University of California Los Angeles
MIMO Enhancements for Air-to-Ground Wireless Communications
A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Electrical Engineering
by
Jesse Thomas Chen
2014 c Copyright by Jesse Thomas Chen 2014 Abstract of the Dissertation
MIMO Enhancements for Air-to-Ground Wireless Communications
by
Jesse Thomas Chen Doctor of Philosophy in Electrical Engineering University of California, Los Angeles, 2014 Professor Babak Daneshrad, Chair
In order to introduce the benefits of Multiple-Input/Multiple-Output (MIMO) wireless so- lutions into the airborne environment for maximal effect, the airborne channel must be fully understood. While there have been theoretical models proposed for the airborne channel, there has been very little work toward providing a practical channel model which has been validated by actual an airborne platform.
This work presents a characterization of practical performance gains of a MIMO sys- tem over a conventional SISO, in a mobile air-to-ground environment. Field measurements were collected with an airborne 4x4 MIMO-OFDM channel-sounding platform at altitudes, speeds and flight patterns approximating medium-endurance vehicles flying over various ter- rain. Ground stations placed in multiple locations (different scattering scenarios) measured channel responses in addition to actual throughput statistics. Our studies indicate that sig- nificant throughput and range gains are achievable with MIMO. We also show that depending on application requirements, these MIMO-enabled gains can be converted into considerable power savings.
We also present a study of the effects of introducing MIMO-enabled signaling techniques (such as eigen beamforming and spatial multiplexing) on the total link-capacity of a system of uncoordinated, air-to-ground link-pairs deployed to a single area of operations. Captured channel measurements from the earlier real-world airborne study were inserted into our multi- ii link simulation environment. Trials were run under several representative aerial-deployment scenarios, revealing significant gains in link capacity.
Finally, we consider the potential throughput enhancement delivered by full-duplex sig- naling and its limitations due to desensitization of receiver hardware by self-generated in- terference (SI). Existing SI cancellation solutions are prohibitive for long-range/airborne applications due to power handling limitations. They are also not easily scalable for an arbitrary number of MIMO antennas in arbitrary positions. A host-agnostic, high-power, adaptive SI canceler design is proposed and a hardware prototype is presented. Performance enhancement with an off-the-shelf host radio was demonstrated in the presence of varying SI signal profiles.
iii The dissertation of Jesse Thomas Chen is approved.
Kung Yao William Kaiser Mario Gerla Babak Daneshrad, Committee Chair
University of California, Los Angeles 2014
iv To my dad.
v Table of Contents
1 Introduction ...... 1
1.1 Motivation ...... 1
1.2 Scope of the Dissertation ...... 2
2 The Airborne Wireless MIMO Channel ...... 4
2.1 Overview ...... 4
2.1.1 Background ...... 4
2.1.2 Prior Art ...... 9
2.1.3 Objectives ...... 10
2.2 Measurement Platform ...... 10
2.2.1 Packet Structure ...... 12
2.2.2 Post Processing Methodology ...... 13
2.3 Field Test Overview ...... 16
2.4 Analysis of Channel Measurements ...... 21
2.4.1 Eigenmode Analysis ...... 21
2.4.2 Theoretical MIMO Capacity Gain ...... 21
2.4.3 Eigen Beamforming Analysis ...... 24
2.4.4 MIMO Power Savings Analysis ...... 26
2.5 Analysis of Actual Data Performance ...... 28
2.5.1 MIMO Throughput Gain ...... 28
2.5.2 Spatial Stream Analysis ...... 30
2.5.3 MIMO Range Extension ...... 31
2.6 Discussion and Conclusions ...... 33
vi 3 Airborne MIMO Concurrent Link Capacity ...... 35
3.1 Overview ...... 35
3.1.1 Background ...... 36
3.1.2 Prior Art ...... 38
3.1.3 Objectives ...... 39
3.2 Mode Selection Algorithm ...... 40
3.2.1 Transmit Bandwidth and Power Constraints ...... 40
3.2.2 Resource Mode Selection Engine ...... 41
3.2.3 Mode Selection Engine in Action ...... 42
3.3 Simulation Engine ...... 44
3.3.1 Framework Overview ...... 44
3.3.2 Air-to-Ground Channel Assumptions ...... 45
3.3.3 Post-Processing SNR Calculation ...... 46
3.3.4 Network Topology Generation ...... 47
3.3.5 Practical Considerations ...... 53
3.4 MIMO Concurrent Link Performance ...... 54
3.4.1 Scenario I: Free Roam Topology ...... 54
3.4.2 Scenario II: Sensor Hotspot Topology ...... 58
3.4.3 Scenario III: Perimeter Patrol Topology ...... 59
3.4.4 Scenario IV: Outpost Deployment Topology ...... 60
3.4.5 Scenario V: Maximum Coverage Topology ...... 61
3.4.6 Scenario VI: Convoy Topology ...... 62
3.4.7 MIMO Concurrent Link Performance Summary ...... 62
3.5 Effectiveness of Individual Features ...... 63
3.5.1 MIMO Spatial Multiplexing Effectiveness ...... 63
vii 3.5.2 MIMO RX Eigen Beamnulling Effectiveness ...... 65
3.5.3 MIMO TX Eigen Beamforming Effectiveness ...... 66
3.5.4 Power Control Effectiveness ...... 67
3.5.5 Link Adaptation, Spectral Segmentation, Variable Bandwidth . . . . 68
3.6 Discussion and Conclusions ...... 69
4 Enabling Airborne Full-Duplex Communications ...... 71
4.1 Overview ...... 71
4.2 Background ...... 73
4.2.1 Prior Art ...... 73
4.2.2 Objectives ...... 75
4.3 Two-Stage Adaptive Self-Interference Cancellation ...... 76
4.3.1 General Architecture Trade-offs ...... 78
4.4 Analog RF Cancellation Design Considerations ...... 80
4.4.1 Gain Resolution, Phase Precision and Power Handling ...... 80
4.4.2 Group Delay ...... 81
4.5 Baseband Cancellation Design Considerations ...... 81
4.5.1 Digital Adaptive Filter ...... 81
4.5.2 Non-Linearity ...... 82
4.5.3 Phase Noise ...... 85
4.5.4 I/Q Imbalance and DC Offset Cancellation ...... 87
4.6 Hardware System Implementation ...... 88
4.6.1 Analog Subsystem ...... 89
4.6.2 Digital Subsystem ...... 92
4.7 Hardware Performance ...... 94
viii 4.7.1 Experimental Setup ...... 94
4.7.2 Radio System Performance ...... 95
4.7.3 Narrowband, Wideband and Tracking Performance ...... 95
4.8 Discussion and Conclusions ...... 98
5 Conclusions and Future Work ...... 100
5.1 Conclusions ...... 100
5.2 Future Work ...... 102
References ...... 105
ix List of Figures
1.1 DoD spending on UAS development [Dod05] ...... 1
2.1 Effect of multipath on wireless transmission [Gol05] ...... 5
2.2 Variation in signal power due to multipath fading [Gol05] ...... 5
2.3 Channel delay spread (τdelay) and coherence bandwidth (Bcoherence) [Gol05] .6
2.4 OFDM subcarriers in a frequency selective channel [Tsa07] ...... 6
2.5 General MIMO system diagram [Gol05] ...... 7
2.6 A 2x2 MIMO channel ...... 8
2.7 A 2x2 MIMO channel after SVD ...... 9
2.8 StreamCaster 3500 high-level hardware architecture ...... 11
2.9 MIMO channel-sounding packet structure ...... 12
2.10 MIMO channel-sounding symbol structure ...... 12
2.11 MIMO/SISO data packet structure ...... 13
2.12 Sounding waveform capture mechanism ...... 14
2.13 MIMO channel-sounding waveform post-processing flow ...... 14
2.14 Google EarthTMformat flight logs ...... 15
2.15 Flightpath for airborne field tests ...... 16
2.16 Cessna-172S aircraft used for the airborne channel measurement campaign, antenna positions circled in white ...... 17
2.17 Airborne antenna (left) and radiation pattern (right) ...... 17
2.18 Ground Station 1, Rooftop Unit 1: patch antennas (spread) ...... 18
2.19 Ground Station 1, Rooftop Unit 2: omnidirectional (left); and Unit 3: patch antenna (right) ...... 19
2.20 Ground Station 2, UCLA: omnidirectional antennas ...... 19
x 2.21 Ground Station 3, Long Beach Airport: omnidirectional antennas ...... 20
2.22 Ground station omnidirectional antenna (left) and radiation pattern (right) . 20
2.23 Ground station directional antenna (left) and radiation pattern (right) . . . 20
2.24 Normalized eigenmode values vs. distance, rooftop patch antenna location . 22
2.25 Normalized eigenmode values vs. distance, UCLA omnidirectional antenna location ...... 22
2.26 Total data transfer capacity vs. time, rooftop patch antenna location . . . . 24
2.27 Channel capacity vs. distance, rooftop patch antenna location ...... 24
2.28 Beamforming capacity gain, rooftop spread patch antenna location ...... 25
2.29 Beamforming capacity gain, airport omni. antenna location ...... 25
2.30 MIMO gain over SISO vs. MPRF, rooftop patch antenna location ...... 27
2.31 MIMO gain over SISO vs. MPRF, airport omnidirectional antenna location . 27
2.32 MIMO/SISO total data transfered (top) and instantaneous throughput (bot- tom) vs. time, rooftop patch antenna location ...... 29
2.33 Observed data throughput vs. distance, rooftop omnidirectional antenna lo- cation ...... 29
2.34 Spatial streams vs. location (blue, red, yellow, green are 1, 2, 3, 4 spatial streams, respectively) ...... 31
2.35 Flightpath for MIMO range extension experiment ...... 32
2.36 MIMO range extension (throughput vs. distance); Rooftop patch antenna location ...... 32
2.37 Effective SNR gain with eigen beamforming: rooftop directional antenna lo- cation ...... 33
2.38 Effective SNR gain with eigen beamforming: UCLA omnidir. antenna location 33
3.1 The current U.S. National Airspace System (NAS) [FAA13] ...... 36
xi 3.2 Resource mode selection engine ...... 42
3.3 Scenario I (Free Roam), final mode distribution ...... 43
3.4 Scenario III (Perimeter Patrol), final mode distribution ...... 43
3.5 Scenario I (Free Roam), final power (per-antenna) distribution ...... 44
3.6 Simulation flow diagram ...... 45
3.7 Free Roam Topology, example realization ...... 48
3.8 Sensor Hotspot Topology, example realization ...... 49
3.9 Perimeter Patrol Topology, example realization ...... 50
3.10 Outpost Deployment Topology, example realization ...... 51
3.11 Maximum Coverage Topology, example realization ...... 52
3.12 Convoy Security Topology, example realization ...... 53
3.13 Scenario I (Free Roam), impact of individual features on link capacity for simulated (left) and captured (right) channels ...... 55
3.14 Scenario I (Free Roam), spatial stream distribution for simulated (left) and captured (right) channels ...... 56
3.15 Scenario I (Free Roam), bandwidth and allowed power distribution for simu- lated (left) and captured (right) channels ...... 57
3.16 Scenario I (Free Roam, captured channels), number of links supported under 70% (left) and 90% (right) coverage guarantee ...... 57
3.17 Scenario II (Sensor Hotspot, captured channels), number of links supported under 70% (left) and 90% (right) coverage guarantee ...... 58
3.18 Scenario III (Perimeter Patrol), number of links supported under 70% (left) and 90% (right) coverage guarantee ...... 59
3.19 Scenario III (Perimeter Patrol), bandwidth and allowed power distribution for simulated (left) and captured (right) channels ...... 60
xii 3.20 Scenario IV (Outpost Deployment), number of links supported under 70% (left) and 90% (right) coverage guarantee ...... 61
3.21 Scenario V (Maximum Coverage Topology), number of links supported under 70% (left) and 90% (right) coverage guarantee ...... 62
3.22 Scenario II (Sensor Hotspot), spatial stream distribution for simulated (left) and captured (right) channels ...... 64
3.23 Scenario III (Perimeter Patrol), spatial stream distribution for simulated (left) and captured (right) channels ...... 65
3.24 Scenario IV (Outpost Deployment), spatial stream distribution for simulated (left) and captured (right) channels ...... 65
3.25 Scenario II (Sensor Hotspot), impact of individual features on link capacity for simulated (left) and captured (right) channels ...... 66
3.26 Scenario III (Perimeter Patrol), impact of individual features on link capacity for simulated (left) and captured (right) channels ...... 67
3.27 Scenario IV (Outpost Deployment), impact of individual features on link ca- pacity for simulated (left) and captured (right) channels ...... 68
3.28 Scenario V (Maximum Coverage), impact of individual features on link ca- pacity for simulated (left) and captured (right) channels ...... 69
4.1 Extending the per-antenna self interference canceler solution to MIMO . . . 73
4.2 System schematic of the entire adaptive self-interference cancellation system 77
4.3 Two-stage hardware prototype adaptive self-interference cancellation system 78
4.4 SI reduction assuming a variable attenuator with 32 dB range and a variable phase shifter with 360 degree range. The resolution of components used in our hardware is 0.5 dB attenuation and 5.625 degrees phase shift...... 80
4.5 Simulation results showing sensitivity of digital adaptive filter output SINR to non-linearity. Larger back-off implies less non-linearity...... 83
xiii 4.6 The estimation error a 6-tap RLS filter for a non-linear system: (top) classic filter (bottom) the RLS adaptive filter including Volterra augmentation . . . 85
4.7 Average Volterra term weights vs. term index, indicating rapid decay . . . . 85
4.8 Simulation results showing sensitivity of digital adaptive filter output SINR to phase noise profile ...... 86
4.9 PSD of phase noise with 0.085 degrees of integrated phase error used for simulation ...... 86
4.10 Spur/noise reduction at the output of the QRD-RLS filter when RX I/Q imbalance at both the wireless and wired (SI reference) is calibrated. The light (red) and dark (black) and curves are before and after I/Q compensation, respectively...... 88
4.11 SI power vs. gain and phase of the PRFC, measured in hardware for L-band. This quasi-convex function can be optimized with LMS ...... 89
4.12 State machine for the PRFC coefficient update algorithm, inclusive of an LMS coefficient update engine ...... 91
4.13 Conversion of a traditional QRD-RLS implementation to an extended systolic array with implicit filter weight calculation ...... 92
4.14 Fixed point simulation study. The SI-free QPSK constellation with -14.75 dB EVM (left), -9.9 dB EVM after 74 dB of floating point SI cancellation (center), -9.04 dB EVM after fixed point cancellation (right) ...... 93
4.15 SOI spectrum distortion using truncation within the QRD-RLS filter (left) is non-existent when true rounding is used (right) ...... 94
4.16 Schematic of the experimental setup with SC3800 OFDM radios ...... 94
4.17 Decoded constellations reported by Silvus SC3800 radio: SI-free SOI with 29.4 dB post-processing SNR (left), 18.5 dB SINR after 70 dB of SI suppression (center) and 9.3 dB SINR after 81 dB of SI suppression (right) ...... 96
4.18 Narrowband SI scenario: SI and SOI at RX antenna input ...... 96
xiv 4.19 Narrowband SI scenario: QRD-RLS input (light/red curve) and final output (dark/blue curve) ...... 97
4.20 Wideband SI cancellation scenario: final (SI cancelled) RF output to host radio 97
4.21 Hopping SI cancellation scenario: SI at RX antenna input (top) and final, SI cancelled RF output to host radio (bottom) ...... 98
xv List of Tables
2.1 RX ground station details ...... 18
2.2 MIMO power savings factor ...... 27
3.1 Resource modes available to the airborne wireless communication system . . 41
3.2 Concurrent links simulation result summary, captured channels ...... 63
4.1 General simulation parameters for design study ...... 79
4.2 Adaptive filter SINR vs. number of taps ...... 82
xvi Vita
2003-2005 Electronics Intern, Leach International (Esterline) Corp.
2005 B.S. (Electrical Engineering), UC Irvine
2005 B.S. (Computer Engineering), UC Irvine
2005-2007 Graduate Student Researcher, Electrical Engineering Department, UCLA. Wireless Integrated Systems Research Group – Prof. Babak Daneshrad.
2008 M.S. (Electrical Engineering), UCLA.
2007–2011 Hardware Engineer, Silvus Technologies, Inc
2011–2012 Senior Hardware Engineer, Silvus Technologies, Inc.
2012–present Director of Hardware Engineering, Silvus Technologies, Inc.
xvii CHAPTER 1
Introduction
1.1 Motivation
The rapidly increasing demand for ubiquitous broadband connectivity in recent years has given rise to the need to provide high-speed, long-range communications systems on mobile airborne platforms. In the commercial sector, we have already begun to see a large increase in wireless data services being offered on passenger airlines. In the military sector, there has been a tremendous increase in the number of UAS (Unmanned Aerial Systems) being brought into operation. By 2015, the US Army is expected to carry out 100% of all aerial surveillance and command/control missions with the use of UAS [Arm10]. As shown in Figure 1.1 below, the United States Department of Defense spending on UAS development has grown from around $300M in 2001 to over $3.2B expected for 2011 [Dod05]. A 2013 market study [Tea13] estimated that annual UAV expenditures will grow from $5.2B in 2013 to $11.6B in 2023, equating to a total of over $89B spent over the next decade.
