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Macrodiversity MIMO Transceivers by Dushyantha A. Basnayaka A dissertation submitted for the degree of Doctor of Philosophy in Electrical and Electronic Engineering University of Canterbury New Zealand Jun 2012 To my parents Abstract In wireless systems, radio signals are corrupted due to fading, interference and noise. In order to handle the effects of fading and interference, modern systems employ various tech- niques including multi-antenna transceivers. Initially, multi-antenna systems were proposed only for point-point communication. More recently, multi-antenna transceivers have been proposed for multiuser (MU) wireless systems. There are various topologies in which multi- antenna transceivers can be used in a multiuser wireless environment. Among them, macro- diversity is an important concept driven by many scenarios, including base station coop- eration, coordinated multipoint (CoMP) transmission and network multiple input multiple output (MIMO). A communication system where antenna elements at both source and receiver are widely (geographically) separated is described as a macrodiversity communication system. For these macrodiversity systems, every link may have a different average signal to noise ra- tio (SNR) since the sources and the receive antennas are all in different locations. This variation in average SNR across the links makes the performance analysis of such systems more complex. For this reason, most of the results currently available are based on simu- lation. However, the value of analytical results can be immense for efficient computation and optimized operation. Therefore, in this thesis we present a comprehensive, and rigorous analytical investigation of various aspects of multiuser macrodiversity MIMO systems. Two main aspects of macrodiversity MIMO systems are considered: the multiple access channel (MAC) and uplink user scheduling. In the earlier chapters of the thesis, we inves- i ii tigate the performance of uplink transmission employing multi-antenna transmitters and receivers. We analyze the signal-to-interference plus noise ratio (SINR) performance, sym- bol error rate (SER) and ergodic sum capacity etc. In a later chapter, we consider multiuser scheduling issues in macrodiversity multiuser MIMO systems. The primary emphasis is on the MIMO-MAC where we present some systematic performance metrics and approaches to multiuser scheduling which only require the long term channel state information (CSI). These methods provide a double advantage over scheduling using instantaneous CSI. First, the computational burden is lower and secondly, the delay between obtaining and using channel estimation is reduced. Acknowledgements During my doctoral studies my work has benefited from many different people and insti- tutions. I would like to express my heartiest gratitude to everyone who gave their hand to me. Above all, the shadow of the University of Canterbury was a huge support for me. She stood by me and my colleagues amidst the unprecedented troubles of the September 2010, earthquake that rattled Christchurch. Furthermore, I would like to acknowledge her for supporting me through a University of Canterbury International Doctoral Scholarship. I am extremely grateful to my principal supervisor Associate Prof. Peter J. Smith for his directions and support. If I am going into detail, I greatly appreciate his suggestions for my PhD topic. Naturally I enjoy working on hard problems and the difficulty of the prob- lems is an extra motivation for me to go further. I would also like to thank him for being patient, helpful and encouraging. I would also like to thank my co-supervisor Dr. Philippa A. Martin, for her support throughout my tenure as a PhD student. Next, I am thankful to Adjunct Prof. Mansoor Shafi for introducing me to Peter and for believing in me. That single decision changed my career forever. If I have to say whose sin- gle decision has made the biggest impact on my career so far, the answer should definitely be Prof. Mansoor Shafi’s. I wish him all the best in his life to be there for many more students like me around the world. Further, I am very grateful to Senior Prof. Nimal Ekanayake at the University of Peradeniya for continuously encouraging me to pursue my post graduate studies and US based Prof. S. R. H. Hoole for the guidance, advice and help he extended me in times when I have given iii iv up doing my post graduate studies which undoubtedly was a huge support. Next, I should remind my friend, Dr. Abdulla Firag, whose