Reconfigurable Antennas for Adaptive MIMO Communication Systems

Total Page:16

File Type:pdf, Size:1020Kb

Reconfigurable Antennas for Adaptive MIMO Communication Systems Reconfigurable Antennas for Adaptive MIMO Communication Systems A Thesis Submitted to the Faculty of Drexel University by Daniele Piazza in partial fulfillment of the requirements for the degree of Doctor of Philosophy June 2009 c 2009 Daniele Piazza. All Rights Reserved. ii Dedications To my family: thanks for your love and support during my studies iii Acknowledgments I would like to express my sincere thanks to Prof. Kapil R. Dandekar for his invaluable and unique guidance during my studies and for his support in preparing this thesis. I would like to thank Prof. Michele D'Amico who gave me the opportunity to continue my research studies at Politecnico di Milano. I am very grateful to all my colleagues and friends from the Drexel Wireless System Laboratory for their continuos collaboration on common research topics. Special thanks to my parents and my brother that have always been close to me and supported me with their continuos help, their patience and their love during my studies and my research. A big thank to my girlfriend Anita for supporting and encouraging me during these years and a special thank to all my friends that have been close to me in these years. iv Table of Contents List of Tables . xi List of Figures . xx Abstract . xxi 1 Introduction . 1 1.1 Antennas for MIMO systems . 3 1.2 Innovative aspects of this thesis . 6 1.3 Thesis outline . 11 2 Antenna diversity and reconfigurability . 14 2.1 Spatial, polarization and pattern diversity . 14 2.1.1 Spatial diversity . 15 2.1.2 Polarization diversity . 16 2.1.3 Pattern diversity . 17 2.2 Reconfigurable antennas . 18 2.2.1 Polarization reconfigurable antennas . 18 2.2.2 Pattern reconfigurable antennas . 21 2.2.3 Compound reconfigurable antennas . 26 2.3 Summary . 30 TABLE OF CONTENTS v 3 MIMO channel models and antenna correlation . 32 3.1 MIMO system . 32 3.2 MIMO channel modeling . 35 3.2.1 Deterministic channel model . 39 3.2.2 Stochastic channel model . 39 3.3 Summary . 42 4 Reconfigurable printed dipole array . 44 4.1 Design motivation . 44 4.2 Reconfigurable printed dipole array design . 45 4.3 Performance in clustered MIMO channel model . 56 4.4 Ray tracing simulation results . 60 4.5 Measurements results . 63 4.6 Summary . 70 5 Reconfigurable circular patch antennas . 71 5.1 Circular patch: pattern and polarization diversity . 72 5.2 Reconfigurable circular patch antennas . 74 5.2.1 Reconfigurable circular patch antenna for pattern diversity (RCPA-PD) 75 5.2.2 Reconfigurable circular patch antenna for pattern and polarization diversity (RCPA-PPD) . 81 5.3 Experimental setup and channel model . 87 5.4 RCPAs performance . 90 5.4.1 RCPAs at one and both ends of the communication link . 91 TABLE OF CONTENTS vi 5.4.2 RCPA configuration performance . 95 5.4.3 Narrowband vs broadband capacity . 99 5.5 Summary . 101 6 Configuration selection scheme for pattern RCPAs . 104 6.1 MIMO system with reconfigurable antennas . 105 6.2 Reconfigurable circular patch antennas . 109 6.3 RCPAs performance in clustered channel model . 113 6.3.1 Clustered channel model and RCPA spatial correlation . 114 6.3.2 Effect of RCPA spatial correlation on configuration selection . 116 6.3.3 Effect of average system SNR on configuration selection . 119 6.4 Selection algorithm . 121 6.5 Effect of pilot assisted estimation on the channel capacity and BER . 124 6.5.1 Ergodic channel capacity . 125 6.5.2 Bit error rate . 128 6.6 Summary . 131 7 Two port reconfigurable CRLH leaky wave antenna . 134 7.1 Generalities of leaky wave antennas . 135 7.2 CRLH leaky wave antennas . 137 7.3 Reconfigurable leaky wave antenna . 140 7.4 System model and experimental setup . 146 7.5 Experimental results . 152 TABLE OF CONTENTS vii 7.5.1 Channel capacity . 153 7.5.2 Power saving . 156 7.6 RLWA in different channel scenarios . 158 7.7 Summary . 160 8 Reconfigurable antennas in MIMO ad hoc networks . 162 8.1 Comparison of RCPA-PD with RPDA . 164 8.2 System model and notation . 165 8.3 Antenna configuration selection methods . 167 8.3.1 Centralized configuration selection technique . 167 8.3.2 Distributed configuration selection technique . 168 8.3.3 Single side reconfigurable antennas . 169 8.4 Data collection . 