Massive MIMO channel estimation under time-correlation block Bayesian algorithm

Massive MIMO has become a key technology in the physical layer of 5G communication systems due to its high array gain and high spectral efficiency. However, in the process of obtaining the accurate state information of large-scale MIMO channels at the base station side of the frequency division duplex system, the pilot overhead consumes a large amount of spectrum resources. Therefore, a block Bayesian compressed sensing channel estimation method based on time correlation and multi-measure vector model (TMBB-CS) is proposed for the case of time-dependent channel and channel sparsity. Due to the time-dependent transmission signal of the antenna at the base station, the time domain channel impulse response of the massive MIMO system is a block sparse structure. This feature is used to measure and estimate the multiuser channel matrix in the downlink, which can greatly reduce the pilot overhead and improve the performance. The simulation results show that the proposed TMBB-CS algorithm performs better than other block Bayesian algorithms in channel estimation.