K-Best Decoders for 5G+ Wireless Communication

K-Best Decoders for 5G+ Wireless Communication

Mehnaz Rahman Gwan S. Choi K-Best Decoders for 5G+ Wireless Communication

Mehnaz Rahman Department of Electrical and Computer Engineering Texas A&M University College Station, TX, USA Gwan S. Choi Department of Electrical and Computer Engineering Texas A&M University College Station, TX, USA ISBN 978-3-319-42808-6 ISBN 978-3-319-42809-3 (ebook) DOI 10.1007/978-3-319-42809-3 Library of Congress Control Number: 2016945170 Springer International Publishing Switzerland 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG Switzerland

To My Parents.

Preface The demand for wireless and high-rate communication system is increasing gradually, and multiple input multiple-output (MIMO) is one of the feasible solutions to accommodate the growing demand for its spatial multiplexing and diversity gain. However, with high number of antennas, the computational and hardware complexity of MIMO increases exponentially. This accumulating complexity is a paramount problem in MIMO detection system, directly leading to large power consumption. Hence, the major focus of this book is algorithmic and hardware development of MIMO decoder with reduced complexity for both real and complex domain, which can be a beneficial solution with power efficiency and high throughput. Both hard and soft domain MIMO detectors are considered. The use of lattice reduction (LR) algorithm and on-demand child expansion for the reduction of noise propagation and node calculation, respectively, are two of the key features of our developed architecture, presented in this literature. The real domain iterative soft MIMO decoding algorithm, simulated for 4 4 MIMO with a different modulation scheme, achieves 1.1 2.7 db improvement over Least Sphere Decoder (LSD) and more than 8 reduction in list size, K, as well as complexity of the detector. Next, the iterative real domain K-Best decoder is expanded to the complex domain with new detection scheme. It attains 6.9 8.0 db improvement over real domain K-Best decoder and 1.4 2.5 db better performance over conventional complex decoder for 8 8 MIMO with 64 QAM modulation scheme. Besides K, a new adjustable parameter, Rlimit, has been introduced in order to append reconfigurability trading-off between complexity and performance. All of the proposed decoders mentioned above are bounded by the fixed K. Hence, an adaptive real domain K-Best decoder is further developed to achieve the similar performance with less K, thereby reducing the computational complexity of the decoder. It does not require accurate SNR measurement to perform the initial estimation of list size, K. Instead, the difference between the first two minimal distances is considered, which inherently eliminates complexity. vii

viii Preface In Summary, a novel iterative K-Best detector for both real and complex domain with efficient VLSI design is proposed in this book. The results from extensive simulation and VHDL with analysis using Synopsys tool are also presented for justification and validation of the proposed works. College Station, TX, USA Mehnaz Rahman, Ph.D. Gwan S. Choi

Acknowledgments I, Mehnaz Rahman, would like to express my heartiest gratitude to my advisor, Dr. Gwan Choi, for his support and guidance toward my research. He consistently encouraged me in all the difficult situations of my life. Last but not least, I want to express my cordial gratitude to my parents, specially my mother, Rokeya Begum. Without their constant support, love, and encouragement, my journey would not be complete. ix

Nomenclature BER BLAST BPSK DFS-LSD DMT LDPC LLR LR LSD LTE Mbps MIMO ML MMSE NLD PED SD SE SIC SISO SM SNR VLSI WiMAX WLAN ZF Bit error rate Bell Labs Layered Space-Time Binary phase shift keying Depth first search least sphere decoder Diversity multiplexing tradeoff Low-density parity check Log likelihood ratio Lattice reduction Least sphere decoder Long-term evolution Mega bits per second Multiple input multiple output Maximum likelihood Minimum mean square error Naive lattice detection Partial Euclidean distance Sphere decoding Schnorr Euchner Successive interference cancelation Soft input soft output Spatial multiplexing Signal-to-noise ratio Very large-scale integration Worldwide interoperability for microwave access Wireless local area network Zero forcing xi

Contents 1 Introduction... 1 1.1 Introduction to MIMO Systems... 1 1.2 Challenges and Motivation... 3 1.3 Contributions... 4 1.4 Book Outline... 5 2 Background... 7 2.1 MIMO System Model... 7 2.2 MIMO Detection Schemes... 9 2.2.1 Optimal MIMO Detection... 9 2.2.2 Suboptimal MIMO Detection... 10 2.2.3 Near-Optimal MIMO Detection... 13 3 Real Domain Iterative K-Best Detector... 17 3.1 Theory of K-Best Algorithm... 17 3.2 Proposed K-Best Algorithm... 17 3.2.1 LR-Aided K-Best Decoder... 18 3.2.2 On-Demand Child Expansion... 19 3.2.3 Soft Decoding... 20 3.2.4 LDPC Decoder... 21 3.3 Discussion... 22 3.3.1 Simulation and Analysis... 22 3.3.2 Choosing Optimum List Size, K... 26 3.3.3 Effect of LLR Clipping on K... 28 4 Complex Domain Iterative K-Best Decoder... 33 4.1 Proposed Complex Domain K-Best Algorithm... 33 4.2 Complex On-Demand Expansion... 35 4.3 Iterative Soft Decoding... 36 4.4 Discussion... 37 xiii

xiv Contents 4.4.1 Simulation and Analysis... 37 4.4.2 Effect of Rlimit on BER... 39 4.4.3 Comparison of Performance... 40 5 Fixed Point Realization of Iterative K-Best Decoder... 43 5.1 Architecture Selection... 43 5.1.1 QR Decomposition... 43 5.1.2 Lattice Reduction... 44 5.1.3 LDPC Decoder... 44 5.2 Fixed Point Conversion with Word-Length Optimization... 45 5.3 Discussion... 45 5.3.1 Comparison of Performance... 45 5.3.2 Optimization of Word-Length... 46 6 Adaptive Real Domain Iterative K-Best Decoder... 49 6.1 Proposed Adaptive K-Best Algorithm... 49 6.2 Discussion... 51 6.2.1 Estimation of Channel... 51 6.2.2 Choosing Threshold Points... 52 6.2.3 Performance of Adaptive K-Best Decoder... 53 7 Conclusion... 55 7.1 Summary of Chapter 2... 55 7.1.1 MIMO System Model... 55 7.1.2 MIMO Detection Schemes... 56 7.2 Summary of Chapter 3... 56 7.2.1 Discussion of Chapter 3... 56 7.3 Summary of Chapter 4... 57 7.3.1 Discussion of Chapter 4... 57 7.4 Summary of Chapter 5... 57 7.4.1 Discussion of Chapter 5... 57 7.5 Summary of Chapter 6... 58 7.5.1 Discussion of Chapter 6... 58 References... 59 Index... 63