Advanced Bayesian Signal Processing
Description
This textbook provides a systematical introduction of Bayesian signal processing from Bayesian parameter estimation, factor graph, and message passing to sparse Bayesian inference (compressive sensing). The book not only provides a systematical introduction of the theory and methods in Bayesian inference, but also discusses advanced Bayesian inference methods recently developed for future wireless systems and its applications in emerging wireless technologies, including massive MIMO, wireless localization, integrated sensing and communications, etc. The authors include a unified framework to incorporate different Bayesian inference methods/algorithms, and a thorough comparison of the pros and cons of different Bayesian inference methods/algorithms and their application scenarios in wireless network and IoT. The authors offer classroom materials such as homework problems, course projects, a solutions manual, PowerPoint slides, and sample code. The book is intended for senior undergraduates, graduate and advanced graduate students and is also suitable for industry researchers.
More details
Persons
Dr. An Liu received the B.S. and Ph.D. degrees in electrical engineering from Peking University, China, in 2004 and 2011, respectively. From 2008 to 2010, he was a Visiting Scholar with the Department of ECEE, University of Colorado at Boulder, USA. He was a Post-Doctoral Research Fellow from 2011 to 2013, a Visiting Assistant Professor in 2014, and a Research Assistant Professor with the Department of ECE, HKUST, Hong Kong, from 2015 to 2017. He is currently an Associate Professor with the College of Information Science and Electronic Engineering, Zhejiang University. An Liu is a Senior Member, IEEE. His research interests include wireless communications, stochastic optimization, compressive sensing, and machine/deep learning for communications. He is serving an Editor for IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, and a member for the Signal Processing for Communications and Networking Technical Committee (SPCOM TC) of IEEE Signal Processing Society. He served as an Editor for IEEE TRANSACTIONSON ON SIGNAL PROCESSING from 2020 to 2024, an Editor for IEEE WIRELESS COMMUNICATIONS LETTERS from 2017 to 2022. He has 130+ IEEE Journal Publications, 90+ IEEE Conference Publications, 3 Book Chapters, 1 US Patents and 10+ China Patents.
Dr. Ming-Min Zhao received the B.Eng. and Ph.D. degrees in information and communication engineering from Zhejiang University, in 2012 and 2017, respectively. From 2015 to 2016, he was a Visiting Scholar with the Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA. From 2017 to 2018, he worked as a Research Engineer with Huawei Technologies Co., Ltd. From 2019 to 2020, he was a Visiting Scholar with the Department of Electrical and Computer Engineering, National University of Singapore. Since 2018, he has been working with Zhejiang University, where he is currently an Associate Professor with the College of Information Science and Electronic Engineering. Ming-Min Zhao is a Senior Member, IEEE. His research interests include algorithm design and analysis for advanced MIMO, signal processing for communication, channel coding, and machine learning for wireless communications. His paper received the IEEE Communications Society Katherine Johnson Young Author Best Paper Award in 2024. He was the Publication and Publicity Co-Chair for the 2022 International Symposium on Wireless Communication Systems (ISWCS), and co-organized workshops/special sessions at IEEE/CIC ICCC 2025, IEEE VTC2025-Fall, IEEE SAM 2026 and IEEE SPAWC 2026. He is currently serving as an Associate Editor for IEEE OPEN JOURNAL OF SIGNAL PROCESSING and IEEE WIRELESS COMMUNICATIONS LETTERS. He has 50+ IEEE Journal Publications, 50+ IEEE Conference Publications, 2 US Patents and 10+ China Patents.
Content
Introduction.- Part I: Bayesian Parameter Estimation for Wireless Communications and Sensing.- Bayesian Decision Theory: A Unified Framework for Parameter Estimation.- Point Estimators.- Bayesian Estimators.- Expectation Maximization.- Factor Graph and Message Passing.- Subspace Methods.- Part II: Sparse Bayesian Inference (Compressive Sensing) for Wireless Communications and Sensing.- Introduction to Compressive Sensing (CS).- Optimization Based CS Recovery Algorithms.- Greedy CS Recovery Algorithms.- Approximate Message Passing.- Sparse Bayesian Learning.- Variational Bayesian Inference.- Part III: Advanced Topics in Future Wireless Networks.- Structure Compressive Sensing with Dynamic Grid.- Turbo Compressive Sensing.- Dynamic Bayesian Learning.- Advanced Variational Bayesian Inference Methods.- Bayesian Deep Learning.- Conclusion.