Deep Reinforcement Learning-Based UAV Data Collection and Offloading in NOMA-Enabled Marine IoT Systems. (12th May 2022)

• Record Type: Journal Article
• Authors: Dai, Yanpeng; Liang, Ziyi; Lyu, Ling; Lin, Bin
• Other Names: Ye Qiang Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2022(2022)
• Publication Date: 2022-05-12
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2022/8805416
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : The rapid growth of maritime wireless communication demand and the complex offshore wireless communication environment have brought challenges to ensure the real-time and reliability of data transmission in the marine Internet of Things (MIoT). Unmanned aerial vehicles (UAVs) have great advantages in enhancing coverage and channel quality. Hence, we investigate a UAV-assisted data collection and data offloading system based on nonorthogonal multiple access (NOMA) technology in this paper. We jointly optimize the buoy-UAV association relationship, transmit powers, and the UAV trajectory to minimize the total mission completion time while ensuring data transmission requirements. We first propose a UAV trajectory optimization algorithm based on deep reinforcement learning (DRL). Then, we design a heuristic algorithm to effectively solve the subproblem of power control and the association relationship. Finally, we propose a joint optimization scheme to solve the minimization problem. Simulation results show the effectiveness of the proposed scheme.