谭颀,
魏延南,
贺兰钦,
唐伦
1.重庆邮电大学通信与信息工程学院 重庆 400065
2.重庆邮电大学移动通信技术重点实验室 重庆 400065
基金项目:国家自然科学基金(6157073)，重庆市教委科学技术研究项目(KJZD-M201800601)
详细信息
作者简介:陈前斌：男，1967年生，教授，博士生导师，研究方向为个人通信、多媒体信息处理与传输、异构蜂窝网络等
谭颀：女，1995年生，硕士生，研究方向为5G网络切片、资源分配、随机优化理论
魏延南：男，1995年生，硕士生，研究方向为5G网络切片、虚拟资源分配、随机优化理论
贺兰钦：男，1995年生，硕士生，研究方向为5G网络切片，机器学习算法
唐伦：男，1973年生，教授，博士，研究方向为下一代无线通信网络、异构蜂窝网络、软件定义无线网络等
通讯作者:陈前斌 cqb@cqupt.edu.cn
中图分类号:TN929.5计量
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被引次数:0
出版历程
收稿日期:2019-07-04
修回日期:2020-01-29
网络出版日期:2020-02-20
刊出日期:2020-06-22
Dynamic Resource Allocation and Energy Management Algorithm for Hybrid Energy Supply in Heterogeneous Cloud Radio Access Networks
Qianbin CHEN,,Qi TAN,
Yannan WEI,
Lanqin HE,
Lun TANG
1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Funds:The National Natural Science Foundation of China (61571073), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-M201800601)
摘要
摘要:针对面向混合能源供应的 5G 异构云无线接入网(H-CRANs)网络架构下的动态资源分配和能源管理问题，该文提出一种基于深度强化学习的动态网络资源分配及能源管理算法。首先，由于可再生能源到达的波动性及用户数据业务到达的随机性，同时考虑到系统的稳定性、能源的可持续性以及用户的服务质量(QoS)需求，将H-CRANs网络下的资源分配以及能源管理问题建立为一个以最大化服务提供商平均净收益为目标的受限无穷时间马尔科夫决策过程(CMDP)。然后，使用拉格朗日乘子法将所提CMDP问题转换为一个非受限的马尔科夫决策过程(MDP)问题。最后，因为行为空间与状态空间都是连续值集合，因此该文利用深度强化学习解决上述MDP问题。仿真结果表明，该文所提算法可有效保证用户QoS及能量可持续性的同时，提升了服务提供商的平均净收益，降低了能耗。
关键词:异构云无线接入网/
混合能源/
资源分配/
能源管理/
深度强化学习
Abstract:Considering the dynamic resource allocation and energy management problem in the 5G Heterogeneous Cloud Radio Access Networks(H-CRANs) architecture for hybrid energy supply, a dynamic network resource allocation and energy management algorithm based on deep reinforcement learning is proposed. Firstly, due to the volatility of renewable energy and the randomness of user data service arrival, taking into account the stability of the system, the sustainability of energy and the Quality of Service(QoS) requirements of users, the resource allocation and energy management issues in the H-CRANs network as a Constrained infinite time Markov Decision Process (CMDP) are modeled with the goal of maximizing the average net profit of service providers. Then, the Lagrange multiplier method is used to transform the proposed CMDP problem into an unconstrained Markov Decision Process (MDP) problem. Finally, because the action space and the state space are both continuous value sets, the deep reinforcement learning is used to solve the above MDP problem. The simulation results show that the proposed algorithm can effectively guarantee the QoS and energy sustainability of the system, while improving the average net income of the service provider and reducing energy consumption.
Key words:Heterogeneous Cloud Radio Access Networks (H-CRANs)/
Hybrid energy/
Resource allocation/
Energy management/
Deep reinforcement learning
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