To address the challenges of robust data transmission over complex time-varying channels, this paper introduces channel learning and enhanced adaptive reconstruction (CLEAR) strategy for semantic communications. CLEAR integrates deep joint source-channel coding (DeepJSCC) with an adaptive diffusion denoising model (ADDM) to form a unique framework. It leverages a trainable encoder-decoder architecture to encode data into complex semantic codes, which are then transmitted and reconstructed while minimizing distortion, ensuring high semantic fidelity. By addressing multipath effects, frequency-selective fading, phase noise, and Doppler shifts, CLEAR achieves high semantic fidelity and reliable transmission across diverse signal-to-noise ratios (SNRs) and channel conditions. Extensive experiments demonstrate that CLEAR achieves a 2.3 dB gain on peak signal-to-noise ratio (PSNR) over the existing state-of-the-art method, DeepJSCC-V. Furthermore, the results verify that CLEAR is robust against varying channel conditions, particularly in scenarios characterized by high Doppler shifts and strong phase noise.
@article{arxiv.2412.08978,
title = {CLEAR: Channel Learning and Enhanced Adaptive Reconstruction for Semantic Communication in Complex Time-Varying Environments},
author = {Hongzhi Pan and Shengliang Wu and Lingyun Wang and Yujun Zhu and Weiwei Jiang and Xin He},
journal= {arXiv preprint arXiv:2412.08978},
year = {2024}
}