Optimizing Pilots for Structured Compressive Sensing Based Channel Estimation in Massive MIMO-OFDM systems

This paper addresses the sparse channel estimation problem in FDD large-scale multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems from the perspective of structured compressive sensing. In this system, the pilots in different transmit antennas are superimposed and the problem of sparse channel estimation can be modeled as the reconstruction problem of structured compressive sensing. To improve the performance of structured compressive sensing-based channel estimation, a mutual coherence-related criterion is proposed to optimize pilots and a random research algorithm is provided to obtain optimized pilots. Simulation results show that, as compared with other randomly generated pilots, employing the pilots obtained by proposed optimizing algorithm can reduce the error of channel estimation obviously, and thus improve the channel estimation performance. The performance gain obtained by employing optimizing pilots is about 2~5dB. Simulation results also show that, when the number of pilots is the same, the performance gains by employing optimizing pilots become larger with the increase of the number of transmit antennas.