Figure 1.1: DoD spending on UAS development [Dod05]
1 As the requirements on range and throughput of airborne wireless data links continue to grow, traditional communications systems that rely on single-input/single-output (SISO) technology will quickly find themselves unable to meet such demands. Multiple-input/multiple- output (MIMO) technology, is the next natural evolution of such systems, but has not been well studied in air-to-ground environments. Some of the major advantages of incorporating MIMO technology into an airborne system include increased throughput capability, range extension and interference mitigation. These benefits can be realized without sacrificing spectral efficiency and will be instrumental in enabling advanced communications systems for emerging airborne technologies such as UAV (Unmanned Aerial Vehicle) “swarms” (groups of ad hoc networked UAV platforms) that must be able to communicate with ground operators (air-to-ground) as well as with each other (air-to-air). Large amounts of sensor/telemetry data will need to be relayed at long ranges while inflicting minimal interference upon other concurrent transmissions. Combining MIMO technology with OFDM (Orthogonal Frequency Division Multiplexing), which is expected to be robust against the potentially high mobility of airborne vehicles [WKD05], should prove to be a powerful combination. While there has been some concern about the reduced effectiveness of MIMO in an airborne LOS (Line-of- Sight) scenario due to poor spatial diversity [GDD05], our studies indicate that MIMO gains are quite significant in several typical scenarios.
1.2 Scope of the Dissertation
Chapter 2 describes the design of an air-to-ground channel sounding and performance mea- surement system. Detailed analysis of the airborne MIMO channel and performance results obtained from an extensive real-world measurement campaign are also presented. Chapter 3 describes the simulation framework for the study of a system of uncooperative air-to-ground MIMO links. Multiple MIMO techniques were employed, tied together with a link adapta- tion algorithm, and studied on both an individual and ensemble level. The channel measure- ments obtained from the real-world measurement campaign in Chapter 2 were incorporated into this simulation effort. Chapter 4 considers the self-interference cancellation system
2 required to enable full-duplex wireless communications in an airborne/long-range environ- ment. The design of a novel, self-contained, adaptive self-interference canceler is described. This scalable design was prototyped in actual hardware and validated with an off-the-shelf, high-performance radio system, against multiple self-interference signal profiles. Analysis of practical self-interference suppression performance is presented.
3 CHAPTER 2
The Airborne Wireless MIMO Channel
2.1 Overview
In order to introduce the benefits of MIMO into the airborne environment for maximal effect, the airborne channel must be fully understood. While there have been various theoretical models proposed for the airborne channel (discussed in section 2.1.2), to date there has been very little work toward providing a practical MIMO channel model which has been validated by actual measurement on an airborne platform. The research presented here will directly contribute to the performance prediction and optimization of modern wideband MIMO com- munication systems across different flight profiles, terrain and antenna configurations.
2.1.1 Background
2.1.1.1 Overview of the Wireless Channel
As illustrated in Fig. 2.1, the wireless channel is made complicated by obstructions in the signaling path between transmitter and receiver. Multipath is the term used to describe the physical phenomena of a single wavefront launched from a transmitter being reflected, refracted or scattered before being received at the receiver. This results in multiple delayed versions of the original wave arriving at the receiver with different amplitudes and phases.
Depending on the position of the transmitter and receiver, these delayed waves may add constructively or destructively, creating a pattern of signal peaks and nulls within the transmission space. As illustrated in Fig. 2.2, even with a very simple “two-ray” (line-of- sight component and a single, bounced ray) model, a mobile receiver can experience wildly
4 changing signal power as it moves through an environment, giving rise to a phenomenon termed Doppler spread.
Figure 2.1: Effect of multipath on wireless transmission [Gol05]
Figure 2.2: Variation in signal power due to multipath fading [Gol05]
Another effect of multipath is frequency selective fading (signal attenuation). Many wireless channels are rich in multipath, and thus the delay spread (time between the first and last echo sensed at the receiver) may be large as compared to the transmitted symbol period. From a time-domain impulse response perspective, this can be seen as multiple impulses with varying arrival times and amplitudes (decaying with time), as illustrated in
5 Fig. 2.3. A single impulse results in a flat frequency spectrum, but the introduction of additional delayed impulses results in a spectrum that is significantly distorted. This delay spread is directly tied to the coherence bandwidth of the channel. The distortion created by this type of channel must be equalized (canceled) in order to extract the original signal sent by the transmitter.
Figure 2.3: Channel delay spread (τdelay) and coherence bandwidth (Bcoherence) [Gol05]
2.1.1.2 Overview of OFDM
Orthogonal Frequency Division Multiplexing (OFDM) combats the frequency selectivity of a wideband wireless channel by using many subcarriers modulated at a lower data rate (increased symbol time) in order to achieve a higher overall data rate. As illustrated in Fig. 2.4, a frequency selective channel can be broken up into multiple flat channels, allowing for a channel equalization scheme of significantly reduced complexity (single-tap equalizers on each subcarrier).
Figure 2.4: OFDM subcarriers in a frequency selective channel [Tsa07]
6 2.1.1.3 Overview of MIMO
The basic idea behind MIMO is to leverage the spatial dimension in order to boost data transmission rate or link robustness without sacrificing bandwidth (spectral efficiency). If there exists sufficient scattering in the channel, the signals transmitted from each antenna undergo independent fading before arriving at the receiver. This characteristic is known as spatial diversity. The general system diagram given in Fig. 2.5 illustrates how an input stream can be split and mapped onto separate spatial streams before passing through a channel and being recombined by the receiver. By using this spatial multiplexing technique, throughput is multiplied by the number of spatial streams that can be supported by the channel. A MIMO system can also leverage transmit diversity through the use of Space- Time Coding (STC), by which strategically coded, redundant copies of the data stream are sent out of multiple antennas in order to improve link reliability. Receiver diversity may be implemented in a number of ways, including antenna-switching (based on best SNR) or maximum ratio (weighted) antenna combining.
Figure 2.5: General MIMO system diagram [Gol05]
Leveraging spatial information on the transmit side, eigen beamforming can also be em- ployed in order to simultaneously boost SNR at the receive antennas and reduce interference in unintended directions. Similarly on the receiver side, nulls can be placed in the direction of an interferer to increase effective SINR (Signal-to-Interference and Noise Ratio). Given the multitude of different ways MIMO can be used, it is critical to first understand the operating environment in order to determine the effectiveness of different techniques. 7 2.1.1.4 Eigenvalues of the MIMO Channel
Eigenvalues and singular values (square roots of the eigenvalues) are of particular importance when discussing the MIMO channel. Considering for the moment, a 2x2 MIMO channel, the received vector y consists of a linear combination of the elements of the transmit vector x, i.e., y1 = x1h11 + x2h12 and y2 = x1h21 + x2h22. In matrix notation, this is simply y = Hx.
Figure 2.6: A 2x2 MIMO channel
y1 h11 h12 x1 = (2.1) y2 h21 h22 x2
Singular value decomposition (SVD) of the H matrix yields the unitary matrices U and V , as well as the diagonal matrix Σ, which contains the singular values of the channel.
∗ ∗ H u11 u12 λ1 0 v11 v21 H = UΣV = (2.2) ∗ ∗ u21 u22 0 λ2 v12 v22
Multiplying our original message with the precoding matrix V on the TX side and filtering with the matrix UH on the RX side yields x = Vx0 and y0 = UHy, respectively. Thus, the received MIMO signal can be written as follows:
y = Hx → y0 = UHUΣVHVx0 = Σx0 (2.3)
In this way, we may view the 2x2 MIMO channel as two parallel spatial streams with their individual SNR governed by the strength of the singular values (or eigenvalues).
8 Figure 2.7: A 2x2 MIMO channel after SVD
2.1.2 Prior Art
In order to gain insight into the effectiveness of airborne MIMO, it is clear that a comprehen- sive study of the airborne channel must first be conducted. Of the studies that propose gen- eral airborne channel models (including satellite-to-ground models [CD04][DFM02]), many do not validate these models with experimental data [Haa02][Eln92][WKD05]. One research effort has approached the urban air-to-ground scenario using ray-tracing simulations and city maps, but again lacks experimental validation [FMT06]. Another channel modeling effort developed custom channel-sounding hardware, but only analyzed experimental data collected over mountainous terrain [RMD04]. Yet another airborne channel modeling effort conducted flight trials in order to statistically characterize low-altitude, multipath delay spread over a university campus [NMD03]. In this study, although a four-element receiver array was used to enable receiver diversity gain, only a single antenna was used at the transmitter (a single- input/multiple-output, SIMO system). Another set of SIMO (1x4) field trials was carried out with the use of standard 802.11b/g radios for the purposes of measuring antenna correlation coefficients and potential network-level diversity gain in a wooded, farmland area [KLL10]. Yet another set of field measurements with an 802.11a system mounted on a UAV, utilized switched-transmit-diversity in order to determine the best type of antenna configuration for what is effectively a conventional SISO link [CHK06]. This survey of existing literature suggests that the MIMO aspect of airborne communications has gone largely untouched.
9 2.1.3 Objectives
The work presented in this chapter characterizes the practical performance gains of a MIMO system over a SISO system - in a mobile air-to-ground environment - based on channel mea- surements and real-world data throughput of an airborne 4x4 MIMO-OFDM system. Exper- imental field trials were executed at altitudes, speeds and flight patterns (take-off/landing, cruising and orbiting) approximating those of medium-endurance airborne vehicles flying over various types of terrain. Multiple ground stations were placed in locations with both rich and sparse local scattering in order to measure channel responses as well as actual data throughput statistics. Analysis of the results indicates that significant MIMO gains in throughput and range are achievable. We also show that if increased throughput or range are less of a requirement for a target application, these MIMO-enabled gains can instead be converted into considerable transmit power savings.
In summary, the two primary scientific contributions presented in Chapter 2 are:
• An extensive measurement campaign and analysis of the airborne wireless MIMO channel
• Quantification of MIMO performance gains on a realistic, MIMO-enabled platform, operating in a mobile airborne environment
2.2 Measurement Platform
The hardware development platform that became the workhorse of this research effort was provided by our industry partner, Silvus Technologies. The Silvus StreamCaster 3500 plat- form [Sil14a], as illustrated in Fig. 2.8, is a fully-functional, high-performance 2.4 GHz MIMO radio. This highly configurable radio platform possesses a multitude of MIMO/SISO modulation and coding mechanisms. With assistance from the Silvus engineering team, full customization of the user-level software, microprocessor drivers and baseband signal process- ing hardware (FPGA) was enabled, transforming the SC3500 into a fully-featured MIMO performance measurement and channel-sounding system. 10 Figure 2.8: StreamCaster 3500 high-level hardware architecture
The transmitter node was designed such that one MIMO channel-sounding packet would be transmitted every 500 ms. In between these sounding packets, data packets (100-byte pay- load, 80µs inter-packet spacing) for link-adaptive performance evaluation were sent round- robin through 8 SISO (1x1, 3 extra antennas shut down) and 31 MIMO (4x4, all antennas active) modulation schemes. The modulation and coding scheme combinations ranged from
1 5 BPSK, r = 2 to 64-QAM, r = 6 . In the case of MIMO, up to 4 spatial streams were exercised. The airborne transmitter node was outfitted with a global positioning system (GPS) unit and inertial measurement unit for logging location, altitude, speed, time and attitude data.
The receiver node was designed to switch between MIMO and SISO configurations at 1-second intervals. The transmitter MIMO/SISO state was encoded into the payload (pro- tected by CRC) so that only packets transmitted and received with four antennas would be considered for MIMO packet error rate (PER) calculations. Similarly for SISO, only pack- ets sent and received with a single antenna would be considered for SISO PER statistics measurement. Channel-sounding packets were always sent in MIMO mode and only those received in MIMO mode were saved for offline processing.
In summary, the following information was recorded by the receiver software:
• Channel-sounding waveform captures
• Receiver (RX) and transmitter (TX) GPS information (time, latitude, longitude, altitude and speed).
• MIMO/SISO received signal power and noise power (SNR)
• Receiver frequency offset 11 • Receiver packet totals for all rates
• Instantaneous PER for all rates (based on payload CRC successes)
2.2.1 Packet Structure
2.2.1.1 Sounding Packets
The received sounding waveforms were captured in hardware, written to long-term storage, and processed offline. This sounding waveform, as pictured in Fig. 2.9, allows for 26 dB of processing gain (averaging) if the channel characteristics remain relatively static over the duration of the packet (400 symbols). The coarse synchronization field is used for automatic gain control (AGC) convergence, coarse symbol-boundary identification and frequency-offset estimation (local-oscillator offset between transmitter and receiver hardware). The fine syn- chronization field is intended for precise symbol boundary recovery and fine frequency-offset correction.
Figure 2.9: MIMO channel-sounding packet structure
Figure 2.10: MIMO channel-sounding symbol structure
While an existing MIMO channel-sounding platform found in the literature utilized a switched-antenna array [MWJ08], our sounding platform allows for simultaneous measure- ment of the channels between all receiver and transmitter antennas. The structure of the sounding symbols is similar to that adopted by the 802.11n standard. As illustrated in 12 Fig. 2.10, total cancellation occurs when multiplying the modulation sequence of any of the transmit streams (periodic with 4 symbols) with any of the other streams and summing over the resulting waveform. This achieves orthogonality between the multiple transmitted channel-sounding antenna streams. In high-SNR situations, this waveform structure also enables tracking of short-term, symbol-to-symbol, variations.
2.2.1.2 Data Packets
In addition to transmitting sounding packets, the hardware platform is designed to send actual data packets of the format pictured in Fig. 2.11. While the preamble is similar to the sounding packet structure, the extended sounding block is replaced with a modulated, random-data payload. This allowed us to seamlessly integrate actual SISO/MIMO perfor- mance testing into the channel-sounding campaign.
Figure 2.11: MIMO/SISO data packet structure
2.2.2 Post Processing Methodology
2.2.2.1 Channel Captures
As mentioned in section 2.2, at least one sounding packet was recorded per second and saved to long-term storage for offline processing. During post-processing, MATLAB is used to parse metadata (time, location, power) and process (compensate/demodulate) the received sounding waveform. The first concern when extracting received channel-state information is the detection of symbol boundaries. In order to properly process each time symbol, the start and end samples of each OFDM symbol must be accurately and reliably identified within the 400-symbol capture. To this end, the trigger for the hardware capture-buffer is tied to a deterministic signal based on the fine timing-recovery circuit as shown in Fig. 2.12. This
13 guarantees that each capture begins at the same time (1-sample uncertainty) relative to the start of each packet. Additionally, the capture trigger is qualified by a cyclic redundancy check (CRC) on the decoded packet header (metadata), ensuring that each saved capture is indeed a valid sounding packet.
Figure 2.12: Sounding waveform capture mechanism
Figure 2.13: MIMO channel-sounding waveform post-processing flow
In addition to proper symbol boundary detection, the system also exports its fine frequency- offset estimation. Since the captured signal is tapped out of the system before any digital signal compensation is applied (with the exception of bandpass filtering), all impairments including frequency offset are still present. A MATLAB-based post-processing framework, as shown in Fig. 2.13, was developed in order to extract an accurate channel frequency-response from these captured waveforms. The exported frequency-offset estimation information is used to accomplish an initial correction on the captured signal. Any residual frequency offset is then removed by a simple autocorrelation method. In order to assist with channel-capacity calculations, a per-capture noise estimate is also extracted at this point. The compensated 14 signal is then demodulated via Fast Fourier Transform (FFT) and further compensated for sampling-clock offset. The result is a 4x4x56 matrix corresponding to the 56 data-subcarrier channel-coefficients for each combination of transmitter-receiver antenna pairs. This chan- nel data can then be used to accomplish eigenmode and capacity analysis for every 1-second interval that the aircraft is within sensitivity range of the receiver.
2.2.2.2 Performance Analysis
1 5 The PER data for all 31 MIMO modes (BPSK, r = 2 to 64-QAM, r = 6 and 1-4 spatial streams) and all 8 SISO modes was recorded for each flight. These data logs were parsed of- fline and MATLAB was again used to assemble this raw data into a Google EarthTMreadable file. This fully interactive flight log proved to be a very useful tool for quickly identifying areas of interest. As demonstrated in Fig. 2.14, any point on this 3D map could be accessed, bringing up MIMO throughput, SISO throughput, SNR, time, location, altitude and speed information.
