SymNet：用于联合无线电地图重建与发射机定位的多任务网络
SymNet: A Multi-Task Network for Joint Radio Map Reconstruction and Transmitter Localization
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中文总结 AI 辅助
SymNet是一个统一框架，通过集成发射机定位预测头与无线电地图重建模块，联合完成定向无线电地图重建和发射机定位任务，在定向场景下性能优于现有基线方法。
中文摘要 AI 辅助
准确预测定向无线电地图对无线应用至关重要，但现有方法主要关注全向信号，且通常将发射机定位与信号地图重建视为独立任务。在全向场景中，预测最大信号位置往往与发射机位置重合，降低了显式联合建模的必要性。但在定向传播场景中，角度效应、反射和建筑遮挡起关键作用，上述假设不再成立。为解决这一差距，我们提出SymNet，这一统一框架可从稀疏信号测量中联合预测定向无线电地图与发射机位置。SymNet集成了发射机定位预测头与无线电地图重建模块，支持两个任务的同步学习。该联合公式利用了任务间的互补信息，相比单独处理两个任务实现了一致性能提升。在具有挑战性的定向场景实验中，SymNet优于现有最先进的基线方法，在无线电地图重建和发射机定位两方面均实现了更优精度。
英文摘要
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.
发表机构
- University of North Texas（北得克萨斯大学）
机构由 AI 辅助整理，请以论文原文为准。