171 8.4.1 Measurement setup . 171 8.4.2 Simulation setup . 173 8.5 Results . 174 8.5.1 RCPA-PD . 175 8.5.2 RPDA . 178 8.5.3 Comparing RCPA-PD with RPDA . 179 8.5.4 Effect of the number of configurations . 182 8.5.5 Effect of correlation between the patterns . 183 8.6 Summary . 185 TABLE OF CONTENTS viii 9 Conclusions . 186 Appendix A: MIMO channel sounder . 191 Appendix B: Table of symbols . 194 Appendix C: Table of acronyms . 197 Bibliography . 197 Vita . 211 ix List of Tables 4.1 List of structural parameters of the reconfigurable printed dipole array (RPDA). 48 4.2 Spatial correlation between patterns generated at two different ports of the RPDA. 51 4.3 Spatial correlation between patterns generated at the same port of the RPDA. 51 4.4 Measured radiation efficiency of the RPDA. 53 4.5 Average measured percentage capacity improvement achievable with the RPDA with respect to the \short-short" configuration when used in a 2×2 MIMO OFDM system. 67 5.1 RCPA-PD structural parameters . 78 5.2 Spatial correlation between patterns generated at two different ports of the RCPA-PD. 80 5.3 Spatial correlation between patterns generated at the same port of the RCPA- PD......................................... 80 5.4 Measured radiation efficiency of the RCPA-PD . 81 5.5 RCPA-PPD structural parameters . 83 5.6 Spatial correlation between patterns generated at two different ports of the RCPA-PPD. 84 5.7 Spatial correlation between patterns generated at the same port of the RCPA- PPD. 85 5.8 Measured radiation efficiency of the RCPA-PPD. 86 LIST OF TABLES x 5.9 Percentage capacity improvement achievable for a probability of 50% when using the RCPA-PD and the RCPA-PPD with respect to a system that em- ployes circular patch antennas operating in \mode 3". Noise level v = 100 nW ......................................... 92 5.10 Average channel capacity with efficiency normalization for the RCPA-PPD and the RCPA-PD used at the receiver. Noise level v = 100 nW . 93 6.1 Spatial correlation between patterns generated at two different ports of the RCPA for the same configuration -r ^1;k;2;k . 111 6.2 Spatial correlation between patterns generated at the same port of the RCPA -r ^1;k;1;m ...................................... 111 6.3 RCPAs characteristics . 114 6.4 Relationship of angle spread to reciprocal condition number for SNR = 5dB 124 6.5 MIMO system configuration . 125 7.1 Unit cell structural paramters. 141 7.2 Bloch impedance. ..
Recommended publications
  • Performance Comparisons of MIMO Techniques with Application to WCDMA Systems
    EURASIP Journal on Applied Signal Processing 2004:5, 649–661 c 2004 Hindawi Publishing Corporation Performance Comparisons of MIMO Techniques with Application to WCDMA Systems Chuxiang Li Department of Electrical Engineering, Columbia University, New York, NY 10027, USA Email: [email protected] Xiaodong Wang Department of Electrical Engineering, Columbia University, New York, NY 10027, USA Email: [email protected] Received 11 December 2002; Revised 1 August 2003 Multiple-input multiple-output (MIMO) communication techniques have received great attention and gained significant devel- opment in recent years. In this paper, we analyze and compare the performances of different MIMO techniques. In particular, we compare the performance of three MIMO methods, namely, BLAST, STBC, and linear precoding/decoding. We provide both an analytical performance analysis in terms of the average receiver SNR and simulation results in terms of the BER. Moreover, the applications of MIMO techniques in WCDMA systems are also considered in this study. Specifically, a subspace tracking algo- rithm and a quantized feedback scheme are introduced into the system to simplify implementation of the beamforming scheme. It is seen that the BLAST scheme can achieve the best performance in the high data rate transmission scenario; the beamforming scheme has better performance than the STBC strategies in the diversity transmission scenario; and the beamforming scheme can be effectively realized in WCDMA systems employing the subspace tracking and the quantized feedback approach. Keywords and phrases: BLAST, space-time block coding, linear precoding/decoding, subspace tracking, WCDMA. 1. INTRODUCTION ing power and/or rate over multiple transmit antennas, with partially or perfectly known channel state information [7].