Figure 2.14: Google EarthTMformat flight logs
15 2.3 Field Test Overview
All flights in our channel measurement campaign were carried out with the use of a Cessna- 172S aircraft. The aircraft traveled on the predetermined flight path shown in Fig. 2.15, taking it over various types of geographic topologies (mountains, urban, ocean) at an aver- age cruising altitude and ground speed of 10,500 ft and 120 mph, respectively. The signal transmitted from this mobile aerial platform was received at three ground locations situated in a handful of environments around the Los Angeles area. Ground stations were positioned in specific locations in order to get a survey of varying types of air-to-ground channel sce- narios. Link data for distances in excess of 50 km was recorded. Table 2.1 is a summary of the ground station configurations.
Figure 2.15: Flightpath for airborne field tests
16 The Cessna-172S was outfitted with four 3-dBi omnidirectional antennas mounted to the wing struts, tail (underside) and nose (underside) of the aircraft shown in Fig. 2.16. Four 0.5” diameter low-loss RF cables ran from the antennas, down the struts and fuselage and into the cabin where they were connected to our radio system. The radio system itself was powered by high-capacity 12V batteries. Additionally, a GPS receiver and inertial sensor were mounted to the dash of the cockpit and measurements were extracted from these devices. The form factor and radiation pattern of the omnidirectional airborne antennas is given in Fig. 2.17.
Figure 2.16: Cessna-172S aircraft used for the airborne channel measurement campaign, antenna positions circled in white
Figure 2.17: Airborne antenna (left) and radiation pattern (right)
Ground Station 1 was located on the roof of an 19-story high-rise (Fig. 2.18 and Fig. 2.19). Three RX nodes were placed at this location with different antenna configurations (omnidirectional- dipole, directional-patch, and directional-patch spread apart). Ground Station 2 was located 17 Ground Station Location Antenna Type Mounting Config. 1-1 Rooftop (19-story high-rise) Directional (6-dBi) 1m apart, 45 deg elev. angle 1-2 Rooftop (19-story high-rise) Directional (6-dBi) 8m apart, 45 deg elev. angle 1-3 Rooftop (19-story high-rise) Omni. (3-dBi) 1m apart, 0 deg elev. angle 2 University campus, field area Omni. (3-dBi) 1m apart, 0 deg elev. angle 3 Airport tarmac, vehicle roof Omni. (3-dBi) <1m apart, 0 deg elev. angle
Table 2.1: RX ground station details in an open field area of the University of California, Los Angeles (UCLA) campus (Fig. 2.20) and Ground Station 3 was located on the tarmac of Long Beach Airport (Fig. 2.21). Ground Station 2 and Ground Station 3 were both outfitted with omnidirectional dipole antennas. The form factors and radiation patterns for both the omnidirectional and directional ground station antennas are depicted in Fig. 2.22 and Fig. 2.23, respectively.
Figure 2.18: Ground Station 1, Rooftop Unit 1: patch antennas (spread)
18 Figure 2.19: Ground Station 1, Rooftop Unit 2: omnidirectional (left); and Unit 3: patch antenna (right)
Figure 2.20: Ground Station 2, UCLA: omnidirectional antennas
19 Figure 2.21: Ground Station 3, Long Beach Airport: omnidirectional antennas
Figure 2.22: Ground station omnidirectional antenna (left) and radiation pattern (right)
Figure 2.23: Ground station directional antenna (left) and radiation pattern (right)
20 2.4 Analysis of Channel Measurements
2.4.1 Eigenmode Analysis
From the measured air-to-ground channels, the eigenmodes (singular values) were extracted. The figures in this section depict the normalized (relative to the largest) eigenmodes of the captured channels. We can view these normalized eigenmodes as directly related to the effective SNR of the multiple spatial data-streams. Fig. 2.24 is a plot of these normalized eigenmodes, as a function of transmission distance, for the first rooftop patch-antenna con- figuration (Ground Station 1-1). From this figure, we can see that when the aircraft is in close proximity to the RX ground station, the spread of the eigenmodes is significantly larger. This indicates that the line-of-sight (LOS) components of the received signal at the antennas are dominant, and thus the transmission is more spatially correlated. While spatial multiplexing will be difficult to achieve, array gains are still possible. At larger distances we observe a smaller spread, which indicates that signal reflections (multipath) off neighboring skycrapers and various rooftop-geometry features have become more of a factor, spatially decorrelating the received signals and allowing spatial multiplexing to become more effective.
A plot of the normalized eigenmodes versus distance, for the university-campus omni- antenna location, is given as Fig. 2.25. We observe that this location experienced a flatter trend in its normalized eigenmodes despite a larger variance. This is likely due to the exis- tence of more local and medium range scatterers in this location – a field sparsely populated with trees and with large, concrete structures at its perimeter. This trend indicates that if the overall SNR is high enough, multiple spatial streams can be supported over the entire transmission range.
2.4.2 Theoretical MIMO Capacity Gain
The following is a discussion of throughput capacity as the theoretical maximum data rate that can be supported with the following assumptions:
• 5 MHz (56 OFDM data-subcarriers) effective signaling bandwidth 21 Figure 2.24: Normalized eigenmode values vs. distance, rooftop patch antenna location
Figure 2.25: Normalized eigenmode values vs. distance, UCLA omnidirectional antenna location
22 • No additional signaling overhead (no preamble/inter-frame spacing)
• Optimal TX precoding
• Optimal TX stream power allocation
• Unlimited modulation capability and perfect, continuous link adaptation
Although the absolute data rate presented here is purely theoretical, the calculations are based on real channel measurements. Thus, theoretical capacity can be used to quan- tify, in a practical air-to-ground channel, the relative MIMO performance gain over a SISO implementation of equal total transmit power.
MIMO capacity was calculated according to the following expression:
56 4 2 X X Pi,jλi,j C = B × log 1 + (2.4) 2 N j=1 i=1 0
In Eq. 2.4, i is the eigenmode index, j is the subcarrier index, B is the subcarrier
2 bandwidth, P is the power allocation to each eigenmode, λ is the eigenmode, and N0 is the noise variance. Pi,j was chosen based on water-filling, which is known to be the optimum P λ2 power loading scheme [Gol05]. The SISO capacity is calculated similarly but with i,j i,j N0 replaced by Ptotal and the inner sum over i removed. N0 The theoretical instantaneous data rates for both MIMO and SISO channels are given in Fig. 2.26, where both MIMO and SISO systems are considered to possess perfect, unlimited, link-adaptation capability. We observe that given the channel conditions corresponding to this particular ground station, MIMO has a theoretical gain of around 2.5x relative to a SISO system of equal total transmit power. If we then plot the channel capacity over average transmission distance, as also shown in Fig. 2.27, we find that the MIMO gain remains quite constant over all distances. These results were consistent at all RX ground station locations, indicating that a MIMO vs. SISO gain of 2-3x is achievable, and is relatively independent of distance and ground station environment.
23 Figure 2.26: Total data transfer capacity vs. time, rooftop patch antenna location
Figure 2.27: Channel capacity vs. distance, rooftop patch antenna location
2.4.3 Eigen Beamforming Analysis
Based on the eigenmodes and measured noise variance of the captured channels, a theoreti- cal post-processing SNR (PPSNR) of each mode (assuming knowledge of the channel at the transmitter) was calculated. This PPSNR represents the SNR of the individual information streams after the transmit signal has been preshaped with an ideal transmit weight-vector (produced by singular value decomposition of the channel response) and decoded via min- imum mean-square-error (MMSE) detection. The resulting PPSNR was used to calculate 24 channel capacity with and without optimum power loading (water-filling).
Figure 2.28: Beamforming capacity gain, rooftop spread patch antenna location
Figure 2.29: Beamforming capacity gain, airport omni. antenna location
Based on the capacity plot shown in Fig. 2.28, we make the observation that given the channel conditions corresponding to the rooftop patch antenna location, the addition
25 of TX eigen beamforming capability may yield an additional gain of around 2.2x over a MIMO system without eigen beamforming. Similar performance was observed at all receiver locations, including the airport location as shown in Fig. 2.29. We observed a steady 2x gain in capacity over all scenarios, which suggests quite strongly that eigen beamforming is very effective in typical aerial channel scenarios. It is understood that optimal power loading does not contribute much to performance gain until the SNR is quite low [TV05]. This is the reason for the more noticeable gap between the equal power-loaded and unequal (optimal) power-loaded curves shown in Fig. 2.29. Measurements conducted at even greater range are expected to display even more significant gain in this respect – though the issue of obtaining accurate channel measurements at long ranges is not trivial.
2.4.4 MIMO Power Savings Analysis
The goal of the analysis in this section is to determine how much power savings can be obtained if we wish to operate a MIMO system at the same overall throughput performance as a SISO system. We begin by defining a MIMO power reduction factor (MPRF) as the amount of reduction in transmit power that a MIMO system is handicapped relative to a SISO system. Again making use of the captured channel measurements, we sweep through the MPRF, calculating total amount of data transferred (for the duration of the flight) at each MPRF point. A plot of MIMO performance gain versus MPRF for the rooftop patch antenna configuration is shown in Fig. 2.30. The 1x-intercept point (red dotted line) indicates how much the MIMO transmission power can be reduced before matching the overall performance of a full-powered SISO system. For the rooftop patch antenna location, we can see that this is as much as 21 dB. The lowest amount of power reduction observed was at the airport omnidirectional antenna location. In this case, the amount of power savings is significantly less (approx. 13 dB, Fig. 2.31), but still large enough to be compelling.
Depending on the ground location type, we have observed a potential MIMO power savings of approximately 13-21 dB (20-145x) with respect to a full-powered SISO system. A summary of recorded potential power savings is given in Table 2.2.
26 Figure 2.30: MIMO gain over SISO vs. MPRF, rooftop patch antenna location
Figure 2.31: MIMO gain over SISO vs. MPRF, airport omnidirectional antenna location
Location MPRF 1x intercept Rooftop patch antenna (both) 21 dB UCLA omnidirectional 18 dB Rooftop omnidirectional 16 dB Airport omnidirectional 13 dB
Table 2.2: MIMO power savings factor
27 2.5 Analysis of Actual Data Performance
2.5.1 MIMO Throughput Gain
As mentioned in Section 2.2, random data packets were sent alongside the channel-sounding waveforms in order to collect empirical data on realizable MIMO gain in a field-ready system. Fig. 2.32 shows MIMO gain in terms of cumulative data transfer as well as instantaneous data rate for the rooftop patch antenna location. Data rate quantities were calculated from the average PER measured for all the transmitted modulation and coding scenarios. Cumulative data quantities were calculated by integrating over this instantaneous data rate. We observe from Fig. 2.32 that compared to a SISO system, our MIMO system (both assuming optimal link-adaptation) is able to achieve around a 2x gain in total data transferred over the duration of the flight. In most cases, the MIMO throughput curve lies significantly higher than the SISO throughput curve. We also note that SISO may occasionally meet or slightly exceed MIMO performance. This is due to the fact that total MIMO and SISO transmit power were made to be equal. Thus, in situations where the mobile airborne platform was oriented such that there was direct line-of-sight between the SISO TX-RX antenna pair, but not between the remaining three MIMO TX and RX antennas (shadowed by aircraft body), the SISO system was given a 6-dB raw SNR advantage. Conversely, when the SISO antenna is shadowed, MIMO gains are considerable.
Fig. 2.33 shows MIMO gain in terms of instantaneous data rate for the rooftop omnidi- rectional antenna location. Comparing the directional antenna and omnidirectional antenna data allows us to make several observations. The maximum data rate of the directional antenna system is around 2x that of the omnidirectional antenna system, and is due largely to the differences in radiation patterns. When the aircraft is directly overhead, the overall SNR is maximized by the directional antenna configuration while there is actually a dip in performance for the omnidirectional antenna system due to its null position. Another trend to note is that while the patch antenna achieves much higher maximum throughput, this gain begins to drop off quickly with distance as the aircraft moves out of the direction of its antenna pattern’s main lobe. The omnidirectional system decays less rapidly with distance, 28 due to the aircraft’s transmission achieving better alignment with the antenna pattern’s main lobe direction.
Figure 2.32: MIMO/SISO total data transfered (top) and instantaneous throughput (bottom) vs. time, rooftop patch antenna location
Figure 2.33: Observed data throughput vs. distance, rooftop omnidirectional antenna location
29 2.5.2 Spatial Stream Analysis
The spatial stream map given as Fig. 2.34 indicates the spatial stream count corresponding to the modulation and coding scheme that resulted in the maximum throughput at the rooftop patch antenna location for any given airborne transmitter location. It is clear that at long ranges, single spatial stream transmissions dominate the performance. This makes intuitive sense given the eigenmode distributions shown previously in Fig. 2.24 and Fig. 2.25. Although multiple spatial streams are theoretically possible, the overall SNR at long ranges is not high enough to support the weaker spatial modes. It is interesting to note however, that there are quite a lot of areas for which two spatial stream transmissions provide the best throughput performance. There is also a sweet spot – corresponding to a peak in the eigenmodes of Fig. 2.24 – at around 20-25km transmission distance where 4 spatial-stream rates actually delivered the maximum throughput. As we will see in Chapter 3, being able to support 2 spatial streams allows a system to deliver the same data rate in half the bandwidth, resulting in a much better spectral interference footprint.
30 Figure 2.34: Spatial streams vs. location (blue, red, yellow, green are 1, 2, 3, 4 spatial streams, respectively)
2.5.3 MIMO Range Extension
In order to fully exercise the range of the airborne link, a slightly modified version of the original experiment was conducted. The data for the rooftop patch antenna system in Fig. 2.36 was collected during a similar flight trial with an extended flight path. As can be seen in Fig. 2.35, the aircraft traveled to a larger range (in excess of 60 km for this trial). The black markers on Fig. 2.36 indicate where link-adaptive MIMO and fixed-rate SISO (QPSK
1 rate = 2 , chosen based on a survey of current UAV communications solutions) modes first degraded to the 0.95 Mbps throughput level. A gain in range of approximately 1.6x was obtained with MIMO in this case, translating into a 2.6x increase in circular coverage area.
With the addition of eigen beamforming, we observe a total effective SNR gain of around 16-18 dB when directly overhead and 3-6 dB at extended ranges, as indicated in Fig. 2.37
31 Figure 2.35: Flightpath for MIMO range extension experiment
Figure 2.36: MIMO range extension (throughput vs. distance); Rooftop patch antenna location and Fig. 2.38. This translates into approximately 1.4-2.0x additional extension in range, assuming free-space path-loss. With the additional 2x boost, the overall range extension increases to 3.6x, yielding an increase in circular coverage area by almost 13x. If water-
32 filling (optimal power loading) is applied, further SNR gain is obtained. We observe as much as an additional 5 dB of SNR at long ranges. At the airport location, a consistent SNR gain of around 5 dB without water-filling and 12 dB with water-filling is observed. While this additional gain may be difficult to achieve due to channel estimation SNR limitations at longer ranges, the lower relative mobility of the channel allows for more sophisticated channel estimate processing techniques. Channel estimation issues aside, this result remains a strong indication that eigen beamforming can significantly extend the communications range and throughput of MIMO-enabled airborne platforms.
Figure 2.37: Effective SNR gain with eigen beamforming: rooftop directional antenna location
Figure 2.38: Effective SNR gain with eigen beamforming: UCLA omnidir. antenna location
2.6 Discussion and Conclusions
This work presents the design and subsequent experimental field tests of a comprehensive, airborne MIMO channel-sounding and throughput-measurement platform. A representative aircraft was outfitted with a 4x4 MIMO-OFDM radio system and flown over different ter- rain, at altitudes and speeds approximating a typical medium-endurance UAV. Analysis of 33 collected data at ground stations, situated in various locations around the Los Angeles area, showed that MIMO-enabled nodes can achieve a significant gain in throughput and range, or alternatively, large TX power savings with respect to a conventional SISO system. Eigen beamforming analysis that was based on captured channel responses indicated that even larger MIMO performance gains are attainable if channel-state information can be effec- tively and accurately fed back to the transmitter. Future work will include validating this claim by introducing a working beamforming solution into the hardware platform.
Delay spread (related to channel coherence bandwidth) and Doppler spread (related to channel coherence time) are other areas of further investigation. The current symbol-based FFT/IFFT method is only capable of resolving multipath components down to 200 nsec (200 ft) and a maximum delay spread of 12.8µs (1,280 ft) which is sufficient for performance prediction for a 5 MHz MIMO-OFDM system. From a Doppler spectrum perspective, we achieve a Doppler resolution of 156 Hz and a maximum measurable limit of 62.5 kHz. By increasing measurement bandwidth and improving our post-processing techniques, we will be able to achieve better resolution. Study of the channel at bandwidths resembling even more modern OFDM systems (tens to hundreds of MHz) is another area of possible improvement. Additionally, we may study the coherence time of the eigenmodes in a fashion similar to how coherence time of the channel can be determined from the autocorrelation of channel taps over time. This gives us a measure of how steady the spatial structure of the MIMO channel remains as the mobile nodes travel through space. Finally, while the channel measurements obtained in this study are only the first step in developing a statistically accurate model of the airborne channel, the database of collected channel measurements (in excess of 30,000 captures) is large enough to be useful for network/system-level simulations such as that which is presented in Chapter 3.
