# SymNet: A Multi-Task Network for Joint Radio Map Reconstruction and Transmitter Localization

arXiv: https://arxiv.org/abs/2608.00087 · HTML: https://mlsanity.com/paper/2608.00087/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 36 references that are in the map.
- First posted on arXiv 30 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [Transformers without Normalization](https://mlsanity.com/paper/2503.10622/) (2025)
- [Deep Completion Autoencoders for Radio Map Estimation](https://mlsanity.com/paper/2005.05964/) (2020)
- [A Scalable and Generalizable Pathloss Map Prediction](https://mlsanity.com/paper/2312.03950/) (2023)
- [Spatial Signal Strength Prediction using 3D Maps and Deep Learning](https://mlsanity.com/paper/2011.03597/) (2020)
- [Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN](https://mlsanity.com/paper/2402.02729/) (2024)
- [ACT-GAN: Radio map construction based on generative adversarial networks with ACT blocks](https://mlsanity.com/paper/2401.08976/) (2024)
- [An empirical study on using CNNs for fast radio signal prediction](https://mlsanity.com/paper/2006.09245/) (2020)

## Abstract (arXiv)

Accurately predicting directional radio maps is essential for wireless applications, yet prior approaches primarily focus on omnidirectional signals and typically treat transmitter localization and signal map reconstruction as separate tasks. In omnidirectional settings, predicting the maximum signal location often coincides with the transmitter position, which limits the need for explicit joint modeling. However, in directional propagation where angular effects, reflections, and building occlusions play critical roles, this assumption no longer holds. To address this gap, we propose SymNet, a unified framework that jointly predicts directional radio maps and transmitter locations from sparse signal measurements. SymNet incorporates a prediction head for transmitter localization alongside radio map reconstruction, enabling simultaneous learning of both tasks. This joint formulation leverages their complementary information and leads to consistent improvements over treating them separately. Experiments on challenging directional scenarios demonstrate that SymNet outperforms state-of-the-art baselines, achieving superior accuracy in both radio map reconstruction and transmitter localization.