用上下文感知的迭代检测与掩码传输，提升无线通信中文本令牌的传输质量。
Context-Aware Iterative Token Detection and Masked Transmission for Wireless Token Communication
- 基于预训练语言模型构建收发端共享的上下文先验，指导令牌检测。
- 通过贝叶斯框架融合上下文与信道观测，实现迭代式令牌恢复。
- 动态跳过高可预测令牌，降低传输率并适配不同信道条件。
大规模语言模型的成功使令牌成为自然语言表示的紧凑且有意义的单元，推动了无线信道上的令牌通信，其中令牌被视为无线传输的基本单位。本文提出一种上下文感知的令牌通信框架，利用预训练掩码语言模型（MLM）作为收发端共享的上下文概率模型。接收端采用基于贝叶斯视角的迭代令牌检测方法，联合利用MLM引导的上下文先验和信道观测。发送端引入上下文感知的掩码策略，跳过高度可预测的令牌传输以降低传输速率。仿真结果表明，该框架显著提升了重建句子的质量，并在多种信道条件下实现了有效的速率自适应。
原文摘要 · Abstract (English)
The success of large-scale language models has established tokens as compact and meaningful units for natural-language representation, which motivates token communication over wireless channels, where tokens are considered fundamental units for wireless transmission. We propose a context-aware token communication framework that uses a pretrained masked language model (MLM) as a shared contextual probability model between the transmitter (Tx) and receiver (Rx). At Rx, we develop an iterative token detection method that jointly exploits MLM-guided contextual priors and channel observations based on a Bayesian perspective. At Tx, we additionally introduce a context-aware masking strategy which skips highly predictable token transmission to reduce transmission rate. Simulation results demonstrate that the proposed framework substantially improves reconstructed sentence quality and supports effective rate adaptation under various channel conditions.
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