support and experience as a recent doctoral graduate was enormously helpful to me. I should mention my many colleagues across the School of Engineering at the University of Canterbury for their company and exchanging their ideas, thoughts and opinions on various subjects such as politics, economics, research towards changing the world. Those friends and ideas were undoubtedly inspirational and were an escape for me. Last, but not least, I would like to thank my parents, to whom I dedicate this work, for their love, sacrifices and support and more importantly for implanting the value of education in my soul. Further, I am grateful to my beloved wife, Ruvini for her courage throughout my studies in New Zealand. Next I like to remind my little daughter, Sanumi, for making my life full of joy and excitement especially in times when I was deeply disappointed and drained due to my studies. Maha Basnayaka Mudiyanselage Pahala Walawwe Dushyantha Anuruddha Basnayakaa aD. A. Basnayaka is also known in his full name as Maha Basnayaka Mudiyanselage Pahala Walawwe Dushyantha Anuruddha Basnayaka. Then the name with initials becomes M. B. M. P. W. D. A. Basnayaka. Contents Acknowledgements iii 1 Introduction 1 1.1 Problem Statement and Focus . .2 1.2 Thesis Outline and Contributions . .3 1.2.1 Macrodiversity Maximal Ratio Combining . .3 1.2.2 Ergodic Sum Capacity of Macrodiversity MIMO Systems . .4 1.2.3 Dual User Macrodiversity MIMO Systems with Linear Receivers . .5 1.2.4 Multiuser Macrodiversity MIMO Systems with Linear Receivers . .5 1.2.5 Maximum Likelihood Detection in Macrodiversity MIMO Systems . .6 1.2.6 Uplink Macrodiversity MU-MIMO Scheduling . .6 1.3 Publications . .7 2 System Background and Assumptions 9 2.1 Wireless Channel and Models . .9 2.1.1 Statistical Channel Models . 13 2.1.2 Modeling SISO Channels . 15 2.1.3 Modeling MIMO Channels . 16 2.2 Multiuser MIMO Channels . 18 2.3 Macrodiversity MIMO Applications . 23 v vi 2.3.1 CoMP Deployment Scenarios . 23 2.3.2 CoMP Transmission Modes . 24 3 Macrodiversity Maximal Ratio Combining. 27 3.1 System Model . 28 3.2 Performance Analysis . 31 3.2.1 A Simple SER Analysis . 31 3.2.2 Extended SER Analysis . 35 3.2.3 A Simple Power Metric . 37 3.2.4 Remarks on Systems with Multiple Co-located Receive Antennas at BSs..................................... 38 3.3 Numerical and Simulation Results . 38 3.4 Summary . 41 4 Ergodic Sum Capacity of Macrodiversity MIMO Systems 45 4.1 System Model . 46 4.1.1 Correlated Channels . 47 4.2 Preliminaries . 48 4.3 Exact Small System Analysis . 50 4.4 Approximate General Analysis . 54 4.5 A Simple Capacity Bound . 57 4.6 Numerical and Simulation Results . 58 4.7 Summary . 60 5 Dual User Macrodiversity MIMO Systems with Linear Receivers 65 5.1 System Model and Receiver Types . 66 5.1.1 System Model . 66 5.1.2 Receiver Types . 67 vii 5.2 System Analysis . 68 5.2.1 Background . 68 5.2.2 ZF Analysis . 68 5.2.3 MMSE Analysis . 74 5.3 High SNR Approximations . 78 5.3.1 ZF Analysis . 78 5.3.2 MMSE Analysis . 79 5.4 Exact High SNR Analysis . 81 5.4.1 ZF Analysis . 81 5.4.2 MMSE Analysis . 82 5.5 Numerical Results . 84 5.6 Summary . 87 6 Multiuser Macrodiversity MIMO Systems with Linear Receivers 91 6.1 System Model . 91 6.2 ZF Analysis . 93 6.2.1 CDF Approximations . 93 6.2.2 High SNR Approximations . 97 6.3 MMSE Analysis . 98 6.3.1 CDF Approximations . 98 6.3.2 High SNR Approximations . 101 6.4 Special Cases . 103 6.5 Simulations and Numerical Results . 104 6.6 Summary . 105 7 Maximum Likelihood Detection in Macrodiversity MIMO Systems 109 7.1 System Model . 110 7.2 Pairwise Error Probability . 110 viii 7.3 Performance Analysis . 114 7.3.1 Diversity Order and High SNR Results . 114 7.3.2 Symbol Error Rate . 115 7.4 Numerical Results . 116 7.5 Summary . 118 8 Uplink Macrodiversity MU-MIMO Scheduling 121 8.1 System Model and Problem Statement . 123 8.2 Group vs Individual Metrics . 124 8.3 Proposed Metrics Based on CDI . 126 8.3.1 Information Theoretical Capacity Based Metrics . 126 8.3.2 Linear Receiver Based Metrics . 127 8.3.3 Maximum Likelihood Criterion Based Metrics . 131 8.4 Summary . 133 9 Conclusions and Future Work 135 9.1 Summary of Analysis and Key Results . 136 9.1.1 MRC . 136 9.1.2 Ergodic Sum Capacity . 137 9.1.3 Dual User MMSE and ZF . 138 9.1.4 Multi User MMSE and ZF . 139 9.1.5 MLD . 140 9.1.6 Multiuser Scheduling . 140 9.2 Future Work . 141 Appendix 141 A Preliminary Mathematical Results 143 A.1 Matrix Identities . 143 ix A.2 Permanent Identities . 144 A.3 Algebraic Identities . ..
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