# Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography

arXiv: https://arxiv.org/abs/2202.03269 · HTML: https://mlsanity.com/paper/2202.03269/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2022.

## Built on by (most cited first, 30 of 31)

- [A Scalable and Generalizable Pathloss Map Prediction](https://mlsanity.com/paper/2312.03950/) (2023)
- [RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks](https://mlsanity.com/paper/2403.16397/) (2024)
- [Generative AI on SpectrumNet: An Open Benchmark of Multiband 3D Radio Maps](https://mlsanity.com/paper/2408.15252/) (2024)
- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [Physics-informed Generalizable Wireless Channel Modeling with Segmentation and Deep Learning: Fundamentals, Methodologies, and Challenges](https://mlsanity.com/paper/2401.01288/) (2024)
- [Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN](https://mlsanity.com/paper/2402.02729/) (2024)
- [Spatial Transformers for Radio Map Estimation](https://mlsanity.com/paper/2411.01211/) (2024)
- [RMTransformer: Accurate Radio Map Construction and Coverage Prediction](https://mlsanity.com/paper/2501.05190/) (2025)
- [WiFi-Diffusion: Achieving Fine-Grained WiFi Radio Map Estimation With Ultra-Low Sampling Rate by Diffusion Models](https://mlsanity.com/paper/2503.12004/) (2025)
- [Radio Map Estimation via Latent Domain Plug-and-Play Denoising](https://mlsanity.com/paper/2501.13472/) (2025)
- [RadioFormer: A Multiple-Granularity Radio Map Estimation Transformer with 1textpertenthousand Spatial Sampling](https://mlsanity.com/paper/2504.19161/) (2025)
- [RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation](https://mlsanity.com/paper/2506.08418/) (2025)
- [ProSpire: Proactive Spatial Prediction of Radio Environment Using Deep Learning](https://mlsanity.com/paper/2308.10193/) (2023)
- [Radio Map Estimation: Empirical Validation and Analysis](https://mlsanity.com/paper/2310.11036/) (2023)
- [Bayesian-Driven Graph Reasoning for Active Radio Map Construction](https://mlsanity.com/paper/2508.09142/) (2025)
- [RadioFlow: Efficient Radio Map Construction Framework with Flow Matching](https://mlsanity.com/paper/2510.09314/) (2025)
- [3D Dynamic Radio Map Prediction Using Vision Transformers for Low-Altitude Wireless Networks](https://mlsanity.com/paper/2511.19019/) (2025)
- [Channel Knowledge Map Construction via Physics-Inspired Diffusion Model Without Prior Observations](https://mlsanity.com/paper/2512.02757/) (2025)
- [Blind Radio Mapping via Spatially Regularized Bayesian Trajectory Inference](https://mlsanity.com/paper/2512.13701/) (2025)
- [FM-RME: Foundation Model Empowered Radio Map Estimation](https://mlsanity.com/paper/2602.22231/) (2026)
- [Accelerating Regularized Attention Kernel Regression for Spectrum Cartography](https://mlsanity.com/paper/2604.25138/) (2026)
- [RADIANCE: Radio-Frequency Adversarial Deep-learning Inference for Automated Network Coverage Estimation](https://mlsanity.com/paper/2308.10584/) (2023)
- [RadioMapMotion: A Dataset and Baseline for Proactive Spatio-Temporal Radio Environment Prediction](https://mlsanity.com/paper/2511.17526/) (2025)
- [ReVeal-MT: A Physics-Informed Neural Network for Multi-Transmitter Radio Environment Mapping](https://mlsanity.com/paper/2512.04100/) (2025)
- [RadioGen3D: 3D Radio Map Generation via Adversarial Learning on Large-Scale Synthetic Data](https://mlsanity.com/paper/2602.18744/) (2026)
- [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)
- [Learning the Channel Gain from Anywhere to Anywhere via Cross-environment Transformer Estimators](https://mlsanity.com/paper/2605.08211/) (2026)
- [RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling](https://mlsanity.com/paper/2605.29538/) (2026)
- [Diffusion Models for Radio Map Estimation: Theoretical Performance Analysis and Sampling Rate Guideline](https://mlsanity.com/paper/2606.25310/) (2026)
- [RadioTrace: Transmitter-Aware Diffusion for Radio Map Estimation without Deployment-Time Fine-Tuning](https://mlsanity.com/paper/2607.20909/) (2026)

## Abstract (arXiv)

Radio maps characterize quantities of interest in radio communication environments, such as the received signal strength and channel attenuation, at every point of a geographical region. Radio map estimation typically entails interpolative inference based on spatially distributed measurements. In this tutorial article, after presenting some representative applications of radio maps, the most prominent radio map estimation methods are discussed. Starting from simple regression, the exposition gradually delves into more sophisticated algorithms, eventually touching upon state-of-the-art techniques. To gain insight into this versatile toolkit, illustrative toy examples will also be presented.