基于近似消息传递的NOMA系统信道和脉冲噪声联合估计方法

李有明; 马冲亚; 吴永宏; 国强

doi:10.11959/j.issn.1000-0801.2024205

您当前的位置：

首页 >

文章列表页 >

基于近似消息传递的NOMA系统信道和脉冲噪声联合估计方法

研究与开发 | 更新时间：2024-11-25

- 基于近似消息传递的NOMA系统信道和脉冲噪声联合估计方法
- Joint channel and impulsive noise estimation method based on approximate message passing for NOMA systems
- 电信科学 2024年40卷第9期页码：44-53
- 作者机构：
  
  1.宁波大学信息科学与工程学院，浙江宁波 315211
  2.中国电波传播研究所，山东青岛 266075
  3.哈尔滨工程大学信息与通信工程学院，黑龙江哈尔滨 150001
- 作者简介：
  
  [ "李有明（1963- ），男，博士，宁波大学信息科学与工程学院教授，主要研究方向为无线宽带通信、电力线通信、协作中继、认知无线电等。" ]
  [ "马冲亚（1999- ），男，宁波大学信息科学与工程学院硕士生，主要研究方向为无线通信中的脉冲噪声抑制。" ]
  [ "吴永宏（1974- ），男，博士，中国电波传播研究所高级工程师，主要研究方向为短波通信、网络规划和信号处理等。" ]
  [ "国强（1972- ），男，博士，哈尔滨工程大学信息与通信工程学院教授，主要研究方向为电子对抗、智能信号处理与识别。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61571250);浙江省自然科学基金资助项目;Zhejiang Provincial Natural Science Foundation of China(LY22F010018);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;The International Cooperation Project of the Ministry of Science and Technology(2018YFE0206500)
- DOI：10.11959/j.issn.1000-0801.2024205
  中图分类号： TN929
- 收稿日期：2024-04-10，
  
  修回日期：2024-08-30，
  
  纸质出版日期：2024-09-20
- 稿件说明：
移动端阅览
李有明,马冲亚,吴永宏等.基于近似消息传递的NOMA系统信道和脉冲噪声联合估计方法[J].电信科学,2024,40(09):44-53.

LI Youming,MA Chongya,WU Yonghong,et al.Joint channel and impulsive noise estimation method based on approximate message passing for NOMA systems[J].Telecommunications Science,2024,40(09):44-53.
李有明,马冲亚,吴永宏等.基于近似消息传递的NOMA系统信道和脉冲噪声联合估计方法[J].电信科学,2024,40(09):44-53. DOI： 10.11959/j.issn.1000-0801.2024205.

LI Youming,MA Chongya,WU Yonghong,et al.Joint channel and impulsive noise estimation method based on approximate message passing for NOMA systems[J].Telecommunications Science,2024,40(09):44-53. DOI： 10.11959/j.issn.1000-0801.2024205.

摘要

针对非高斯脉冲噪声背景下的非正交多址接入（non-orthogonal multiple access，NOMA）系统的信道估计问题，利用信道和脉冲噪声的稀疏特性，提出一种基于近似消息传递的信道和脉冲噪声联合估计方法。首先构建全子载波的压缩感知方程，然后基于稀疏贝叶斯学习理论提出一种信道、脉冲噪声和数据符号的联合估计优化问题。为解决这一超参量非线性非凸问题，设计了一种基于高斯广义近似消息传递和稀疏贝叶斯学习理论的期望最大化实现算法。仿真结果表明，与基于期望最大化的稀疏贝叶斯学习方法相比，所提算法在信道和脉冲噪声估计的均方误差、误码率等方面性能虽略有下降，但算法复杂度降低了1个数量级。

Abstract

To address the channel estimation problem for non-orthogonal multiple access (NOMA) systems under non-Gaussian impulsive noise

a joint channel and impulsive noise estimation method based on approximate message passing was proposed

by exploiting the joint sparsity of the channel and impulsive noise. Firstly

based on sparse Bayesian learning theory

a compressed sensing equation was constructed by using all subcarriers

and then a joint estimation optimization problem for the channel

impulsive noise

and data symbols was proposed. To address this hyperparameter nonlinear non-convex problem

an expectation maximization (EM) implementation algorithm based on Gaussian generalized approximation message passing and sparse Bayesian learning (SBL) theory was designed. Simulation results show that compared to the SBL method based on EM

the proposed algorithm exhibited a slight degradation in terms of mean square error (MSE) for channel and impulsive noise estimation

bit error rate (BER). However

the complexity of the proposed algorithm was reduced by one order of magnitude.

关键词

Keywords

references

DING Z G , LEI X F , KARAGIANNIDIS G K , et al . A survey on non-orthogonal multiple access for 5G networks: research challenges and future trends [J ] . IEEE Journal on Selected Areas in Communications , 2017 , 35 ( 10 ): 2181 - 2195 .