    [Show full text]
  • Miniaturized Frequency Reconfigurable Pentagonal MIMO Slot Antenna for Interweave CR Applications
    Miniaturized frequency reconfigurable pentagonal MIMO slot antenna for interweave CR applications Item Type Article Authors Hussain, Rifaqat; Raza, Ali; Khan, Muhammad U.; Shamim, Atif; Sharawi, Mohammad S. Citation Hussain R, Raza A, Khan MU, Shammim A, Sharawi MS (2019) Miniaturized frequency reconfigurable pentagonal MIMO slot antenna for interweave CR applications. International Journal of RF and Microwave Computer-Aided Engineering: e21811. Available: http://dx.doi.org/10.1002/mmce.21811. DOI 10.1002/mmce.21811 Publisher Wiley Journal International Journal of RF and Microwave Computer-Aided Engineering Download date 01/10/2021 07:53:51 Link to Item http://hdl.handle.net/10754/653090 Pentagonal Slot MIMO Reconfigurable Antenna 1 Miniaturized Frequency Reconfigurable Pentagonal MIMO Slot Antenna for Interweave CR Applications Rifaqat Hussain 1, Ali Raza 2, Muhammad U. Khan 3, Atif Shammim 4 and Mohammad S. Sharawi 5 1 Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia, Email: [email protected]. 2 Department of Electrical Engineering, University of Engineering and Technology Lahore, Faisalabad Campus, Pakistan 3 Research Institute for Microwave and Millimeter-Wave Studies, National University of Science and Technology, Islamabad, 44000, Pakistan 4 Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University (KAUST) of Science and Technology, Thuwal 23955-6900, Saudi Arabia 5 Electrical Engineering Department, Polytechnique Montr´eal, Montreal, QC H3T 1J4, Canada, Email: [email protected] ABSTRACT: In this paper, a miniaturized 4-element frequency reconfigurable multiple- input-multiple-output (MIMO) antenna system is presented. The proposed design is low profile with planar configuration. The design consists of pentagonal slot-based frequency reconfigurable antenna elements.
    [Show full text]
  • An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems
    1 An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems Robert W. Heath Jr., Nuria Gonzalez-Prelcic, Sundeep Rangan, Wonil Roh, and Akbar Sayeed Abstract Communication at millimeter wave (mmWave) frequencies is defining a new era of wireless com- munication. The mmWave band offers higher bandwidth communication channels versus those presently used in commercial wireless systems. The applications of mmWave are immense: wireless local and personal area networks in the unlicensed band, 5G cellular systems, not to mention vehicular area networks, ad hoc networks, and wearables. Signal processing is critical for enabling the next generation of mmWave communication. Due to the use of large antenna arrays at the transmitter and receiver, combined with radio frequency and mixed signal power constraints, new multiple-input multiple-output (MIMO) communication signal processing techniques are needed. Because of the wide bandwidths, low complexity transceiver algorithms become important. There are opportunities to exploit techniques like compressed sensing for channel estimation and beamforming. This article provides an overview of signal processing challenges in mmWave wireless systems, with an emphasis on those faced by using MIMO communication at higher carrier frequencies. I. INTRODUCTION The millimeter wave (mmWave) band is the frontier for commercial – high volume consumer – wireless communication systems [1]. MmWave makes use of spectrum from 30 GHz to 300 GHz whereas most arXiv:1512.03007v1 [cs.IT] 9 Dec 2015 R. W. Heath Jr. is with The University of Texas at Austin, Austin, TX, USA (email: [email protected]). Nuria Gonzalez- Prelcic is with the University of Vigo, Spain, (email: [email protected]).
