An optimization algorithm based on deep reinforcement learning for maritime MEC task offloading and resource allocation

XU Yanli; ZHOU Zirui

doi:10.11959/j.issn.1000-0801.2025227

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An optimization algorithm based on deep reinforcement learning for maritime MEC task offloading and resource allocation

Topic: Technologies for Underwater Communication Networks | 更新时间：2025-11-06

- An optimization algorithm based on deep reinforcement learning for maritime MEC task offloading and resource allocation
- Telecommunications Science Vol. 41, Issue 10, Pages: 102-121(2025)
- 作者机构：
  
  上海海事大学信息工程学院，上海 201306
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62271303);The Natural Science Foundation of Shanghai(20ZR1423200)
- DOI：10.11959/j.issn.1000-0801.2025227
  CLC： TP393;TN92
- Received：30 June 2025，
  
  Revised：2025-09-25，
  
  Accepted：28 September 2025，
  
  Published：20 October 2025
- 稿件说明：
移动端阅览
徐艳丽,周子睿.一种基于深度强化学习的海上MEC任务卸载和资源分配优化算法[J].电信科学,2025,41(10):102-121.

XU Yanli,ZHOU Zirui.An optimization algorithm based on deep reinforcement learning for maritime MEC task offloading and resource allocation[J].Telecommunications Science,2025,41(10):102-121.
徐艳丽,周子睿.一种基于深度强化学习的海上MEC任务卸载和资源分配优化算法[J].电信科学,2025,41(10):102-121. DOI： 10.11959/j.issn.1000-0801.2025227.

XU Yanli,ZHOU Zirui.An optimization algorithm based on deep reinforcement learning for maritime MEC task offloading and resource allocation[J].Telecommunications Science,2025,41(10):102-121. DOI： 10.11959/j.issn.1000-0801.2025227.

摘要

移动边缘计算被认为是减少回程压力和提高服务质量的重要解决方案，但现有的资源管理策略在高动态的海洋环境下适应性较差。为解决该问题，提出了一种基于改进双延迟深度确定性策略梯度的任务卸载和资源分配算法。该算法可系统地协调无人机部署与边缘节点资源，联合优化通信资源分配和计算任务调度，同时考虑海洋边缘节点的能量限制和海洋网络的时变特性。具体而言，问题被表述为一个非凸优化框架，目标是在用户设备严格的服务质量要求下最大化吞吐量。提出的算法通过资源协调动态适应海洋环境变化，有效平衡了时延和能耗。仿真结果表明，在高动态的海事通信场景中，提出的算法显著优于现有的基准方法，证明该方法的有效性和可行性。

Abstract

Mobile edge computing is considered as an important solution to reduce backhaul pressure and improve quality of service

yet existing resource management strategies are poorly adapted in highly dynamic ocean environments. To address this problem

a task offloading and resource allocation algorithm based on an improved twin-delayed deep deterministic policy gradient was proposed. The algorithm was designed to systematically coordinate servo UAV deployment with edge node resources to jointly optimize communication resource allocation and computational task scheduling

while taking into account the energy constraints of ocean edge nodes and the time-varying characteristics of ocean networks. Specifically

the problem was formulated as a non-convex optimization framework with the objective of maximizing throughput under stringent quality of service requirements of user devices. The proposed algorithm dynamically adapted to the changing ocean environment through resource coordination

effectively balancing delay and energy consumption. Simulation results show that the proposed algorithm significantly outperforms existing benchmark methods in highly dynamic maritime communication scenarios

demonstrating the effectiveness and feasibility of the approach.

关键词

Keywords

references

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