Spatial Channel Covariance Estimation for Hybrid mmWave Multi-User MIMO Systems

Channel estimation is crucial to beamforming techniques in directional millimetre wave (mmWave) communications, which is generally designed based on channel state information with the assumption that the channel is static. However, due to the Doppler effect caused by the mobility of the users in highly mobile applications, the mmWave channel is changing rapidly. Spatial channel covariance, defined by long-term statistic information of channels, is a promising solution to reduce channel estimation frequency, and which can be used to design hybrid precoders. In this paper, we investigate compressive sensing based spatial channel covariance estimation for hybrid mmWave multiuser (MU) multiple input multiple output (MIMO) system. The updated sparse Bayesian learning (Updated-SBL) algorithm is proposed which is achieved by reducing the total squared mutual coherence of the sensing matrix in it. Simulations demonstrate that the total squared mutual coherence of the proposed Updated-SBL algorithm is dramatically reduced and the superiority of the proposed algorithm is validated by comparing to the other benchmark methods.