34 CHAPTER 3
Airborne MIMO Concurrent Link Capacity
3.1 Overview
Recent years have seen a marked increase in the number of unmanned aerial vehicles (UAV) being deployed in all manner of scenarios. As discussed in Chapter 1, all branches of the military are expected to dramatically increase their UAV fleets. In the commercial and public sectors we are already beginning to see the emergence of large-scale (“swarm”) deployments of UAVs for the purposes of storm chasing, geographical mapping, wildlife preservation, agricultural crop monitoring and search and rescue [FAA13][Geo13]. With Amazon.com and Domino’s Pizza recently announcing plans to deploy UAV swarms for the purposes of rapid order delivery [BBC13][CNN13], we are entering a new era of airborne vehicle proliferation. As the number of UAVs operating in the same geographical area increases, we quickly encounter a spectrum congestion problem due to sizable interference footprints from these multiple concurrent transmissions.
In the work presented in this chapter, we show that by making use of MIMO-enabled sig- naling techniques such as eigen beamforming, eigen beamnulling and spatial multiplexing (in addition to power control, link-adaptation and spectral segmentation), we can significantly increase the number of effective concurrent links in a given geographical area. Under several generic airborne deployment topologies, we show that by enabling these MIMO techniques, the number of effective concurrent links can be increased by more than a factor of 10x with respect to a conventional SISO system.
35 Figure 3.1: The current U.S. National Airspace System (NAS) [FAA13]
3.1.1 Background
3.1.1.1 MIMO Eigen Beamforming, Beamnulling and Spatial Multiplexing
MIMO eigen beamforming is analogous to traditional phased array beamforming, but elim- inates the need for precise control of antenna position and orientation while providing a similar level of gain. Based on the singular value decomposition (SVD) of transmit-side channel state information, a matrix of weights can be calculated from the eigenvalues of the channel. When applied to the transmit signal vector, maximum SNR is produced at the receiver.
Spatial multiplexing refers to the use of multiple antennas to transmit multiple informa- tion streams in parallel. These streams are referred to as spatial streams and are transmitted at the same time and in the same frequency band. MIMO spatial multiplexing is used in our proposed solution to enable us to maintain throughput when forced to operate within a narrower bandwidth.
There exists an inherent trade-off between multiplexing gain and array gain (beamform- ing) and we must adaptively decide how to best make use of our spatial degrees of freedom. For example, we may decide that it is best to transmit data along two spatial streams, beam- formed with our 4 antennas. Recall from the previous chapter that our channel matrix H can be decomposed via SVD as follows: 36 λ1 0 0 0 ∗ ∗ u11 ··· u14 v11 ··· v41 0 λ 0 0 H . . . 2 . . . H = UΣV = . .. . . .. . (3.1) 0 0 λ3 0 ∗ ∗ u41 ··· u44 v14 ··· v44 0 0 0 λ4
We can construct four TX antenna signals (x) from a linear combination of 2 spatial- stream data, to be beamformed along the first and second eigenmodes. This is done by multiplying our original TX signal vector (x0) with the V matrix obtained from SVD as follows:
0 0 0 x1 v11x1 + v12x2 v11 ··· v14 x0 v x0 + v x0 0 . . . 2 21 1 22 2 x = Vx = . .. . = (3.2) 0 0 0 v31x1 + v32x2 v41 ··· v44 0 0 0 v41x1 + v42x2 MIMO eigen beamnulling is a technique that relies on multiple antennas at the receiver. As demonstrated in [SZD08][SD11], using the SVD of the interference covariance matrix measured at the antennas, we can identify the largest eigenmode of the interference channel and null it out via linear spatial filter. This technique has been proven effective in providing upwards of 20 dB of interference suppression under experimental conditions both indoors and outdoors and in the presence of non-cooperative interference.
3.1.1.2 Spectral Segmentation, Power Control and Link Adaptation
Spectral segmentation requires the sensing of the spectrum to find frequency sub-bands of interest that are not occupied by another user or excessive amounts of interference. Our approach augments the traditional sensing techniques with a spatial measure of the inter- ference, and limits the choice of possible bandwidths to 8 sub-bands of the baseline legacy radio.
Power control refers to modulating the transmit power so that just enough SNR (with some margin) is used to close the link to the receiver. This is a very effective technique 37 for minimizing the interference footprint of a link by helping to improve frequency reuse and thus increase the number of concurrent links. Link adaptation is the complement to power control. For a given transmit power and set of channel conditions, link adaptation will choose the modulation-coding scheme (MCS) that maximizes throughput. Implicitly, link adaptation requires the ability of the radio to provide multiple MCS choices to be selected from on a per-packet basis.
3.1.2 Prior Art
One approach that has been studied in the literature is to treat this spectrum conges- tion problem as an interference alignment problem. There has been a tremendous amount progress in this area and we will not attempt to give a complete survey here. The upper bound of performance of such a multi-node, interference aligned network has been studied [CJ08] and was also later extended to include MIMO [GJ10]. In most of the studies of interference alignment, performance is measured in terms of sum capacity. While this is certainly an important metric to be aware of, in the air-to-ground situations we will study, it is assumed that each link-pair (point-to-point connection) is only concerned about meeting a specific data rate requirement while simultaneously reducing its own interference footprint.
Other work has taken a cognitive approach to this problem [ZDG08][PDL08], mostly operating on the principle of primary and secondary users. In the scenarios we are inter- ested in, we assume multiple point-to-point links with no explicit cooperation between nodes outside their own link-pair. Thus, in our work we assume links of equal priority and seek to maximize the total number of concurrent MIMO links that are supportable under their individual data rate delivery requirements. There has also been study of the impact of prac- tical, physical layer (PHY) operating conditions on the performance of a multi-user ad hoc MIMO network [ZDW11]. While this work considered the idea of combining eigen beam- forming and link adaptation, our work also considers spatial multiplexing, power control and spectral segmentation assuming link-pairs that are completely uncooperative with others.
Realistically, the combination of all of these techniques could easily lead to several hun-
38 dred unique modes of communications, complicating a practical implementation of such a system on an airborne platform. Finding the optimum mode for each airborne link-pair is not a straightforward task. Fortunately, this work is able to show that even with a heavily constrained search space and basic search algorithm, significant gains in total number of supportable MIMO concurrent links can be achieved.
3.1.3 Objectives
Techniques that enable multiple aerial systems to operate within the interference footprint of one another and successfully communicate with their respective ground stations in an uncoordinated manner will certainly enhance mission effectiveness. The aim of this work is to investigate an array of signaling-enhancement techniques that together, create a significant increase in the number of aerial systems that can operate within receiver-sensitivity range of one another. An important element of our proposed approach is that it increases the number of concurrent links within the same bandwidth as was allocated to the original (baseline) system of interest.
The signaling-enhancement techniques that will be studied are as follows:
• MIMO Spatial Multiplexing
• MIMO TX Eigen Beamforming
• MIMO RX Eigen Beamnulling
• Spectral Segmentation
• Power Control
• Link Adaptation
This study required the development of a link-adaption protocol (mode selection) to automatically combine and tune these enhancement techniques in realistic deployment sce- narios. The individual and ensemble impact of the aforementioned signaling techniques on 39 simultaneous uncoordinated link capacity was initially studied assuming a Rician channel model. These studies were repeated with actual channel measurements obtained from the channel measurement campaign in Chapter 2.
3.2 Mode Selection Algorithm
It is recognized that combining all of the signaling-enhancement techniques could easily lead to hundreds of unique configurations for even a handful of airborne nodes attempting to operate within close proximity of one another. It is important to have an effective and prac- tical means of going through the trade-off space, allowing the overall system to maximize the number of concurrent links without requiring the airborne nodes to explicitly coordinate with one another. In response to this challenge we first constrained the TX power and band- width combinations that the aerial systems were allowed to utilize. Secondly, we developed a mode-selection protocol to independently govern the behavior of each aerial system.
3.2.1 Transmit Bandwidth and Power Constraints
We first define the “baseline system” as a conventional SISO system with QPSK modulation,
1 r= 2 convolutional coding rate and 4 W total TX power. We then impose the following constraints on the combination of TX power and bandwidth relative to this baseline. We define 100%-bandwidth and 100%-power as the bandwidth and TX power, respectively, that would be used by the baseline system. In order to link the simulation effort with the channel study in Chapter 2, a 100%-bandwidth of 5 MHz was assumed (as well as a center frequency of 2.4 GHz). This band of operation was then partitioned into eight equal sub-bands of 7 OFDM subcarriers each (for a total of 56 subcarriers). The airborne nodes were allowed to choose any combination of these sub-bands, subject to the signaling/resource mode currently in use. The allowable resource modes are given in Table 3.1:
As an example, an airborne node may desire to use a bandwidth equal to 25% of the baseline system, and transmit up to 2x (200%) higher power than the baseline system (Mode 5). It must then deliver the same final data rate as the baseline system, requiring a higher- 40 Resource Mode Spatial Streams Bandwidth TX Power Modulation
1 1 4 25% Up to 100% QPSK r = 2 1 2 2 50% Up to 100% QPSK r = 2 1 3 1 100% Up to 100% QPSK r = 2 1 4 4 12.5% Up to 200% 16-QAM r = 2 1 5 2 25% Up to 200% 16-QAM r = 2 1 6 1 50% Up to 200% 16-QAM r = 2 2 7 2 12.5% Up to 400% 64-QAM r = 3 2 8 1 25% Up to 400% 64-QAM r = 3
Table 3.1: Resource modes available to the airborne wireless communication system order modulation type and an additional spatial stream. Further, as it has decided to use 25% of the total bandwidth, it may determine that subcarriers 1-7 as well as 50-56 are experiencing the least amount of interference and will use these for transmission. As a final step, the TX power will be trimmed down to the minimum amount required to close the link (within a reasonable margin), subject to the current signaling parameters.
3.2.2 Resource Mode Selection Engine
The decision protocol for automatic resource mode selection of the airborne communication system is shown in Fig. 3.2. This diagram illustrates how spectrum sensing, spectrum parti- tioning, and power control are all used in our proposed system. Our proposed protocol takes a simple approach whereby the autonomous airborne node first tries to establish a connection using Mode 1. This index utilizes 100%-power, but only a fraction of the bandwidth and allocates itself to the sub-bands experiencing the least of amount of interference. If its link is not supportable under the current path-loss, eigen beamforming/beamnulling SINR gains and interference environment, the airborne node sequentially moves through its available modes until a supportable mode is encountered. Transmit power optimization (based on post-processing SNR projections) is then applied to further reduce the interference footprint as much as possible.
41 Figure 3.2: Resource mode selection engine
3.2.3 Mode Selection Engine in Action
An overall evaluation of this operating protocol can obtained by looking at the probability distribution of the different modes as a function of the number of desired concurrent links. As an example, the results of Scenario I (Free Roam Topology) are presented in Fig. 3.3. When the number of concurrent links is equal to 1, the distribution of selected modes is dominated by modes 1-3. As the number of nodes increases, it becomes increasingly harder to close the link with these first three modes and thus, modes 5, 6 and 8 begin to play a serious role. It makes intuitive sense that as more links are added, we begin to see more links spreading out into the other resource access options. Similarly, for the mode distribution of Scenario III (Perimeter Patrol Topology) shown Fig. 3.4, we see an initially dominant mode 8 giving way to modes 6 and 7 as the number of simultaneous links increases. It is clear from these two examples that the optimal mode distribution is heavily dependent on the particular airborne deployment pattern.
We gain insight into the behavior of the power control algorithm by looking at the distribution (over 1000 runs) of transmitted power for 10 concurrent link-pairs. This is shown for Scenario I in Fig. 3.5, where it is seen that a majority of the links are closed with less than 2 W of transmit power per antenna. We also observe that power control attempts to spread the distribution of links away from the maximum power limits (1, 2 and 4 W per
42 antenna) imposed by the resource modes, resulting in a continuous distribution of optimal power.
Figure 3.3: Scenario I (Free Roam), final mode distribution
Figure 3.4: Scenario III (Perimeter Patrol), final mode distribution
43 Figure 3.5: Scenario I (Free Roam), final power (per-antenna) distribution
3.3 Simulation Engine
3.3.1 Framework Overview
The overall simulation framework is illustrated in Fig. 3.6. For each trial, a random deploy- ment of airborne nodes is generated, subject to the scenario-specific boundaries discussed in Section 3.3.4. The normalized fading channels are then generated and received power is calculated depending on path loss (transmission distance) factors. A modified version of this simulation engine replaces channel generation with a random selection of actual cap- tured channels from the measurement campaign of Chapter 2. Transmit eigen beamforming weights are then applied on a per-subcarrier basis before passing all parameters to the deci- sion engine which handles mode selection, sub-band selection and power optimization.
This process is carried out in an iterative fashion. As each update of an individual link- pair’s transmission mode changes the interference environment for all other link-pairs, the mode selection algorithm must iterate several times before completion. The simulation ter- minates when the system reaches a steady-state. Initial versions of the simulation framework were inspired by previous work on MIMO concurrent link simulations [ZDG08][ZD11].
44 Figure 3.6: Simulation flow diagram
3.3.2 Air-to-Ground Channel Assumptions
The simulated mobile air-to-ground channel is assumed to be Rician [NMD03] and the chan- nel matrix H is randomly generated between each TX and RX node according to the fol- lowing expression:
r K r 1 H = H H H + H (3.3) K + 1 LOS LOS K + 1 Rayleigh
In Eq. 3.3, the line-of-sight (LOS) component is assumed to have a random arrival phase and the multipath fading component is assumed to be Rayleigh distributed. The K factor used for all simulated channels was assumed to be 10 dB. In the alternate simulation envi- ronment, captured channels from the various ground locations of the channel measurement campaign in Chapter 2 were pooled together and binned according to link distance. Depend- ing on the link distances randomly generated by the topology generator, normalized channel captures were randomly selected from their associated distance-bin and scaled to match the calculated received signal strength.
Each channel was scaled according to the associated link’s path loss which was calculated
45 for each link-pair as follows:
4πdf p L = 10 log (3.4) path 10 c
In Eq. 3.4, d is the transmission distance in meters, f is the center frequency (assumed to be 2.4 GHz in this study), p is the path loss exponent (equal to 2, assuming free-space) and c is the speed of light.
3.3.3 Post-Processing SNR Calculation
Post-processing SNR is calculated during each iteration to guide the the mode selection process. It is again calculated for each link-pair after all link resource modes have been selected, in order to determine if the resulting link pairs meet their SNR requirements. All pairs that meet their respective requirements will be declared as supported and the rest are declared as unsupported. During post-processing SNR calculation for a specific link pair, the interference channels between all undesired TX nodes and the desired RX node are first calculated. When calculating these interference channels, eigen beamforming is taken into account as the other TX nodes attempt to steer their transmissions toward their respective intended receivers. The ideal MMSE detector seeks to find the coefficient W , which will minimize the criterion: E [W y − x][W y − x]H (3.5) where, y = H x + n (3.6)
In Eq. 3.6, y is the received waveform, x is the original waveform and H is the channel matrix. Solving for W yields:
−1 H H −1 W = Ryy Ryx = H (H H + N0I ) (3.7)
Since we assume knowledge of the interference channels between all TX nodes and the RX node of interest, we can expand the H matrix to include this information as follows:
h i H = H link H intf,1 ··· H intf,n (3.8) 46 All H matrices in the above expression have been modified by their respective beamforming V matrices and scaled according to received power (resulting from individual link’s TX power and path-loss factors). The resulting MMSE receiver will attempt to extract all data streams including interference data streams. Since the diagonal elements of the effective post-
processing channel matrix (H eff = WH ) represent the data stream signal power while the off-diagonal elements represent residual post processing noise and interference contributions, the PPSNR can be calculated for each data/interference stream as follows:
2 (H eff,ii) PPSNRi = (3.9) Nlinks 2 Σk6=i (H eff,ik) + noise
3.3.4 Network Topology Generation
Before execution of each simulation trial, a topology generator randomly places all TX and RX nodes within the confines of a geometric area defined by a deployment scenario. These scenarios will be described in the following subsections. In all scenarios, airborne nodes were given an altitude randomly selected in the range of 3-4 km (10,000-13,000 ft).
3.3.4.1 Scenario I: Free Roam Topology
The Free Roam topology is the most generic deployment scenario, characteristic of a truly uncoordinated environment. Unlike the remaining deployment scenarios, the mission objec- tives of each link are likely to be completely unrelated. All TX and RX nodes are allowed to be placed anywhere within the 100 km x 100 km square area. It is expected that this scenario will create the most diverse set of interference scenarios. The Free Roam topology is depicted in Fig. 3.7.
47 Figure 3.7: Free Roam Topology, example realization
3.3.4.2 Scenario II: Sensor Hotspot Topology
In the Sensor Hotspot topology, multiple airborne nodes are operating over a single “hot” area, communicating with different ground stations placed outside a keep-out zone. Disaster response is an example of a scenario where we might see a deployment configuration such as this, with airborne assets transmitting situational awareness information back to command posts safely outside of the threat area. We assume that the aerial systems operate within a 20 km radius at the center, and that the ground stations are randomly placed within a ring with an inner radius of 40 km, and an outer radius of 100 km. The Sensor Hotspot topology is depicted in Fig. 3.8.
