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.

翻译：为应对复杂时变信道下鲁棒数据传输的挑战，本文提出一种面向语义通信的信道学习与增强自适应重构（CLEAR）策略。CLEAR将深度联合信源信道编码（DeepJSCC）与自适应扩散去噪模型（ADDM）相结合，构建出独特的框架。该框架利用可训练的编码器-解码器架构将数据编码为复杂语义码，在传输与重构过程中最小化失真，从而确保高语义保真度。通过处理多径效应、频率选择性衰落、相位噪声及多普勒频移等问题，CLEAR能够在不同信噪比（SNR）与信道条件下实现高语义保真度与可靠传输。大量实验表明，CLEAR在峰值信噪比（PSNR）指标上较现有最优方法DeepJSCC-V提升了2.3 dB。此外，实验结果验证了CLEAR对变化信道条件（尤其在高多普勒频移与强相位噪声场景中）具有优异的鲁棒性。