基于压缩感知的多小区MASSIVE MIMO信道估计

针对多小区多用户大规模多输入多输出（MASSIVE MIMO）系统信道估计在低信噪比情况下估计精度较差的问题，提出了一种基于群智能搜索的果蝇分段正交匹配追踪（FF-StOMP）压缩感知算法。该算法在分段正交匹配追踪（StOMP）求解不同阈值下的信道矩阵参数与归一化最小均方误差的基础上，采用果蝇优化算法动态搜索出最小归一化均方误差与其对应的阈值，达到自适应参数设定的目的。仿真结果表明，与StOMP算法相比，信噪比在0~10 dB情况下，所提出的FF-StOMP算法信道估计性能能够提升0.5~1 dB；信噪比在11~20 dB时，信道估计性能能够提升0.2~0.3 dB。当小区用户数发生变化时，所提出的算法能实现自适应信道估计，能够有效提升MASSIVE MIMO系统低信噪比情况下的信道估计精度。

Multi-cell channel estimation based on compressive sensing in MASSIVE MIMO system

Focused on the issue that the channel estimation accuracy of multi-cell multi-user MASSIVE Multi-Input Multi-Output (MASSIVE MIMO) system was poor in the case of low Signal-to-Noise Ratio (SNR), a compressive sensing algorithm named Fruit Fly Stagewise Orthogonal Matching Pursuit (FF-StOMP) based on group intelligent search was proposed. Based on the Stagewise Orthogonal Matching Pursuit (StOMP) solution to the channel matrix parameters and the normalized minimum mean square error under different thresholds, the algorithm was used to search the minimum normalized mean square error and its corresponding threshold by the fruit fly optimization algorithm to achieve the adaptive parameter setting. The simulation results show that the channel estimation performance of FF-StOMP algorithm can be improved by 0.5 to 1 dB when the SNR is 0 to 10 dB compared with the StOMP algorithm. When the SNR is 11 to 20 dB, the channel estimation performance can be improved by 0.2 to 0.3 dB. When the number of cell users changes, the proposed algorithm can realize the adaptive channel estimation, which can effectively improve the channel estimation accuracy in the case of MASSIVE MIMO system with low SNR.