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Token-Domain Multiple Access: Exploiting Semantic Orthogonality for Collision Mitigation

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arxiv 2502.06118 v2 pith:V5XENAGZ submitted 2025-02-10 cs.IT eess.SPmath.IT

classification cs.ITeess.SPmath.IT
keywords semantictokendevicesmultipleaccesscommunicationcontextimage
verification ladder T0 review T1 audit T2 compute T3 formal
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Token communications is an emerging generative semantic communication concept that reduces transmission rates by using context and transformer-based token processing, with tokens serving as universal semantic units. In this paper, we propose a semantic multiple access scheme in the token domain, referred to as ToDMA, where a large number of devices share a tokenizer and a modulation codebook for source and channel coding, respectively. Specifically, the source signal is tokenized into sequences, with each token modulated into a codeword. Codewords from multiple devices are transmitted simultaneously, resulting in overlap at the receiver. The receiver detects the transmitted tokens, assigns them to their respective sources, and mitigates token collisions by leveraging context and semantic orthogonality across the devices' messages. Simulations demonstrate that the proposed ToDMA framework outperforms context-unaware orthogonal and non-orthogonal communication methods in image transmission tasks, achieving lower latency and better image quality.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Adaptive Semantic Token Communication for Transformer-based Edge Inference

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A single adaptive deep joint source-channel coding model with budget-conditioned token selection and Lyapunov-based resource allocation achieves better accuracy-compression trade-offs than static DJSCC and digital bas...

  2. Text-Guided Token Communication for Wireless Image Transmission

    cs.IT 2025-07 reject novelty 5.0 of 10

    A text-guided token transmission system using pre-trained image and text models outperforms a deep JSCC baseline on perceptual and semantic metrics, but relies on an assumption that text is available at the receiver.

  3. Low-Complexity Semantic Packet Aggregation for Token Communication via Lookahead Search

    eess.SP 2025-06 conditional novelty 5.0 of 10

    SemPA-Look groups tokens into packets using a leave-one-out residual semantic score and a fixed-width lookahead search, matching near-optimal ATS at linear text-encoding complexity.

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