c Copyright 2012 Daniel Chaim Halperin Simplifying the Configuration of 802.11 Wireless Networks with Effective SNR Daniel Chaim Halperin A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2012 Reading Committee: David J. Wetherall, Chair Thomas E. Anderson, Chair Jitendra D. Padhye Program Authorized to Offer Degree: Computer Science and Engineering University of Washington Abstract Simplifying the Configuration of 802.11 Wireless Networks with Effective SNR Daniel Chaim Halperin Co-Chairs of the Supervisory Committee: Professor David J. Wetherall Computer Science and Engineering Professor Thomas E. Anderson Computer Science and Engineering Advances in the price, performance, and power consumption of Wi-Fi (IEEE 802.11) tech- nology have led to the adoption of wireless functionality in diverse consumer electronics. These trends have enabled an exciting vision of rich wireless applications that combine the unique features of different devices for a better user experience. To meet the needs of these applications, a wireless network must be configured well to provide good performance at the physical layer. But because of wireless technology and usage trends, finding these configurations is an increasingly challenging problem. Wireless configuration objectives range from simply choosing the fastest way to encode data on a single wireless link to the global optimization of many interacting parameters over multiple sets of communicating devices. As more links are involved, as technology advances (e.g., the adoption of OFDM and MIMO techniques in Wi-Fi), and as devices are used in changing wireless channels, the size of the configuration space grows. Thus algorithms must find good operating points among a growing number of options. The heart of every configuration algorithm is evaluating of the performance of a wireless link in a particular operating point. For example, if we know the performance of all three links between a source, a destination, and a potential relay, we can easily determine whether or not using the relay will improve aggregate throughput. Unfortunately, the two standard approaches to this task fall short. One approach uses aggregate signal strength statistics to estimate performance, but these do not yield accurate predictions of performance. Instead, the approach used in practice measures performance by actually trying the possible con- figurations. This procedure takes a long time to converge and hence is ill-suited to large configuration spaces, multiple devices, or changing channels, all of which are trends today. As a result, the complexity of practical configuration algorithms is dominated by optimizing this performance estimation step. In this thesis, I develop a comprehensive way to rapidly and accurately predict the performance of every operating point in a large configuration space. I devise a simple but powerful model that uses a single low-level channel measurement and extrapolates over a wide configuration space. My work makes the most complex step of today’s configuration algorithms—estimating the effectiveness of a particular configuration—trivial, achieving better performance in practice and enabling the practical solution of larger problems. TABLE OF CONTENTS Page List of Figures . .v List of Tables . ix Chapter 1: Introduction . .1 1.1 The Problem . .3 1.2 Approach . .9 1.3 Hypothesis and Contributions . 10 1.4 Organization of this Thesis . 12 Chapter 2: Background . 13 2.1 Digital Communication Principles . 13 2.2 The IEEE 802.11n Standard . 21 2.3 Summary . 23 Chapter 3: Problem and Approach . 25 3.1 Problem: Rate Control for a Single Link . 25 3.2 Existing Statistics-based Approaches . 26 3.3 Channel-based Approaches . 30 3.4 Further Wireless Configuration Problems . 34 3.5 My Approach: An Effective SNR-based Model for Wi-Fi . 37 3.6 Summary . 41 Chapter 4: Effective SNR Model . 43 4.1 Overview of a MIMO-OFDM Link . 