Learnable Residual-Based Latent Denoising in Semantic Communication
This work addresses the problem of reliable image transmission in noisy environments for semantic communication systems, representing an incremental improvement with specific optimizations.
The paper tackles robust image transmission over noisy channels by proposing a latent denoising semantic communication framework, which incorporates a learnable latent denoiser at the receiver to remove channel noise and recover semantic information, resulting in enhanced decoded image quality and reduced communication latency as demonstrated in simulations.
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.