Description
The book focuses on utilizing sparse signal processing techniques in designing massive MIMO communication systems. As the number of antennas has been increasing rapidly for years, extremely high-dimensional channel matrix and massive user access urge for algorithms with much higher efficiency. This book provides in-depth discussions on compressive sensing techniques and simulates the performance on wireless systems. The easy-to-understand instructions with detailed simulations and open-sourced codes provide convenience for readers such as researchers, engineers, and graduate students in the fields of wireless communications.
Table of Contents
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.
