基于稀疏贝叶斯学习的MIMO-OFDM电力线通信系统接收机设计

丰富的脉冲噪声干扰对基于MIMO-OFDM技术的电力线通信系统接收机设计带来了巨大挑战。针对这个问题,提出了一种联合估计电力线信道和脉冲噪声的接收机设计方案。该方案主要利用电力信道多径模型参数在频域上的稀疏性和脉冲噪声在时域上的稀疏性特征,将待估计信道模型参数和脉冲噪声联合视作一个稀疏向量,同时利用MIMO系统的空间相关性,构建了一个基于多测量向量的压缩感知模型,并引入多测量向量稀疏贝叶斯学习理论,设计了一种联合估计MIMO信道模型参数和脉冲噪声的方法。仿真结果表明,与传统的MIMO信道估计与脉冲噪声抑制相互分离的接收机方案相比,新方法在估计性能和误比特率性能上有明显提升。

Receiver design of sparse Bayesian learning based MIMO-OFDM power line communication system

The rich impulsive noise in the power line channel poses a huge challenge to the design of MIMO-OFDM transceiver.To solve this problem,a design scheme that can jointly estimate the channel and impulsive noise was proposed,which exploited the parametric sparsity of the classical multipath model and the sparsity of the time do-main impulsive noise.In this scheme,the unknown channel model parameters and the impulsive noise were jointly regarded as a sparse vector.By observing the spatial correlation of MIMO system,a compressed sensing model based on multiple measurement vectors was constructed.The multiple response sparse Bayesian learning theory was intro-duced to jointly estimate the MIMO channel parameters and impulsive noise.The simulation results show that,com-pared with the traditional receiver scheme that considers MIMO channel estimation and impulsive noise suppression separately,the receiver proposed has a significant improvement in channel estimation performance and bit error rate performance.