# QuaMoE-DRF: Proactive Beam and Rate Adaptation via Multimodal Dynamic Radio Map Forecasting in ISAC Networks

arXiv: https://arxiv.org/abs/2607.00974 · HTML: https://mlsanity.com/paper/2607.00974/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 47 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Builds on (most cited first, 30 of 34)

- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G](https://mlsanity.com/paper/2309.07460/) (2023)
- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [RME-GAN: A Learning Framework for Radio Map Estimation based on Conditional Generative Adversarial Network](https://mlsanity.com/paper/2212.12817/) (2022)
- [A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies](https://mlsanity.com/paper/1804.01908/) (2018)
- [Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff](https://mlsanity.com/paper/2102.09527/) (2021)
- [LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I Communications](https://mlsanity.com/paper/2203.05548/) (2022)
- [Millimeter Wave Cellular Wireless Networks: Potentials and Challenges](https://mlsanity.com/paper/1401.2560/) (2014)
- [Joint Radar and Communication Design: Applications, State-of-the-art, and the Road Ahead](https://mlsanity.com/paper/1906.00789/) (2019)
- [LIDAR Data for Deep Learning-Based mmWave Beam-Selection](https://mlsanity.com/paper/1908.07488/) (2019)
- [Computer Vision Aided mmWave Beam Alignment in V2X Communications](https://mlsanity.com/paper/2207.11409/) (2022)
- [Environment-Aware Hybrid Beamforming by Leveraging Channel Knowledge Map](https://mlsanity.com/paper/2206.08707/) (2022)
- [Denoising Diffusion Probabilistic Model for Radio Map Estimation in Generative Wireless Networks](https://mlsanity.com/paper/2501.06604/) (2025)
- [Cram'er-Rao Bound Optimization for Joint Radar-Communication Design](https://mlsanity.com/paper/2101.12530/) (2021)
- [Radiomap Inpainting for Restricted Areas based on Propagation Priority and Depth Map](https://mlsanity.com/paper/2305.15526/) (2023)
- [Radar-assisted Predictive Beamforming for Vehicular Links: Communication Served by Sensing](https://mlsanity.com/paper/2001.09306/) (2020)
- [Enabling Joint Communication and Radar Sensing in Mobile Networks -- A Survey](https://mlsanity.com/paper/2006.07559/) (2020)
- [Directional Initial Access for Millimeter Wave Cellular Systems](https://mlsanity.com/paper/1511.06483/) (2015)
- [Environment Semantics Aided Wireless Communications: A Case Study of mmWave Beam Prediction and Blockage Prediction](https://mlsanity.com/paper/2301.05837/) (2023)
- [Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios](https://mlsanity.com/paper/2308.06868/) (2023)
- [Diffraction and Scattering Aware Radio Map and Environment Reconstruction using Geometry Model-Assisted Deep Learning](https://mlsanity.com/paper/2403.00229/) (2024)
- [Learning-based Handover in Mobile Millimeter-wave Networks](https://mlsanity.com/paper/2003.11009/) (2020)
- [Fundamental CRB-Rate Tradeoff in Multi-Antenna ISAC Systems with Information Multicasting and Multi-Target Sensing](https://mlsanity.com/paper/2307.11337/) (2023)
- [GAC-KAN: An Ultra-Lightweight GNSS Interference Classifier for GenAI-Powered Consumer Edge Devices](https://mlsanity.com/paper/2602.11186/) (2026)
- [SKANet: A Cognitive Dual-Stream Framework with Adaptive Modality Fusion for Robust Compound GNSS Interference Classification](https://mlsanity.com/paper/2601.12791/) (2026)
- [PhyG-MoE: A Physics-Guided Mixture-of-Experts Framework for Energy-Efficient GNSS Interference Recognition](https://mlsanity.com/paper/2601.12798/) (2026)
- [JSR-GFNet: Jamming-to-Signal Ratio-Aware Dynamic Gating for Interference Classification in future Cognitive Global Navigation Satellite Systems](https://mlsanity.com/paper/2602.00042/) (2026)
- [Harnessing Multimodal Sensing for Multi-user Beamforming in mmWave Systems](https://mlsanity.com/paper/2406.05300/) (2024)
- [Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learning](https://mlsanity.com/paper/2504.06173/) (2025)

## Built on by (most cited first, 1 of 1)

- [SPOC-Net: Single-Primitive Online Composition Network for GNSS Jamming Set Recognition](https://mlsanity.com/paper/2609.34875/) (2026)

## Abstract (arXiv)

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.