Energy Efficiency Maximization for Multi-UAV-IRS-Assisted Marine Vehicle Systems

• Published in: Journal of marine science and engineering Vol. 12; no. 10; p. 1761
• Main Authors: Zhang, Chaoyue, Lin, Bin, Li, Chao, Qi, Shuang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 04.10.2024
• Subjects: Algorithms; Approximation; Benchmarks; Communication; Computation; Computation offloading; Computing time; Convex analysis; Convexity; Deep learning; deep reinforcement learning; Edge computing; Efficiency; Energy; Energy consumption; Energy efficiency; Machine learning; Marine technology; marine vehicle systems; Methods; Mixed integer; Mobile computing; Multiagent systems; Nonlinear programming; Optimization; Power; Reconfigurable intelligent surfaces; Simulation; Surface vehicles; unmanned aerial vehicle; Unmanned aerial vehicles; unmanned surface vehicle; Unmanned vehicles; Vehicles; Workloads
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse12101761

Mobile edge computing is envisioned as a prospective technology for supporting time-sensitive and computation-intensive applications in marine vehicle systems. However, the offloading performance is highly impacted by the poor wireless channel. Recently, an Unmanned Aerial Vehicle (UAV) equipped with an Intelligent Reflecting Surface (IRS), i.e., UIRS, has drawn attention due to its capability to control wireless signals so as to improve the data rate. In this paper, we consider a multi-UIRS-assisted marine vehicle system where UIRSs are deployed to assist in the computation offloading of Unmanned Surface Vehicles (USVs). To improve energy efficiency, the optimization problem of the association relationships, computation resources of USVs, multi-UIRS phase shifts, and multi-UIRS trajectories is formulated. To solve the mixed-integer nonlinear programming problem, we decompose it into two layers and propose an integrated convex optimization and deep reinforcement learning algorithm to attain the near-optimal solution. Specifically, the inner layer solves the discrete variables by using the convex optimization based on Dinkelbach and relaxation methods, and the outer layer optimizes the continuous variables based on the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3). The numerical results demonstrate that the proposed algorithm can effectively improve the energy efficiency of the multi-UIRS-assisted marine vehicle system in comparison with the benchmarks.