解决无线信道丢包导致语义失真的问题，实现无额外开销的鲁棒语义恢复。
Token Encoding for Semantic Recovery
- 设计无额外开销的令牌编码框架TokCode，支持即插即用部署。
- 在40%至60%令牌丢失条件下，仍可逼近性能上限。
- 提出句义引导的模型适配算法，避免昂贵的端到端训练。
基于令牌的语义通信有望在未来无线网络中发挥作用，因其能在极低信道容量下压缩语义令牌。然而，恶劣的无线信道常导致令牌丢失，造成严重失真，使接收端难以可靠恢复语义。本文提出一种用于鲁棒语义恢复的令牌编码框架（TokCode），不增加传输开销，支持即插即用部署。为高效优化令牌编码器，我们开发了一种句义引导的基础模型适配算法（SFMA），避免了昂贵的端到端训练。基于提示驱动的生成式图像传输仿真结果表明，TokCode能有效缓解语义失真，在40%至60%令牌随机丢失的恶劣信道下仍可接近性能上界。
原文摘要 · Abstract (English)
Token-based semantic communication is promising for future wireless networks, as it can compact semantic tokens under very limited channel capacity. However, harsh wireless channels often cause missing tokens, leading to severe distortion that prevents reliable semantic recovery at the receiver. In this article, we propose a token encoding framework for robust semantic recovery (TokCode), which incurs no additional transmission overhead and supports plug-and-play deployment. For efficient token encoder optimization, we develop a sentence-semantic-guided foundation model adaptation algorithm (SFMA) that avoids costly end-to-end training. Based on simulation results on prompt-based generative image transmission, TokCode mitigates semantic distortion and can approach the performance upper-bound, even under harsh channels where 40% to 60% of tokens are randomly lost.
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