# Radio Map Estimation: Empirical Validation and Analysis

arXiv: https://arxiv.org/abs/2310.11036 · HTML: https://mlsanity.com/paper/2310.11036/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 47 references that are in the map.
- First posted on arXiv 17 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [Deep Completion Autoencoders for Radio Map Estimation](https://mlsanity.com/paper/2005.05964/) (2020)
- [Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography](https://mlsanity.com/paper/2202.03269/) (2022)
- [Learning Power Spectrum Maps from Quantized Power Measurements](https://mlsanity.com/paper/1606.02679/) (2016)
- [Spatial Signal Strength Prediction using 3D Maps and Deep Learning](https://mlsanity.com/paper/2011.03597/) (2020)
- [Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs](https://mlsanity.com/paper/2201.04125/) (2022)
- [Transfer Learning-Based Received Power Prediction with Ray-tracing Simulation and Small Amount of Measurement Data](https://mlsanity.com/paper/2005.00833/) (2020)

## Built on by (most cited first, 1 of 1)

- [Spatial Transformers for Radio Map Estimation](https://mlsanity.com/paper/2411.01211/) (2024)

## Abstract (arXiv)

Radio maps provide metrics such as the received signal strength at every location in a geographical region of interest. Extensive research has been carried out in this context, but it relies almost exclusively on synthetic-data experiments. Thus, the practical aspects of the radio map estimation (RME) problem as well as the performance of existing estimators in the real world remain unknown. To fill this gap end, this paper puts forth the first comprehensive, rigorous, and reproducible study of RME with real data. The main contributions include (C1) an assessment of the viability of RME based on the estimation error that can be achieved, (C2) the analysis of the main phenomena and trade-offs involved in RME, including the experimental verification of theoretical findings in the literature, and (C3) a thorough evaluation of a wide range of estimators on realworld data. Remarkably, this reveals that the performance gain of existing deep estimators in their pure form may not compensate for their complexity. A simple enhancement (C4) is proposed to alleviate this issue. The vast amount of data collected for this study is published along with the developed simulator to enable research on new schemes, hopefully bringing RME one step closer to practical deployment.