Meeting Stringent QoS Requirements in AAV-Assisted Networks: Resource Allocation and AAVs Positioning

Abstract

Providing quick and reliable emergency communication in situations of natural disasters or unforeseen incidents may be crucial. In such situations, traditional communication infrastructure, such as ground-based wireless base stations, may become temporarily damaged or unavailable to support emergency teleoperations. Considered a promising solution, autonomous aerial vehicles (AAVs) can be deployed as flying base stations or relays to provide fast and reliable communication between physicians and remote robots in both uplink and downlink directions, while meeting strict transmission requirements. This paper addresses the joint optimization problem of AAV positioning and resource allocation in AAV-assisted wireless networks to minimize the number of deployed AAVs, all while satisfying stringent transmission quality demands. The formulated problem is a non-convex mixed-integer programming problem, which we prove to be $\mathcal {NP}$ -hard. We first develop efficient greedy and metaheuristic genetic algorithms. Then, we propose an efficient centralized deep reinforcement learning solution based on the deep deterministic policy gradient (DDPG), where the agent learns optimal AAV positions and resource allocation. Simulation results demonstrate that the greedy solution closely matches the performance of both the genetic and deep reinforcement learning approaches, with a significant reduction in computational complexity. Furthermore, the results highlight the effectiveness of the deep reinforcement learning solution in minimizing the number of AAVs required to fully satisfy the transmission requirements of all users in both uplink and downlink directions.