A Scalable and Generalizable Pathloss Map Prediction

2023/12/06 by Ju-Hyung Lee, Andreas F. Molisch, Lee, Ju-Hyung +1 · 9 citations
Engineering · #Advanced MIMO Systems Optimization #FOS: Computer and information sciences #FOS: Electrical engineering #Indoor and Outdoor Localization Technologies #Information Theory (cs.IT) #Machine Learning (cs.LG) #Millimeter-Wave Propagation and Modeling #Networking and Internet Architecture (cs.NI) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2312.03950

openalex publication_date 2023/12/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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• WiCo-PG: Wireless Channel Foundation Model for Pathloss Map Generation via Synesthesia of Machines
• EPFL-REMNet: Efficient Personalized Federated Digital Twin Towards 6G Heterogeneous Radio Environment
• AIRMap -- AI-Generated Radio Maps for Wireless Digital Twins
• Radio Propagation Modelling: To Differentiate or To Deep Learn, That Is The Question
• A Multi-Scale Feature Extraction and Fusion UNet for Pathloss Prediction in UAV-Assisted mmWave Radio Networks
• Machine Learning based Radio Environment Map Estimation for Indoor Visible Light Communication

Related

• PMNet: Robust Pathloss Map Prediction via Supervised Learning
• IPP-Net: A Generalizable Deep Neural Network Model for Indoor Pathloss Radio Map Prediction
• EA-RMENet -- Path Loss Prediction in Urban Environments using Deep Learning
• RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks
• RMTransformer: Accurate Radio Map Construction and Coverage Prediction