This book takes a comprehensive study on turbo message passing algorithms for structured signal recovery, where the considered structured signals include 1) a sparse vector/matrix (which corresponds to the compressed sensing (CS) problem), 2) a low-rank matrix (which corresponds to the affine rank minimization (ARM) problem), 3) a mixture of a sparse matrix and a low-rank matrix (which corresponds to the robust principal component analysis (RPCA) problem). The book is divided into three parts. First, the authors introduce a turbo message passing algorithm termed denoising-based Turbo-CS (D-Turbo-CS). Second, the authors introduce a turbo message passing (TMP) algorithm for solving the ARM problem. Third, the authors introduce a TMP algorithm for solving the RPCA problem which aims to recover a low-rank matrix and a sparse matrix from their compressed mixture. With this book, we wish to spur new researches on applying message passing to various inference problems.

• Provides an in depth look into turbo message passing algorithms for structured signal recovery
• Includes efficient iterative algorithmic solutions for inference, optimization, and satisfaction problems through message passing
• Shows applications in areas such as wireless communications and computer vision

Provides an in depth look into turbo message passing algorithms for structured signal recovery Includes efficient iterative algorithmic solutions for inference, optimization, and satisfaction problems through message passing Shows applications in areas such as wireless communications and computer vision

Autorentext
Bin Duo received his Ph.D. degrees in information and communication engineering from Harbin Institute of Technology, China in 2014 and from the University of Sydney, Australia, in 2016, respectively. From 2018 to 2019, he visited the Department of Electrical and Computer Engineering, National University of Singapore, Singapore. Since 2018, he has been with the National Laboratory of Science and Technology on Communications, the University of Electronic Science and Technology of China, as a Research Fellow. He is currently a Professor with the College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, China. His research interests include convex optimization theory, UAV communications for 6G and physical layer security. Xiaojun Yuan received the Ph.D. degree in Electrical Engineering from the City University of Hong Kong in 2008. From 2009 to 2011, he was a research fellow at the Department of Electronic Engineering, the City University of Hong Kong. He was also a visiting scholar at the Department of Electrical Engineering, the University of Hawaii at Manoa in spring and summer 2009, as well as in the same period of 2010. From 2011 to 2014, he was a research assistant professor with the Institute of Network Coding, The Chinese University of Hong Kong. From 2014 to 2017, he was an assistant professor with the School of Information Science and Technology, ShanghaiTech University. He is now a professor with the University of Electronic Science and Technology of China. His research interests cover a broad range of wireless communications, statistical signal processing, and information theory including multi-antenna techniques, network coding, cooperative communications, compressed sensing, etc. He has published over 220 peer-reviewed research papers in the leading international journals and conferences, and has served on a number of technical programs for international conferences. He is now serving as an editorof IEEE Transactions on Wireless Communications, as well as of IEEE Transactions on Communications. He was a co-recipient of a number of Best Paper Awards of IEEE journals and conferences. Yifan Liu received the B.E. degree in IoT Engineering from Chengdu University of Technology, China, in 2015. He is currently pursuing his M.S. degree at Chengdu University of Technology, in the School of Electrical and Mechanical Engineering. His research interests include convex optimization theory, UAV communications, and reconfigurable intelligent surfaces.

Inhalt

Introduction.- Turbo Message Passing for Compressed Sensing.- Turbo Message Passing for Affine Rank Minimization.- Turbo Message Passing for Compressed Robust Principal Component Analysis.- Learned Turbo Message Passing Algorithms.- Future Research Directions.- Conclusion.