Compressive sensing based pilot reduction technique for massive MIMO systems

Massive multi-input multi-output (MIMO) technique deploys a number of transmit antennas in base-station (BS) to support large number of users and high data throughput. Since BS needs to acquire channel state information from all transmit antennas, substantial amount of downlink pilot signals is required. In this paper, we suggest a new downlink pilot allocation strategy, inspired by the compressed sensing principle, that reduces the density of the pilot significantly. Key observation in the proposed approach is that the sparse structure of the channel impulse response (CIR) tends to change slower than the OFDM symbol rate. Through computer simulations, we show that the proposed scheme outperforms the conventional compressed sensing methods, achieving the performance bound provided by the Oracle-based Kalman smoother.