基于压缩感知的大规模MIMO信道估计与反馈

针对大规模 MIMO系统信道估计精度低及反馈方案较为复杂的问题，在差分信道估计及反馈方案上提出了一种基于系数相关性的压缩采样匹配追踪（BCC-CoSAMP）算法。该算法将CoSAMP算法中衡量两个向量之间关系的内积替换为基于相关系数的向量关系判定，从而较快地选出与原始信号相关性强的原子，达到提高信道估计精度的目的。仿真结果表明，与CoSAMP算法相比，所提出的BCC-CoSAMP算法在低信噪比情况下，信道估计精度平均有5 dB的提高，同时能平均提高系统总速率1.25 bit/(s.Hz)。

Massive MIMO channel estimation and feedback based on compressed sensing

Focused on the issue that the problem of low precision of channel estimation and complex feedback scheme in massive MIMO system,a compression sampling matching pursuit (BCC-CoSAMP) algorithm based on coefficient correlation was proposed in the differential channel estimation and feedback scheme.In this algorithm,the inner product of the relationship between two vectors in the CoSAMP algorithm was replaced by the vector relation based on the correlation coefficient,so the atoms with strong correlation of the original signal were quickly selected to improve the accuracy of channel estimation.The simulation results show that the proposed BCC-CoSAMP algorithm has an improved channel estimation accuracy compared with the CoSAMP algorithm and an average increase of the total system rate of 1.25 bit/(s.Hz) at low signal to noise ratio (SNR).