基于动态无线电图预测，实现智能感知与通信网络中的主动波束与速率自适应。
QuaMoE-DRF: Proactive Beam and Rate Adaptation via Multimodal Dynamic Radio Map Forecasting in ISAC Networks
- 融合静态几何与移动事件的多模态质量感知专家模型，预测未来波束-SINR场。
- 在城市多基站场景下实现402.5 Mbps有效速率，中断概率降至0.0417。
- 适合研究智能感知通信一体化、无线资源管理与动态信道预测的学者。
静态无线电图提供位置相关的传播先验，但无法捕捉由移动物体引起的短期遮挡。直接依赖感知的波束预测也受限于波束索引忽略SINR余量、调制编码阶数阈值、基站备选方案及通信等效邻近波束等问题。本文提出QuaMoE-DRF，一种面向感知与通信一体化（ISAC）网络的质量感知多模态动态无线电图预测框架，用于主动波束与速率自适应。其核心表征为未来波束-SINR场，证明该全多基站波束-SINR场足以支持有限码本下的基站、波束、调制编码阶数、吞吐量与中断决策。为提升可计算性，模型学习紧凑的参考基站局部场，辅以基站级监督、基站-波束联合监督与隐式网络上下文；同时指出仅靠此压缩投影不足以完成基站关联。QuaMoE-DRF通过质量感知的专家混合模块，融合静态几何、事件型运动观测、结构化感知状态与无线历史数据，受异方差模态误差下逆方差融合启发。它联合预测通信导向的无线电图信道及主动基站、波束与调制编码阶数决策。在动态多基站多用户城市基准测试中，该方法实现402.5 Mbps有效速率、0.0417中断概率与0.1836地图均方根误差，相比最强基准有效速率提升5.67%，中断概率降低8.35%。当前验证使用紧凑遮挡/路径损耗模拟器生成标签，射线追踪仅用于校准与合理性检验。
原文摘要 · Abstract (English)
Static radio maps provide location-dependent propagation priors, but they cannot capture short-term blockage caused by moving objects. Direct sensing-assisted beam prediction is also limited because a beam index discards SINR margins, MCS thresholds, BS alternatives, and communication-equivalent neighboring beams. This paper proposes QuaMoE-DRF, a quality-aware multimodal dynamic radio map forecasting framework for proactive beam and rate adaptation in ISAC networks. Its core representation is a future beam-SINR field. We show that the full multi-BS beam-SINR field is sufficient for finite-codebook threshold-rate BS, beam, MCS, goodput, and outage decisions. For tractability, the implemented model learns a compact reference-BS local field, complemented by BS-level supervision, joint BS--beam supervision, and latent network context; we also clarify that this compact projection alone is not sufficient for BS association. QuaMoE-DRF fuses static geometry, event-like motion observations, structured sensing states, and wireless history through a quality-aware mixture-of-experts module motivated by inverse-variance fusion under heteroscedastic modality errors. It jointly predicts communication-oriented map channels and proactive BS, beam, and MCS decisions. On a dynamic multi-BS and multi-UE urban benchmark, QuaMoE-DRF achieves 402.5 Mbps effective rate, 0.0417 outage probability, and 0.1836 map RMSE, improving the effective rate by 5.67% and reducing outage by 8.35% over the strongest completed effective-rate baseline. The current validation uses labels from a compact blockage/path-loss simulator, with ray tracing used only for calibration and sanity checking.
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