Presentation Details
  1. Home
  2. Presentation List
  3. Presentation Details
A Signal Denoising Model Based on an Optimized GAN Network Using U-Net
ID：51 View Protection：ATTENDEE Updated Time：2025-11-03 12:31:36 Hits：380 Oral Presentation

Start Time：2025-11-09 09:45（Asia/Shanghai）

Duration：15min

Session：S5 5.AI-driven technology » S55.AI-driven technology

Presentation File

Tips: Only the registered participant can access the file. Please sign in first.

Abstract
Signal denoising in complex electromagnetic environments is a key challenge in 5G communication, autonomous driving, and other fields. Traditional methods (wavelet transform, empirical mode decomposition) suffer from feature degradation in non-stationary noise processing, and existing generative adversarial networks (GANs) have the defect of losing high-frequency details in one-dimensional signal denoising. To address these issues, this paper proposes a U-Net-based generative adversarial network (U-NetGAN) for signal denoising. The model combines the skip connection structure of U-Net with residual modules through a multi-scale feature fusion mechanism, achieving collaborative optimization of signal global trends and local transient features. A piecewise weighted local mean square error loss function is designed to enhance the detail reconstruction capability in critical regions. Experiments show that on a composite linear frequency modulation signal dataset, under 0 dB noise conditions, U-NetGAN achieves a signal-to-noise ratio increment (ΔSNR) of 22.97 dB, a 21.47% improvement over the best traditional method, and a structural similarity (SSIM) of 0.746. Additionally, the parameter count (3.1 million) is reduced by 35% compared to similar GAN models. Ablation experiments confirm that the skip connection mechanism contributes a ΔSNR gain of 1.77 dB, and the local weighted loss improves SSIM by 1.8%. This study provides new insights for high-precision signal processing in dynamic noise environments and has potential applications in radar target recognition and medical monitoring devices.

 
Keywords
signal denoising,generative adversarial networks,U-Net,Encoder-Decoder structure
Speaker
Shuai Bai
Southwest Jiaotong University

Submission Author
Liang Dong Southwest Jiaotong University;the School of Electrical Engineering
Shuai Bai Southwest Jiaotong University
Submit Comment
Verify Code Change Another
All Comments
Important Date

  • Conference Date

    Nov 07

    2025

    to

    Nov 09

    2025

  • Oct 30 2025

    Draft paper submission deadline

  • Nov 10 2025

    Registration deadline

Sponsored By
IEEE西南交通大学IAS学生分会
Organized By
西南交通大学电气工程学院
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队
Contact Information
Login to view full contact details
Previous Conferences