A Low Complexity Correlation Algorithm for Compressive Channel Estimation in Massive MIMO System

Channel state information is essential for base station (BS) to fully exploit the merits of massive multiple input multiple output, which consumes large amount of pilot overhead attributed to tremendous number of BS antennas. Accordingly, huge computational complexity of correlation operation tends to be an obstacle for the implementation of compressive channel estimation algorithms, especially for greedy algorithms. In this paper, pilot overhead problem lightens by exploiting common support property due to the close space of BS antenna array. Furthermore, a low complexity correlation algorithm is proposed for each iteration of greedy algorithm, which exploits the inherent of pilot distribution and sensing matrix composed of pilot sequence. Complexity of proposed algorithm related to pilot distribution is also investigated. Performance analysis and simulation results prove that the proposed algorithm maintains the same performance, while achieves much less computational complexity than the original greedy algorithm.