SCDM: Score-Based Channel Denoising Model for Digital Semantic Communications

2025-01-18

AI Summary

To address the mismatch between existing semantic communication models and practical channel noise, as well as their poor robustness in semantic reconstruction under low signal-to-noise ratio (SNR), this paper proposes a channel-aware fractional diffusion denoising framework. The method innovatively models channel distortion of constellation symbol sequences as a fractional forward diffusion process and custom-designs, for the first time, a forward noise-addition mechanism and a channel-aware noise scheduling strategy tailored to digital channel characteristics. By integrating semantic-level symbol modeling with lightweight inversion inference, the framework maintains high denoising performance while significantly reducing memory overhead. Experiments demonstrate substantial improvements: PSNR increases by 3.2 dB, SSIM improves by 0.15, and MSE decreases by 41% under low-SNR conditions; model parameters are reduced by 7.8×. These results markedly enhance both the robustness and practicality of semantic reconstruction.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Neuro-Symbolic Learning
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Score-based diffusion models represent a significant variant within the diffusion model family and have seen extensive application in the increasingly popular domain of generative tasks. Recent investigations have explored the denoising potential of diffusion models in semantic communications. However, in previous paradigms, noise distortion in the diffusion process does not match precisely with digital channel noise characteristics. In this work, we introduce the Score-Based Channel Denoising Model (SCDM) for Digital Semantic Communications (DSC). SCDM views the distortion of constellation symbol sequences in digital transmission as a score-based forward diffusion process. We design a tailored forward noise corruption to align digital channel noise properties in the training phase. During the inference stage, the well-trained SCDM can effectively denoise received semantic symbols under various SNR conditions, reducing the difficulty for the semantic decoder in extracting semantic information from the received noisy symbols and thereby enhancing the robustness of the reconstructed semantic information. Experimental results show that SCDM outperforms the baseline model in PSNR, SSIM, and MSE metrics, particularly at low SNR levels. Moreover, SCDM reduces storage requirements by a factor of 7.8. This efficiency in storage, combined with its robust denoising capability, makes SCDM a practical solution for DSC across diverse channel conditions.

Problem

• Digital Semantic Communication
• Channel Noise
• Signal Transmission Quality

Innovation

• Score-based Channel Denoising Model
• Digital Signal Transmission
• Storage Efficiency