Joint Semantic-Channel Coding and Modulation for Token Communications

• URL: http://arxiv.org/abs/2511.15699v1
• Date: Wed, 19 Nov 2025 18:56:32 GMT
• Title: Joint Semantic-Channel Coding and Modulation for Token Communications
• Authors: Jingkai Ying, Zhijin Qin, Yulong Feng, Liejun Wang, Xiaoming Tao,
• Abstract summary: We consider the problem of token communication, studying how to transmit tokens efficiently and reliably.<n>We propose a joint semantic-channel and modulation scheme for the token encoder, mapping point tokens to standard digital constellation points.<n>The proposed method outperforms both joint semantic-channel coding and traditional separate coding.
• Score: 37.814311208185906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, the Transformer architecture has achieved outstanding performance across a wide range of tasks and modalities. Token is the unified input and output representation in Transformer-based models, which has become a fundamental information unit. In this work, we consider the problem of token communication, studying how to transmit tokens efficiently and reliably. Point cloud, a prevailing three-dimensional format which exhibits a more complex spatial structure compared to image or video, is chosen to be the information source. We utilize the set abstraction method to obtain point tokens. Subsequently, to get a more informative and transmission-friendly representation based on tokens, we propose a joint semantic-channel and modulation (JSCCM) scheme for the token encoder, mapping point tokens to standard digital constellation points (modulated tokens). Specifically, the JSCCM consists of two parallel Point Transformer-based encoders and a differential modulator which combines the Gumel-softmax and soft quantization methods. Besides, the rate allocator and channel adapter are developed, facilitating adaptive generation of high-quality modulated tokens conditioned on both semantic information and channel conditions. Extensive simulations demonstrate that the proposed method outperforms both joint semantic-channel coding and traditional separate coding, achieving over 1dB gain in reconstruction and more than 6x compression ratio in modulated symbols.

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