The LLM-RIMSA dataset, designed to advance 6G networks through ultra-massive connectivity and intelligent radio environments. The dataset is built around a novel framework that integrates large language models (LLMs) with a reconfigurable intelligent metasurface antenna (RIMSA) architecture. This integration addresses limitations in hardware efficiency, dynamic control, and scalability seen in existing RIS technologies.
The growing antenna array scale, the uncorrelated fadings between downlink and uplink of frequency division duplex (FDD) or analog beamforming design increases the difficulty of channel sounding or estimation. Non-wireless channel detection or beam weight prediction method is a promising solution to help obtain timely and accurate wireless channel state. Furthermore, beamforming can be enhanced by the powerful sensing capability of cameras.
