MIMO电力线载波通信中基于压缩感知的信道与脉冲噪声联合估计方法

Joint Estimation of Channel and Impulse Noise based on Compressed Sensing in MIMO Power Line Carrier Communication

在多输入多输出电力线通信(Multiple-input Multiple-output Power Line Communication,MIMO-PLC)系统中,信道状态信息的准确性将直接影响系统的整体性能,且电力线信道中复杂的脉冲噪声会使信道估计技术性能降低。因此,针对在脉冲噪声影响下进行信道估计的问题,提出一种基于快速块稀疏贝叶斯学习算法的信道与脉冲噪声联合估计方法。该方法将信道与脉冲噪声联合估计转换为压缩感知问题,利用MIMO-PLC系统信道间的相关性以及信道冲击响应和脉冲噪声的稀疏特性进行求解。仿真结果表明,与传统信道估计和脉冲噪声抑制方法相比,在不同导频数量下提出的联合估计方法均有更好的性能。

In MIMO-PLC(multiple-input multiple-output power line communication)system,the accuracy of the channel state information will directly affect the overall performance of the system,and the complex impulse noise in the power line channel will degrade the performance of the channel estimation technology.Therefore,this paper proposes a joint channel and impulse noise estimation method based on fast marginalized block sparse Bayesian learning algorithm for the problem of channel estimation under the influence of impulse noise.This method transforms the joint estimation of channel and impulse noise into a compressed sensing problem,and uses the correlation between the channels of MIMO-PLC system and the sparse characteristics of channel impulse response and impulse noise to solve this problem.Simulation results indicate that compared with the traditional channel estimation and impulse noise suppression methods,the joint estimation method proposed in this paper has better performance under different numbers of pilots.