NOMA系统中基于深度学习的压缩感知脉冲噪声抑制方法

胡进; 李有明

doi:10.11959/j.issn.1000-0801.2025191

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NOMA系统中基于深度学习的压缩感知脉冲噪声抑制方法

研究与开发 | 更新时间：2025-11-06

- NOMA系统中基于深度学习的压缩感知脉冲噪声抑制方法
- Deep learning-based compressed sensing impulsive noise suppression in NOMA systems
- 电信科学 2025年41卷第10期页码：143-150
- 作者机构：
  
  宁波大学信息科学与工程学院，浙江宁波 315211
- 作者简介：
  
  [ "胡进（2000- ），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问题。" ]
  [ "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。" ]
- 基金信息：
  
  国家重点研发计划项目(2023YFC2809400);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)
- DOI：10.11959/j.issn.1000-0801.2025191
  中图分类号： TP913
- 收稿：2025-01-23，
  
  修回：2025-04-27，
  
  纸质出版：2025-10-20
- 稿件说明：
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胡进,李有明.NOMA系统中基于深度学习的压缩感知脉冲噪声抑制方法[J].电信科学,2025,41(10):143-150.

HU Jin,LI Youming.Deep learning-based compressed sensing impulsive noise suppression in NOMA systems[J].Telecommunications Science,2025,41(10):143-150.
胡进,李有明.NOMA系统中基于深度学习的压缩感知脉冲噪声抑制方法[J].电信科学,2025,41(10):143-150. DOI： 10.11959/j.issn.1000-0801.2025191.

HU Jin,LI Youming.Deep learning-based compressed sensing impulsive noise suppression in NOMA systems[J].Telecommunications Science,2025,41(10):143-150. DOI： 10.11959/j.issn.1000-0801.2025191.

摘要

随着物联网技术的广泛应用，非正交多址接入（non-orthogonal multiple access，NOMA）技术作为一种高效的多址接入技术，由于能满足大规模连接需求并能提升频谱效率而受到关注。然而，脉冲噪声作为一种广泛存在的干扰因素，会严重影响NOMA系统的性能。为此，提出了一种基于深度学习的压缩感知脉冲噪声抑制方法，基于NOMA系统中脉冲噪声的时域稀疏性，通过数据驱动方式，利用深度学习网络来估计脉冲噪声。具体来说，首先使用线性映射网络代替压缩感知算法，求伪逆获取脉冲噪声的初始解，然后将初始解输入压缩感知重构WaveNet（compressed sensing reconstruction WaveNet，CSR-WaveNet），通过网络学习脉冲噪声的稀疏特征，以实现对脉冲噪声的精确估计。仿真结果表明，相较现有技术，新算法实现了更低的误码率。

Abstract

With the widespread application of Internet of things technology

non-orthogonal multiple access (NOMA) has been recognized as an efficient multiple access technique

offering significant advantages in large-scale connectivity and spectral efficiency. However

impulsive noise

a common source of interference

has been found to severely degrade the performance of NOMA systems. To address this issue

a deep learning-based compressed sensing method was proposed for impulsive noise suppression. By exploiting the temporal sparsity of impulsive noise in NOMA systems

a data-driven approach was employed

with the network being used to estimate the impulsive noise. Firstly

a linear mapping network was used to replace the compressed sensing algorithm in computing the pseudoinverse

providing an initial estimate of the impulsive noise. This estimate was then fed into a compressed sensing reconstruction WaveNet (CSR-WaveNet)

which could learn the sparse characteristics of the impulsive noise for more accurate noise estimation. Simulation results show that

compared to existing methods

the proposed approach can achieve lower bit error rates.

关键词

Keywords

references

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