Abstract

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 …

Date

September 9, 2024

Authors

Ju-Hyung Lee, Dong-Ho Lee, Joohan Lee, Jay Pujara

Journal

IEEE Transactions on Wireless Communications

Volume

23

Issue

11

Pages

17266-17278

Publisher

IEEE