用大模型实现可变速率的低比特语义通信
Ada-TokenCom: Rate-Adaptive Token Communications via Large-Model-Driven Token Compression and Generation
- 利用大模型预测与算术编码压缩令牌，实现高效语义传输
- 在动态网络下仍保持优异性能，优于传统数字与深度联合编码方案
- 适合对带宽敏感的多模态语义通信场景
Token Communications（TokenCom）作为一种新范式，将令牌作为通信与计算的统一单元，实现了高效的多模态语义与目标导向传输。本文提出基于自回归大模型的率自适应TokenCom框架Ada-TokenCom，通过将下一令牌预测与算术编码结合，在令牌层面实现超低比特率语义通信。我们设计了一种混合重构/生成方案：发送端利用预训练的自回归大模型编码并传输序列开头的高信息量令牌，接收端使用相同模型预测后续内容。此外，我们设计了一种基于李雅普诺夫的算法，动态优化源压缩率与调制编码方案，以适应时变网络条件。仿真结果表明，所提出的Ada-TokenCom框架在性能上显著优于基于数字和深度联合源信道编码的语义通信基线。
原文摘要 · Abstract (English)
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.
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