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Ada-TokenCom: Rate-Adaptive Token Communications via Large-Model-Driven Token Compression and Generation — arXiv:2608.28086

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.28086
License (as archived): CC0-1.0
Archived: 2026-10-17T11:27:16.519Z
SHA-256 of the archived text: 748ee7849d541710f6508999387786044f800f9654a73935aedb279b54e944d8
Stable URL of this page: https://metraipolis.com/documento/dc_e8a8ef53e4fa

How to cite

Ada-TokenCom: Rate-Adaptive Token Communications via Large-Model-Driven Token Compression and Generation — arXiv:2608.28086 — arXiv (en), https://arxiv.org/abs/2608.28086 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e8a8ef53e4fa (Metraipolis archive dc_e8a8ef53e4fa, sha256 748ee7849d541710f6508999387786044f800f9654a73935aedb279b54e944d8)

BibTeX

@misc{dce8a8ef53e4fa,
  title = {Ada-TokenCom: Rate-Adaptive Token Communications via Large-Model-Driven Token Compression and Generation — arXiv:2608.28086},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.28086},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e8a8ef53e4fa; sha256 748ee7849d541710f6508999387786044f800f9654a73935aedb279b54e944d8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.28086. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: matematica e informatica teorica (retro-catalogo arXiv: annunci del 2026-08-31 in math.IT).
Categorie arXiv: Information Theory (cs.IT) ; Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV).
Pagina del lavoro: https://arxiv.org/abs/2608.28086 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T11:07:15.574Z dalla pagina https://arxiv.org/catchup/math.IT/2026-08-31?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).