5G异构网络中基于多目标Actor-Critic的资源分配
Resource allocation based on multi-objective Actor-Critic in 5G heterogeneous networks
曾韦健 1李晖2
作者信息
- 1. 南京信息工程大学电子与信息工程学院 南京 210044
- 2. 中国航空研究院研究生院 扬州 225006
- 折叠
摘要
在5G异构网络(heterogeneous network,HetNet)中广泛部署小基站可以提高网络容量和用户速率,但密集部署也会产生严重干扰和更高能耗问题.为了最大化网络能量效率(energy efficiency,EE)并保证用户服务质量(quality of service,QoS),提出了一种在小蜂窝基站中嵌入能量收集器供电的资源分配方案.首先,针对网络系统的下行链路,将频谱和小基站发射功率分配问题建模为联合优化系统能效和用户满意度的多目标优化问题.其次,提出了基于深度强化学习的多目标演员-评论家(multi-objective actor-critic,MAC)资源分配算法求解所建立的优化模型.最后,仿真结果表明,相比于其他传统学习算法,能量效率提高了11.96%～12.37%,用户满意度提高了11.45%～27.37%.
Abstract
The widespread deployment of small base stations in 5G heterogeneous networks(HetNet)can improve network capacity and user rates,but the dense deployment will also cause severe interference and higher energy consumption problems.In order to maximize the network energy efficiency(EE)and guarantee the user quality of service(QoS),this paper presents a resource allocation scheme that embeds energy harvester power supply in small cell base stations.Firstly,for the downlink of the network system,the spectrum and small base station transmit power allocation problem was modeled as a multi-objective optimization problem to jointly optimize system energy efficiency and user satisfaction.Secondly,a multi-objective actor-critic(MAC)resource allocation algorithm based on deep reinforcement learning was proposed to solve the established optimization model.Finally,simulation results show that compared with other traditional learning algorithms,the energy efficiency of the proposed algorithm is improved by 11.96%～12.37%,and the user satisfaction is improved by 11.45%～27.37%.
关键词
5G异构网络/能量效率/用户满意度/多目标优化/深度强化学习Key words
5G heterogeneous network/energy efficiency/user satisfaction/multi-objective optimization/deep reinforce-ment learning引用本文复制引用
基金项目
国家自然科学基金(61661018)
江苏省基础研究计划青年基金(BK20210064)
无锡市科技创新创业资金(WX03-02B0137-022200-34)
出版年
2024
NETL
NSTL
万方数据