Channel Estimation and Beamforming Techniques for mm Wave-Massive MIMO: Recent Trends, Challenges and Open Issues

Abstract

The fifth-generation (5G) wireless communication system requires massive connectivity with high data rates and low latency. One of the technologies to meet these requirements is mm Wave massive MIMO. This work, therefore, aspires to have an in-depth look at the channel estimation and beamforming techniques jointly with their respective architectures for mm Wave massive MIMO system. In particular; sparse, compressed sensing, machine learning and array signal processing based channel estimation are addressed from 5G channel estimation techniques. On the other hand, beamforming techniques like hybrid beamforming and the low-complexity hybrid block diagonalization schemes with their mathematical analysis are included. This work also discusses in detail the challenges, optimization methods and mitigation techniques of pilot contamination, signal detection, channel estimation and hybrid beamforming for mm Wave massive MIMO system. The result asserts that partially connected block-diagonal hybrid bema forming with array signal processing based channel estimation is more optimal than the others with respect to over all performance, complexity and energy consumption. Finally, open research directions and challenges are pointed out.