关键词: 大规模多输入输出, 信道估计, 压缩感知, 稀疏度自适应匹配追踪, 虚拟角域

Abstract: Focused on the issue that the channel estimation accuracy was poor in virtual angular domain channel for frequency division duplex based massive multi-input multi-output(MASSIVE MIMO)systems, a compressive sensing algorithm based on Threshold Sparsity Adaptive Matching (BT-SAMP) was proposed. This algorithm extended the adaptive characteristic of Sparsity Adaptive Matching (SAMP) algorithm, and the "adding atom" rule of Backtracking Orthogonal Matching (BAOMP) algorithm was applied in the atomic selection of SAMP algorithm. To improving the accuracy of SAMP algorithm and reducing the algorithm's iterative time. The simulation results show that the channel estimation accuracy is improved compared with SAMP algorithm, especially when the signal-to-noise ratio is 0 ~ 10dB, the estimation accuracy is improved by 4dB, and the running time of the algorithm is reduced about 62.5%.

Key words: Massive Multi-input Multi-output(MASSIVE MIMO), Channel Estimation, Compression Sensing, Sparsity Adaptive Matching Pursuit(SAMP), Virtual Angular Domain