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CTD-Diff: Cooperative Time-Division Diffusion for Multi-User Semantic Communication Systems

Chengyang Liang, Dong Li

Abstract

Semantic communication (SemCom) has emerged as a transformative paradigm for efficient information transmission by emphasizing the exchange of task-relevant meaning rather than raw data. While diffusion-based SemCom models have demonstrated remarkable generative capabilities, existing studies predominantly focus on point-to-point links, overlooking the potential of multi-user (MU) cooperation in MU wireless environments. To address this limitation, we propose a Cooperative Time-Division Diffusion (CTD-Diff) framework. Unlike traditional approaches that view channel noise solely as a detriment, our framework innovatively integrates the noisy wireless transmission process directly into the forward diffusion chain. Specifically, we design a multi-user cooperation mechanism based on Time-Division Multiple Access (TDMA), where idle users overhearing the active transmitter act as semantic collaborators. To maximize the signal fidelity, the receiver employs direct signal aggregation to fuse the direct signal with cooperative copies. This aggregated noisy semantic representation serves as the condition for the reverse diffusion process, allowing the receiver to reconstruct high-fidelity data by mitigating the cumulative channel distortions. By effectively converting physical channel noise into diffusion noise, the proposed method significantly enhances the transmission reliability. Extensive experiments demonstrate that CTD-Diff outperforms various baselines regarding the reconstruction accuracy and the perceptual quality, particularly under challenging low signal-to-noise ratio (SNR) conditions.

CTD-Diff: Cooperative Time-Division Diffusion for Multi-User Semantic Communication Systems

Abstract

Semantic communication (SemCom) has emerged as a transformative paradigm for efficient information transmission by emphasizing the exchange of task-relevant meaning rather than raw data. While diffusion-based SemCom models have demonstrated remarkable generative capabilities, existing studies predominantly focus on point-to-point links, overlooking the potential of multi-user (MU) cooperation in MU wireless environments. To address this limitation, we propose a Cooperative Time-Division Diffusion (CTD-Diff) framework. Unlike traditional approaches that view channel noise solely as a detriment, our framework innovatively integrates the noisy wireless transmission process directly into the forward diffusion chain. Specifically, we design a multi-user cooperation mechanism based on Time-Division Multiple Access (TDMA), where idle users overhearing the active transmitter act as semantic collaborators. To maximize the signal fidelity, the receiver employs direct signal aggregation to fuse the direct signal with cooperative copies. This aggregated noisy semantic representation serves as the condition for the reverse diffusion process, allowing the receiver to reconstruct high-fidelity data by mitigating the cumulative channel distortions. By effectively converting physical channel noise into diffusion noise, the proposed method significantly enhances the transmission reliability. Extensive experiments demonstrate that CTD-Diff outperforms various baselines regarding the reconstruction accuracy and the perceptual quality, particularly under challenging low signal-to-noise ratio (SNR) conditions.

Paper Structure

This paper contains 25 sections, 24 equations, 9 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Transmitter Design
  4. Source Transmission (User $k$)
  5. Cooperative Forwarding (User $i$)
  6. Receiver Design
  7. Diffusion-based Semantic Reconstruction
  8. Forward Process as Channel Distortion
  9. Reverse Denoising Process
  10. CTD-Diff Framework and Cooperative Mechanism
  11. CTD-Diff Framework
  12. Conditional Diffusion Reconstruction
  13. Hybrid-Noise Training Strategy
  14. Numerical Experiments
  15. Experiments Setting
  16. ...and 10 more sections

Figures (9)

  • Figure 1: Illustration of the proposed semantic communication system.
  • Figure 2: Architecture of the proposed conditional diffusion network in CTD-Diff.
  • Figure 3: Comparison of the PSNR performance in different datasets with AWGN and Rayleigh fading.
  • Figure 4: Comparison of the MS-SSIM performance in different datasets with AWGN and Rayleigh fading.
  • Figure 5: Reconstruction Comparison With vs. Without Cooperation on the CIFAR-100 dataset for 10 users at 10 dB AWGN channel.
  • ...and 4 more figures

Theorems & Definitions (1)

  • Remark 1