Deep Reinforcement Learning Based Joint Edge Resource Management in Maritime Network

• 作 者: Fangmin Xu Fan Yang Chenglin Zhao Sheng Wu
• 机 构: School of Information and Communication Engineering,Beijing University of Posts and Telecommunicatio
• 出 处: 中国通信（英文版）
• 发表日期: 2020年5期
• 关键词: maritime network edge comput-ing computation offload computation laten-cy reinfo

Due to the rapid development of the maritime networks,there has been a growing demand for computation-intensive applica-tions which have various energy consumption,transmission bandwidth and computing la-tency requirements.Mobile edge computing(MEC)can efficiently minimize computational latency by offloading computation tasks by the terrestrial access network.In this work,we in-troduce a space-air-ground-sea integrated net-work architecture with edge and cloud com-puting components to provide flexible hybrid computing service for maritime service.In the integrated network,satellites and unmanned aerial vehicles(UAVs)provide the users with edge computing services and network access.Based on the architecture,the joint commu-nication and computation resource allocation problem is modelled as a complex decision process,and a deep reinforcement leing based solution is designed to solve the com-plex optimization problem.Finally,numerical results verify that the proposed approach can improve the communication and computing efficiency greatly.