# ACT-GAN: Radio map construction based on generative adversarial networks with ACT blocks

arXiv: https://arxiv.org/abs/2401.08976 · HTML: https://mlsanity.com/paper/2401.08976/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [Aggregated Contextual Transformations for High-Resolution Image Inpainting](https://mlsanity.com/paper/2104.01431/) (2021)
- [RME-GAN: A Learning Framework for Radio Map Estimation based on Conditional Generative Adversarial Network](https://mlsanity.com/paper/2212.12817/) (2022)
- [Deep Completion Autoencoders for Radio Map Estimation](https://mlsanity.com/paper/2005.05964/) (2020)
- [Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.07574/) (2020)
- [Radio Map Based 3D Path Planning for Cellular-Connected UAV](https://mlsanity.com/paper/1912.00021/) (2019)
- [Learning Power Spectrum Maps from Quantized Power Measurements](https://mlsanity.com/paper/1606.02679/) (2016)
- [Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs](https://mlsanity.com/paper/2201.04125/) (2022)
- [Non-cooperative Aerial Base Station Placement via Stochastic Optimization](https://mlsanity.com/paper/1905.03988/) (2019)

## Built on by (most cited first, 6 of 6)

- [Generative AI on SpectrumNet: An Open Benchmark of Multiband 3D Radio Maps](https://mlsanity.com/paper/2408.15252/) (2024)
- [An Indoor Radio Mapping Dataset Combining 3D Point Clouds and RSSI](https://mlsanity.com/paper/2511.00494/) (2025)
- [Learned Elevation Models as a Lightweight Alternative to LiDAR for Radio Environment Map Estimation](https://mlsanity.com/paper/2604.05520/) (2026)
- [Scene-Conditioned PINN-GNN for Multipath RF Maps: Cross-Scene Generation and In-Scene Completion](https://mlsanity.com/paper/2607.01777/) (2026)
- [SymNet: A Multi-Task Network for Joint Radio Map Reconstruction and Transmitter Localization](https://mlsanity.com/paper/2608.00087/) (2026)
- [SymNetPro: LOS-Aware Directional Multi-Transmitter Localization from Sparse Radio Observations](https://mlsanity.com/paper/2609.33964/) (2026)

## Abstract (arXiv)

The radio map, serving as a visual representation of electromagnetic spatial characteristics, plays a pivotal role in assessment of wireless communication networks and radio monitoring coverage. Addressing the issue of low accuracy existing in the current radio map construction, this paper presents a novel radio map construction method based on generative adversarial network (GAN) in which the Aggregated Contextual-Transformation (AOT) block, Convolutional Block Attention Module (CBAM), and Transposed Convolution (T-Conv) block are applied to the generator, and we name it as ACT-GAN. It significantly improves the reconstruction accuracy and local texture of the radio maps. The performance of ACT-GAN across three different scenarios is demonstrated. Experiment results reveal that in the scenario without sparse discrete observations, the proposed method reduces the root mean square error (RMSE) by 14.6% in comparison to the state-of-the-art models. In the scenario with sparse discrete observations, the RMSE is diminished by 13.2%. Furthermore, the predictive results of the proposed model show a more lucid representation of electromagnetic spatial field distribution. To verify the universality of this model in radio map construction tasks, the scenario of unknown radio emission source is investigated. The results indicate that the proposed model is robust radio map construction and accurate in predicting the location of the emission source.