Turbo Message Passing Algorithms for Structured Signal Recovery
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 m
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Xiaojun Yuan Zhipeng Xue
Turbo Message Passing Algorithms for Structured Signal Recovery 123
SpringerBriefs in Computer Science Series Editors Stan Zdonik, Brown University, Providence, RI, USA Shashi Shekhar, University of Minnesota, Minneapolis, MN, USA Xindong Wu, University of Vermont, Burlington, VT, USA Lakhmi C. Jain, University of South Australia, Adelaide, SA, Australia David Padua, University of Illinois Urbana-Champaign, Urbana, IL, USA Xuemin Sherman Shen, University of Waterloo, Waterloo, ON, Canada Borko Furht, Florida Atlantic University, Boca Raton, FL, USA V. S. Subrahmanian, University of Maryland, College Park, MD, USA Martial Hebert, Carnegie Mellon University, Pittsburgh, PA, USA Katsushi Ikeuchi, Meguro-ku, University of Tokyo, Tokyo, Japan Bruno Siciliano, Università di Napoli Federico II, Napoli, Italy Sushil Jajodia, George Mason University, Fairfax, VA, USA Newton Lee, Institute for Education, Research and Scholarships, Los Angeles, CA, USA
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Xiaojun Yuan • Zhipeng Xue
Turbo Message Passing Algorithms for Structured Signal Recovery
Xiaojun Yuan Center for Intelligent Networking & Communication University of Electronic Science & Technology of China Chengdu, China
Zhipeng Xue School of Information Science and Technology ShanghaiTech University Shanghai, China
ISSN 2191-5768 ISSN 2191-5776 (electronic) SpringerBriefs in Computer Science ISBN 978-3-030-54761-5 ISBN 978-3-030-54762-2 (eBook) https://doi.org/10.1007/978-3-030-54762-2 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the
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