# Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach

arXiv: https://arxiv.org/abs/2106.12556 · HTML: https://mlsanity.com/paper/2106.12556/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 97 references that are in the map.
- First posted on arXiv 23 Jun 2021.

## Builds on (most cited first, 23 of 23)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Integral Human Pose Regression](https://mlsanity.com/paper/1711.08229/) (2017)
- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [Numerical Coordinate Regression with Convolutional Neural Networks](https://mlsanity.com/paper/1801.07372/) (2018)
- [Human Pose Regression by Combining Indirect Part Detection and Contextual Information](https://mlsanity.com/paper/1710.02322/) (2017)
- [Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis](https://mlsanity.com/paper/1906.06007/) (2019)
- [Millimeter Wave Base Stations with Cameras: Vision Aided Beam and Blockage Prediction](https://mlsanity.com/paper/1911.06255/) (2019)
- [Adaptive Neural Signal Detection for Massive MIMO](https://mlsanity.com/paper/1906.04610/) (2019)
- [ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection](https://mlsanity.com/paper/1905.10750/) (2019)
- [Laplace Landmark Localization](https://mlsanity.com/paper/1903.11633/) (2019)
- [A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals](https://mlsanity.com/paper/2012.11171/) (2020)
- [Machine Learning for Reliable mmWave Systems: Blockage Prediction and Proactive Handoff](https://mlsanity.com/paper/1807.02723/) (2018)
- [Proof of Witness Presence: Blockchain Consensus for Augmented Democracy in Smart Cities](https://mlsanity.com/paper/1907.00498/) (2019)
- [Deep Energy Autoencoder for Noncoherent Multicarrier MU-SIMO Systems](https://mlsanity.com/paper/2002.08710/) (2020)
- [RF Fingerprinting and Deep Learning Assisted UE Positioning in 5G](https://mlsanity.com/paper/2001.00977/) (2020)
- [Deep Learning-Aided Tabu Search Detection for Large MIMO Systems](https://mlsanity.com/paper/1909.01683/) (2019)
- [Site-specific online compressive beam codebook learning in mmWave vehicular communication](https://mlsanity.com/paper/2005.05485/) (2020)
- [Intelligent User Association for Symbiotic Radio Networks using Deep Reinforcement Learning](https://mlsanity.com/paper/1905.04041/) (2019)
- [Maximum correntropy criterion for robust TOA-based localization in NLOS environments](https://mlsanity.com/paper/2009.06032/) (2020)
- [Probabilistic Time of Arrival Localization](https://mlsanity.com/paper/1910.06569/) (2019)

## Built on by (most cited first, 12 of 12)

- [Dataset of Pathloss and ToA Radio Maps With Localization Application](https://mlsanity.com/paper/2212.11777/) (2022)
- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [Radio Map Prediction from Aerial Images and Application to Coverage Optimization](https://mlsanity.com/paper/2410.17264/) (2024)
- [LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning](https://mlsanity.com/paper/2202.00738/) (2022)
- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [Empowering Wireless Network Applications with Deep Learning-based Radio Propagation Models](https://mlsanity.com/paper/2408.12193/) (2024)
- [U6G XL-MIMO Radiomap Prediction: Multi-Config Dataset and Beam Map Approach](https://mlsanity.com/paper/2603.06401/) (2026)
- [Data-and-Semantic Dual-Driven Spectrum Map Construction for 6G Spectrum Management](https://mlsanity.com/paper/2501.12853/) (2025)
- [Blind Radio Mapping via Spatially Regularized Bayesian Trajectory Inference](https://mlsanity.com/paper/2512.13701/) (2025)
- [On the Effective Usage of Priors in RSS-based Localization](https://mlsanity.com/paper/2212.00728/) (2022)
- [PathFinder: Advancing Path Loss Prediction for Single-to-Multi-Transmitter Scenario](https://mlsanity.com/paper/2512.14150/) (2025)
- [When Simultaneous Localization and Mapping Meets Wireless Communications: A Survey](https://mlsanity.com/paper/2602.06995/) (2026)

## Abstract (arXiv)

Global Navigation Satellite Systems typically perform poorly in urban environments, where the likelihood of line-of-sight conditions between devices and satellites is low. Therefore, alternative location methods are required to achieve good accuracy. We present LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task, which is able to estimate the position of a user from the received signal strength (RSS) of a small number of Base Stations (BS). Using estimations of pathloss radio maps of the BSs and the RSS measurements of the users to be localized, LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps. The proposed method does not require generating RSS fingerprints of each specific area where the localization task is performed and is suitable for real-time applications. Moreover, two novel datasets that allow for numerical evaluations of RSS and ToA methods in realistic urban environments are presented and made publicly available for the research community. By using these datasets, we also provide a fair comparison of state-of-the-art RSS and ToA-based methods in the dense urban scenario and show numerically that LocUNet outperforms all the compared methods.