时间相关块贝叶斯算法下的大规模MIMO信道估计

因具有高的阵列增益和高的频谱效率，大规模MIMO已成为5G通信系统物理层关键技术，但在频分双工系统基站侧获取大规模MIMO信道准确状态信息的过程中，存在导频开销占用大量频谱资源问题。为此，针对时间相关信道和信道稀疏度未知的情况，提出一种基于时间相关和多测量矢量模型的块贝叶斯压缩感知（TMBB-CS）信道估计方法。因基站端天线发射信号时间相关，所以大规模MIMO系统的时域信道脉冲响应呈块稀疏结构，利用该特性对下行链路中的多用户信道矩阵进行测量估计，可较大幅度减少导频开销，提升性能。实验仿真结果表明，与其他块贝叶斯算法相比，所提出的TMBB-CS算法信道估计性能更好。 

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.