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

电信科学 ›› 2022, Vol. 38 ›› Issue (2): 25-34.doi: 10.11959/j.issn.1000-0801.2022036

基于稀疏贝叶斯学习的MIMO-OFDM电力线通信系统接收机设计

吕新荣¹, 李有明², 吴永清³^,⁴, 唐小波⁵

¹ 宁波大学科学技术学院，浙江宁波 315300
² 宁波大学信息科学与工程学院，浙江宁波 315211
³ 中国科学院声学研究所，北京 100190
⁴ 中国科学院大学，北京 100190
⁵ 宁波奥克斯高科技有限公司，浙江宁波 315034

修回日期:2022-02-10 出版日期:2022-02-20 发布日期:2022-02-01
作者简介:吕新荣（1976- ），男，博士，宁波大学科学技术学院讲师，主要研究方向为电力线通信、无线通信、稀疏信号处理、压缩感知
李有明（1963- ），男，宁波大学信息科学与工程学院教授、博士生导师，主要研究方向为宽带通信、电力线通信、协作中继、认知无线电等
吴永清（1968- ），男，中国科学院声学研究所研究员，中国科学院大学教授、博士生导师，主要研究方向为水声通信、水下目标检测和识别等
唐小波（1976- ），男，宁波奥克斯高科技有限公司电力研发中心科技管理部总监，主要研究方向为电力线通信
基金资助:
科技部战略性国际科技创新合作项目重点专项基金资助项目(2018YFE0206500);浙江省自然科学基金资助项目(LY22F010018);宁波市江北区重大专项基金资助项目(201801A04)

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

Xinrong LYU¹, Youming LI², Yongqing WU³^,⁴, Xiaobo TANG⁵

¹ College of Science ＆Technology, Ningbo University, Ningbo 315300, China
² Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211, China
³ Institute of Acoustics, Chinese Academy of Science, Beijing 100190, China
⁴ University of Chinese Academy of Science, Beijing 100190, China
⁵ Ningbo Aux HighTech Co.Ltd, Ningbo 315034, China

Revised:2022-02-10 Online:2022-02-20 Published:2022-02-01
Supported by:
The International Cooperation Project of the Ministry of Science and Technology(2018YFE0206500);Zhejiang Provincial Natural Science Foundation of China(LY22F010018);The Foundation of Ningbo Jiangbei District Science and Technology Bureau(201801A04)

摘要/Abstract

摘要：

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。

关键词: MIMO, OFDM, 脉冲噪声, 电力线通信, 稀疏贝叶斯学习

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

中图分类号:

TN913

吕新荣, 李有明, 吴永清, 唐小波. 基于稀疏贝叶斯学习的MIMO-OFDM电力线通信系统接收机设计[J]. 电信科学, 2022, 38(2): 25-34.

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.

图/表 5

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基于稀疏贝叶斯学习的MIMO-OFDM电力线通信系统接收机设计

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

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