The burgeoning field of on-device AI communication, where devices exchange information directly through embedded foundation models, such as language models (LMs), requires robust, efficient, and generalizable communication frameworks. However, integrating these frameworks with existing wireless systems and effectively managing noise and bit errors pose significant challenges. In this work, we introduce a practical ondevice AI communication framework, integrated with physical layer (PHY) communication functions, demonstrated through its performance on a link-level simulator. Our framework incorporates end-to-end training with channel noise to enhance resilience, incorporates vector quantized variational autoencoders (VQ-VAE) for efficient and robust communication, and utilizes pre-trained encoder-decoder transformers for improved generalization capabilities. Simulations, across various communication scenarios, reveal that our framework achieves a 50% reduction in transmission size while demonstrating substantial generalization ability and noise robustness under standardized 3GPP channel models.

翻译：设备端人工智能通信这一新兴领域要求设备通过嵌入式基础模型（如语言模型）直接交换信息，这需要构建鲁棒、高效且可泛化的通信框架。然而，将这些框架与现有无线系统集成，并有效管理噪声与比特错误，构成了重大挑战。本研究提出一种实用的设备端人工智能通信框架，该框架与物理层通信功能相集成，并通过链路级仿真器展示了其性能。我们的框架采用包含信道噪声的端到端训练以增强鲁棒性，引入矢量量化变分自编码器以实现高效且鲁棒的通信，并利用预训练的编码器-解码器Transformer模型以提升泛化能力。在不同通信场景下的仿真结果表明，在标准化的3GPP信道模型下，我们的框架在实现传输规模降低50%的同时，展现出显著的泛化能力与噪声鲁棒性。