Latency minimization in IRS-UAV assisted WPT-MEC systems: An ID-AOPDDQN-based trajectory and phase shift optimization approach

Abstract

Intelligent Reflectors (IRS) assisted Wireless Power Transmission and Mobile Edge Computing (WPT-MEC) are considered as solutions for implementing sustainable Internet of Things (IoT) networks that can effectively improve network performance and reduce data transmission latency. There are still challenges such as flexible deployment of IRSs and multivariate joint optimization remain. In this paper, we study the task offloading problem of unmanned aerial vehicles (UAVs) carrying IRSs (IRS-UAV) assisted WPT-MEC, which exploits the flexibility of the UAV to dynamically improve the energy harvesting and task offloading channel transmission between ground equipment (GD) and access point (AP). In this system, we consider the association relationship between the hovering points (HPs) of the IRS-UAV and the GDs, the phase shift of the IRS-UAV, the flight trajectory, the beamforming vector, and the offloading decision and transmit power of the GDs to minimize latency performance under the constraint of energy consumption. To solve this multivariable non-convex problem, we propose an ID-AOPDDQN (ISODATA clustering, successive convex approximation and parametric Dueling deep $Q$-network) algorithm. At first, we cluster the HPs and the association relationship between GDs and HPs through an efficient load balancing algorithm (ISODATA), so as to cover all GDs to the maximum extent. Secondly, on the basis of clustering, we divide the target problem into two sub-problems. For the first sub-problem, Successive Convex Approximation (SCA) is used to transform it into a convex problem, and the phase shift and beamforming vectors of radio energy transmission are optimized alternately. For the second subproblem, we design the PDDQN (a combination of DDPG and Dueling DQN) algorithm to process the mixed space based on the first problem of the solution, where DDPG processes continuous motion (such as phase shift) and Dueling DQN processes discrete action (such as offloading decisions). Simulation results show that the ID-AOPDDQN algorithm significantly improves the performance of the system in latency.