    [Show full text]
  • Full-Azimuth Beam Steering MIMO Antenna Arranged in a Daisy Chain Array Structure
    micromachines Article Full-Azimuth Beam Steering MIMO Antenna Arranged in a Daisy Chain Array Structure Kazuhiro Honda 1,*, Taiki Fukushima 2 and Koichi Ogawa 1 1 Graduate School of Engineering, Toyama University, 3190 Gofuku, Toyama 930-8555, Japan; [email protected] 2 Panasonic System Networks R&D Lab. Co., Ltd., Technology Center, 2-5 Akedori, Izumi-ku, Sendai, Miyagi 981-3206, Japan; [email protected] * Correspondence: [email protected]; Tel.: +81-76-445-6759 Received: 28 August 2020; Accepted: 18 September 2020; Published: 19 September 2020 Abstract: This paper presents a multiple-input, multiple-output (MIMO) antenna system with the ability to perform full-azimuth beam steering, and with the aim of realizing greater than 20 Gbps vehicular communications. The MIMO antenna described in this paper comprises 64 elements arranged in a daisy chain array structure, where 32 subarrays are formed by pairing elements in each subarray; the antenna yields 32 independent subchannels for MIMO transmission, and covers all communication targets regardless of their position relative to the array. Analytical results reveal that the proposed antenna system can provide a channel capacity of more than 200 bits/s/Hz at a signal-to-noise power ratio (SNR) of 30 dB over the whole azimuth, which is equivalent to 20 Gbps for a bandwidth of 100 MHz. This remarkably high channel capacity is shown to be due to two significant factors; the improved directivity created by the optimum in-phase excitation and the low correlation between the subarrays due to the orthogonal alignment of the array with respect to the incident waves.
    [Show full text]
  • 802.11Ac MU-MIMO Bridging the MIMO Gap in Wi-Fi
    Title Qualcomm Atheros, Inc. 802.11ac MU-MIMO: Bridging the MIMO Gap in Wi-Fi January, 2015 Qualcomm Atheros, Inc. Not to be used, copied, reproduced, or modified in whole or in part, nor its contents revealed in any manner to others without the express written permission of Qualcomm Atheros Inc. Qualcomm, Snapdragon, and VIVE are trademarks of Qualcomm Incorporated, registered in the United States and other countries. All Qualcomm Incorporated trademarks are used with permission. Other product and brand names may be trademarks or registered trademarks of their respective owners. This technical data may be subject to U.S. and international export, re-export, or transfer (“export”) laws. Diversion contrary to U.S. and international law is strictly prohibited. Qualcomm Atheros, Inc. 1700 Technology Drive San Jose, CA 95110 U.S.A. ©2014-15 Qualcomm Atheros, Inc. All Rights Reserved. MAY CONTAIN U.S. AND INTERNATIONAL EXPORT CONTROLLED INFORMATION Page 2 Table of Contents 1 Executive Summary ................................................................................................................................ 4 2 Why MU-MIMO? ..................................................................................................................................... 5 3 11ac Advanced Features: Transmit Beamforming (TxBF) and MU-MIMO ............................................. 6 3.1 Standardized Closed Loop TxBF .................................................................................................... 6 3.2 MU-MIMO or MU-TxBF ..................................................................................................................
