基于稀疏贝叶斯学习的MIMO-OFDM电力线通信系统接收机设计

doi:10.11959/j.issn.1000-0801.2022036

Abstract

Abstract:

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 domain 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 introduced to jointly estimate the MIMO channel parameters and impulsive noise.The simulation results show that, compared 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.

Key words: MIMO, OFDM, impulsive noise, power line communication, sparse Bayesian learning

CLC Number:

TN913

Xinrong LYU, Youming LI, Yongqing WU, Xiaobo TANG. Receiver design of sparse Bayesian learning based MIMO-OFDM power line communication system[J]. Telecommunications Science, 2022, 38(2): 25-34.

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References 20

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