# RadioNet: Transformer based Radio Map Prediction Model For Dense Urban Environments

arXiv: https://arxiv.org/abs/2105.07158 · HTML: https://mlsanity.com/paper/2105.07158/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2021.

## Built on by (most cited first, 15 of 15)

- [Dataset of Pathloss and ToA Radio Maps With Localization Application](https://mlsanity.com/paper/2212.11777/) (2022)
- [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)
- [Spatial Transformers for Radio Map Estimation](https://mlsanity.com/paper/2411.01211/) (2024)
- [Vision Transformers for Efficient Indoor Pathloss Radio Map Prediction](https://mlsanity.com/paper/2412.09507/) (2024)
- [Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction from Variable-Sized Maps](https://mlsanity.com/paper/2310.04570/) (2023)
- [Radio Propagation Modelling: To Differentiate or To Deep Learn, That Is The Question](https://mlsanity.com/paper/2509.19337/) (2025)
- [RadioDiff-FS: Physics-Informed Manifold Alignment in Few-Shot Diffusion Models for High-Fidelity Radio Map Construction](https://mlsanity.com/paper/2603.18865/) (2026)
- [RadioFlow: Efficient Radio Map Construction Framework with Flow Matching](https://mlsanity.com/paper/2510.09314/) (2025)
- [Channel Knowledge Map Construction via Physics-Inspired Diffusion Model Without Prior Observations](https://mlsanity.com/paper/2512.02757/) (2025)
- [Accelerating Regularized Attention Kernel Regression for Spectrum Cartography](https://mlsanity.com/paper/2604.25138/) (2026)
- [MARS: Radio Map Super-resolution and Reconstruction Method under Sparse Channel Measurements](https://mlsanity.com/paper/2506.04682/) (2025)
- [Fusion of Pervasive RF Data with Spatial Images via Vision Transformers for Enhanced Mapping in Smart Cities](https://mlsanity.com/paper/2508.03736/) (2025)
- [EA-RMENet -- Path Loss Prediction in Urban Environments using Deep Learning](https://mlsanity.com/paper/2607.16449/) (2026)
- [CityDeploy-Bench: Benchmarking Physics-Grounded Spatial Set Planning for Multi-Transmitter Network Deployment](https://mlsanity.com/paper/2610.11065/) (2026)

## Abstract (arXiv)

Radio Map Prediction (RMP), aiming at estimating coverage of radio wave, has been widely recognized as an enabling technology for improving radio spectrum efficiency. However, fast and reliable radio map prediction can be very challenging due to the complicated interaction between radio waves and the environment. In this paper, a novel Transformer based deep learning model termed as RadioNet is proposed for radio map prediction in urban scenarios. In addition, a novel Grid Embedding technique is proposed to substitute the original Position Embedding in Transformer to better anchor the relative position of the radiation source, destination and environment. The effectiveness of proposed method is verified on an urban radio wave propagation dataset. Compared with the SOTA model on RMP task, RadioNet reduces the validation loss by 27.3%, improves the prediction reliability from 90.9% to 98.9%. The prediction speed is increased by 4 orders of magnitude, when compared with ray-tracing based method. We believe that the proposed method will be beneficial to high-efficiency wireless communication, real-time radio visualization, and even high-speed image rendering.