QuaMoE-DRF: Proactive Beam and Rate Adaptation via Multimodal Dynamic Radio Map Forecasting in ISAC Networks
2026-07-01 • Information Theory
Information TheoryComputer Vision and Pattern Recognition
AI summaryⓘ
The authors present QuaMoE-DRF, a new system that predicts how well wireless signals will work in moving environments by combining different types of data like building layouts and moving objects. Instead of just relying on fixed radio maps or direct sensing that miss some details, their model forecasts a future map of signal quality tied to specific beams. This helps decide the best base stations, beams, and data rates to use ahead of time. On a city data benchmark, their method improved wireless speed and reduced connection problems compared to existing approaches. They tested their model with simulated signal blockages and calibrated it using detailed ray tracing methods.
radio mapbeamformingSINRISAC networksmixture-of-expertsbeam predictionbase station associationheteroscedastic errorsray tracingMCS (Modulation and Coding Scheme)
Authors
Zhihan Zeng, Kaihe Wang, Zhongpei Zhang, Chongwen Huang
Abstract
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.