RFWeave: Learning Wireless Propagation Across Scenes and Transceiver Configurations
In this work, we introduce RFWeave, a learned wireless propagation model designed to generalize across scenes, transceiver locations, and transceiver configurations.
ProofPaper ↗
Key points
- Wireless propagation modeling is foundational to the evaluation and design of modern communication systems.
- To achieve accurate wireless propagation prediction, recent learning-based approaches leverage scene geometry and materials, demonstrating promising generalization across transceiver locations and unseen scenes.
- A configuration-conditioned decoder predicts the spatial spectrum and received power.
- Extensive evaluations show that RFWeave achieves state-of-the-art performance across all evaluated generalization settings, improving spatial spectrum structural similarity (SSIM) by up to 15.7% and reducing received power mean absolute error (MAE) by up to 35.4% over the strongest baselines on unseen scenes.
Sources (1)
- [1]RFWeave: Learning Wireless Propagation Across Scenes and Transceiver ConfigurationsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 12:11 PM
In this work, we introduce RFWeave, a learned wireless propagation model designed to generalize across scenes, transceiver locations, and transceiver configurations.
Wireless propagation modeling is foundational to the evaluation and design of modern communication systems.
Extractive summary: sentences quoted from the sources.