Energy Efficiency Optimization of IRS and UAV-Assisted Wireless Powered Edge Networks

Abstract

With the surge in the number of Internet of Things (IoT) devices and latency-sensitive services such as smart cities and smart factories, Next Generation Multiple Access (NGMA) technologies (e.g., Intelligent Reflecting Surface (IRS) and millimeter wave), which can efficiently process a large number of user accesses and low-latency services, have gained much attention. Among them, due to the ability to optimize wireless channels and improve data and energy transmission efficiency, IRS has been applied to Unmanned Aerial Vehicle (UAV)-assisted wireless powered edge networks. However, scheduling multi-dimensional resources in multi-UAVs, multi-IRSs and multi-devices coexistence scenarios always leads to a large number of highly coupled variables and complicated optimization problems. To address the above challenges, we propose a multi-agent Deep Reinforcement Learning (DRL)-based distributed scheduling algorithm for IRS and UAV-assisted wireless powered edge networks to jointly optimize charging time, phase shift matrices of IRSs, association scheduling of UAVs and UAV trajectories. First, to satisfy UAV time constraints and device energy consumption constraints, we formulate an energy efficiency maximization problem and represent it as a corresponding Markov Decision Process (MDP). Then, we propose a lightweight scheduling algorithm based on multi-agent DRL with value function decomposition. Finally, experiments show that the proposed algorithm has significant advantages in terms of algorithm convergence and system energy efficiency.