Single-Channel Co-Frequency Blind Signal Separation Based on Dynamic Graph Neural Networks and Contrastive Learning

LUO Ming; LAI Jinshan; SUN Pengzhao; ZHANG Kun; ZHANG Fengli; WANG Ruijin

doi:10.12263/DZXB.20260400

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Single-Channel Co-Frequency Blind Signal Separation Based on Dynamic Graph Neural Networks and Contrastive Learning

RF Computing | 更新时间：2026-10-16

- Single-Channel Co-Frequency Blind Signal Separation Based on Dynamic Graph Neural Networks and Contrastive Learning
- ACTA ELECTRONICA SINICA Vol. 54, Issue 6, Pages: 2493-2508(2026)
- 作者机构：
  
  1.电子科技大学，四川成都 610054
  2.成都菁蓉联创科技有限公司，四川成都 610041
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62271128);Major Scientific and Technological Project of Sichuan Province(2025ZDZX0093);Science and Technology Project of Sichuan Province(2025ZHRG0006);Key Research and Development Support Project of Chengdu(2025-YF08-00128-GX;2025-YF12-00029-RC)
- DOI：10.12263/DZXB.20260400
  CLC： TP18;
- Received：26 April 2026，
  
  Accepted：19 May 2026，
  
  Published：25 June 2026
- 稿件说明：
移动端阅览
罗鸣, 赖金山, 孙鹏钊, 等. 基于动态图神经网络与对比学习的单通道同频盲信号分离方法[J]. 电子学报, 2026, 54(06): 2493-2508.

LUO Ming, LAI Jinshan, SUN Pengzhao, et al. Single-Channel Co-Frequency Blind Signal Separation Based on Dynamic Graph Neural Networks and Contrastive Learning[J]. Acta Electronica Sinica, 2026, 54(06): 2493-2508.
罗鸣, 赖金山, 孙鹏钊, 等. 基于动态图神经网络与对比学习的单通道同频盲信号分离方法[J]. 电子学报, 2026, 54(06): 2493-2508. DOI：10.12263/DZXB.20260400

LUO Ming, LAI Jinshan, SUN Pengzhao, et al. Single-Channel Co-Frequency Blind Signal Separation Based on Dynamic Graph Neural Networks and Contrastive Learning[J]. Acta Electronica Sinica, 2026, 54(06): 2493-2508. DOI：10.12263/DZXB.20260400

摘要

在复杂电磁环境下，目标信号与干扰信号在时域、频域高度重叠，单通道接收条件下的同频盲信号分离面临较大挑战。现有基于卷积的分离方法在复杂非平稳干扰条件下，主要存在非局部相关性建模不足、目标信号与干扰信号隐空间解耦能力有限、对未知干扰模式泛化性较弱等问题。本文提出基于动态图神经网络与对比学习的单通道同频盲信号分离方法（Dynamic Graph and Contrastive Learning network，DGCL）。首先，将复基带混合序列映射为I/Q双通道表示，并依托动态图神经网络实现多尺度图时序特征的有效提取，通过多尺度分段、动态构图与图消息传递的协同作用，增强对复杂非平稳干扰的表征能力；其次，设计目标信号匹配驱动的对比学习自编码器，在结构匹配重构的基础上引入对比约束，提升目标信号与干扰信号在隐空间中的可分性；此外，在训练时设计隐空间流形混合增强策略，通过扩展深层特征分布，提升模型对未知干扰模式的鲁棒性。在单载波、多载波及多类干扰条件下的实验表明，DGCL在误码率与均方误差两项指标上均表现出显著优势。相较于常规基准模型WaveNet框架，DGCL的MSE（Mean Square Error）降低了6.7 dB，BER（Bit Error Rate）下降了1.72 × 10

-2

，这些结果验证了本文方法的有效性。

Abstract

In complex electromagnetic environments

target signals and interference signals heavily overlap in both time and frequency domains

posing significant challenges for co-frequency blind signal separation under single-channel reception conditions. Under complex non-stationary interference conditions

existing convolution-based separation methods primarily suffer from insufficient non-local correlation modeling

limited decoupling capability between target and interference signals in the latent space

and weak generalization to unknown interference patterns. To address these issues

this paper proposes a single-channel co-frequency blind signal separation method based on dynamic graph neural networks and contrastive learning (DGCL). First

the complex baseband mixture s

equence is mapped into an in-phase/quadrature (I/Q) dual-channel representation

and dynamic graph neural networks are utilized to effectively extract multi-scale graph temporal features. Through the synergistic effects of multi-scale segmentation

dynamic graph construction

and graph message passing

the representation capability for complex non-stationary interference is enhanced. Second

a contrastive learning autoencoder driven by target signal matching is designed. By introducing contrastive constraints on top of structural matching reconstruction

the separability of the target signal and the interference signal in the latent space is improved. Furthermore

a latent space manifold mixup enhancement strategy is designed during training. By expanding the deep feature distribution

the model’s robustness against unknown interference patterns is elevated. Experiments conducted under single-carrier

multi-carrier

and multi-class interference conditions indicate that DGCL demonstrates significant advantages in both bit error rate and mean square error metrics.Compared with the conventional WaveNet baseline

DGCL reduces MSE (Mean Square Error) by 6.7 dB and decreases BER (Bit Error Rate) by 1.72 × 10

-2

. These results verify the effectiveness of the proposed method.

关键词

Keywords

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