Compressive Sensing Based Time-varying Channel Estimation for Millimeter Wave Systems

Channel estimation for millimeter wave (mmWave) systems over time-varying channels is a challenging problem, since a large number of channel coefficients need to be estimated. In this paper, by exploiting the sparsity of mmWave channel in the angular domain, we propose an efficient sparse channel estimation scheme based on compressive sensing (CS) theory. Specifically, considering that the angles of arrival/departure (AoAs/AoDs) vary more slowly than the path gains, we formulate the channel estimation into a block-sparse signal recovery problem, and then propose a novel greedy algorithm consistent with the block structure to estimate AoAs/AoDs. Based on the estimated angles, we design optimal training hybrid precoders and combiners to maximize array gains, followed by estimating the path gains utilizing the least square (LS) method. The simulation results demonstrate that our proposed scheme performs better than the existing mmWave channel estimators in both estimation accuracy and spectral efficiency over time-invariant channels, and further verify that our proposed scheme is suitable for time-varying channels.