用生成式补全技术提升语义通信抗错能力，适合6G场景。
Generative Feature Imputing -- A Technique for Error-resilient Semantic Communication
- 通过空间聚类编码使误码集中，降低修复难度。
- 采用扩散模型恢复丢包导致的缺失特征，提升重建质量。
- 按语义重要性分配功率，实现不等错误保护。
语义通信（SemCom）利用人工智能提取并传输源数据的深层语义，是第六代（6G）网络实现空前通信效率的有前景范式。然而，在数字系统中部署语义通信面临新挑战，尤其需确保对传输错误的鲁棒性，避免关键语义内容受损。本文提出一种新型框架——生成式特征补全，包含三项关键技术：首先，提出空间误差集中分组策略，基于信道映射对特征元素进行编码，使特征失真在空间上集中，这对后续技术的有效性和复杂度降低至关重要；其次，基于该策略，提出生成式特征补全方法，利用扩散模型高效重构因包丢失造成的缺失特征；最后，设计语义感知功率分配方案，根据每个包的语义重要性分配传输功率，实现不等错误保护。实验结果表明，所提框架在块衰落条件下优于传统方法（如深度联合源信道编码DJSCC和JPEG2000），达到更高的语义准确率和更低的感知图像块相似性（LPIPS）得分。
原文摘要 · Abstract (English)
Semantic communication (SemCom) has emerged as a promising paradigm for achieving unprecedented communication efficiency in sixth-generation (6G) networks by leveraging artificial intelligence (AI) to extract and transmit the underlying meanings of source data. However, deploying SemCom over digital systems presents new challenges, particularly in ensuring robustness against transmission errors that may distort semantically critical content. To address this issue, this paper proposes a novel framework, termed generative feature imputing, which comprises three key techniques. First, we introduce a spatial error concentration packetization strategy that spatially concentrates feature distortions by encoding feature elements based on their channel mappings, a property crucial for both the effectiveness and reduced complexity of the subsequent techniques. Second, building on this strategy, we propose a generative feature imputing method that utilizes a diffusion model to efficiently reconstruct missing features caused by packet losses. Finally, we develop a semantic-aware power allocation scheme that enables unequal error protection by allocating transmission power according to the semantic importance of each packet. Experimental results demonstrate that the proposed framework outperforms conventional approaches, such as Deep Joint Source-Channel Coding (DJSCC) and JPEG2000, under block fading conditions, achieving higher semantic accuracy and lower Learned Perceptual Image Patch Similarity (LPIPS) scores.
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