发表机构
Beijing Institute of Technology; Zhongguancun Academy; The University of Hong Kong; University of Surrey; Shanghai Jiao Tong University; Beijing University of Posts and Telecommunications; Southeast University; University of Southampton; Ocean University of China; Singapore University of Technology and Design（北京理工大学; 中关村学院; 香港大学; 萨里大学; 上海交通大学; 北京邮电大学; 东南大学; 南安普顿大学; 中国海洋大学; 新加坡科技设计大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出令牌通信（TokenCom）作为大模型驱动的6G智能连接的新范式，通过将令牌作为比特之上的通信抽象，实现语义通信的互操作性和可扩展性，并综述了其演变、技术及开放挑战。
AI 中文摘要
第六代（6G）网络的宏伟需求正推动通信系统从可靠的比特传输向语义感知和任务导向的连接转变。大模型（LMs）凭借其强大的多模态理解和生成能力，加速了这一转变，并使语义通信（SemCom）日益实用化。然而，当前由大模型驱动的语义通信仍然支离破碎：语义表示通常与特定的模态、模型或任务绑定。虽然比特为数字传输提供了通用单位，但仍然没有用于表示和处理语义的类似单位，这限制了互操作性、理论统一性和可扩展的系统设计。我们认为，令牌为这一缺失的抽象提供了自然的候选方案。两个趋势支持这一点：统一的多模态大模型现在在一个令牌空间中编码文本、图像、音频、视频和机器人动作，而分布式大模型推理已经通过专家路由、缓存传输和推测解码产生了大量的令牌级流量。令牌通信（TokenCom）通过统一这些趋势而出现，将大模型的原生处理单元用作比特级别之上的通信抽象，并直接在令牌粒度上实现重要性分配、错误处理和资源分配。本综述追溯了从大模型驱动的语义通信到令牌通信的演变。我们回顾了大模型驱动的语义通信的三个主要方向：以源为中心的语义编码、用于物理层任务的信道语义，以及协作的边缘设备智能。然后，我们考察了令牌抽象、其所需的传输技术，以及两种新兴范式，即用于大模型服务的令牌通信和用于具身及智能体智能的令牌通信。最后，我们指出了迈向统一、可扩展和AI原生的6G通信系统的开放挑战。
英文摘要
The ambitious requirements of sixth-generation (6G) networks are driving communication systems from reliable bit delivery toward meaning-aware and task-oriented connectivity. Large models (LMs), with strong multimodal understanding and generation capabilities, have accelerated this shift and made semantic communication (SemCom) increasingly practical. Yet current LM-driven SemCom remains fragmented: semantic representations are typically tied to specific modalities, models, or tasks. While the bit provides a universal unit for digital transport, there is still no analogous unit for representing and processing semantics, which limits interoperability, theoretical unification, and scalable system design. We argue that tokens provide a natural candidate for this missing abstraction. Two trends support this: unified multimodal LMs now encode text, images, audio, video, and robot actions in one token space, while distributed LM inference already generates substantial token-level traffic through expert routing, cache transfer, and speculative decoding. Token communication (TokenCom) emerges by unifying these trends, using the LM's native processing unit as a communication abstraction above the bit level and enabling importance assignment, error handling, and resource allocation directly at token granularity. This survey traces the evolution from LM-driven SemCom to TokenCom. We review three major directions of LM-driven SemCom: source-centric semantic coding, channel semantics for physical-layer tasks, and collaborative edge-device intelligence. We then examine the token abstraction, the transmission techniques it requires, and two emerging paradigms, namely TokenCom for LM services and for embodied and agentic intelligence. Finally, we identify open challenges toward unified, scalable, and AI-native 6G communication systems.
Comments39 pages, 10 figures, 10 tables, 182 references. Submitted to Science China Information Sciences
