李子煜,,
管令进,
陈前斌
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(62071078)，重庆市教委科学技术研究项目(KJZD-M201800601)，重庆市重大主题专项项目(cstc2019jscx-zdztzxX0006)
详细信息
作者简介:唐伦：男，1973年生，教授、博士生导师，研究方向为新一代无线通信网络、异构蜂窝网络、软件定义无线网络等
李子煜：女，1995年生，硕士生，研究方向为资源分配、机器学习
管令进：男，1995年生，硕士生，研究方向为网络功能虚拟化、无线资源分配、机器学习
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、下一代移动通信网络等
通讯作者:李子煜 lzy395682410@qq.com
中图分类号:TN929.5计量
文章访问数:511
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被引次数:0
出版历程
收稿日期:2020-04-28
修回日期:2020-10-05
网络出版日期:2020-10-12
刊出日期:2021-06-18
Energy Efficiency Optimization Algorithm Based On PD-NOMA Under Heterogeneous Cloud Radio Access Networks
Lun TANG,Ziyu LI,,
Lingjin GUAN,
Qianbin CHEN
1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. Key Laboratory of Mobile Communication Technology, Chongqing University of Post and Telecommunications, Chongqing 400065, China
Funds:The National Natural Science Foundation of China (62071078), The Science and Technology Research Project of Chongqing Education Commission (KJZD-M201800601), The Major Theme Projects in Chongqing (cstc2019jscxzdztzxX0006)
摘要
摘要:针对异构云无线接入网络的频谱效率和能效问题，该文提出一种基于功率域-非正交多址接入(PD-NOMA)的能效优化算法。首先，该算法以队列稳定和前传链路容量为约束，联合优化用户关联、功率分配和资源块分配，并建立网络能效和用户公平的联合优化模型；其次，由于系统的状态空间和动作空间都是高维且具有连续性，研究问题为连续域的NP-hard问题，进而引入置信域策略优化(TRPO)算法，高效地解决连续域问题；最后，针对TRPO算法的标准解法产生的计算量较为庞大，采用近端策略优化(PPO)算法进行优化求解，PPO算法既保证了TRPO算法的可靠性，又有效地降低TRPO的计算复杂度。仿真结果表明，该文所提算法在保证用户公平性约束下，进一步提高了网络能效性能。
关键词:异构云无线接入网络/
资源分配/
网络能效/
深度强化学习
Abstract:In view of the spectrum efficiency and energy efficiency of Heterogeneous Cloud Radio Access Networks (H-CRAN), an energy efficiency optimization algorithm based on Power Domain Non-Orthogonal Multiple Access (PD-NOMA) is proposed. First, the algorithm takes queue stability and forward link capacity as constraints, jointly optimizes user association, power allocation and resource block allocation, and it establishes a joint optimization model of network energy efficiency and user fairness. Secondly, because the state space and action space of the system are both high-dimensional and continuity, the research problem is the NP-hard problem of the continuous domain, and then Trust Region Policy Optimization (TRPO) algorithm is introduced to solve efficiently the continuous domain issue. Finally, the amount of calculations generated by the standard solution for the TRPO algorithm is too large, and Proximal Policy Optimization (PPO) algorithm is used to optimize the solution. The PPO algorithm not only ensures the reliability of the TRPO algorithm, but also reduces effectively the TRPO calculation complexity. Simulation results show that the algorithm proposed in this paper improves further the energy efficiency performance of the network under the constraint of ensuring user fairness.
Key words:Heterogeneous Cloud Radio Access Networks(H-CRAN)/
Resource allocation/
Network energy efficiency/
Deep Reinforcement Learning(DRL)
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https://jeit.ac.cn/article/exportPdf?id=57e4a079-2444-4bce-8bdd-a4cb1cc1b334
