Compressive Sensing Based Hybrid Beamforming for Adaptively-Connected Structure

Hybrid multiple-input multiple-output (MIMO) has been thought as a promising technology for 5G communications. Compared with the fully-connected structure in hybrid MIMO systems, the adaptively-connected structure requires a significantly reduced number of analog phase shifters (APSs) and no radio frequency (RF) adder. In this paper, we focus on the multi-user massive MIMO system with adaptively-connected structure and propose a compressive sensing (CS) based method to design hybrid beamforming. The RF combiner for each user is independently designed based on the decomposition of the channel. By exploiting the sparse structure of RF precoder, we develop an iterative greedy algorithm to jointly design the RF precoder and baseband precoder, aiming at maximizing the effective signal power as well as eliminating the multi-user interference. Moreover, upper bound of the achievable sum rate for the proposed scheme is derived. The numerical results demonstrate that the proposed scheme can approach the performance of fully-connected scheme and achieve a higher sum rate than the existing schemes in both Rayleigh fading channel and millimeter wave channel.