    [Show full text]
  • Directional Beamforming for Millimeter-Wave MIMO Systems
    Directional Beamforming for Millimeter-Wave MIMO Systems Vasanthan Raghavan, Sundar Subramanian, Juergen Cezanne and Ashwin Sampath Qualcomm Corporate R&D, Bridgewater, NJ 08807 E-mail: {vraghava, sundars, jcezanne, asampath}@qti.qualcomm.com Abstract—The focus of this paper is on beamforming in a that allows the deployment of a large number of antennas in millimeter-wave (mmW) multi-input multi-output (MIMO) set- a fixed array aperture. up that has gained increasing traction in meeting the high data- Despite the possibility of multi-input multi-output (MIMO) rate requirements of next-generation wireless systems. For a given MIMO channel matrix, the optimality of beamforming communications, mmW signaling differs significantly from with the dominant right-singular vector (RSV) at the transmit traditional MIMO architectures at cellular frequencies. The end and with the matched filter to the RSV at the receive most optimistic antenna configurations1 at cellular frequencies end has been well-understood. When the channel matrix can are on the order of 4 × 8 with a precoder rank (number of be accurately captured by a physical (geometric) scattering layers) of 1 to 4; see, e.g., [9]. Higher rank signaling requires model across multiple clusters/paths as is the case in mmW 2 MIMO systems, we provide a physical interpretation for this multiple radio-frequency (RF) chains which are easier to real- optimal structure: beam steering across the different paths with ize at lower frequencies than at the mmW regime. Thus, there appropriate power allocation and phase compensation. While has been a growing interest in understanding the capabilities of such an explicit physical interpretation has not been provided low-complexity approaches such as beamforming (that require hitherto, practical implementation of such a structure in a mmW only a single RF chain) in mmW systems [10]–[15].
    [Show full text]
  • A Study of MIMO Precoding Algorithm for WCDMA Uplink
    A study of MIMO precoding algorithm in WCDMA uplink Master's Thesis in Signals and Systems Sheng Huang & Mengbai Tao Department of Signals & Systems Communication Engineering Chalmers University of Technology Gothenburg, Sweden 2014 Master's Thesis EX044/2014 Abstract With rapidly increasing demand in high-speed data transmission, Multiple-Input Multiple-Output (MIMO) techniques are widely used in current communication net- works, like Wideband Code Division Multiple Access (WCDMA). MIMO in downlink of WCDMA has been studied for a long time, while in WCDMA uplink, the specification of how to choose precoder is still not defined yet. Precoding is an essential element in MIMO, aiming at mitigating interference between different data streams that are se- quences of digitally coded signals. Currently, a MIMO system suffer from two problems regarding precoding. Since channel information is needed for the precoding algorithm, one problem then is that it is difficult to model the channel characteristics in a frequency selective channel. The other problem is that only limited feedback is applicable in real system, since the cost of feedback overhead is far larger than the benefits from precod- ing if full channel information needs to be sent to the transmitter. The objective of this thesis is to investigate the maximum potential gain in the required Signal-to-Noise Ratio (SNR) range for a given Block Error Rate (BLER) by using precoding and selecting a suitable precoding algorithm in the case of limited feedback in the WCDMA uplink. The maximum potential gain of precoding is considerable (1:2 dB at 0:5 BLER com- pared to a fixed precoder) in the case of limited feedback according to our simulations.
    [Show full text]
  • MIMO Technology in Wifi Systems
    MIMO in WiFi Systems Rohit U. Nabar Smart Antenna Workshop Aug. 1, 2014 WiFi • Local area wireless technology that allows communication with the internet using 2.4 GHz or 5 GHz radio waves per IEEE 802.11 • Proliferation in the number of devices that use WiFi today: smartphones, tablets, digital cameras, video-game consoles, TVs, etc • Devices connect to the internet via wireless network access point (AP) Advantages • Allows convenient setup of local area networks without cabling – rapid network connection and expansion • Deployed in unlicensed spectrum – no regulatory approval required for individual deployment • Significant competition between vendors has driven costs lower • WiFi governed by a set of global standards (IEEE 802.11) – hardware compatible across geographical regions WiFi IC Shipment Growth 1.25x 15x Cumulative WiFi Devices in Use Data by Local Access The IEEE 802.11 Standards Family Standard Year Ratified Frequency Modulation Channel Max. Data Band Bandwidth Rate 802.11b 1999 2.4 GHz DSSS 22MHz 11 Mbps 802.11a 1999 5 GHz OFDM 20 MHz 54 Mbps 802.11g 2003 2.4 GHz OFDM 20 MHz 54 Mbps 802.11n 2009 2.4/5 GHz MIMO- 20,40 MHz 600 Mbps OFDM 802.11ac 2013 5 GHz MIMO- 20, 40, 80, 6.93 Gbps OFDM 160 MHz 802.11a/ac PHY Comparison 802.11a 802.11ac Modulation OFDM MIMO-OFDM Subcarrier spacing 312.5 KHz 312.5 KHz Symbol Duration 4 us (800 ns guard interval) 3.6 us (400 ns guard interval) FFT size 64 64(20 MHz)/512 (160 MHz) FEC BCC BCC or LDPC Coding rates 1/2, 2/3, 3/4 1/2, 2/3, 3/4, 5/6 QAM BPSK, QPSK, 16-,64-QAM BPSK, QPSK, 16-,64-,256- QAM Factors Driving the Data Rate Increase 6.93 Gbps QAM 802.11ac (1.3x) FEC rate (1.1x) MIMO (8x) 802.11a Bandwidth (8x) 54 Mbps 802.11 Medium Access Control (MAC) Contention MEDIUM BUSY DIFS PACKET Window • Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) • A wireless node that wants to transmit performs the following sequence 1.
