RadioSight: Predictive mmWave XR Network Optimization from Dynamic Neural Radio Fields

2026-08-29

AI Summary

为解决mmWave通信中因用户移动和障碍物导致的频繁中断问题，RadioSight通过实时多模态无线电场系统预测无线传播并进行主动波束成形优化。

Abstract

Next-generation extended reality (XR) networks rely on mmWave communication for multi-gigabit throughput, yet highly directional links are vulnerable to user mobility and blockages, causing frequent outages under reactive beam management. Emerging neural radio fields can predict radio propagation, but prior work remains limited to offline channel reconstruction. We introduce RadioSight, a real-time multi-modal radio field system for predictive mmWave optimization and proactive Multi-User MIMO beamforming. RadioSight combines backward beam-tracing with real-time semantic object synchronization to anticipate RF geometry changes without full model retraining. Implemented as an edge-executable pipeline for commercial 28 GHz arrays, RadioSight determines each scheduling window's beams during the preceding window without exhaustive beam sweeps. Experiments show that RadioSight reduces beam-search error by up to ~50%, improves median throughput by 2x, and enhances link stability.

Problem

• mmWave
• XR networks
• beam management
• neural radio fields
• offline channel reconstruction

Innovation

• neural radio fields
• predictive mmWave optimization
• proactive Multi-User MIMO beamforming
• backward beam-tracing
• real-time semantic object synchronization