Learnable Residual-Based Latent Denoising in Semantic Communication

TL;DR

This paper introduces a learnable latent denoising framework for semantic communication that enhances image transmission robustness over noisy channels by adaptively removing noise and improving image quality.

Contribution

It proposes a novel iterative residual learning-based latent denoiser integrated into semantic communication systems, with adaptive denoising steps guided by channel SNR and similarity scores.

Findings

Effective noise removal across various SNR levels

Improved image reconstruction quality

Reduced communication latency through adaptive denoising

Abstract

A latent denoising semantic communication (SemCom) framework is proposed for robust image transmission over noisy channels. By incorporating a learnable latent denoiser into the receiver, the received signals are preprocessed to effectively remove the channel noise and recover the semantic information, thereby enhancing the quality of the decoded images. Specifically, a latent denoising mapping is established by an iterative residual learning approach to improve the denoising efficiency while ensuring stable performance. Moreover, channel signal-to-noise ratio (SNR) is utilized to estimate and predict the latent similarity score (SS) for conditional denoising, where the number of denoising steps is adapted based on the predicted SS sequence, further reducing the communication latency. Finally, simulations demonstrate that the proposed framework can effectively and efficiently remove the channel noise at various levels and reconstruct visual-appealing images.