Three-Dimensional Trajectory and Resource Allocation Optimization in Multi-Unmanned Aerial Vehicle Multicast System: A Multi-Agent Reinforcement Learning Method

Abstract

Unmanned aerial vehicles (UAVs) are able to act as movable aerial base stations to enhance wireless coverage for edge users with poor ground communication quality. However, in urban environments, the link between UAVs and ground users can be blocked by obstacles, especially when complicated terrestrial infrastructures increase the probability of non-line-of-sight (NLoS) links. In this paper, in order to improve the average throughput, we propose a multi-UAV multicast system, where a multi-agent reinforcement learning method is utilized to help UAVs determine the optimal altitude and trajectory. Intelligent reflective surfaces (IRSs) are also employed to reflect signals to solve the blocking problem. Furthermore, since the UAV’s onboard power is limited, this paper aims to minimize the UAVs’ energy consumption and maximize the transmission rate for edge users by jointly optimizing the UAVs’ 3D trajectory and transmit power. Firstly, we deduce the channel capacity of ground users in different multicast groups. Subsequently, the K-medoids algorithm is utilized for the multicast grouping problem of edge users based on transmission rate requirements. Then, we employ the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to learn an optimal solution and eliminate the non-stationarity of multi-agent training. Finally, the simulation results show that the proposed system can increase the average throughput by 14% approximately compared to the non-grouping system, and the MADDPG algorithm can achieve a 20% improvement in reducing the energy consumption of UAVs compared to traditional deep reinforcement learning (DRL) methods.