48 Figure 3.8: Sensor Hotspot Topology, example realization
3.3.4.3 Scenario III: Perimeter Patrol Topology
In the Perimeter Patrol topology, we consider airborne assets patrolling a circular perimeter surrounding a protected area. We may see deployment scenarios such as this in situations such as border patrol and early warning systems for high-value ground assets. All receivers lie within a 20 km radius, as depicted in Figure 4. All aerial systems operate in an annular region bounded by two circles of 80 km and 100 km radius (concentric with the 20 km radius base-circle). The Perimeter Patrol topology is depicted in Fig. 3.9.
49 Figure 3.9: Perimeter Patrol Topology, example realization
3.3.4.4 Scenario IV: Outpost Deployment topology
The Outpost Deployment topology represents a situation in which a small, forward operating base or outpost is constructed in a remote location. All airborne assets are communicating with a single command post possessing multiple antennas spread out over a 50 meter base of operations. With only a 12 km maximum flight radius, the baseline per-antenna TX power for airborne nodes in this scenario was lowered from 1W to 16 mW (18 dB power reduction). The Outpost Deployment topology is depicted in Fig. 3.10.
50 Figure 3.10: Outpost Deployment Topology, example realization
3.3.4.5 Scenario V: Maximum Coverage Topology
In the Maximum Coverage topology, we consider a situation where ground stations are spaced just far enough apart such that their airborne coverage areas only intersect enough to provide full coverage of a rectangular area. Possible applications for this type of deployment include agricultural/crop monitoring and surveillance. All aerial systems maintain a maximum flight radius of 12 km from their respective ground stations. It is expected that this 11-node inter- ference environment will be quite benign and that users may desire to increase surveillance effectiveness in each region by deploying additional airborne nodes for each ground station. Thus, we examine the cases where the number of airborne nodes per monitoring zone is increased to 2, and 3 nodes (for a total of 22 and 33 nodes, respectively). As in the Outpost Deployment topology, due to the 12 km maximum flight radius, the baseline per-antenna TX power for airborne nodes in this scenario was lowered from 1W to 16 mW. The Maximum Coverage topology is depicted in Fig. 3.11.
51 Figure 3.11: Maximum Coverage Topology, example realization
3.3.4.6 Scenario VI: Convoy Security Topology
The Convoy Security topology considers a convoy traveling laterally with airborne surveil- lance units providing support to forward and rear protection assets via a centrally-located command unit. This particular convoy possesses 11 airborne assets and a single ground terminal to which all 11 airborne nodes send data. Three of the aerial systems, operating as early warning units, fly within circular regions with a 20 km radius. The remaining 8 aerial systems, operating as direct support to ground units, have a 5 km roam radius centered just ahead and behind the convoy. The Convoy Security topology is depicted in Fig. 3.12.
52 Figure 3.12: Convoy Security Topology, example realization
3.3.5 Practical Considerations
Several practical nonidealities were considered in the design of the simulation engine. Es- timation of the channel between TX and RX in a link pair was assumed to be corrupted by noise. Similarly, measurement of the channel between interferer and RX node of interest was also assumed to be corrupted by noise. In addition to this, transmitter-side channel state information (CSI) was also considered to contain noise. The noise was generated as a random variable with a zero-mean Gaussian distribution (variance equal to thermal noise level) on I (real) and Q (imaginary) components. Since I and Q are independent, their combination results in a Rayleigh amplitude distribution with a uniformly distributed (0 to 2π) random phase. The same model for noise on channel estimation was applied to the receiver (data channel and interference channel) as well as the transmitter (channel used for beamforming calculations). We assumed the existence of four training symbols for channel estimation (inspired by the 802.11n standard) enabling a 6 dB averaging gain for channel estimates. It was also assumed that channel estimation between desired nodes could be ob- tained interference-free. In reality, this could become a performance-limiting factor, requiring more sophisticated training, learning or adaptive cancellation techniques to be addressed in
53 future work.
3.4 MIMO Concurrent Link Performance
For each deployment topology outlined in Section 3.3.4, several simulation configurations were studied. Each simulation run consisted of 1000 deployment topology realizations. Sep- arate runs for total concurrent link counts of 1 through 10 were executed (for a total of 10,000 runs per simulation configuration). In order to gauge the effectiveness of some of the individual features of our system, additional simulations were run with various features disabled. For fair comparison, the same topology realizations that were generated for the full-featured simulation were reused for simulations of the disabled systems. This entire set of simulation configurations was executed with simulated channels as well as with channels obtained by actual air-to-ground channel measurements.
3.4.1 Scenario I: Free Roam Topology
As mentioned in Section 3.3.4.1, this scenario involves multiple nodes that are randomly placed in an operational area of 100 km x 100 km. For a total attempted concurrent link count of 10, an average of 8.41 links were considered supportable when simulated channels were used, as shown in Fig. 3.28. This metric rose to 8.98 when real-world channels were used. Although the concurrent link capacities of the simulated channels and actual channels were similar, the behavior of the mode adaptation engine differed significantly. We also observe from Fig. 3.28 that the contributions of TX eigen beamforming, RX eigen beamnulling, power control (optimization) and mode control (link adaptation) become increasingly compelling as the number of concurrent links operating in the area increases. It is also clear that without MIMO capabilities, the number of concurrent links becomes severely limited as the link count grows past 4-5. The MIMO-disabled simulation involved disabling RX eigen beamnulling, TX eigen beamforming, removing modes with more than 1 spatial stream, and enabling only 1 antenna on the TX and RX side (TX power was boosted by 6 dB for fairness). The “fully disabled” system is equivalent to the baseline system (100%-bandwidth, 100%power, QPSK r 54 1 = 2 ) and required disabling link adaptation and power control as well as the aforementioned MIMO features. Such a system is rendered totally ineffective after the link count exceeds a single link.
Figure 3.13: Scenario I (Free Roam), impact of individual features on link capacity for simulated (left) and captured (right) channels
We can see from Fig. 3.14 that in the case of simulated channels, 2 and 4 spatial stream modes initially dominated. As the interference environment became more congested (moving toward 10 links on the x-axis), we see that supporting 4 spatial streams became too difficult. As the 4 spatial stream rates became less dominant, the single spatial stream modes became more dominant. This implies that although the channels may have been conditioned well enough for 4 spatial streams, in a high-interference environment we are generally better served by backing down to 1-2 spatial stream modes and relying more heavily on other techniques (e.g., TX eigen beamforming, RX eigen beamnulling and spectral segmentation). Interestingly, when real-world channels were used it was found that 4 spatial stream modes were generally harder to support. While 2 spatial stream modes were also slightly harder to support (resulting in even more single spatial stream dominance), they nonetheless played a significant role in minimizing the spectral interference footprint of links that could support multiple spatial streams.
Fig. 3.15 tracks the power and bandwidth utilization of the concurrent links as the total link count is increased. In terms of maximum power allowances, we see in both the simulated 55 Figure 3.14: Scenario I (Free Roam), spatial stream distribution for simulated (left) and captured (right) channels and actual channel cases that as the link count increases, we move toward a more even distribution among the available transmit power limits. We note that within these three power limit choices lies a continuum of optimized power values which are ultimately used by each link. As shown previously in Fig. 3.5, the power control engine works to reduce final transmit power subject to the mode-specific SINR requirements for the target data rate. The bandwidth utilization, which is also given in Fig. 3.15, indicates that although the distribution of 100%, 50% and 25%-BW modes differed between simulation and actual channels, they all play a significant role in optimizing the overall interference environment. We also note that the 12.5% bandwidth modes do not see much usage. This is largely because of the fact that this low of a bandwidth requires higher overall SINR due to the use of higher order modulation types (16-QAM and 64-QAM) and/or multiple spatial streams in order to meet the target data rate.
From a deployment planning perspective, the metric used in Fig. 3.28 (average number of supportable links) is not as useful for predicting practical user satisfaction. Thus, we define the coverage metric as the number of supportable links for a specified percentage of deploy- ment realizations. From Fig. 3.16, we can see that for Scenario I (using actual channels), up to 9 links were supportable for at least 70% of the physical deployment realizations. When a
56 Figure 3.15: Scenario I (Free Roam), bandwidth and allowed power distribution for simulated (left) and captured (right) channels
Figure 3.16: Scenario I (Free Roam, captured channels), number of links supported under 70% (left) and 90% (right) coverage guarantee
90% coverage guarantee was used instead, this metric fell to 8 links. In contrast to this, with all MIMO features disabled, zero links are supportable even with a 70% coverage guarantee when the link count exceeds 7.
For comparison simplicity of overall concurrent link capacity between deployment scenar-
57 ios, only coverage-guarantee metrics will be given for the remainder of this section. Analysis of individual features and mode distributions for the remaining scenarios is presented in Section 3.5.
3.4.2 Scenario II: Sensor Hotspot Topology
As mentioned in Section 3.3.4.2, this scenario involves airborne nodes which are randomly placed within a central, 20 km radius area-of-interest. The ground stations are randomly placed within a ring with an inner radius of 40 km and an outer radius of 100 km. Our simulations indicated that on average, 8.95 and 9.28 out of 10 links were supportable for simulated and real channels, respectively. From Fig. 3.17, we can see that for Scenario II (using actual channels), up to 9 links were supportable for at least 70% of the physical deployment realizations. When a 90% coverage guarantee was used instead, this metric fell to 8 links. When all MIMO capabilities are disabled, and 7 or more link pairs exist in the deployment area, zero links are supportable under even a 70% coverage guarantee. Additionally, the total number of supported links never exceeds 2.
Figure 3.17: Scenario II (Sensor Hotspot, captured channels), number of links supported under 70% (left) and 90% (right) coverage guarantee
58 3.4.3 Scenario III: Perimeter Patrol Topology
As described in Section 3.3.4.3, this scenario considers a group of 10 airborne assets patrolling a circular perimeter surrounding a base of operations. All receivers lie within a 20 km radius. All aerial systems operate in an annular region concentric with the 20 km radius base-circle and bounded by two circles of 80 km and 100 km radius. Our simulations indicated that on average, 7.85 and 8.12 out of 10 links were supportable for simulated and real channels, respectively. From Fig. 3.18, we can see that for Scenario III (using actual channels), up to 7 links were supportable for at least 90% of the physical deployment realizations. When all links are reduced to SISO-only capabilities, and 6 or more link pairs exist in the deployment area, zero links are supportable under even a 70% coverage guarantee. Additionally, the total number of supported links never exceeds 1. In comparison to Scenario I and II, the reduced performance of this scenario makes intuitive sense, as we have more tightly restricted the roaming areas of the TX and RX nodes. The more the links become physically aligned, the harder it is to isolate individual interference footprints.
Figure 3.18: Scenario III (Perimeter Patrol), number of links supported under 70% (left) and 90% (right) coverage guarantee
The bandwidth and power allocations for Scenario III are given below as Fig. 3.19. It is interesting to note that unlike all other scenarios, the Perimeter Patrol topology resulted in the lowest utilization of 100%-bandwidth and 100%-power. This implies that the mode 59 adaptation engine attempted to separate the physically aligned signal paths into smaller frequency bands with less overlap.
Figure 3.19: Scenario III (Perimeter Patrol), bandwidth and allowed power distribution for simulated (left) and captured (right) channels
3.4.4 Scenario IV: Outpost Deployment Topology
As mentioned in Section 3.3.4.4, all airborne assets in this scenario are communicating with a single command post possessing multiple antennas, spread out over a 50 meter base of operations. With only a 12 km maximum flight radius, the baseline TX power for airborne nodes in this scenario was lowered by 18 dB. Our simulations indicated that on average, 9.63 and 9.67 out of 10 links were supportable for simulated and real channels, respectively. From Fig. 3.20, we can see that for Scenario IV (using actual channels), up to 9 links were supportable for at least 90% of the physical deployment realizations. Relaxing the coverage guarantee to 70% allows all 10 links to be supported concurrently. Similar to previous scenarios, a SISO-only system completely fails after 7 links enter the area of operations and the total number of supported links never exceeds 2.
60 Figure 3.20: Scenario IV (Outpost Deployment), number of links supported under 70% (left) and 90% (right) coverage guarantee
3.4.5 Scenario V: Maximum Coverage Topology
As described in Section 3.3.4.5, this scenario considers a situation where a wide area of assets must be monitored. All aerial systems maintain a maximum flight radius of 12 km from their 11 respective ground stations, leading to only a small amount of physical overlap. In order to increase surveillance effectiveness, the number of airborne assets per roam area was doubled to 22 and then tripled to 33 total links. Our simulations indicated that on average, 28.44 and 31.74 out of 33 links were supportable for simulated and real channels, respectively. From Fig. 3.21, we can see that for Scenario V (using actual channels), excellent coverage is provided. Up to 30 links were supportable for at least 90% of the physical deployment realizations. As expected, the SISO version of this deployment cannot even support 5 links under a 70% coverage guarantee and comes to a complete halt when 3 nodes per roam area are deployed.
61 Figure 3.21: Scenario V (Maximum Coverage Topology), number of links supported under 70% (left) and 90% (right) coverage guarantee
3.4.6 Scenario VI: Convoy Topology
As mentioned in Section 3.3.4.6, in this scenario we consider a convoy with 11 airborne assets and a single ground station to which all 11 airborne nodes send data. Three of the aerial systems are operating as early warning systems with a 20 km roam radius. The other 8 aerial systems, operating as direct convoy support, have a 5 km radius of operation just ahead and behind the convoy. Our simulations indicated that on average, 9.07 and 9.59 out of 11 links were supportable for simulated and real channels, respectively. Up to 9 MIMO links (and only 1 SISO link) were supportable for at least 90% of the physical deployment realizations.
3.4.7 MIMO Concurrent Link Performance Summary
A summary of all coverage-guarantee results under actual (captured) channel realizations is given as Table 3.2:
62 Scenario Total Links Avg. Supported 70% Coverage 90% Coverage I [Free Roam] 10 9.0 (90%) 9 (90%) 8 (80%) II [Hotspot] 10 9.3 (93%) 9 (90%) 8 (80%) III [Patrol] 10 8.1 (81%) 7 (70%) 7 (70%) IV [Outpost] 10 9.7 (97%) 10 (100%) 9 (90%) V [Max Cover] 33 31.7 (96%) 33 (100%) 31 (94%) VI [Convoy] 11 9.6 (87%) 9 (82%) 9 (82%)
Table 3.2: Concurrent links simulation result summary, captured channels
3.5 Effectiveness of Individual Features
Quantification of performance impact due to individual features is not a straightforward process. Clearly, the benefits of each of the proposed techniques are a function of not only channel conditions and deployment topology but also the interaction between features. In order to gain insight into the contribution to overall concurrent link performance of each of the techniques as well as gain an appreciation for their necessity in the ensemble of proposed methods, additional simulations were run with various features disabled. For fair comparison, the same topology realizations that were generated for the full-featured simulation were reused for simulations of the disabled systems. The techniques which were not disabled remained active, and the decision engine continued to make the best choice of transmission mode given its now reduced feature-set. Both simulated channels as well as actual channels were considered.
3.5.1 MIMO Spatial Multiplexing Effectiveness
The utilization probability of 1, 2 and 4 spatial stream modes for Scenarios II-V are given below as Fig. 3.22-3.24, respectively. As mentioned in the earlier discussion of Scenario I results (Section 3.4.1), while the simulated channels were able to support 4 spatial stream modes, runs using real-world captured channels were effectively limited to 2 spatial streams. This observation aligns exactly with the spatial stream capability analysis of the actual air-to-ground field trials in Chapter 2. In both the simulated and real channel cases, as
63 the number of concurrent links in the area increased, single spatial stream modes became dominant. In these high interference-congestion situations, it is clear that the links were better served by backing down to one spatial stream and relying more heavily on other MIMO techniques (e.g., eigen beamforming/nulling and spectral segmentation). Despite this, two spatial stream modes served to minimize the bandwidth of interference for links that could support multiple spatial streams, enabling support for the last several links that would otherwise be unsupportable. With the exception of Scenario III (Perimeter Patrol), around 10-20% of the final selected modes still made use of spatial multiplexing when a high link count was reached.
