RFWeave: Learning Wireless Propagation Across Scenes and Transceiver Configurations
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. However, these approaches remain largely confined to a fixed configuration of array size, receiver orientation, and transmit beam. In this work, we introduce RFWeave, a learned wireless propagation model designed to generalize across scenes, transceiver locations, and transceiver configurations. To accommodate varying array structures, RFWeave incorporates variable-length tr...
| Authors | Shurui Xu, Xiao Ye, Yijie Bian et al. |
|---|---|
| Primary category | eess.SP |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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