基于扩散的语义通信中联合信源信道加噪与自适应去噪
Joint Source-Channel Noise Adding with Adaptive Denoising for Diffusion-Based Semantic Communications
AI总结：
本文提出JSCNA-AD框架，将信道噪声转化为扩散重建中的有益成分，并通过基于注意力的自适应去噪按语义重要性动态分配去噪步骤，在多种噪声条件下显著优于现有语义通信方案。
摘要：
语义通信（SemCom）旨在传递消息的预期含义，而不仅仅是传输比特，从而提供更高的效率和鲁棒性，特别是在资源受限或存在噪声的环境中。在本文中，我们提出了一种新的框架，称为联合信源信道加噪与自适应去噪（JSCNA-AD），用于基于扩散模型（DM）的语义通信。与传统的编码器-解码器设计不同，我们的方法在传输过程中有意引入信道噪声，有效地将有害的信道噪声转化为基于扩散的语义重建过程中的建设性组成部分。此外，我们引入了一种基于注意力的自适应去噪机制，其中传输的图像被划分为多个区域，并根据每个区域的语义重要性动态分配去噪步骤的数量。该设计通过优先处理关键语义信息，有效地平衡了接收质量和推理延迟。大量实验表明，我们的方法在各种噪声条件下显著优于现有的语义通信方案，突显了基于扩散的模型在下一代通信系统中的潜力。
英文摘要：
Semantic communication (SemCom) aims to convey the intended meaning of messages rather than merely transmitting bits, thereby offering greater efficiency and robustness, particularly in resource-constrained or noisy environments. In this paper, we propose a novel framework which is referred to as joint source-channel noise adding with adaptive denoising (JSCNA-AD) for SemCom based on a diffusion model (DM). Unlike conventional encoder-decoder designs, our approach intentionally incorporates the channel noise during transmission, effectively transforming the harmful channel noise into a constructive component of the diffusion-based semantic reconstruction process. Besides, we introduce an attention-based adaptive denoising mechanism, in which transmitted images are divided into multiple regions, and the number of denoising steps is dynamically allocated based on the semantic importance of each region. This design effectively balances the reception quality and the inference latency by prioritizing the critical semantic information. Extensive experiments demonstrate that our method significantly outperforms existing SemCom schemes under various noise conditions, underscoring the potential of diffusion-based models in next-generation communication systems.
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