Overhead-efficient Channel Estimation and Beamforming for Hybrid Architecture-based mmWave Systems

Abstract

In millimeter wave (mmWave) communications, massive multiple-input multiple-output (MIMO) systems can achieve high gain and increase spectral efficiency significantly. To reduce the hardware complexity and energy consumption of massive MIMO systems, hybrid architecture has attracted extensive attention. Channel estimation and beamformer design based on the hybrid architecture are two critical techniques. Motivated by the advantages of sparse recovery, we propose an algorithm based on compressive sensing (CS) for channel estimation to estimate the dominant paths in the sparse mmWave channel. Moreover, benefited by the limited components of multipath in the angle-domain, an overhead-efficient method for hybrid beamformer design without singular value decompose (SVD) for obtaining the optimal hybrid precoder is proposed followed by the mmWave channel estimation, and only the indices of orthogonal array response vectors of the estimated dominated paths need to be transmitted as the feedback information for hybrid precoder design. Numerical experiments demonstrate that the proposed algorithm for channel estimation can recovery the channel matrix by estimating the parameters of dominant paths. Through the theoretical analysis and numerical experiments, we prove that the proposed algorithm for hybrid beamformer design can perform near-optimal spectral efficiency. Finally, we analyze the feedback overhead cost by the proposed algorithm and prove that the proposed algorithm can significantly reduce the feedback overhead.