Sparsity-Enhancing Basis for Compressive Sensing Based Channel Feedback in Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) systems have attracted extensive attention recently due to their potentials to provide high system capacity. To obtain the benefits of massive MIMO systems, channel state information (CSI) at the transmitter is essential. The high overhead of traditional channel estimation and feedback scheme for downlink massive MIMO systems makes frequency division duplexing (FDD) impractical. Compressive sensing (CS) is a potential way to alleviate the problem. In this paper, we mainly focus on the CS- based feedback design. To guarantee the performance of the CS-based algorithms, a proper basis to reveal the sparsity of the channel is important. Here, we use the statistical information of the angle-of-departure (AoD) of physical channel paths for the basis design. A $l_0$-norm based basis optimization problem is first formulated. Then, the problem is relaxed by a weighted $l_1$-norm and is solved by an iterative algorithm. The mean-square-error (MSE) performance of the CS-based feedback scheme based on our proposed basis is better than the traditional discrete Fourier transform (DFT) basis.