A deep reinforcement learning-based UAV-smallcell system for mobile terminals geolocalization in disaster scenarios

Abstract

Deep reinforcement learning (DRL) techniques have the potential to significantly improve the ability of Unmanned Aerial Vehicles (UAVs) for mobile device localization in disaster scenarios by optimizing flight paths and enhancing signal detection accuracy using Reference Signal Received Power (RSRP) measurements. DRL allows UAVs to learn optimal navigation strategies autonomously in dynamic and complex environments, leading to more efficient and accurate localization of mobile devices. The integration between UAVs and 4G/5G technology allows for more accurate and timely localization of mobile devices under the rubble, thereby improving the overall effectiveness of the system. Smallcells, low-power cellular base stations, are used to enhance coverage and capacity. In this study, we propose a DRL-based UAV-Smallcell system that can quickly and efficiently localize devices in large disaster areas. The performance of the proposed system is evaluated through an extensive simulation campaign to demonstrate that our approach significantly improves the effectiveness of mobile device localization compared to other state-of-the-art approaches.