43 4.2 Effective SNR Model Overview . 44 4.3 Computing Effective SNR from Subchannel SNRs . 45 4.4 Modeling the Receiver: Computing Subchannel SNRs from Effective CSI . 51 4.5 Applications: Adapting CSI to Compute Effective CSI . 53 4.6 Protocol Details . 56 i 4.7 Comparison to Other Techniques . 60 4.8 Summary . 63 Chapter 5: Experimental Platform . 65 5.1 Experimental 802.11n Wireless Testbeds . 65 5.2 Node Configuration . 65 5.3 Node Software: 802.11n CSI Tool and Research Platform . 68 5.4 Computing 802.11n SNR and Effective SNR using IWL5300 Measurements . 70 5.5 Summary . 71 Chapter 6: Evaluating Effective SNR for MIMO-OFDM Channels . 73 6.1 Experimental Data . 73 6.2 Packet Delivery with Effective SNR . 74 6.3 Transmit Power Control . 86 6.4 Interference . 88 6.5 Summary . 89 Chapter 7: Rate Selection with Effective SNR . 91 7.1 Experimental Methodology . 91 7.2 SISO Rate Adaptation Results . 96 7.3 MIMO Rate Adaptation . 98 7.4 Enhancements: Transmit Antenna Selection . 100 7.5 Summary . 101 Chapter 8: Further Applications of Effective SNR . 103 8.1 Experimental Methodology . 103 8.2 Access Point Selection . 104 8.3 Channel Selection . 109 8.4 Path Selection . 112 8.5 Mobility Classification . 119 8.6 Summary . 128 Chapter 9: Related Work . 129 9.1 Understanding Real 802.11 Wireless Channels . 129 9.2 Theoretical Analysis of Channel Metrics . 130 9.3 Wireless Network Configuration Algorithms . 131 9.4 Follow-on Research . 136 ii Chapter 10: Conclusions and Future Work . 137 10.1 Thesis and Contributions . 137 10.2 Future Work . 139 10.3 Summary . 140 Bibliography . 143 iii iv LIST OF FIGURES Figure Number Page 1.1 A single Wi-Fi link . .4 1.2 Approaches to rate selection . .5 (a) The theoretical approach to rate selection based on SNR measurement5 (b) The probe-based approach to rate selection . .5 1.3 The three key configuration problems in multi-device networks . .7 1.4 Effective SNR-based approach to making application decisions . .9 2.1 The Shannon Capacity of a communications channel with Gaussian noise . 16 2.2 Constellation diagrams for the BPSK, QPSK, and 16-QAM modulations . 16 2.3 BER vs SNR for the four 802.11n modulation schemes . 17 2.4 Capacity vs SNR for 802.11n modulation and coding schemes . 18 3.1 The rate-related 802.11n configurations that use three antennas . 26 3.2 Rate adaptation search pattern for 802.11a . 27 3.3 Three different rate maps for 802.11n links . 28 3.4 Rate maps for the links in Figure 3.3 mapped into one dimension . 28 3.5 Rate maps for real 802.11n wireless links . 29 3.6 Rate adaptation search pattern for 802.11n . 29 3.7 SNR vs. PRR for a wired 802.11n link . 32 3.8 SNR vs. PRR for many wireless 802.11n channels . 32 3.9 Channel gains on four links that perform about equally well at 52 Mbps . 33 3.10 Simplified overview of an RF link operating over multiple subchannels . 38 4.1 An 802.11n link . 43 4.2 Model overview . 45 4.3 Packet SNR and Effective SNRs for a sample faded link . 48 5.1 My two indoor 802.11n testbeds . 66 (a) The testbed at Intel Labs Seattle . 66 (b) The testbed at UW CSE . 66 5.2 A custom antenna stand used to achieve consistent spatial geometry . 67 5.3 A custom laptop antenna stand . 67 v 5.4 The Intel Wireless Wi-Fi Link 5300 . 68 6.1 PRR vs SNR for all SISO modulations . 75 6.2 PRR vs Effective SNR for all SISO modulations . 76 6.3 Rate confusion for different antenna configurations . 79 (a) SISO configurations . 79 (b) SIMO configurations . 79 (c) MIMO2 configurations . 79 (d) MIMO3 configurations . 79 6.4 Thresholds and False Negative/Positive Rates with Packet SNR . 82 6.5 Thresholds and False Negative/Positive Rates with Effective SNR . 83 6.6 Balanced error rates for Effective SNR and Packet SNR . 84 6.7 Balanced error rate for Effective SNR relative to Packet SNR . 84 6.8 Performance from selecting rates with Effective SNR and Packet SNR . 84 6.9 Effective SNR vs Packet SNR for four faded links . ..