aiqbench analysis
SymNet introduces a novel neural architecture capable of jointly reconstructing directional radio maps and localizing transmitters, addressing limitations in prior omnidirectional models. By leveraging complementary information between these tasks within a unified deep learning framework, the system achieves superior accuracy under complex propagation conditions involving reflections and occlusions. This advancement represents significant progress in applying machine learning to advanced wireless signal processing challenges.
SymNet: A Multi-Task Network for Joint Radio Map Reconstruction and Transmitter Localization
arXiv:2608.00087v1 Announce Type: cross Abstract: 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.
Abstract: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.
Signal Processing (eess.SP); Artificial Intelligence (cs.AI)
arXiv:2608.00087 [eess.SP]
(or arXiv:2608.00087v1 [eess.SP] for this version)
https://doi.org/10.48550/arXiv.2608.00087 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1109/WACV61042.2026.00023 Focus to learn more DOI(s) linking to related resources
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