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.

翻译：基于令牌的语义通信在未来无线网络中具有前景，因为它能在极有限的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，从而造成严重失真，阻碍接收端可靠地恢复语义。本文提出了一种用于鲁棒语义恢复的令牌编码框架（TokCode），该框架不增加额外传输开销，且支持即插即用部署。为了实现高效的令牌编码器优化，我们开发了一种基于句子语义引导的基础模型适配算法（SFMA），避免了昂贵的端到端训练。基于基于提示的生成式图像传输仿真结果，TokCode能够减轻语义失真，并接近性能上限，即使在40%至60%令牌随机丢失的恶劣信道条件下也是如此。