# A Scalable and Generalizable Pathloss Map Prediction

arXiv: https://arxiv.org/abs/2312.03950 · HTML: https://mlsanity.com/paper/2312.03950/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 6 Dec 2023.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Do Better ImageNet Models Transfer Better?](https://mlsanity.com/paper/1805.08974/) (2018)
- [RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation](https://mlsanity.com/paper/1611.06612/) (2016)
- [Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network](https://mlsanity.com/paper/1703.02719/) (2017)
- [What makes ImageNet good for transfer learning?](https://mlsanity.com/paper/1608.08614/) (2016)
- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [NeRF2: Neural Radio-Frequency Radiance Fields](https://mlsanity.com/paper/2305.06118/) (2023)
- [Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography](https://mlsanity.com/paper/2202.03269/) (2022)
- [Dataset of Pathloss and ToA Radio Maps With Localization Application](https://mlsanity.com/paper/2212.11777/) (2022)
- [Real-Time Digital Twins: Vision and Research Directions for 6G and Beyond](https://mlsanity.com/paper/2301.11283/) (2023)
- [6G Digital Twin Networks: From Theory to Practice](https://mlsanity.com/paper/2212.02032/) (2022)
- [PMNet: Robust Pathloss Map Prediction via Supervised Learning](https://mlsanity.com/paper/2211.10527/) (2022)
- [The First Pathloss Radio Map Prediction Challenge](https://mlsanity.com/paper/2310.07658/) (2023)

## Built on by (most cited first, 18 of 18)

- [Radio Map Prediction from Aerial Images and Application to Coverage Optimization](https://mlsanity.com/paper/2410.17264/) (2024)
- [Differentiable and Learnable Wireless Simulation with Geometric Transformers](https://mlsanity.com/paper/2406.14995/) (2024)
- [Vision Transformers for Efficient Indoor Pathloss Radio Map Prediction](https://mlsanity.com/paper/2412.09507/) (2024)
- [RMTransformer: Accurate Radio Map Construction and Coverage Prediction](https://mlsanity.com/paper/2501.05190/) (2025)
- [Radio Propagation Modelling: To Differentiate or To Deep Learn, That Is The Question](https://mlsanity.com/paper/2509.19337/) (2025)
- [RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation](https://mlsanity.com/paper/2506.08418/) (2025)
- [Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Prediction](https://mlsanity.com/paper/2409.09978/) (2024)
- [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)
- [Channel Knowledge Map Construction via Physics-Inspired Diffusion Model Without Prior Observations](https://mlsanity.com/paper/2512.02757/) (2025)
- [An Efficient and Explainable KAN Framework for Wireless Radiation Field Prediction](https://mlsanity.com/paper/2601.11656/) (2026)
- [Intelligent Base Station Deployment in Urban Wireless Networks: A Geographic Data-Informed Digital Twin Approach](https://mlsanity.com/paper/2608.14599/) (2026)
- [ReVeal-MT: A Physics-Informed Neural Network for Multi-Transmitter Radio Environment Mapping](https://mlsanity.com/paper/2512.04100/) (2025)
- [Learning the Channel Gain from Anywhere to Anywhere via Cross-environment Transformer Estimators](https://mlsanity.com/paper/2605.08211/) (2026)
- [Multi-Modal Conditioned High-Resolution Transformer for Urban Electromagnetic Field Map Prediction Download PDF](https://mlsanity.com/paper/2606.27671/) (2026)
- [Active Learning for Channel Knowledge Map Construction via Bayesian Inference Diffusion Models](https://mlsanity.com/paper/2606.29862/) (2026)
- [SymNet: A Multi-Task Network for Joint Radio Map Reconstruction and Transmitter Localization](https://mlsanity.com/paper/2608.00087/) (2026)
- [Should I Use This Synthetic Dataset for Training? How to Test with Minimal Real Data](https://mlsanity.com/paper/2608.27996/) (2026)

## Abstract (arXiv)

Large-scale channel prediction, i.e., estimation of the pathloss from geographical/morphological/building maps, is an essential component of wireless network planning. Ray tracing (RT)-based methods have been widely used for many years, but they require significant computational effort that may become prohibitive with the increased network densification and/or use of higher frequencies in B5G/6G systems. In this paper, we propose a data-driven, model-free pathloss map prediction (PMP) method, called PMNet. PMNet uses a supervised learning approach: it is trained on a limited amount of RT (or channel measurement) data and map data. Once trained, PMNet can predict pathloss over location with high accuracy (an RMSE level of 10-2) in a few milliseconds. We further extend PMNet by employing transfer learning (TL). TL allows PMNet to learn a new network scenario quickly (x5.6 faster training) and efficiently (using x4.5 less data) by transferring knowledge from a pre-trained model, while retaining accuracy. Our results demonstrate that PMNet is a scalable and generalizable ML-based PMP method, showing its potential to be used in several network optimization applications.