RADIO: Reinforcement Learning-Aided Deployment of Wi-Fi Routers in 5G Networks for Indoor Drone Orchestrating

In the near future, drones will play a key role in streamlining the delivery processes between businesses and their clients by utilizing high-accuracy localization. This study introduces a system that employs a 5G network, enhanced by multiple radio access technologies (RAT), to address the challenges of accurate positioning for drones. For indoor settings, where the 5G signal strength is not as robust as it is outdoors, we use Wireless Fidelity (Wi-Fi) routers, here referred to as Wi-5G TRPs, to enhance location accuracy. These points connect through the 5G infrastructure to relay information to the same location management function (LMF) that manages the cellular 5G network. The core of our research demonstrates how the placement of Wi-5G TRPs inside buildings significantly influences how accurately they can pinpoint a location. By developing an innovative deep reinforcement learning (DRL) framework, we tackle the complex challenge of determining the most effective arrangement of Wi-5G TRPs to achieve high-accuracy three-dimensional (3D) tracking of moving targets. Our findings indicate that the overall location accuracy is influenced by both the measurement errors and the spatial error distribution, known as geometric dilution of precision (GDOP). Our extensive tests confirm that inaccuracies are predominantly due to vertical measurements rather than horizontal ones. Lastly, by analyzing the outcomes of strategically placing the Wi-5G TRPs utilizing our proposed DRL-based solution, we can reduce the location estimation errors compared to arbitrary placements.