In this paper, we address the problem of Received Signal Strength map reconstruction based on location-dependent radio measurements and utilizing side knowledge about the local region; for example, city plan, terrain height, gateway position. Depending on the quantity of such prior side information, we employ Neural Architecture Search to find an optimized Neural Network model with the best architecture for each of the supposed settings. We demonstrate that using additional side information enhances the final accuracy of the Received Signal Strength map reconstruction on three datasets that correspond to three major cities, particularly in sub-areas near the gateways where larger variations of the average received signal power are typically observed.

翻译：本文研究了基于位置相关无线电测量并利用局部区域辅助信息（例如城市规划、地形高度、网关位置）的接收信号强度地图重建问题。根据此类先验辅助信息的数据量，我们采用神经架构搜索为每种假设场景寻找具有最佳架构的优化神经网络模型。我们证明，在对应于三个主要城市的三组数据集上，使用额外辅助信息能够提升接收信号强度地图重建的最终精度，尤其是在网关附近子区域——该区域通常观测到平均接收信号功率的较大波动。