Ada-TokenCom: Rate-Adaptive Token Communications via Large-Model-Driven Token Compression and Generation

2026-08-28

AI Summary

本文提出Ada-TokenCom框架，通过大模型驱动的token压缩与生成实现自适应速率token通信，以解决高效多模态语义传输问题。

Abstract

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.

Problem

• Token Communications
• Rate-Adaptive
• Semantic Communication
• Large Autoregressive Models

Innovation

• Rate-Adaptive
• Token Communications
• Large Autoregressive Models
• Mixed Reconstruction/Generation Scheme
• Lyapunov-based Algorithm