Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive Sensing

Abstract

Channel estimation is a fundamental problem for downlink transmission in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. This paper proposes a channel estimation algorithm by exploiting the separable structured sparsity of mmWave massive MIMO channel. The mmWave downlink channel is firstly formulated as a two dimensional (2D) separable compressive sensing (CS) model according to the sparsity structure of the channel in angle of arrivals (AoAs) and angle of departures (AoDs) domains. Then a separable compressive sampling match pursuit (SCoSaMP) algorithm is proposed to solve the separable CS recovery problem for channel estimation. Based on the separable sparsity structure of the channel, we design the precoding and combining matrices under the metric of mutual information to further improve the performance of channel estimation. Simulations demonstrate the advantages of the proposed algorithm over the traditional CS-based channel estimation methods.