Dimension-Deficient Channel Estimation of Hybrid Beamforming Based on Compressive Sensing

Abstract

Due to high hardware costs for digital beamforming, hybrid beamforming (HBF) is widely employed in millimeter-wave (mmWave) communications systems. However, the number of radio frequency chains in the analog part of HBF is far less than that of antennas, which causes a serious dimension-deficient problem. In order to overcome this problem, this paper proposes a compressive sensing algorithm using an adaptive overcomplete dictionary to estimate the sparse channel in the HBF-based mmWave system. The algorithm adaptively generates the dictionary by using the received signal to accurately reconstruct the mmWave channel. The simulation results are presented to demonstrate that the proposed algorithm outperforms its traditional counterparts in terms of the normalized mean square error and the spectral efficiency.