Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems

Abstract

Compressed sensing (CS) has great potential in channel estimation for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. To solve such a CS-based channel estimation problem, three categories of algorithms, namely convex relaxation algorithms, greedy iteration algorithms and Bayesian inference algorithms, are widely used. In this paper, with a unified massive MIMO framework, comprehensive comparisons among three categories of algorithms are presented in the perspective of the estimated accuracy, which is affected by the received signal-to-noise ratio (SNR), the number of resolvable paths, angular quantization error, the number of pilot symbols and hardware impairments. Specifically, it shows that convex relation algorithms achieve the best estimation accuracy at the high SNR range and it is mainly affected by the received SNR and transmitter’s hardware impairments. At the low SNR range, greedy iteration algorithms outperform others and the estimated accuracy is then limited by the angle quantization error. Furthermore, a tradeoff between the estimated error and complexity is achieved in Bayesian inference algorithms, a;though its estimated error is sensitive to the number of available pilot symbols.