    [Show full text]
  • MIMO Fading Channel Capacity, Massive MIMO, Beamforming, Diversity Vs
    Lecture 15 - EE 359: Wireless Communications - Winter 2020 MIMO Fading Channel Capacity, Massive MIMO, Beamforming, Diversity vs. Multiplexing, RX Design Lecture Outline • MIMO Fading Channel Capacity • Massive MIMO • Beamforming • MIMO Diversity/Multiplexing Tradeoffs • MIMO RX Design 1. MIMO Fading Channel Capacity • In fading, capacity with both transmitter and receiver knowledge is the average of the capacity for the static channel, with power allocated either by an instantaneous or average power constraint. Under the instantaneous constraint power is optimally allocated over the spatial dimension only. Under the average constraint it is allocated over both space and time. • In fading, if the channel is unknown at transmitter, uniform power allocation is optimal, but this leads to an outage probability since the transmitter doesn’t know what rate R to transmit at: ρ H Pout = p H : B log2 det IMr + HH <R . Mt 2. Massive MIMO: • When the number of TX (or RX) antennas is large, the channel becomes “static” due to the law of large numbers. Specifically 1 H lim HH = IMr . Mt→∞ Mt • The MIMO channel capacity then becomes ρ H lim B log2 det IMr + HH = B log2 det [IMr + ρIMr ] = MrB log2(1 + ρ). Mt→∞ Mt • Defining M = min(Mt, Mr), this implies that as Mt grows large, the MIMO channel capacity in the absence of TX CSI approaches C = MB log2(1 + ρ) for ρ the SNR, and hence grows linearly in M. This is also true for finite Mt and Mr but at large SNR. 3. MIMO Systems: Beamforming • Beamforming sends the same symbol over each transmit antenna with a different scale factor.
    [Show full text]
  • MIMO RX Design, ISI Mitigation Techniques, Multicarrier Modulation and OFDM
    Lecture 16 - EE 359: Wireless Communications - Winter 2020 MIMO RX Design, ISI Mitigation Techniques, Multicarrier Modulation and OFDM Lecture Outline • MIMO RX Design • ISI Mitigation Techniques • Multicarrier Modulation • Overlapping Subcarriers and OFDM 1. MIMO Receiver Design: Maximum Likelihood Receiver • Optimal MIMO receiver is maximum-likelihood (ML) receiver solves the following problem. 2 xˆ(y) = argminx∈X Mt ky − Hxk . • Must search over all possible x ∈ X Mt , i.e. over all possible transmitted vectors which has exponential complexity in Mt and in the constellation size. • Alternate approaches using linear processing of input vector Ax. 2. Linear MIMO Receivers: • Multiplies y with a MIMO equalization matrix A ∈ CMt×Mr to get x˜(y) ∈ CMt . • Zero-forcing receiver forces all interference from other symbols to zero: x˜(y) = H†y, where H† is H−1 if the matrix is square and invertible, otherwise we use the pseudo inverse. • When Mt ≤ Mr, and there are at least Mt linearly independent columns in H (typical for many propagation environments), the pseudo inverse (Moore-Penrose pseudoin- verse) is H† =(HHH)−1HH. † • Complexity of obtaining H is roughly cubic in Mt for square matrices. However ob- taining xˆ(y) from x˜(y) is linear in Mt. ZF can result in significant noise enhancement. • L-MMSE receiver: trades off cancellation of interference from other symbols for noise enhancemen with a regularization term that depends on SNR. The solution is x˜(y)=(HHH + λI)−1HHy = HH(HHH + λI)−1y, which reduces to zero forcing in the absence of noise (infinite SNR). • Compared to the ML detector, both the ZF and MMSE linear detectors are simpler to implement, but their BER performances are worse.
