# PMNet: Robust Pathloss Map Prediction via Supervised Learning

arXiv: https://arxiv.org/abs/2211.10527 · HTML: https://mlsanity.com/paper/2211.10527/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2022.

## Built on by (most cited first, 16 of 16)

- [A Scalable and Generalizable Pathloss Map Prediction](https://mlsanity.com/paper/2312.03950/) (2023)
- [Radio Map Prediction from Aerial Images and Application to Coverage Optimization](https://mlsanity.com/paper/2410.17264/) (2024)
- [Radio Map Estimation -- An Open Dataset with Directive Transmitter Antennas and Initial Experiments](https://mlsanity.com/paper/2402.00878/) (2024)
- [Diffraction and Scattering Aware Radio Map and Environment Reconstruction using Geometry Model-Assisted Deep Learning](https://mlsanity.com/paper/2403.00229/) (2024)
- [RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments](https://mlsanity.com/paper/2406.16907/) (2024)
- [RadioFormer: A Multiple-Granularity Radio Map Estimation Transformer with 1textpertenthousand Spatial Sampling](https://mlsanity.com/paper/2504.19161/) (2025)
- [Taming Vision Priors for Data Efficient mmWave Channel Modeling](https://mlsanity.com/paper/2603.13383/) (2026)
- [AutoBS: Autonomous Base Station Deployment with Reinforcement Learning and Digital Network Twins](https://mlsanity.com/paper/2502.19647/) (2025)
- [AIRMap: AI-Generated Radio Maps for Wireless Digital Twins](https://mlsanity.com/paper/2511.05522/) (2025)
- [Point2Radio: A Foundation Model for Cross-Scene Radio Fields from Material-Aware Point Clouds](https://mlsanity.com/paper/2607.28994/) (2026)
- [Intelligent Base Station Deployment in Urban Wireless Networks: A Geographic Data-Informed Digital Twin Approach](https://mlsanity.com/paper/2608.14599/) (2026)
- [Photon Splatting: A Physics-Guided Neural Surrogate for Real-Time Wireless Channel Prediction](https://mlsanity.com/paper/2507.04595/) (2025)
- [Learning Coverage- and Power-Optimal Transmitter Placement from City Maps: A Comparative Study of Direct and Indirect Neural Approaches](https://mlsanity.com/paper/2604.22056/) (2026)
- [Bridging the Sampling Distribution Shift in Radio Map Estimation: A Trajectory-Aware Paradigm](https://mlsanity.com/paper/2605.28234/) (2026)
- [RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling](https://mlsanity.com/paper/2605.29538/) (2026)
- [EA-RMENet -- Path Loss Prediction in Urban Environments using Deep Learning](https://mlsanity.com/paper/2607.16449/) (2026)

## Abstract (arXiv)

Pathloss prediction is an essential component of wireless network planning. While ray tracing based methods have been successfully used for many years, they require significant computational effort that may become prohibitive with the increased network densification and/or use of higher frequencies in 5G/B5G (beyond 5G) systems. In this paper, we propose and evaluate a data-driven and model-free pathloss prediction method, dubbed PMNet. This method uses a supervised learning approach: training a neural network (NN) with a limited amount of ray tracing (or channel measurement) data and map data and then predicting the pathloss over location with no ray tracing data with a high level of accuracy. Our proposed pathloss map prediction-oriented NN architecture, which is empowered by state-of-the-art computer vision techniques, outperforms other architectures that have been previously proposed (e.g., UNet, RadioUNet) in terms of accuracy while showing generalization capability. Moreover, PMNet trained on a 4-fold smaller dataset surpasses the other baselines (trained on a 4-fold larger dataset), corroborating the potential of PMNet.