# Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN

arXiv: https://arxiv.org/abs/2402.02729 · HTML: https://mlsanity.com/paper/2402.02729/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [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)
- [Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI](https://mlsanity.com/paper/2207.00969/) (2022)
- [Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography](https://mlsanity.com/paper/2202.03269/) (2022)
- [Radio Map Based 3D Path Planning for Cellular-Connected UAV](https://mlsanity.com/paper/1912.00021/) (2019)
- [UAV-aided Radio Map Construction Exploiting Environment Semantics](https://mlsanity.com/paper/2107.10574/) (2021)
- [Propagation Map Reconstruction via Interpolation Assisted Matrix Completion](https://mlsanity.com/paper/2207.13473/) (2022)
- [Low-Latency Cooperative Spectrum Sensing via Truncated Vertical Federated Learning](https://mlsanity.com/paper/2208.03694/) (2022)

## Built on by (most cited first, 7 of 7)

- [RadioDiff-Inverse: Diffusion Enhanced Bayesian Inverse Estimation for ISAC Radio Map Construction](https://mlsanity.com/paper/2504.14298/) (2025)
- [RadioMamba: Breaking the Accuracy-Efficiency Trade-off in Radio Map Construction via a Hybrid Mamba-UNet](https://mlsanity.com/paper/2508.09140/) (2025)
- [Two-Stage Radio Map Construction with Real Environments and Sparse Measurements](https://mlsanity.com/paper/2410.18092/) (2024)
- [3D Dynamic Radio Map Prediction Using Vision Transformers for Low-Altitude Wireless Networks](https://mlsanity.com/paper/2511.19019/) (2025)
- [RadioGen3D: 3D Radio Map Generation via Adversarial Learning on Large-Scale Synthetic Data](https://mlsanity.com/paper/2602.18744/) (2026)
- [RadioTrace: Transmitter-Aware Diffusion for Radio Map Estimation without Deployment-Time Fine-Tuning](https://mlsanity.com/paper/2607.20909/) (2026)
- [SymNet: A Multi-Task Network for Joint Radio Map Reconstruction and Transmitter Localization](https://mlsanity.com/paper/2608.00087/) (2026)

## Abstract (arXiv)

In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications. This calls for fast and accurate estimation on the distribution of the radio resources, which is usually represented by the spatial signal power strength over the geographical environment, known as a radio map. In this paper, we present a cooperative radio map estimation (CRME) approach enabled by the generative adversarial network (GAN), called as GAN-CRME, which features fast and accurate radio map estimation without the transmitters' information. The radio map is inferred by exploiting the interaction between distributed received signal strength (RSS) measurements at mobile users and the geographical map using a deep neural network estimator, resulting in low data-acquisition cost and computational complexity. Moreover, a GAN-based learning algorithm is proposed to boost the inference capability of the deep neural network estimator by exploiting the power of generative AI. Simulation results showcase that the proposed GAN-CRME is even capable of coarse error-correction when the geographical map information is inaccurate.