Sparse Signal Processing for Massive MIMO Communications

Zhen Gao received the B.S. degree in information engineering from Beijing Institute of Technology, Beijing, China, in 2011 and the Ph.D. degree in communication and signal processing from Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, China, in 2016. He is currently Assistant Professor at Beijing Institute of Technology. His research interests are in wireless communications, with a focus on multi-carrier modulations, multiple antenna systems, and sparse signal processing. He was a recipient of the IEEE Broadcast Technology Society 2016 Scott Helt Memorial Award (Best Paper), the Exemplary Reviewer of IEEE COMMUNICATIONS LETTERS in 2016, the IET ELECTRONICS LETTERS Premium Award (Best Paper) 2016, the Young Elite Scientists Sponsorship Program (2018–2020) from China Association for Science and Technology, the Elsevier China Highly Cited Scholar (2020), and the First Prize of Natural Science granted by Chinese Institute of Electronics.

• Introduction.- Massive MIMO Performance Analysis and Channel Estimation Scheme in Sparse Channels.- Channel Estimation Based on Structured Compressed Sensing Theory in FDD Massive MIMO Systems.- Channel Feedback Based on Distributed Compressed Sensing Theory in FDD Massive MIMO Systems.-  Channel Estimation and Beamforming Based on Compressed Sensing Theory in mmWave Massive MIMO Systems.- Sparse Channel Estimation Based on Spectral Estimation Theory for mmWave Massive MIMO Systems.- Quasi-Optimal Signals Detection for Massive Spatial Modulation MIMO Systems Based on Structured Compressed Sensing.-  Multiuser Signal Detection Based on Compressed Sensing for Massive Media Modulation MIMO Systems.- Compressed Sensing Mass Access Techniques in Medium Modulation Assisted IoT Machine Type Communication.- Time-varying Channel Estimation Based on Compressed Sensing Theory for TDS-OFDM Systems.- Summary and Prospects for Massive MIMOTechnology.