Textbook in PDF format

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.
Introduction
Super-Resolution Sparse MIMO-OFDM Channel Estimation Based on Spatial and Temporal Correlations
Structured Compressive Sensing Based Spatio-Temporal Joint Channel Estimation for FDD Massive MIMO
Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO
Channel Estimation for Millimeter-Wave Massive MIMO with Hybrid Precoding over Frequency-Selective Fading Channels
Super-Resolution Channel Estimation for Millimeter-Wave Massive MIMO with Hybrid Precoding
Near-Optimal Signal Detector Based on Structured Compressive Sensing for Massive SM-MIMO
Compressive Sensing Based Multi-user Detector for the Large-Scale SM-MIMO Uplink
Compressive Sensing Based Massive Access for IoT Relying on Media Modulation Aided Machine Type Communications
Priori-Information Aided Iterative Hard Threshold: A Low-Complexity High-Accuracy Compressive Sensing Based CE for TDS-OFDM