发表机构
University of Georgia（佐治亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对毫米波XR网络波束管理易受移动和遮挡导致中断的问题，提出RadioSight系统，结合反向波束追踪与语义同步，实现实时预测性波束优化，显著降低误差、提升吞吐量与链路稳定性。
AI 中文摘要
下一代扩展现实（XR）网络依赖毫米波（mmWave）通信实现多吉比特吞吐量，但高定向链路易受用户移动和遮挡影响，在反应式波束管理下会导致频繁中断。新兴的神经无线电场可预测无线电传播，但现有工作仅局限于离线信道重建。我们提出RadioSight，一种用于预测性毫米波优化和主动多用户多输入多输出（Multi-User MIMO）波束成形的实时多模态无线电场系统。RadioSight将反向波束追踪与实时语义对象同步相结合，可在无需完整模型重训练的情况下预判射频几何变化。作为面向商用28 GHz阵列的边缘可执行流水线实现，RadioSight在前一个调度窗口内确定当前窗口的波束，无需穷尽式波束扫描。实验表明，RadioSight可将波束搜索误差降低约50%，将中值吞吐量提升2倍，并增强链路稳定性。
英文摘要
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.
CommentsAccepted to ACM MobiCom 2026. 21 pages, 20 figures, 3 tables
