arXiv AI By Li Qiao, Mahdi Boloursaz Mashhadi, Zhen Gao, Robert Schober, Deniz G\"und\"uz

ToDMA: Large Model-Driven Massive Token Communications for Semantic Multiple Access

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arXiv:2505. 10946v3 Announce Type: replace-cross Abstract: Token communications (TokenCom) is an emerging generative semantic communication paradigm, where tokens serve as compact representation units across modalities.

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arXiv Computer Vision
Aug 31

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

Ada-TokenCom is a rate‑adaptive token communication framework that uses large autoregressive models to compress and transmit tokens efficiently. It combines next‑token prediction with arithmetic coding, sending only the most informative tokens and letting the receiver generate the rest. A Lyapunov‑based algorithm dynamically adjusts compression and modulation to match changing network conditions, and simulations show it outperforms existing digital and deep joint source‑channel coding baselines.

By Zijun Zhang, Li Qiao, Mahdi Boloursaz Mashhadi, Zhen Gao, Mehdi Bennis, Kaibin Huang
arXiv Machine Learning
Sep 10

Token Encoding for Semantic Recovery

The paper introduces TokCode, a token encoding framework that enhances robustness in generative semantic communication by restructuring redundancy in the semantic domain. TokCode leverages a lightweight adapter to transform a large language model into a token encoder, avoiding the need for a dedicated deep model. A channel-quality-aware distillation method (CADET) trains the adapter across diverse erasure rates, producing a reconfigurable low‑rank adapter that enables efficient reinforcement learning and achieves significant improvements in image similarity over existing receiver‑side recovery benchmarks.

By Jingzhi Hu, Ouya Wang, Geoffrey Ye Li
arXiv AI
Sep 15

From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity

arXiv:2609.10714v1 Announce Type: cross Abstract: The ambitious requirements of sixth-generation (6G) networks are driving communication systems from reliable bit delivery toward meaning-aware and ta...

By Yu Ma, Zhen Gao, Li Qiao, Xiaoyuan Zhang, Mahdi Boloursaz Mashhadi, Yin Xu, Wenjun Xu, Xiaodong Xu, Kaibin Huang, Jiangzhou Wang, Rahim Tafazolli, Sheng Chen, Tony Q. S. Quek, Ping Zhang
arXiv Machine Learning
Aug 27

Token-Oriented Semantic Communication with Pretrained Vision Transformers

The paper introduces a token‑oriented semantic communication framework that transmits only task‑relevant image latents instead of full token embeddings, reducing communication cost and improving interoperability. It leverages a spatial alignment between vision transformer patch tokens and learned image compression latents, enabling token‑level relevance estimation and selective transmission. Experiments on ImageNet demonstrate a superior rate–accuracy trade‑off compared to existing semantic communication methods and hand‑crafted codecs.

By Jiwoong Im, Minwoo Kim, Jaeho Lee, Yo-Seb Jeon, Yongjune Kim
arXiv Machine Learning
Jul 24

Wireless TokenCom: RL-Based Tokenizer Agreement for Multi-User Wireless Token Communications

arXiv:2602. 12338v2 Announce Type: replace Abstract: Token Communications (TokenCom) has recently emerged as an effective new paradigm, where tokens are the unified units of multimodal communications and computations, enabling efficient digital semantic- and goal-oriented communications in future wireless networks.

By Farshad Zeinali, Mahdi Boloursaz Mashhadi, Rahim Tafazolli