CONVERGE: A Multi-Agent Vision-Radio Architecture for xApps
This addresses the challenge of improving wireless network reliability and performance for telecommunications systems by enabling Integrated Sensing and Communications, though it appears incremental as it builds on existing O-RAN and sensing frameworks.
The paper tackles the problem of integrating visual data with radio sensing to predict and overcome channel dynamics in high-frequency wireless networks, proposing a multi-agent architecture for real-time delivery to O-RAN xApps, with experimental results showing sensing delay under 1 ms and successful real-time control of 5G/6G RAN.
Telecommunications and computer vision have evolved independently. With the emergence of high-frequency wireless links operating mostly in line-of-sight, visual data can help predict the channel dynamics by detecting obstacles and help overcoming them through beamforming or handover techniques. This paper proposes a novel architecture for delivering real-time radio and video sensing information to O-RAN xApps through a multi-agent approach, and introduces a new video function capable of generating blockage information for xApps, enabling Integrated Sensing and Communications. Experimental results show that the delay of sensing information remains under 1\,ms and that an xApp can successfully use radio and video sensing information to control the 5G/6G RAN in real-time.