ANTONIALI M , VERSOLATTO F , TONELLO A M . An experimental characterization of the PLC noise at the source [J ] . IEEE Transactions on Power Delivery , 2016 , 31 ( 3 ): 1068 - 1075 .

XU Z P , YANG C C , TAN Z W , et al . Raptor code-enabled reliable data transmission for in-vehicle power line communication systems with impulsive noise [J ] . IEEE Communications Letters , 2017 , 21 ( 10 ): 2154 - 2157 .

SELIM B , ALAM M S , EVANGELISTA J V C , et al . NOMA-based IoT networks: impulsive noise effects and mitigation [J ] . IEEE Communications Magazine , 2020 , 58 ( 11 ): 69 - 75 .

LIU S C , YANG F , DING W B , et al . Double kill: compressive-sensing-based narrow-band interference and impulsive noise mitigation for vehicular communications [J ] . IEEE Transactions on Vehicular Technology , 2016 , 65 ( 7 ): 5099 - 5109 .

SACUTO F , LABEAU F , AGBA B L . Wide band time-correlated model for wireless communications under impulsive noise within power substation [J ] . IEEE Transactions on Wireless Communications , 2014 , 13 ( 3 ): 1449 - 1461 .

MAHMOOD A , CHITRE M , ARMAND M A . Detecting OFDM signals in alpha-stable noise [J ] . IEEE Transactions on Communications , 2014 , 62 ( 10 ): 3571 - 3583 .

SELIM B , ALAM M S , KADDOUM G , et al . Effect of impulsive noise on uplink NOMA systems [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 3 ): 3454 - 3458 .

ZHIDKOV S V . Analysis and comparison of several simple impulsive noise mitigation schemes for OFDM receivers [J ] . IEEE Transactions on Communications , 2008 , 56 ( 1 ): 5 - 9 .

KORKI M , ZHANG J X , ZHANG C S , et al . Block-sparse impulsive noise reduction in OFDM systems—a novel iterative Bayesian approach [J ] . IEEE Transactions on Communications , 2016 , 64 ( 1 ): 271 - 284 .

LIN J , NASSAR M , EVANS B L . Impulsive noise mitigation in powerline communications using sparse Bayesian learning [J ] . IEEE Journal on Selected Areas in Communications , 2013 , 31 ( 7 ): 1172 - 1183 .

FENG X , WANG J F , KUAI X Y , et al . Message passing-based impulsive noise mitigation and channel estimation for underwater acoustic OFDM communications [J ] . IEEE Transactions on Vehicular Technology , 2022 , 71 ( 1 ): 611 - 625 .

NASSAR M , SCHNITER P , EVANS B L . A factor-graph approach to joint OFDM channel estimation and decoding in impulsive noise channels [C ] // Proceedings of the 2013 Asilomar Conference on Signals, Systems and Computers . Piscataway : IEEE Press , 2013 : 1929 - 1933 .

LYU X R , LI Y M , WU Y Q , et al . Joint channel estimation and impulsive noise mitigation method for OFDM systems using sparse Bayesian learning [J ] . IEEE Access , 2019 ( 7 ): 74500 - 74510 .

WIPF D P , RAO B D . Sparse Bayesian learning for basis selection [J ] . IEEE Transactions on Signal Processing , 2004 , 52 ( 8 ): 2153 - 2164 .

LYU X R , LI Y M , WU Y Q , et al . Kalman filter based recursive estimation of slowly fading sparse channel in impulsive noise environment for OFDM systems [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 3 ): 2828 - 2835 .

WANG S C , HE Z Q , NIU K , et al . New results on joint channel and impulsive noise estimation and tracking in underwater acoustic OFDM systems [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 4 ): 2601 - 2612 .

ALAM M S , SELIM B , AHMED I , et al . Bursty impulsive noise mitigation in NOMA: a MAP receiver-based approach [J ] . IEEE Communications Letters , 2021 , 25 ( 9 ): 2790 - 2794 .

HUSSAIN M , SHAKIR H , RASHEED H . Deep learning approaches for impulse noise mitigation and classification in NOMA-based systems [J ] . IEEE Access , 2021 ( 9 ): 143836 - 143846 .

AL-SHOUKAIRI M , SCHNITER P , RAO B D . A GAMP-based low complexity sparse Bayesian learning algorithm [J ] . IEEE Transactions on Signal Processing , 2018 , 66 ( 2 ): 294 - 308 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于稀疏贝叶斯学习的电力线载波通信接收机设计

一种基于ADMM的多用户联合的RIS信道估计方案

太赫兹频段高速移动场景参考信号方案研究

中继选择NOMA无线系统的物理层安全

基于复数生成对抗网络的5G OFDM信道估计方法