RAPID: Reinforcement Learning-Aided Femtocell Placement for Indoor Drone Localization

Mobile networks are swiftly advancing to accommodate the burgeoning spectrum of applications. The architecture of 5G networks integrates the principle of network slices, logically isolated end-to-end segments tailored to offer specific services. In this architectural schema, drones have emerged as a significant service category. Achieving the successful deployment of drone networks is heavily contingent upon the ability to accurately localize them in a three-dimensional (3D) setting, beyond the critical requirement for tight latency control. Transitioning from 4G to 5G, these networks are characterized by their operation at elevated frequency spectrums and more densely packed deployment configurations. Within such environments, the task of ensuring precise indoor localization poses a significant challenge, primarily due to the distinctive signal behavior at higher frequencies. To achieve this goal, we propose the RAPID framework, utilizing foundational principles from the third-generation partnership project (3GPP) to design a radio access network (RAN) that includes 5G femtocells. This architecture aims to shift positioning responsibilities from outdoor base stations (BSs) to improve indoor localization performance. Our study's principal contribution is the demonstration of how the spatial distribution of 5G femtocells significantly influences the accuracy of drone positioning. To address the challenges inherent in femtocell deployment, we develop an innovative optimization framework coupled with a deep reinforcement learning (DRL) strategy, aimed at solving the NP-hard problem. Our findings reveal that adopting our DRL-based placement strategy significantly improves positioning accuracy compared to regular arbitrary deployment approaches.