大规模MIMO系统的贝叶斯匹配追踪信道估计算法

Bayesian matching pursuit channel estimation algorithm on massive MIMO system

大规模MIMO技术能成倍地提高通信系统的容量和传输速率,但是系统性能的进一步提升会受到导频污染的制约.采用压缩感知进行信道估计,能够减少导频开销,从而可以有效解决这一问题.为此,本文提出了基于压缩感知的支持不可知贝叶斯匹配追踪信道估计算法(SABMP).仿真结果表明,在低信噪比时,该算法仅需较少的导频开销就能达到较低的误码率(BER)及归一化均方误差(NMSE)性能,提高频谱效率.

Massive MIMO technology can multiply the system capacity and data transfer rate. However, the pilot contamination is a limiting factor for the performance improvement of massive MIMO system. Adopting compressed sensing for channel estimation can reduce pilot overhead and solve this problem effectively. In this respect, a channel estimation algorithm based on compressed sensing called support agnostic Bayesian matching pursuit（SABMP） was proposed.Simulation results showed that this algorithm can achieve good bit error rate（BER） and normalized mean squared error（NMSE） performance with fewer pilot overhead in low SNR scenario, which can improve spectral efficiency.