AI-Native Radio Transceiver Signal Processing for Next-Generation Mobile Communication Systems

This invited paper presents our vision of Artificial Intelligence (AI)-Native radio transceiver design for next-generation (6G) mobile communication systems by integrating AI models into the conventional radio signal processing pipeline. The proposed design and architecture can efficiently handle wireless signal processing, enabling reliable and high-performance mobile communications. Unlike traditional transceiver designs, which are agnostic to the environment, the proposed AI-Native approach leverages dynamic learning of existing regularities and patterns in the wireless environment (e.g., channel, hardware nonlinearity, traffic, and user mobility models) to improve system efficiency. We demonstrate how AI can enhance the performance of key functions such as channel estimation, beamforming, link adaptation, and phased-array antenna calibration. Unlike prior work, we emphasize the practical deployment of AI models in a millimeter-wave communication testbed and demonstrate a realistic training approach based on real-world data, moving beyond the constraints of synthetic datasets. Finally, we present AI-enabled extended reality as a promising 6G application and highlight its requirements and the challenges it poses on the AI-Native radio access network.