Figure 3.22: Scenario II (Sensor Hotspot), spatial stream distribution for simulated (left) and captured (right) channels
64 Figure 3.23: Scenario III (Perimeter Patrol), spatial stream distribution for simulated (left) and captured (right) channels
Figure 3.24: Scenario IV (Outpost Deployment), spatial stream distribution for simulated (left) and cap- tured (right) channels
3.5.2 MIMO RX Eigen Beamnulling Effectiveness
In the fully featured system, the estimated interference channels are used in the calculation of a weight matrix used to suppress interference and extract desired TX spatial streams. In the disabled system, interference estimation is not considered during the weight vector calculation for mode selection, and thus negatively impacts the final PPSNR after MMSE
65 detection. We observe from the results of Scenario II (Sensor Hotspot) in Fig. 3.25 that for lower concurrent link counts, disabling RX beamnulling does not significantly degrade performance. The mode adaptation algorithm is able to effectively compensate for this disability until a knee point at around 7 links. After this breaking point, the absence of RX beamnulling creates a severe reduction in concurrent link capacity as the system is no longer able to deal with the added congestion. Similar behavior is apparent in Scenarios III (Fig. 3.26, Perimeter Patrol), IV (Fig. 3.27, Outpost Deployment) and to a lesser degree, Scenario I (Fig. 3.13, Free Roam).
Figure 3.25: Scenario II (Sensor Hotspot), impact of individual features on link capacity for simulated (left) and captured (right) channels
3.5.3 MIMO TX Eigen Beamforming Effectiveness
In the fully featured system, SVD is carried out on all desired TX-RX channels (SVD{H} = UDVH ). This produces a spatial TX weight vector, V, which is multiplied into the transmit signal vector in order to beamform the spatial streams along the strongest eigenmodes of the channel. In doing so, we create a new effective channel matrix, producing optimal per- spatial-stream SNR. In order to disable eigen beamforming, the TX weight vector produced by SVD is simply ignored and the original channel matrix H remains unchanged. Concurrent link performance generally degrades for all scenarios when eigen beamforming is removed, but it is most apparent in Scenario III (Perimeter Patrol) as shown in Fig. 3.26 below. Due 66 to the high probability of physical alignment of signal paths and long transmission distances in this scenario, TX beamforming becomes a critical factor in isolating unintended receivers from interference. Without this feature, the system is never really able to support more than 2 links. Scenarios I (Fig. 3.13, Free Roam), II (Fig. 3.25, Sensor Hotspot) and IV (Fig. 3.27, Outpost Deployment) also show significantly degraded concurrent link capacity, with the number of supported links quickly saturating at roughly half the number of links desired.
Figure 3.26: Scenario III (Perimeter Patrol), impact of individual features on link capacity for simulated (left) and captured (right) channels
3.5.4 Power Control Effectiveness
When considering the impact of power control, there are two components at work. The upper limit of transmission power is set according to the resource index (mode) selection as indicated by Table 3.1. This can be seen as first level (coarse) power control. The second level of power control optimizes within these upper limits, reducing power down to the mini- mum necessary to close the link (with a 3 dB margin). Although both levels of optimization can be independently disabled, we study the effect of disabling both, i.e., the transmit power for all nodes is fixed at 100% regardless of resource index and no secondary optimization is attempted. The results shown in Fig. 3.27 (Scenario IV, Outpost Deployment) are rep- resentative of most scenarios and indicate that, similar to TX beamforming, the general impact of this feature becomes most obvious at larger concurrent link counts. Lack of power 67 control in Scenario III (Fig. 3.26, Perimeter Patrol) in particular, leads to disastrous results as transmitters easily desensitize the unintended receivers in the ground station cluster.
Figure 3.27: Scenario IV (Outpost Deployment), impact of individual features on link capacity for simulated (left) and captured (right) channels
3.5.5 Link Adaptation, Spectral Segmentation, Variable Bandwidth
Separating link-adaptation (mode control), spectral segmentation and variable bandwidth techniques is not as straightforward as with previous techniques since they are tightly cou- pled. This being the case, we studied the effect of removing all three features as a whole. To this end, the disabled simulation was limited to the resource mode most similar to that of the baseline SISO system: 1 spatial stream, 100%-bandwidth, up to 100%-power, and QPSK r =
1 2 modulation. Second-level power control (optimization) remained active, allowing the TX power to drop below 100%. The result of disabling mode control was quite consistent across most scenarios, limiting all the 10-link scenarios to around 4 supportable links as shown in Scenarios I (Fig. 3.13, Free Roam), II (Fig. 3.25, Sensor Hotspot), III (Fig. 3.26, Perimeter Patrol), and IV (Fig. 3.27, Outpost Deployment). As depicted in Fig. 3.28, Scenario V (Maximum Coverage) was only able to support 23/33 concurrent links despite its relatively benign interference environment.
68 Figure 3.28: Scenario V (Maximum Coverage), impact of individual features on link capacity for simulated (left) and captured (right) channels
3.6 Discussion and Conclusions
The goal of this work was to explore the potential benefits of incorporating several powerful MIMO-enabled processing techniques into a radio system in order to improve transmission conditions for multiple air-to-ground wireless link-pairs. To this end, a simple decision engine algorithm was proposed which could be very easily implemented and deployed in an actual system. A simulation engine was built around this, capturing the effects of link- adaptation, spectral segmentation, variable bandwidth, power control, eigen beamforming, eigen beamnulling and spatial multiplexing individually, and in concert with each other. Realistic implementation losses and realistic aerial vehicle deployment topologies were also taken into account in our simulations. Under all deployment scenarios and given a realistic user data rate requirement, we desire to have as many concurrent MIMO link pairs as possible within a space where only one simple SISO link-pair can operate effectively. Across all six scenarios which were simulated, large gains in the number of supportable concurrent links were observed. By disabling individual features, it is apparent that all features are critical to overall uncoordinated link-capacity in a confined area of operation.
Across all deployment scenarios studied in this work, we observe a compelling increase in aggregate uncoordinated link-capacity. As shown in Scenarios I (Fig. 3.13, Free Roam),
69 II (Fig. 3.25, Sensor Hotspot), III (Fig. 3.26, Perimeter Patrol), and IV (Fig. 3.27, Outpost Deployment), without MIMO capabilities, link capacity hits a performance knee after around 4 links enter the deployment area. The MIMO-disabled simulation consisted of disabling RX eigen beamnulling, TX eigen beamforming, removing modes with more than 1 spatial stream, and enabling only 1 antenna on the TX and RX side (TX power was boosted by 6 dB for fairness). The interference congestion is so severe in this case that by the time 8 links have entered the area, virtually none of the link pairs are able to deliver adequate throughput. Disabling the remaining features (power control and link adaptation) returned the system to the original baseline system which can only support a single link pair. As soon as two or more uncoordinated link pairs enter the area, throughput capacity comes to an abrupt halt. For the airborne 2.4 GHz system case study in this work (5 MHz available bandwidth, 1W of TX power per-antenna), performance was generally better when using the channels captured from the measurement study of Chapter 2. This is an interesting result, especially when considering that the simulated channels were generally of higher rank (better able to support 4 spatial streams).
While results of this study are promising, there are several practical considerations that require more investigation. The first and foremost is the issue of obtaining reliable forward and reverse channel estimates in the presence of interference. It is likely that more sophis- ticated channel training will be required. The situation is made more complicated by the potentially high mobility of airborne links, limiting the amount of time that the systems have to properly estimate channel conditions and converge within their adaptation loops. Beyond these issues, we also recognize that only one set of transmission modes was studied in this work. We make no claims that this set of modes is near optimum. The good news is that even with such a basic set of resource modes available to the link adaptation engine, very good results were achieved. It is left to future work to propose a more optimal approach to the MIMO link adaptation aspect.
70 CHAPTER 4
Enabling Airborne Full-Duplex Communications
4.1 Overview
Following the positive results of the uncoordinated MIMO concurrent-link capacity study in Chapter 3, and seeking additional methods to further increase aggregate throughput for air- borne links, we consider the well-known concept of full-duplex communications. The poten- tial throughput enhancement delivered by full-duplex signaling is limited by desensitization of the receiver hardware due to self-generated interference (SI). A survey of the literature (discussed in Section 4.2.1) found that current SI cancellation solutions are prohibitive for long-range/airborne applications due to power handling limitations. Additionally, no solu- tions were easily scalable for an arbitrary number of MIMO antennas and arbitrary antenna placements. An ideal solution from an airborne MIMO perspective would be a self-contained (in-line deployable) and adaptive system requiring no manual calibration or specific antenna placement.
The vast majority of wireless communications systems in use today continue to operate in only half-duplex mode. It is generally taken for granted that a wireless node cannot easily simultaneously transmit and receive in the same channel. In recent years, there has been renewed interest in increasing network capacity by revisiting the idea of full-duplex wireless communications and research into this area has produced many promising results. Several multi-stage self-interference (SI) cancellation techniques have been proposed (described in Section 4.2.1), indicating that total suppression in the range of 60-80 dB is quite feasible. Most of these works have focused on indoor, WiFi-like applications, implying typical trans- mission powers of around 100 mW (20 dBm), typical indoor channel models with delay
71 spreads on the order of tens of nanoseconds (rooms with dimensions in the tens of feet) and low mobility (e.g. human walking speed of 2-3 mph). Many works achieve good suppres- sion but impose restrictions on antenna configuration, placement or type. Others require explicit signaling or control information which are internal to the existing radio system being augmented for full-duplex. Some also make assumptions about the structure of the signal (desired RX signal and TX-SI).
This work presents a design that is versatile and fully self-contained. We do not impose significant restrictions on antenna type or antenna placement, nor do we require a second TX antenna for null-forming as presented in [CJS10] and [RGK10]. This design also does not require externally measured channel-state information (CSI), or a copy of the TX baseband signal from the radio system (or both, as in [JCK11], [SPS11]) or any a priori knowledge of the structure of the RX signal-of-interest (SOI) or TX self-interference (SI) signals. Our only requirement is a clean (wired) copy of the RF TX signal, which is typically simple to obtain. These features enable our SI cancellation system to be attached at the antenna port of virtually any existing radio system with minimal modification of that radio system. Further, in order to enable full-duplex wireless communications for outdoor/airborne communications we must consider the fact that such systems could be deployed in a variety of scenarios ranging from urban canyon to rural or woodland areas. Thus, the system must not only be adaptable but also allow tuning of adaption parameters to meet quality-of-service (QoS) goals. In addition, TX output power in such situations is often much greater than in typical indoor WiFi scenarios. As an example, first-responder radios possess output capabilities of >1W (+30 dBm), and even up to >100W (+50 dBm) [Mot13]. Cellular base-stations have been providing more than 100W [TIT99] for over a decade and state-of-the-art cell tower amplifiers have reached levels >350W [WMO05]. The general approach of our multi-stage solution is to first cancel the dominant (typically, line-of-sight) component in the RF domain. We then remove, in the digital domain, the residual dominant-tap contribution as well as additional multi-path components. While these general concepts have existed in literature for many years, applying these techniques to create a self-contained, self-interference cancellation solution that is able to operate in a variety of channel conditions and is agnostic to the host
72 radio system, presents a significant challenge.
A prototype of our universal, per-antenna SI cancellation design was built to gauge feasibility from a hardware complexity perspective as well as to gain insight into the potential practical issues that would limit such a system in the field. While this prototype was built for testing on a single antenna system, it can be extended to MIMO by simply cascading SI cancelers and tapping off SI reference signals for all MIMO transmit antennas. As illustrated in Fig. 4.1, cancellation for each individual receiving MIMO antenna would proceed in order of received signal strength, i.e., co-site SI first, then next closest antenna and so on.
Figure 4.1: Extending the per-antenna self interference canceler solution to MIMO
4.2 Background
4.2.1 Prior Art
As previously mentioned, recent work on SI cancellation for enabling practical full-duplex wireless generally employs a multi-stage approach by stacking the suppression effects of each stage to achieve a larger amount of total suppression (well in excess of 60 dB, nearing 80 dB in some cases). Recent experimental results were obtained with the use of off-the-shelf (OTS) open-source radio platforms such as USRP [Res14] and WARP [WAR14]. The following is 73 a brief review of individual SI cancellation techniques that essentially fall into three classes: Antenna-Based Suppression, RF Cancellation and Digital Cancellation.
4.2.1.1 Antenna-Based Suppression
Existing literature on use of antenna nulling solutions have demonstrated SI suppression of around 20-30 dB [CJS10][RGK10]. Generally, a second TX antenna is used in tandem with the primary TX antenna in order to produce a null in the TX signal at the RX antenna. This imposes restrictions on antenna placement, as the required spacing is tightly coupled to signal wavelength. Achievable suppression may also be sensitive to multipath if major reflectors exist in the vicinity. It was pointed out in [RGK10] that unintended signal attenuation may be created elsewhere in the transmission area, leading to reduced link coverage. Other antenna-based SI suppression solutions include relying purely on path loss or shadowing to reduce the observed SI power via physical separation of TX and RX antennas [DS10][SPS11]. Similarly, the use of directional antennas has been studied, relying on signal reduction of the co-site TX signal as it is focused away from the RX antenna [EDD11]. These solutions may not always be feasible, and depend on the system form factor and deployment scenario requirements of a particular application.
4.2.1.2 RF Cancellation
RF cancellation typically requires a clean (wired) reference copy of the co-site TX signal before it is launched over the air. This signal’s delay, phase and gain is modified and sub- sequently summed with the signal at the RX antenna in order to produce SI cancellation. The results of [CJS10] and [RGK10] show 20-30 dB of SI suppression with a tuning loop closed by an OTS component (Quellen QHx220). An improvement on [CJS10] by [JCK11] makes use of balun-based signal inversion to allow for wideband cancellation. Again using the QHx220 along with a gradient descent control algorithm, around 45 dB of cancellation was achieved. Unfortunately, the QHx220 becomes non-linear somewhere near the -40 dBm region, which is not acceptable for any outdoor system (e.g. cellular base stations transmit-
74 ting in excess of +50 dBm) or even some indoor systems. The introduction of an additional TX chain (baseband and RF) was proposed in [SPS11]. This solution requires channel-state information (CSI) between the primary TX antenna and RX antenna as well as between the output of the second TX chain and RF combiner at the RX antenna. This imposes the requirement that CSI must be made available, and puts the SI cancellation engine at the mercy of the accuracy of external channel estimation.
4.2.1.3 Digital Cancellation
Assuming the signal at the RX antenna is brought to within the dynamic range of the ADCs, digital domain cancellation is possible. In [CJS10], the transmitted baseband TX signal is aligned with the received SI and combined with the downconverted signal at the RX antenna. Approximately 10 dB of SI cancellation was expected (MATLAB simulation of captured data) with this technique but could not be verified due to use of OTS radios (USRP). An improvement upon [CJS10] was made in [JCK11] by incorporating CSI between the TX and RX antennas, yielding 25-30 dB of SI suppression. Again, the issue of external CSI access and accuracy must be considered. An interference-free period is also required to obtain accurate channel estimation. If this is not available, or the quasi-static channel assumption does not hold (i.e., packet time-on-air is greater than channel coherence time), performance will be severely degraded.
4.2.2 Objectives
The principle requirements that guided our system design and development were the follow- ing:
Deployable in an arbitrary, dynamic environment The SI cancellation system should be able to adapt to a dynamic channel such as that of an outdoor, mobile platform. The system should also be able to handle large transmit powers such as those typically associated with outdoor wide-area and mission-critical systems. 75 Independent of radio signal structure The SI cancellation system should be independent of the host radio’s communication waveform and should not require knowledge of a particular waveform structure or timing, thus minimizing application-specific tailoring.
In-line deployable with any existing radio system The SI cancellation system should not require access to any internal part of the host radio, nor should it place any restrictions on antenna type or placement. All that is required is access to the antenna ports (TX and RX) of the host radio.
Without loss of generality of our methods, we target L-band (1-2 GHz), a popular band for cellular telecom applications, among others.
An observation was made in [DDS12], that characterizing analog and digital cancellation in isolation is not necessarily predictive of integrated system performance. To that end, we will demonstrate aggregate PHY-layer performance improvement by inspecting the received EVM and achievable throughput of a fully-functional, high-performance OFDM host radio. The proposed multi-stage SI cancellation design was implemented in hardware, utilizing both analog RF and digital domain cancellation techniques. Both analog and digital cancellation mechanisms were driven with tunable adaptive engines: a modified LMS algorithm for RF single-tap cancellation and a QRD-RLS adaptive filter for cancellation in the baseband digital domain. The prototype was validated by attaching it to an off-the-shelf, flexible OFDM radio system (Silvus StreamCaster 3800 [Sil14b]). Experimental results in the L-band were obtained for varying transmission power and signal bandwidths, demonstrating total SI suppression in the range of 50 dB to 90 dB.
4.3 Two-Stage Adaptive Self-Interference Cancellation
A dynamically adaptive engine is necessary to meet the three principle design goals stated above. In addition, analog RF components possess multiple non-idealities such as phase noise, DC/IQ offset and temperature drifts, several of which need to be tracked and cancelled
76 while the system is on-line. A multi-stage approach is necessary because a single stage typically does not offer enough cancellation (as discussed in Section 4.2.1). Although much focus in this work is placed on the design of the digital cancellation engine, cancellation in the analog domain is critical due to the fact that practical ADCs have limited dynamic range. If the signal cannot be accurately represented in the digital domain, there is little hope for SI cancellation, let alone successful decoding by the host radio.
