Learned Elevation Models as a Lightweight Alternative to LiDAR for Radio Environment Map Estimation
Next-generation wireless systems such as 6G operate at higher frequency bands, making signal propagation highly sensitive to environmental factors such as buildings and vege- tation. Accurate Radio Environment Map (REM) estimation is therefore increasingly important for effective network planning and operation. Existing methods, from ray-tracing simulators to deep learning generative models, achieve promising results but require detailed 3D environment data such as LiDAR-derived point clouds, which are costly to acquire, several gigabytes per km2 in size, and quickly outdated in dynamic environments. We propose a two-stage framework that eliminates the need for 3D data at inference time: ...
| Authors | Ljupcho Milosheski, Fedja Močnik, Mihael Mohorčič et al. |
|---|---|
| Primary category | eess.SP |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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