AI 中文总结
本研究提出基于大自回归模型的速率自适应令牌通信框架Ada-TokenCom，结合下一个令牌预测与算术编码，通过混合重建/生成方案及李雅普诺夫算法优化，实现优于基线的超低比特率语义通信。
AI 中文摘要
令牌通信（TokenCom）近来成为一种新范式，其中令牌作为通信与计算的统一单元，支持高效的多模态语义及面向目标的传输。本文提出Ada-TokenCom，一种基于大自回归模型的速率自适应TokenCom框架，它将下一个令牌预测与算术编码相结合，在令牌层面实现超低比特率的语义通信。我们提出一种混合重建/生成方案：发射端利用预训练的自回归大模型对令牌序列开头信息含量高的令牌进行编码与传输，接收端则使用相同模型预测剩余令牌。此外，我们设计了一种基于李雅普诺夫的算法，动态优化信源压缩率与调制编码方案，以适应时变网络状况。仿真结果表明，本文提出的Ada-TokenCom框架优于基于数字及深度联合信源信道编码的语义通信基线方法。
英文摘要
Token Communications (TokenCom) has recently emerged as a new paradigm in which tokens serve as unified units for communication and computation, enabling efficient multimodal semantic and goal-oriented transmission. In this paper, we develop Ada-TokenCom, a rate-adaptive TokenCom framework based on large autoregressive models, which integrates next-token prediction with arithmetic coding to achieve ultra-low bitrate semantic communication at the token level. We propose a mixed reconstruction/generation scheme, where the transmitter encodes and transmits the highly informative tokens at the beginning of the token sequence leveraging a pre-trained autoregressive large model, while the receiver uses an identical model to predict the rest. Moreover, we design a Lyapunov-based algorithm to dynamically optimize both the source compression rate and the modulation and coding scheme, adapting to time-varying network conditions. Simulation results demonstrate that our proposed Ada-TokenCom framework outperforms both digital and deep joint source-channel coding-based semantic communication baselines.