The achievable SI suppression is ultimately limited by non-idealities of the hardware implementation. For this reason, an extensive simulation study was carried out in order to identify the effects of various hardware impairments and guide the hardware prototype design. A summary of typical simulation parameters is given as Table 4.1. The RF impair- ment parameters were selected based on device data for popular, commercially available RF components. Unless noted, these RF impairment and channel profile parameters were used in all simulations of interest. Several results from this study will be presented as part of the RF and baseband design considerations.
Figure 4.2: System schematic of the entire adaptive self-interference cancellation system
77 Figure 4.3: Two-stage hardware prototype adaptive self-interference cancellation system
4.3.1 General Architecture Trade-offs
A survey of existing methods for interference cancellation yielded techniques that generally fell into three categories: 1) Passive RF Cancellation (PRFC), 2) Active RF Cancellation (ARFC) and 3) Digital Baseband Cancellation (DBBC).
Our proposed system (shown in Fig. 4.2) utilizes a PRFC stage to bring the signal to within the linear range of the RF downcoverter and ADC before passing to a DBBC stage to cancel all channel effects. In the following, we provide a very brief overview of the PRFC and DBBC techniques. The ARFC technique is not described as it is costly, and imposes undue requirements on the quality of the RF components needed to achieve deep nulls.
78 Parameter Value Phase Noise RMS Error 0.15 deg IIP3 Back-Off 40 dB I/Q Phase Imbalance 0.2 deg I/Q Imbalance Sideband Power -30 dBc RF Attenuator Resolution 5.625 deg Channel Delay Spread 500 nsec Channel Reflectors 3 SI-to-SOI Ratio 75 dB SOI SNR 10 dB
Table 4.1: General simulation parameters for design study
PRFC techniques typically take a clean version of the TX (SI) signal, modify its gain, phase and/or delay and combine this signal directly after the RX antenna port to cancel the SI. This is a simple method, is relatively unaffected by RF impairments due to its passive nature, and consumes very little power. A survey of RF components that could be used in a PRFC implementation indicated that many have large power handling. Expected suppression is typically low to moderate (10-30 dB).
DBBC solutions may involve highly mathematically intensive implementations due to the tremendous resources available in modern ASIC and FPGA technologies. Cancellation in the digital domain can be very high (in excess of 50 dB) with moderate to low power consumption, depending on sampling rate (system clock speed). The main difficulty is providing a distortion-free signal to the digital domain, placing the performance bottleneck at the mixed-signal interface (ADC dynamic range).
As shown in Fig. 4.2, our approach aims to combine an adaptive PRFC stage designed specifically for high power handling with the high cancellation performance of a computation- ally intensive, digital adaptive filter stage. To our knowledge, a self-interference cancellation system for full-duplex communications with this level of potential adaptability has not been reported in the literature.
79 4.4 Analog RF Cancellation Design Considerations
4.4.1 Gain Resolution, Phase Precision and Power Handling
In order to accomplish its main function and accommodate large input power, the PRFC was designed assuming the availability of amplitude and phase shifters that could accom- modate high power. It is clear that the SI suppression performance of the PRFC is heavily dependent on the resolution of these components. The sensitivity of SI cancellation to gain and phase resolution was simulated and is presented in Fig. 4.4. A survey of available hard- ware indicates that gain and phase shifters with 1-dB compression points of about +30 dBm have approximately 0.5 dB and 5 degrees of gain and phase resolution, respectively. Accord- ing to our simulations, we should expect to achieve an average SI suppression of 25-35 dB (Fig. 4.4) if the PRFC is implemented correctly. A discussion of the operational scenario in Section 4.6.1.2 addresses the use of +30 dBm capable RF components in the presence of a +50 dBm co-site transmitter.
Figure 4.4: SI reduction assuming a variable attenuator with 32 dB range and a variable phase shifter with 360 degree range. The resolution of components used in our hardware is 0.5 dB attenuation and 5.625 degrees phase shift.
80 4.4.2 Group Delay
The group delay of the RF components must also be considered for the PRFC. The envelopes of the wireless (RX) and wired (TX-SI) paths must experience roughly the same group delay for optimum cancellation. The wireless path delay consists primarily of the group delay of the antenna and propagation delay over-the-air. The delay of the wired path consists of the delay of the cable and any cascaded RF attenuators or phase shifters. The effect of group delay on the alignment of the SI at RX and TX reference paths can be quantified as follows:
∆SI(t) = A [I(t − τ1) − I(t − τ2)] cos(ωct + θ) + A [Q(t − τ1) − Q(t − τ2)] sin(ωct + θ) (4.1)
In Eq. 4.1, assuming perfect matching of gain and carrier phase of the two paths, A and
θ are the gain and phase of both paths and τ1 and τ2 are the group delay of the two paths. The significance of group delay mismatch increases with the bandwidth of the baseband signals. Simulations indicate that for about 20 dB of SI suppression, good alignment is
1 achieved when τ1 − τ2 < , where fBB is the signal bandwidth. For example, when 20πfBB the bandwidth is 1 MHz, the group delay matching should be better than 16 nsec. An array of programmable delay lines can be used to eliminate this effect, but since the group delay needs to be calibrated and can vary with antenna type, it adds a complication to arbitrary deployment scenarios. Although cross-correlation methods [LYZ08] could be used to accurately determine the delay on-the-fly, it is a relatively static quantity that will not require significant tracking. A one-time power-on calibration of the system using a simple sweep of delay values is likely all that is necessary.
4.5 Baseband Cancellation Design Considerations
4.5.1 Digital Adaptive Filter
A typical adaptive filter consists of a filter and a coefficient update algorithm. For our purposes, the LMS and RLS algorithms were considered in conjunction with an FIR im- plementation for the DBBC adaptive filter (see Fig. 4.2). There are three main design
81 Number of Filter Taps Output SINR (dB) 6 -12 8 -2.6 10 +3.3 12 +4.3 16 +4.3
Table 4.2: Adaptive filter SINR vs. number of taps parameters that determine the performance of our FIR adaptive filter: 1) tap spacing, 2) filter order (number of taps), and 3) update algorithm. The tap spacing is dictated by the baseband sampling rate which also puts requirements on the ADC sampling rate and the digital system clock speed. Statistical-based update algorithms such as LMS are simple to implement and have low computational complexity. RLS algorithms have higher convergence speed and less steady state error [Far98]. Our studies revealed that LMS could not track a time-varying channel as well as RLS for parameters that produced comparable steady-state SI cancellation, and even failed to converge in several cases where RLS did not.
The remaining parameter of interest is the filter order. The advantage of a larger filter (more taps) is the ability to match channels with higher delay spreads while the disadvantage is higher computational complexity. Simulations were run to characterize the sensitivity of cancellation performance to filter tap-length for a representative outdoor channel (3-tap Rician channel, 500 nsec maximum delay spread under free-space path loss). These results are given in Table 4.2 and were inclusive of the other RF impairments from Table 4.1. Table 4.2 shows that a performance plateau is reached after 12 taps, indicating that the last significant channel tap was covered.
4.5.2 Non-Linearity
As mentioned in Section 4.4.1, the PRFC stage is assumed to be largely unaffected by non-linearity. Linking the PRFC to the DBBC is a downconverter (mixer) and ADC (see Fig. 4.2). Assuming the ADCs have enough dynamic range to avoid saturation, most of the
82 non-linearity contribution will come from the mixer component. Non-linearity of balanced, differential mixers can be modeled using a third-order polynomial. For fair comparison, we have simulated the effect of non-linearity as a function of back-off level from the compression point of the non-linear stage. Simulations were run under the following conditions: 1 MHz of bandwidth, 4-tap adaptive filter, 100 nsec channel delay spread, and 24-bit ADC. Non-linear effects in terms of required back-off levels are only useful when considered in conjunction with SI power levels and compression points of available analog hardware. As suggested by the simulation results shown in Fig. 4.5, in order to have 80 dB of SI cancellation in the presence of nonlinearity, 40 dB back-off from the third order intercept (IIP3) - or equivalently, 30 dB from the 1-dB compression point (P1dB) - is required.
If we assume a +20 dBm P1dB (the upper limit for most OTS RF components), the maximum acceptable power level that can enter the downconverter stage is -10 dBm for 80 dB suppression. Our hardware prototype satisfies this requirement (see Section 4.6.1.2). It is important to mention that non-linearity simulations were performed under a harsh environment, with the waveform peak-to-average ratio (PAR) measuring close to 15 dB - as may be exhibited by many wideband OFDM systems.
Figure 4.5: Simulation results showing sensitivity of digital adaptive filter output SINR to non-linearity. Larger back-off implies less non-linearity.
Allowing operation deep into the non-linear region of a device (e.g. 0-dB back-off from P1dB) would allow us to accommodate systems with even greater TX power - tens of dB greater. An adaptive filter implementation can be augmented with non-linear input compo- 83 nents. We studied the impact of including a generalized RLS adaptive filter with Volterra series augmentation [Ram02]. A full explanation of such non-linear adaptive filtering is lengthy and beyond the scope of this paper, but due to the potential of its enhancements, the results of our simulation study are presented. Assuming a two-tap channel impulse re- sponse represented as h(n) = δ(n) + δ(n − 1) and device non-linearity which is commonly modeled as y(n) = x(n) + x3(n), the time domain response of the channel cascaded with non-linearity is given by:
y(n) = x(n) + x(n − 1) + x3(n) + 3x2(n)x(n − 1) + 3x(n)x2(n − 1) + x3(n − 1) (4.2)
These six terms imply that the non-linear channel can be matched with a 6-tap nonlinear adaptive RLS filter. Using a Volterra series expansion of Eq. 4.2 as the inputs to this filter, we simulated the tracking capabilities of this channel subject to the remaining parameters in Table 4.1. Fig. 4.6 shows that while the classic RLS filter fails to converge, the RLS filter with Volterra series augmentation converges in less than 100 samples. While this is a very encouraging result, we were not able to implement such a filter in hardware as its com- putational complexity increases exponentially with channel tap-length. Further simulations revealed that under the assumption of weak nonlinearity and a rapid decay of higher order paths of the wireless channel, many of the Volterra series terms become negligible (as shown in Fig. 4.7), easing the computational burden. This is promising, but further study into the application of this method in our system is needed.
84 Figure 4.6: The estimation error a 6-tap RLS filter for a non-linear system: (top) classic filter (bottom) the RLS adaptive filter including Volterra augmentation
Figure 4.7: Average Volterra term weights vs. term index, indicating rapid decay
4.5.3 Phase Noise
Phase noise is the manifestation of random fluctuations in oscillator phase and can be mod- eled both in the time domain and in the frequency domain. Fig. 4.8 shows the sensitivity of the adaptive filter’s output SINR (assuming original SOI SNR of 10 dB) to integrated phase error at two typical levels: 0.175 and 0.085 degrees. The power spectral density (PSD) of the
85 noisy oscillator for the 0.085 degrees case is shown in Fig. 4.9. The studied phase noise levels and power spectral densities were selected based on device data for commercially available, low-phase-noise synthesizers. The Holzworth HSM2001A synthesizer used in our hardware prototype has a phase noise profile that hits -127 dBc/Hz at a 10 kHz offset, which is very close to what was simulated (Fig. 4.9). The results of this simulation indicate phase noise will not be the limiting factor in our hardware prototype since we do not expect (with 30 dB of suppression from the PRFC, as in Fig. 4.4) the SI-to-SOI power ratio to exceed 90 dB at the digital baseband input.
Figure 4.8: Simulation results showing sensitivity of digital adaptive filter output SINR to phase noise profile
Figure 4.9: PSD of phase noise with 0.085 degrees of integrated phase error used for simulation
86 4.5.4 I/Q Imbalance and DC Offset Cancellation
It was found that even moderate levels of I/Q imbalance significantly degraded the per- formance of the RLS filter. Fig. 4.10 indicates that without I/Q imbalance calibration, a significant amount of noise and unwanted sidebands will pass through to the host radio sys- tem, potentially rendering the cancellation ineffective. To cancel this hardware impairment we first estimate gain (G) and phase (P ) imbalance from a hardware waveform capture (window size = N) as represented by Eq. 4.3 and Eq. 4.4.
s PN (Im{x[n]}2) G = 0 (4.3) PN 2 0 (Re{x[n]} )
PN (Re{x[n]}· Im{x[n]}) P = 0 (4.4) Re{x[n]}2
We then apply G and P to obtain a new, compensated complex signal given as Eq. 4.5 and Eq. 4.6:
Re{x[n]}comp = G · Re{x[n]} (4.5)
(Im{x[n]} − P · Re{x[n]}) Im{x[n]}comp = √ (4.6) 1 − P 2 DC offset is an important consideration, but its cancellation can be carried out easily with a simple averaging function to determine the mean values over several waveform captures. The resulting DC offset is simply subtracted from the incoming waveform.
I/Q imbalance and DC offset calibration are carried out separately for both SI reference and wireless inputs and cancelled in the digital system as shown in Fig. 4.2. A measurement study on the actual hardware platform confirmed that both sets of parameters are relatively stable, even with moderate temperature fluctuation.
87 Figure 4.10: Spur/noise reduction at the output of the QRD-RLS filter when RX I/Q imbalance at both the wireless and wired (SI reference) is calibrated. The light (red) and dark (black) and curves are before and after I/Q compensation, respectively.
4.6 Hardware System Implementation
As stated earlier, it is our aim to construct a hardware system that can easily interface with an existing radio system directly at the antenna port. It was also observed in [DDS12] that there are some situations where self-interference cancellation is not as effective (e.g. SI level is low), as the self-interference estimation mechanism may not be able to accurately estimate the channel between TX and RX antennas. We can safely assume large self-interference levels by assuming the self-interfering antenna is relatively co-located with the RX antenna. From a realistic deployment perspective, this is a more likely situation than one where the TX antenna is so far away from the RX antenna that its SI contribution approaches the level of the SOI.
88 4.6.1 Analog Subsystem
The PRFC, as described in Section 4.4.1, was built to accommodate an input power of up to +15 dBm at the wireless input and up to +55 dBm at the reference SI input. The variable attenuators have a range of 32 dB with a precision of 0.5 dB and the variable phase shifters have a 360 degree range with 5.625 degree resolution. OTS L-band mixers and 16-bit ADCs from Texas Instruments were utilized.
4.6.1.1 Modified LMS Engine
A modified least-mean-squares (LMS) algorithm was used to align the phase and amplitude of the SI reference signal with the strongest signal component (line-of-sight) of the over- the-air SI signal with the aid of the variable attenuators and phase shifters depicted in Fig. 4.2. Fig. 4.11 illustrates the results of a study, using measured data from the hardware prototype, of the phase-amplitude parameter search-space for the reference SI signal. The z-axis represents residual SI power after cancellation. As can be seen, this optimization problem has a quasi-convex shape with a single minimum and can thus be handled by an LMS algorithm.
Figure 4.11: SI power vs. gain and phase of the PRFC, measured in hardware for L-band. This quasi- convex function can be optimized with LMS
89 The classical formulation of the LMS coefficient update expression is given as follows:
h(n + 1) = h(n) + µE{x(n)e∗(n)} (4.7)
In Eq. 4.7, µ denotes the step size, which controls the tradeoff between convergence speed and accuracy, and E{·} denotes the expectation operator. The expression h(n) represents the combined gain and phase modification, x(n) is the reference SI, and e(n) is the filter output. In our hardware implementation, the expectation operation has been replaced by the unbiased estimator given in Eq. 4.8
N 1 X x(n − k)e∗(n − k) (4.8) N k=0
The observation length, N, has been set in the range of 256 to 8192 samples, with the optimal value dependent on bandwidth and hardware noise performance. In addition, the error correlation term x(n − k)e∗(n − k) was normalized by the norm of the reference signal, kx(n)k. Several practical issues surfaced after observing the behavior of the LMS algorithm running on our platform. We implemented protection against run-away coefficient error when the difference in strength between the reference signal and the over-the-air signal is greater than the range (31.5 dB) of the variable attenuator. Additionally, in order to prevent oscillation around the optimum, the difference between consecutive coefficient updates is monitored. If this difference approaches a threshold (small number), the LMS coefficient output is disconnected from the hardware attenuator/phase shifter and a quick, local search (±1 phase step, ±1 attenuation step) is used to lock out at the optimum value. The LMS algorithm is further augmented by an initialization procedure (two-stage search) to speed up initial power-on acquisition as indicated in Fig. 4.12.
90 Figure 4.12: State machine for the PRFC coefficient update algorithm, inclusive of an LMS coefficient update engine
4.6.1.2 Prototype Hardware Design Limits
Clearly, no design can handle unlimited power. That being said, it was the aim of this work to produce an SI canceller that could be used in the presence of high-power systems. To this end, we consider an arbitrary, 100 W (+50 dBm) L-band transmitter installed on an aircraft. If we assume free-space path loss and an approximate antenna separation of 2 meters, then the power incident on the antenna is expected to be +9 dBm (41 dB path loss) to which we will add a 6 dB design margin, bringing the power limit at the RX antenna to +15 dBm. A fixed attenuator on the wired SI reference is required in order to bring the +50 dBm SI reference signal down to acceptable levels for cancellation. The mixers used in this design have an input 1-dB compression point (P1dB) of approximately +10 dBm. In order to ensure linearity, we set our target mixer input power at +3 dBm (a 7 dB backoff from the mixer’s P1dB). Assuming a 3 dB loss at the RF combiner, the +15 dBm at the system’s wireless input becomes +12 dBm at the mixer input before cancellation is applied. This implies that an absolute minimum cancellation of 9 dB is required to preserve the linearity of the mixers that downconvert the signal to baseband for second-stage processing - a safe expectation for the PRFC.
