移动通信2024，Vol.48Issue(6)：86-90,5.DOI:10.3969/j.issn.1006-1010.20240229-0001
基于多智能体深度强化学习的智慧医疗网络计算卸载方法
Multi-agent Deep Reinforcement Learning Based Edge Computing Offloading Method in Smart Healthcare Network
摘要
Abstract
The significant breakthroughs in 6G and mobile edge computing technologies have driven the flourishing development of smart healthcare services.In order to meet the requirements of low latency and high reliability in healthcare services,a computational offloading method based on multi-agent deep reinforcement learning is proposed.Considering the latency and energy consumption,a mixed integer nonlinear programming(MINLP)task offloading problem is formulated.The resource allocation problem is solved by a traditional optimization algorithm and the offloading decision problem is solved by a multi-agent deep reinforcement learning algorithm.Simulation results show that the proposed algorithm can perform real-time task offloading in dynamically changing environments of smart healthcare networks compared with several existing approaches.关键词
智慧医疗网络/深度强化学习/移动边缘计算/资源分配/计算卸载Key words
smart healthcare network/deep reinforcement learning/mobile edge computing/resource allocation/computation offloading分类
电子信息工程引用本文复制引用
方馨,秦子健,高新平,闻煜超,苏新..基于多智能体深度强化学习的智慧医疗网络计算卸载方法[J].移动通信,2024,48(6):86-90,5.基金项目
国家自然科学基金项目(62371181)"面向海洋移动边缘计算的任务卸载与隐私保护关键技术研究" （62371181）
常州市政策引导类计划"常州文化遗产智慧复原与沉浸式交互文化产业应用示范"(国际科技合作/港澳台科技合作CZ20230029) （国际科技合作/港澳台科技合作CZ20230029）
