Rate adaptation for 802.11 multiuser mimo networks

In multiuser MIMO (MU-MIMO) networks, the optimal bit rate of a user is highly dynamic and changes from one packet to the next. This breaks traditional bit rate adaptation algorithms, which rely on recent history to predict the best bit rate for the next packet. To address this problem, we introduce TurboRate, a rate adaptation scheme for MU-MIMO LANs. TurboRate shows that clients in a MU-MIMO LAN can adapt their bit rate on a per-packet basis if each client learns two variables: its SNR when it transmits alone to the access point, and the direction along which its signal is received at the AP. TurboRate also shows that each client can compute these two variables passively without exchanging control frames with the access point. A TurboRate client then annotates its packets with these variables to enable other clients to pick the optimal bit rate and transmit concurrently to the AP. A prototype implementation in USRP-N200 shows that traditional rate adaptation does not deliver the gains of MU-MIMO WLANs, and can interact negatively with MU-MIMO, leading to low throughput. In contrast, enabling MU-MIMO with TurboRate provides a mean throughput gain of 1.7x and 2.3x, for 2-antenna and 3-antenna APs respectively.National Science Council (China) (contract No. NSC 100-2221-E-001-005-MY2)National Science Foundation (U.S.) (NSF Grant CNS-1117194

Random Access Heterogeneous Mimo Networks

This paper presents the design and implementation of 802.11n+, a fully distributed random access protocol for MIMO networks. 802.11n+ allows nodes that differ in the number of antennas to contend not just for time, but also for the degrees of freedom provided by multiple antennas. We show that even when the medium is already occupied by some nodes, nodes with more antennas can transmit concurrently without harming the ongoing transmissions. Furthermore, such nodes can contend for the medium in a fully distributed way. Our testbed evaluation shows that even for a small network with three competing node pairs, the resulting system about doubles the average network throughput. It also maintains the random access nature of today's 802.11n networks.United States. Defense Advanced Research Projects Agency. Information Theory for Mobile Ad-Hoc Networks ProgramNational Science Foundation (U.S.)

Solving hidden terminal problem in MU-MIMO WLANs with fairness and throughput-aware precoding and a degrees-of-freedom-based MAC design

© 2016, Shrestha et al. We generally emphasize that the zeroforcing (ZF) technique backed by an appropriate medium access control (MAC) protocol can be used to address the inevitable hidden terminal (HT) problem in multi-user multiple input multiple output (MU-MIMO) wireless local area network (WLAN) settings. However, to address the implementation-specific requirements of MU-MIMO WLANs, such as fairness in client access and throughput of the network, we propose a fairness and a throughput-aware ZF precoding in our design at the physical layer (PHY). This precoding scheme not only solves the HT problem but also meets the fairness and the throughput requirements of MU-MIMO WLANs. Besides, we design a MAC layer protocol, supportive to PHY, which decides transmission opportunities (TXOPs) among access points (APs) based on the available degrees of freedom (DoF). We make a mandatory provision in our design that APs should have a sufficient DoF. This can ensure collision-free transmission whenever APs/transmitters transmit in the HT scenario. Additionally, we design an improved channel sounding process for MU-MIMO WLANs with a less signaling overhead than IEEE802.11ac. We demonstrate the feasibility of our PHY in a USRP2/GNU Radio testbed prototype in the lab settings. It is found that our PHY improves the SNR and effective SNR of the received signal from about 5 to 11 dB in the HT scenario. The performance of our MAC design is checked with simulation studies in a typical six-antenna AP and clients scenario. We observe that our MAC protocol has a slightly higher signaling overhead than traditional ready to send/clear to send (RTS/CTS) due to design constraints; however, the signaling time overheads are reduced by 98.67 μs compared to IEEE802.11ac. Another interesting aspect to highlight is the constant Throughput gain of four to five times that of the traditional RTS/CTS. Our MAC protocol obtains this gain as early as 98.67 μs compared to IEEE802.11ac

Improving MIMO Performance in Wi-Fi Networks by using Collision Resolution and User Selection

