In millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems, channel estimation is a challenging task in terms of acquiring the instantaneous channel state information (CSI), because both the estimation complexity and the overhead required for pilot symbols and feedback grow drastically as the number of antennas increases. Alternatively, some channel statistics in the form of partial CSI are sufficient for transceiver optimization and noncoherent detection in time-varying wireless environments. To obtain such useful statistical information accurately and efficiently, this paper proposes a new channel statistics estimation method using the compressive covariance sensing technique, which directly estimates the desired second-order statistics of the channel while bypassing the intermediate recovery of the instantaneous channel matrix itself. A diagonal-search orthogonal matching pursuit (DS-OMP) algorithm is developed for fast channel estimation. The proposed algorithm has low computational complexity and reduced overhead in training and feedback, owing to its proper utilization of the joint sparsity structure of the channel covariance matrix.
['Yue Wang', 'Zhi Tian', 'Shulan Feng', 'Philipp Zhang']
Efficient channel statistics estimation for millimeter-wave MIMO systems