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 on-device 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：在设备端AI通信这一新兴领域中，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，并有效管理噪声和比特错误，面临重大挑战。本研究提出一种实用的设备端AI通信框架，该框架与物理层通信功能集成，并通过链路级仿真器上的性能表现进行验证。该框架采用端到端训练方法，引入信道噪声以增强鲁棒性；采用向量量化变分自编码器实现高效且鲁棒的通信；并利用预训练的编码器-解码器变压器来提升泛化能力。在多种通信场景下的仿真结果表明，该框架在标准化3GPP信道模型下，传输规模减少50%，同时展现出显著的泛化能力和噪声鲁棒性。