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 김종권.Multiple-Input Multiple-Output (MIMO) technologies have emerged as a key component to increase the capacity of wireless networks. The MIMO scheme either simultaneously transmits to multiple users at a time or focuses energy towards a single user to enhance the data rate. A number of Wi-Fi standards based on MIMO technology have been developed, and recently, several commercial products have been successfully deployed on the market. Unfortunately, many commercial MIMO-based Wi-Fi products fail to fully exploit the advantages of the MIMO technology, even though the MIMO technology could play a key role in improving the wireless network performance. MIMO nodes cannot provide their higher data rates, especially when they coexist with SISO nodes. Meanwhile, in Wi-Fi networks, significant Channel State Information (CSI) feedback overhead has been obstacle to the performance of MU-MIMO transmission and user selection. Most of these problems are observed to root in the inefficient PHY and MAC design of current MIMO based Wi-Fi systems: the MAC simply abstracts the advancement of PHY technologies as a change of data rate. Hence, the benefit of new PHY technologies are either not fully exploited, or they even may harm the performance of existing network protocols. In this dissertation we introduce three co-designs of PHY/MAC layers for MIMO based Wi-Fi networks, in order to overcome the intrinsic limitations of the current MIMO based Wi-Fi network and improve the network capacity. First, we show the Interference Alignment and Cancelation (IAC) based collision resolution scheme for heterogeneous MIMO based Wi-Fi systems. Second, we present a practical user selection scheme for MU-MIMO Wi-Fi networks. Finally, we improve the proposed user selection scheme by exploiting a frequency domain signaling scheme and using a capacity gain as a selection metric. We have validated the feasibility and performance of our designs using extensive analysis, simulation and USRP testbed implementation.ABSTRACT i CONTENTS iii LIST OF FIGURES vi LIST OF TABLES ix CHAPTER I: Introduction 1 1.1 Background and Motivation 1 1.2 Goal and Contribution 8 1.3 Thesis Organization 9 CHAPTER II: MIMO based Collision Resolution 10 2.1 Introduction 10 2.2 Related Work 12 2.3 Background 14 2.3.1 Packet Collision Problems in MIMO Networks 14 2.3.2 IAC 15 2.4 802.11mc 17 2.4.1 Protocol Overview 17 2.4.2 Packet Collision Resolution via IAC 19 2.4.3 Collisions between Multiple CTSs 22 2.4.4 Optimal p 23 2.4.5 Discussion 28 2.5 USRP Experiments 33 2.5.1 Micro Benchmark 33 2.5.2 Macro Benchmark 39 2.6 NS-2 Simulations 43 2.6.1 Setting 43 2.6.2 Packet Loss Rate due to Collision 44 2.6.3 CWMin 45 2.6.4 Data Size 46 2.6.5 Number of Node Pairs (N) 49 2.6.6 Proportion of MIMO Receivers (q_2) 50 2.6.7 Postamble Probability (p) 52 2.6.8 Performance in Dynamic Network Configurations 54 2.7 Conclusion 55 CHAPTER III: User Selection for MU-MIMO Transmission 56 3.1 Introduction 56 3.2 Related Work 58 3.3 Background 60 3.3.1 System Model 60 3.3.2 User Selection 61 3.4 802.11ac+ 62 3.4.1 Overview 62 3.4.2 Channel Hint Broadcasting 63 3.4.3 Active CSI Feedback 66 3.5 Fair Scheduling 72 3.5.1 RR-11ac+ 72 3.5.2 PF-11ac+ 73 3.5.3 Summary 73 3.6 Performance Evaluation 75 3.6.1 Setting 75 3.6.2 802.11ac+ Performance 76 3.6.3 Fair Scheduling Protocol Performance 79 3.7 Conclusion 82 CHAPTER IV: Distributed Frequency Domain User Selection 83 4.1 Introduction 83 4.2 Motivation 84 4.3 DiFuse 88 4.3.1 Protocol Overview 88 4.3.2 Distributed Feedback Contention 89 4.3.3 Slot Threshold Design 95 4.3.4 Proportional Fair Selection 97 4.3.5 Discussions 98 4.4 Performance Evaluation 101 4.4.1 Micro Benchmark 101 4.4.2 System-Level Performance 105 4.5 Conclusion 113 CHAPTER V: Conclusion 114 BIBLIOGRAPHY 115 초 록 122Docto