Overview
- 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
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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Table of contents (5 chapters)
Keywords
About this book
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
Authors and Affiliations
About the authors
Dr. 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 Center for Intelligent Networking and Communications (CINC), 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 160 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 editor of 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.
Zhipeng Xue received the B.E. degree in communication engineering from Southwest Jiaotong University, China, in 2015. He is currently pursuing the Ph.D. degree at ShanghaiTech University, in the School of Information Science and Technology. His research interests include statistical signal processing and machine learning.
Bibliographic Information
Book Title: Turbo Message Passing Algorithms for Structured Signal Recovery
Authors: Xiaojun Yuan, Zhipeng Xue
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-030-54762-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
Softcover ISBN: 978-3-030-54761-5Published: 14 October 2020
eBook ISBN: 978-3-030-54762-2Published: 13 October 2020
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XI, 105
Number of Illustrations: 10 b/w illustrations, 20 illustrations in colour
Topics: Communications Engineering, Networks, Signal, Image and Speech Processing, Computer Communication Networks