91 4.6.2 Digital Subsystem
4.6.2.1 QRD-RLS Adaptive Filter
Baseband cancellation is accomplished with the use of a QRD-based RLS engine implemented within an Altera Arria II GX FPGA. Least-squares algorithms find the orthogonal projection of the desired signal on the input sample space. The computational core of the QRD-RLS algorithm is a QR matrix decomposition. Several techniques can be found for implementing QR decomposition, most notably: Gram-Schmitt, Household and Givens methods. Among these methods, the Givens method of rotations is the most hardware-friendly approach due to its parallel computational nature [Apo09]. The approach for our implementation of QRD- RLS uses Coordinate Rotation Digital Computer (CORDIC) blocks to implement the Givens rotations in a systolic array structure [Far98]. This systolic array structure can be extended to directly produce the adaptive filters error signal [Apo09], as illustrated in Fig. 4.13. This implementation is more efficient and greatly reduces integration effort and system latency by eliminating the explicit back-substitution and FIR filter modules.
Figure 4.13: Conversion of a traditional QRD-RLS implementation to an extended systolic array with implicit filter weight calculation
4.6.2.2 Fixed-Point Design
Numerical stability should be carefully analyzed when any algorithm is considered for hard- ware implementation. Some algorithms which behave properly in floating-point arithmetic exhibit unstable behavior when implemented in fixed-point hardware. There are several dif-
92 ferent implementations of the RLS algorithm. While there is no difference in the algebraic solution of these implementations, their behavior can differ dramatically when implemented in finite-precision arithmetic. QRD-RLS was specifically chosen because it is the only im- plementation of the RLS algorithm that is guaranteed to be numerically stable [Apo09]. A fixed point MATLAB model was created and vector-matched to our RTL implementation of the QRD-RLS engine. This was then used for a simulation study of the numerical accuracy of our design. Signals were captured on hardware before being taken to MATLAB for EVM analysis. A QPSK OFDM waveform with 16.4 dB SNR and -14.75 dB EVM was used as the RX SOI. An SI signal which was more than 40 dB stronger than the SOI was used as the other input to this simulation. As can be seen in Fig. 4.14, after SI cancellation in floating-point, the SOI SNR loss was limited to 7.8 dB and an EVM degradation of 5.1 dB was observed. The fixed point design lost only an additional 0.86 dB of EVM, indicating minimal impact to overall performance.
Figure 4.14: Fixed point simulation study. The SI-free QPSK constellation with -14.75 dB EVM (left), -9.9 dB EVM after 74 dB of floating point SI cancellation (center), -9.04 dB EVM after fixed point cancellation (right)
4.6.2.3 Round-Off Error
Errors due to rounding are another serious concern when implementing mathematical algo- rithms that contain implicit recursion or feedback. Interestingly, we observed that use of truncation-mode rounding (zero implementation overhead) instead of true-rounding (minor overhead) for the CORDICs and RLS forgetting factor, resulted in catastrophic distortion of the output SOI. Using true-rounding restored the spectrum of the OFDM SOI. This effect
93 is illustrated in Fig. 4.15.
Figure 4.15: SOI spectrum distortion using truncation within the QRD-RLS filter (left) is non-existent when true rounding is used (right)
4.7 Hardware Performance
4.7.1 Experimental Setup
The hardware prototype (Fig. 4.2) was validated using two experimental configurations. The primary configuration, demonstrated practical performance by incorporating two Silvus StreamCaster 3800 (SC3800) OFDM radios operating in the L-band, as shown in Fig. 4.16. In an alternate configuration, the TX SC3800 producing the desired RX SOI was replaced with an RF signal generator and the RX SC3800 was replaced with a spectrum analyzer. This was done in order to support an even more diverse set of experimental scenarios.
Figure 4.16: Schematic of the experimental setup with SC3800 OFDM radios
94 4.7.2 Radio System Performance
The TX SC3800 producing the desired 1.25 MHz RX SOI cycled through 8 different modu- lation types and data rates ranging from 400 kbps to 4.1 Mbps. As shown in Fig. 4.17, the RX SOI without the presence of an SI has a very tight constellation. The average SNR and maximum average data rate reported by the system was 29.4 dB and 4.0 Mbps, respectively. An SI signal with an SI-to-SOI ratio of 52 dB at the antenna port was enabled and the system came to a halt with no packets decodable. Fig. 4.17 shows the constellation reported by the radio after the PRFC and DBBC SI cancellation engines were enabled. An SNR of 18.5 dB was reported with a maximum average data rate of 3.8 Mbps. This indicates that about 70 dB of SI suppression was achieved. The SI was increased by 20 dB (SI-to-SOI ratio of 72 dB) and Fig. 4.17 shows that the constellation began to degrade further. An SNR of 9.3 dB was reported by the radio with a maximum average data rate of 1.5 Mbps. This indicates that about 81 dB of SI suppression was achieved.
4.7.3 Narrowband, Wideband and Tracking Performance
Using the alternate experimental setup, a series of tests were conducted under various signal profiles. A set of representative results is presented here. Narrowband performance with an SI-to-SOI level of 72 dB is shown in Fig. 4.18. The SI and SOI are separated by 100 kHz so that they may be visually differentiated in a spectrum plot. The SI is suppressed to below the SOI by 18 dB, indicating approximately 90 dB of SI suppression (Fig. 4.19).
A wideband experiment was performed in which the SI bandwidth (1 MHz) was almost twice that of the SOI (600 kHz) and the SI-to-SOI ratio was approximately 45 dB. Fig. 4.20 shows the resulting output waveform after SI cancellation. The wideband SI is still visible but has been suppressed below the SOI, resulting in a suppression factor of about 50 dB.
Another interesting experiment was carried out involving an agile, narrowband SI signal, sweeping through an entire 1 MHz band in 10 hops with 1 msec dwell time per hop. Previous results indicated that narrowband cancellation up to 90 dB was possible, but as the SI hops around in the band, the SI cancellation system is forced to continuously unlock and reacquire.
95 Figure 4.17: Decoded constellations reported by Silvus SC3800 radio: SI-free SOI with 29.4 dB post- processing SNR (left), 18.5 dB SINR after 70 dB of SI suppression (center) and 9.3 dB SINR after 81 dB of SI suppression (right)
Figure 4.18: Narrowband SI scenario: SI and SOI at RX antenna input
96 Figure 4.19: Narrowband SI scenario: QRD-RLS input (light/red curve) and final output (dark/blue curve)
Figure 4.20: Wideband SI cancellation scenario: final (SI cancelled) RF output to host radio
Fig. 4.21 shows the signal at the RX SOI input with a 500 kHz SOI and an SI (mid-sweep) that is 62 dB higher than the SOI. The canceller was able to track the hopping SI and achieve an average of about 75 dB of SI suppression despite the agility of the SI signal.
97 Figure 4.21: Hopping SI cancellation scenario: SI at RX antenna input (top) and final, SI cancelled RF output to host radio (bottom)
4.8 Discussion and Conclusions
Implementing a versatile SI cancellation system that can easily adapt to any environment or existing host radio and accommodate large RF power is certainly a challenge. The consider- ation of practical hardware impairments is a critical step toward understanding the merits and feasibility of any approach to solving this problem. In designing and building both the RF and digital baseband cancellation stages of our SI canceller from the ground up, we were able to gain important insight into the interplay of the two independent adaptation loops and also how their adaptation parameters (e.g. step size, forgetting factor, tap length) and hardware impairments affect steady-state suppression and tracking capabilities. 98 Our hardware validation of the system yielded promising results, indicating that such a system could be used effectively with an arbitrary host radio system without requiring signaling information or feedback from said host radio. Ultimately, the performance of our hardware platform was limited by FPGA resources. QRD-RLS requires a significant amount of concurrent mathematical operations to be executed within each sample interval. This required in a significant amount of time-sharing of FPGA resources, lowering the achiev- able processing bandwidth of the system (subject to the maximum achievable FPGA clock speed). An ASIC implementation would not have such problems, as the highly regular struc- ture of a QRD-RLS systolic array is very amenable to high speed VLSI design techniques. Although we were limited to 3.25 MHz of bandwidth and a QRD-RLS tap length of 8 for this iteration of the prototype, the design is very easily extended to higher sampling rates and filter tap-lengths. Increasing these parameters would allow deeper suppression, better wide- band performance, and channel-matching accuracy. Integrating a balun into our PRFC as demonstrated in [JCK11] or the addition of a group-delay alignment circuit would certainly aid wideband performance. Sub-banding in a divide and conquer approach is also a possible area of further exploration. Finally, adding a higher level adaptation loop that operates on the LMS step size and RLS forgetting factor is also another topic of interest as this could lead to adaptation of the SI canceller to overall channel dynamics. This would enable the system to adaptively trade off steady-state suppression depth for the higher tracking ability needed to accommodate extremely dynamic channels.
99 CHAPTER 5
Conclusions and Future Work
5.1 Conclusions
In this dissertation we began with the design and subsequent field measurement campaign of a comprehensive, airborne MIMO channel-sounding and performance-measurement platform. A representative aircraft (Cessna-172), outfitted with a 4x4 MIMO-OFDM radio system, was flown over different terrain types at altitudes and speeds approximating a typical medium- altitude, medium-endurance UAV system. Analysis of collected data at ground stations, situated in various locations around the Los Angeles area, showed that MIMO-enabled nodes can achieve a significant gain in throughput and range – both in the range of 200-250%. These gains can instead by traded off for large transmit power savings (in the range of 10-20 dB) over a conventional SISO system. Eigen beamforming analysis of the captured channel responses indicated that a 200% gain in long-range channel capacity is attainable if the channel-state information can be effectively and accurately fed back to the transmitter. Channel captures obtained during this field study numbered well over 30,000 and were of great utility for subsequent simulation efforts.
Further exploring the potential benefits of incorporating MIMO-enabled processing tech- niques into a radio system, we studied the problem of interference mitigation in a system of multiple concurrent, uncoordinated air-to-ground transmissions. A simple decision algorithm was proposed which could be very easily implemented and deployed. The simulation frame- work that was developed, captured the effects of eigen beamforming, eigen beamnulling, spatial multiplexing, link-adaptation, spectral segmentation, variable bandwidth, and power control. Simulations were constructed to evaluate these techniques individually as well as
100 in concert with each other. Realistic implementation losses and realistic aerial vehicle de- ployment topologies were also taken into account. Under all deployment scenarios and given a realistic data rate requirement, it was observed that many concurrent MIMO link-pairs could operate within a space where only one basic SISO link-pair could be supported. Across all six scenarios that were simulated, large gains in the number of supportable concurrent links were observed. By disabling individual features, it became apparent that all features are critical to overall uncoordinated link capacity after just a handful of pairs enter the area of operations. Without any MIMO capabilities, link-capacity hits a performance knee after around 4 concurrent links. This MIMO-disabled simulation consisted of disabling RX eigen beamnulling, TX eigen beamforming, removing modes with more than 1 spatial stream, and enabling only 1 antenna on the TX and RX side (TX power was boosted by 6 dB for fairness). The interference congestion was so severe by the time 8 links entered the area that virtually none of the link-pairs were able to deliver adequate throughput. Disabling the remaining features (power control and link adaptation) returned the system to the original baseline system which could essentially only support a single link-pair. As soon as two or more uncoordinated link-pairs enter the area, concurrent link capacity approaches zero. For the airborne 2.4 GHz system case study in this work (5 MHz available bandwidth, 1W TX power per-antenna), performance was generally better when using the channels captured from the measurement study of Chapter 2 as opposed to simulated channels. This is a promising result, indicating that even though the simulated channels were generally of higher rank (better able to support 4 spatial streams), MIMO techniques can be very effective when applied to the actual airborne channel.
Following the promising results of the uncoordinated, MIMO concurrent-link capacity study in Chapter 3, we turned to the well-known concept of full-duplexing as an additional method to increase throughput for uncoordinated airborne links. A survey of prior art found that current self-interference canceling solutions are prohibitive for long-range/airborne ap- plications due to power handling limitations. Additionally, no solutions were easily scalable to an arbitrary number of MIMO antennas and arbitrary antenna placements. To address this, we conceived of a novel design that is versatile and fully self-contained. We do not
101 impose restrictions on antenna type or antenna placement, nor do we require a second TX antenna for null-forming. This design also does not require channel-state information, a copy of the TX baseband signal from the radio system, or any a priori knowledge of the structure of the RX signal-of-interest and TX self-interference signals. These two concepts enable our SI cancellation system to be attached at the antenna port of virtually any existing radio system without requiring modification of that radio system. Our hardware validation of the system yielded promising results, indicating that such a system can be used effectively with an ar- bitrary host radio system with minimal signaling information or feedback. Self-interference suppression ratios were achieved on the order of 50 dB for wideband scenarios and 90 dB for narrowband. The tracking capability of our prototype proved to be quite effective at sup- pressing frequency-agile signals as well, with suppression of narrowband, frequency-hopping self-interference on the order of 75 dB. Implementing a versatile SI cancellation system that can easily adapt to any environment or existing host radio and accommodate large RF power is certainly a challenge. The consideration of practical hardware impairments is a critical step toward understanding the merits and feasibility of any approach to solving this problem. In designing and building both the RF and digital baseband cancellation stages of our SI canceler, we were able to gain important insight into the interplay of the two independent adaptation loops and also how their adaptation parameters and hardware impairments affect steady state suppression and tracking capabilities.
5.2 Future Work
Future work on the airborne MIMO channel measurement front will include validating the TX eigen beamforming gain claim by introducing a working beamforming solution into the hardware platform. Delay spread and Doppler spread are other areas of further investigation. The current symbol-based FFT/IFFT method is only capable of resolving multipath com- ponents down to 200 nsec (200 ft) and a maximum delay spread of 12.8µs (1,280 ft) which was sufficient for performance prediction for a 5 MHz MIMO-OFDM system. Theoretically, since we have the entire waveform captured we can achieve better resolution by using a more
102 sophisticated method of post processing. Study of the channel at larger bandwidths (tens to hundreds of MHz) more closely approximating modern OFDM systems is another area of possible improvement. Additionally, we may study the coherence time of the eigenmodes in a fashion similar to how coherence time of the channel can be determined from the au- tocorrelation of channel-taps over time. This gives us a measure of how steady the spatial structure of the MIMO channel remains as the mobile nodes travel through space. Finally, while the channel measurements obtained in this study are a good first step in developing a statistically accurate model of the airborne channel, more measurements should be collected and organized by flight-profile type in order to derive and validate a generalized statistical expression of the channel.
With regards to uncoordinated, concurrent MIMO link capacity, there are several prac- tical considerations that require more investigation. The first and foremost is the issue of obtaining reliable forward and reverse channel estimates in the presence of interference. It is likely that more sophisticated channel training will be required. The situation is made more complicated by the potentially high mobility of airborne links, limiting the amount of time that the systems have to properly estimate channel conditions and converge within their adaptation loops. Beyond these issues, we also recognize that only one set of transmission modes was studied in this work. We make no claims that this set of modes is optimal and it is left to future work to propose a better approach to the MIMO link adaptation problem.
Finally, while the performance of our full-duplex hardware platform is promising, the self-interference canceling capabilites were limited by FPGA resources. QRD-RLS requires a significant amount of concurrent mathematical operations to be executed within each sample interval. This resulted in a significant amount of time-sharing of FPGA resources, lowering the achievable processing bandwidth of the system (subject to the maximum achievable FPGA clock speed). As a topic for future work, an ASIC implementation would not have such problems, as the QRD-RLS structure - implemented as a systolic array - is very amenable to high speed VLSI layout techniques due to its highly regular structure. Another area of improvement lies in addressing the fact that we were limited to 3.25 MHz of bandwidth and a QRD-RLS tap length of 8 for this iteration of the prototype. This design can be very easily 103 extended to higher sampling rates and filter tap-lengths. Increasing these parameters and applying sub-banding would surely allow deeper suppression, better wideband performance and channel-matching accuracy. Yet another idea involves integrating a balun into our RF subsection as implemented in [JCK11] or a means of group-delay calibration and tracking in order to boost wideband performance. Finally, adding a higher level adaptation loop that operates on the LMS step size and RLS forgetting factor is another topic of interest. This could lead to adaptation of the SI canceler to overall channel dynamics, enabling the system to adaptively trade off steady-state suppression depth for a faster tracking ability, thus acommodating extremely dynamic channels.
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