Radio Propagation Modeling

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3 papers in the last four weeks, down 25% on the four weeks before. 0.0% of all new papers.

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Learning the Channel Gain from Anywhere to Anywhere via Cross-environment Transformer Estimators
May 6, 2026Prasenjit Dhara, Daniel RomeroRadio Propagation Modeling
Dept. of Information and Communication Technology, University of Agder, Grimstad, Norway.
Machine-Learning-Based Classification of Radio Frequency Building Loss
Apr 27, 2026Jiayi Tan, Neelabhro Roy, James Gross +2Wireless Communications Radio Propagation Modeling
Ericsson AB, Stockholm, Sweden · KTH Royal Institute of Technology, Stockholm, Sweden
GeNeRT: A Physics-Informed Approach to Intelligent Wireless Channel Modeling via Generalizable Neural Ray Tracing
Jun 23, 2025Kejia Bian, Meixia Tao, Shu Sun +2Neural Network Generalization Wireless Communications
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China · Shanghai Innovation Institute, Shanghai 200231, China
Sionna RT: Technical Report
Apr 30, 2025Fayçal Aït Aoudia, Jakob Hoydis, Merlin Nimier-David +3Wireless Communications Radio Propagation Modeling
EM-GANSim: Real-time and Accurate EM Simulation Using Conditional GANs for 3D Indoor Scenes
May 27, 2024Ruichen Wang, Dinesh ManochaWireless Communications Radio Propagation Modeling
Department of Computer Science and the Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742 USA
The Vienna 4G/5G Drive-Test Dataset
Date pendingWilfried Wiedner, Lukas Eller, Mariam Mussbah +4Wireless Communications Radio Propagation Modeling
Institute of Telecommunications, TU Wien, Gusshausstraße 25, 1040 Vienna, Austria · Christian Doppler Laboratory for Digital Twin assisted AI for sustainable Radio Access Networks, TU Wien

May 6, 2026, eess.SPJ/K move · Enter open · S save

Learning the Channel Gain from Anywhere to Anywhere via Cross-environment Transformer Estimators

Prasenjit Dhara, Daniel Romero

Dept. of Information and Communication Technology, University of Agder, Grimstad, Norway.

Radio Propagation Modeling

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Channel-gain maps provide the channel gain between any two locations in a geographical region. They find numerous applications, from resource allocation and interference control to path planning for autonomous vehicles. Channel-gain map estimation (CGME) is considerably more challenging than conventional radio map estimation (RME) because channel-gain maps are functions over a 6-dimensional input space. This calls for specialized methods, which currently rely on the (inaccurate) radio tomographic model or require a prohibitively large number of measurements since they do not exploit any spatial structure. This paper overcomes this issue by leveraging spatial patterns that channel-gain maps exhibit across environments, as dictated by the laws of physics and typical environmental characteristics (e.g. building materials and layouts). Adopting a metalearning perspective, a transformer-based estimator is proposed to implicitly learn this common structure from measurements collected in multiple environments. This enables CGME in new environments from significantly fewer measurements (five times less in our experiments). To maximize learning efficiency, the transformer is composed with a feature map that enforces the invariances of CGME, such as those following from reciprocity. Numerical experiments corroborate the merits of the proposed estimator relative to existing methods.

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Latest papers 36

Learning the Channel Gain from Anywhere to Anywhere via Cross-environment Transformer Estimators

Machine-Learning-Based Classification of Radio Frequency Building Loss

GeNeRT: A Physics-Informed Approach to Intelligent Wireless Channel Modeling via Generalizable Neural Ray Tracing

Sionna RT: Technical Report

EM-GANSim: Real-time and Accurate EM Simulation Using Conditional GANs for 3D Indoor Scenes

The Vienna 4G/5G Drive-Test Dataset