Joint Beamforming and Power Optimization for D2D Underlaying Cellular Networks

This paper studies the optimal joint beamforming and power control strategy for device-to-device (D2D) communication underlaying multiuser multiple-input multiple-output cellular networks. We consider multiple antennas at the base station (BS) and a single antenna at each cellular user (CU), D2D transmitter (DT) and D2D receiver (DR). We aim to minimize the total transmission power of the system by jointly designing the transmit beamforming at the BS and the transmit powers for both BS and DTs, while satisfying the signal-to-interference-plus-noise ratio based quality-of-service constraints for both CUs and DRs. Due to the non-convex nature of the problem, we apply the semidefinite relaxation technique to find the optimal solution, which always satisfies the rank-one constraint. We also investigate three sub-optimal fixed beamforming schemes: zero-forcing (ZF), regularized ZF and hybrid maximum ratio transmission-ZF, where the focus is to minimize the total transmission power while reducing complexity. When perfect channel information is not available, we propose a robust transmit power minimization strategy with ZF beamforming which only requires limited feedback based channel direction information at the BS. Finally, computer simulation results are presented to demonstrate the effectiveness of the proposed schemes

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