5G Network on Wings: A Deep Reinforcement Learning Approach to the UAV-based Integrated Access and Backhaul

TL;DR

This paper proposes a deep reinforcement learning method to dynamically control UAV-based base stations in disaster scenarios, optimizing their 3D placement to ensure reliable communication for emergency services.

Contribution

It introduces a novel DRL algorithm for joint 3D placement of multiple UAV-BSs in MC scenarios, enhancing coverage and reliability during emergencies.

Findings

The DRL algorithm effectively supports autonomous UAV navigation.

It improves user throughput in disaster scenarios.

It reduces drop rates for emergency communications.

Abstract

Fast and reliable wireless communication has become a critical demand in human life. In the case of mission-critical (MC) scenarios, for instance, when natural disasters strike, providing ubiquitous connectivity becomes challenging by using traditional wireless networks. In this context, unmanned aerial vehicle (UAV) based aerial networks offer a promising alternative for fast, flexible, and reliable wireless communications. Due to unique characteristics such as mobility, flexible deployment, and rapid reconfiguration, drones can readily change location dynamically to provide on-demand communications to users on the ground in emergency scenarios. As a result, the usage of UAV base stations (UAV-BSs) has been considered an appropriate approach for providing rapid connection in MC scenarios. In this paper, we study how to control multiple UAV-BSs in both static and dynamic environments. We use a system-level simulator to model an MC scenario in which a macro BS of a cellular network is out of service and multiple UAV-BSs are deployed using integrated access and backhaul (IAB) technology to provide coverage for users in the disaster area. With the data collected from the system-level simulation, a deep reinforcement learning algorithm is developed to jointly optimize the three-dimensional placement of these multiple UAV-BSs, which adapt their 3-D locations to the on-ground user movement. The evaluation results show that the proposed algorithm can support the autonomous navigation of the UAV-BSs to meet the MC service requirements in terms of user throughput and drop rate.