    [Show full text]
  • MIMO Self-Encoded Spread Spectrum with Iterative Detection Over Rayleigh Fading Channels Shichuan Ma
    University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln CSE Journal Articles Computer Science and Engineering, Department of 2010 MIMO Self-Encoded Spread Spectrum with Iterative Detection over Rayleigh Fading Channels Shichuan Ma Lim Nguyen Won Mee Jang Yaoqing Yang Follow this and additional works at: https://digitalcommons.unl.edu/csearticles This Article is brought to you for free and open access by the Computer Science and Engineering, Department of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in CSE Journal Articles by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Hindawi Publishing Corporation Journal of Electrical and Computer Engineering Volume 2010, Article ID 492079, 9 pages doi:10.1155/2010/492079 Research Article MIMO Self-Encoded Spread Spectrum with Iterative Detection over Rayleigh Fading Channels Shichuan Ma, Lim Nguyen, Won Mee Jang, and Yaoqing (Lamar) Yang Department of Computer and Electronics Engineering, University of Nebraska-Lincoln, Omaha, NE 68182, USA Correspondence should be addressed to Shichuan Ma, [email protected] Received 15 February 2010; Revised 15 July 2010; Accepted 16 August 2010 Academic Editor: Christian Schlegel Copyright © 2010 Shichuan Ma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Self-encoded spread spectrum (SESS) is a novel communication technique that derives its spreading code from the randomness of the source stream rather than using conventional pseudorandom noise (PN) code. In this paper, we propose to incorporate SESS in multiple-input multiple-output (MIMO) systems as a means to combat against fading effects in wireless channels.
    [Show full text]
  • MIMO in HSPA: the Real-World Impact MIMO in HSPA: the Real-World Impact
    MIMO in HSPA: the Real-World Impact MIMO in HSPA: the Real-World Impact Contents 1 Executive Summary 2 2 The Role of MIMO in HSPA 3 2.1 What is MIMO, and What Does it Provide? 3 3 Implementation and Deployment 4 3.1 Co-existence of MIMO and Legacy Terminals 4 3.2 An Implementation Example 5 3.3 Deployment Considerations 7 4 Experiencing MIMO: from Field Trials to Network Deployments 9 4.1 Case Study at 2100MHz 9 4.2 Case Study at 900 MHz (Polsat) 11 5 The MIMO Ecosystem and Outlook 12 6 MIMO Evolution in 3GPP 13 6.1 MIMO Continues to Evolve in 3GPP 13 6.2 Leveraging Today’s MIMO Investments through Multi-User MIMO: 13 7 Conclusion and Recommendations 15 2 MIMO in HSPA: the Real-World Impact Figure List Figure 2.1: MIMO Improves Data Rates for all Users and Increases Capacity 3 Figure 3.1: Virtual Antenna Mapping 4 Figure 3.2: Overview of Huawei Co-Carrier Solution for MIMO & legacy Terminals 5 Figure 3.3: MIMO User Throughput Gain in Huawei Solution 6 Figure 3.4: MIMO Cell Throughput Gain in Huawei Solution 6 Figure 3.5: Different Evolution Strategies, where MIMO Plays a Fundamental Role 7 Figure 3.6: Easy Long-Term Evolution Steps of HSPA and LTE (Typical Example) 8 Figure 4.1: Field Test, Relative Average Cell Gain of MIMO Compared to 16 QAM and to 64 QAM 9 Figure 4.2: Field Measurement of Single User HSPA+ Performance (UDP Traffic) 10 Figure 4.3: Field Measurement of Multi-User HSPA+ Performance (2 Users with UDP Traffic) 10 Figure 4.4: Field Measurement of Dual Carrier vs Single Carrier (SIMO) HSPA+ Capacity 10 Figure 4.5: Average Throughput of Single User at More Than 30 Test Locations./ Peak Rate in Lab Test.
    [Show full text]