基于深度学习的家庭基站下行链路功率分配

doi:10.19678/j.issn.1000-3428.0055640

计算机工程 ›› 2020, Vol. 46 ›› Issue (10): 188-192. doi: 10.19678/j.issn.1000-3428.0055640

基于深度学习的家庭基站下行链路功率分配

吕亚平¹, 贾向东^1,2, 路艺¹, 叶佩文¹

1. 西北师范大学计算机科学与工程学院, 兰州 730070;
2. 南京邮电大学江苏省无线通信重点实验室, 南京 210003

收稿日期:2019-08-05 修回日期:2019-09-26 发布日期:2019-10-17
作者简介:吕亚平(1994-),女,硕士研究生,主研方向为移动通信;贾向东,教授、博士;路艺、叶佩文,硕士研究生。
基金资助:
国家自然科学基金（61261015，61561043）。

Downlink Power Allocation of Home Base Station Based on Deep Learning

Lü Yaping¹, JIA Xiangdong^1,2, LU Yi¹, YE Peiwen¹

1. College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China;
2. Wireless Communication Key Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Received:2019-08-05 Revised:2019-09-26 Published:2019-10-17

摘要/Abstract

摘要： 为提高室内无线通信服务质量以满足用户需求，基于深度Q学习（DQL）算法进行家庭基站的下行链路功率分配，旨在最大化系统吞吐量。在办公区域密集部署家庭基站的系统模型中，将家庭基站的物理位置建模为泊松点过程，移动用户随机分布在各个位置。在此基础上，构建含有两层隐藏层的深度神经网络，优化网络的非线性，提高网络的拟合能力。仿真结果表明，DQL算法相较于贪婪算法和Q学习算法能有效提高系统吞吐量和收敛速度。

关键词: 家庭基站, 密集部署, 功率分配, 神经网络, 深度学习

Abstract: In order to improve the service quality of indoor wireless communication to meet user demand,the dowmlink power allocation of home base station based on Deep Q Learning (DQL) algorithm is carried out to maximize system throughput.In the system model of densely deploying home base stations in office areas,the physical location of home base stations is modeled as a Poisson point process,and mobile users are randomly distributed in each location.On this basis,a deep neural network with two hidden layers is constructed to optimize the nonlinearity of the network and improve its fitting ability.Simulation results show that DQL algorithm can effectively improve the system throughput and convergence speed compared with greedy algorithm and Q learning algorithm.

Key words: home base station, intensive deployment, power allocation, neural network, deep learning

中图分类号:

TN929.5

吕亚平, 贾向东, 路艺, 叶佩文. 基于深度学习的家庭基站下行链路功率分配[J]. 计算机工程, 2020, 46(10): 188-192.

Lü Yaping, JIA Xiangdong, LU Yi, YE Peiwen. Downlink Power Allocation of Home Base Station Based on Deep Learning[J]. Computer Engineering, 2020, 46(10): 188-192.

http://www.ecice06.com/CN/Y2020/V46/I10/188

图/表 6

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参考文献

[1] HAN Song,LI Xinbin,LIU Zhixin,et al.Distributed hierarchical game-based algorithm for downlink power allocation in OFDMA femtocell networks[J].Computer Networks,2016,94:176-188.
[2] CHANDRASEKHAR V,ANDREWS J,GATHERER A,et al.Femtocell networks:a survey[J].IEEE Communications Magazine,2008,46(9):59-67.
[3] ARISTOMENOPOULOS G,KASTRINOGIANNIS T,LAMPRINAKOU S,et al.Optimal power control and coverage management in two-tier femtocell networks[J].EURASIP Journal on Wireless Communications and Networking,2012(1):1-13.
[4] ESTRADA R,JARRAY A,OTROK H,et al.Base station selection and resource allocation in macro-femtocell networks under noisy scenario[J].Wireless Networks,2014,20(1):115-131.
[5] LIU Z,ZHANG P,CHAN K Y,et al.Robust resource allocation for rates maximization using fuzzy estimation of dynamic channel states in OFDMA femtocell networks[J].Computer Networks,2019,159:110-124.
[6] MU Shiyao.Research on resource allocation algorithm based on disturbance coordination in home base station system[D].Nanjing:Nanjing University of Posts and Telecommunications,2014.(in Chinese)穆施瑶.家庭基站系统中基于干扰协调的资源分配算法研究[D].南京:南京邮电大学,2014.
[7] SAAD H,MOHAMED A,ELBATT T,et al.Cooperative Q-learning techniques for distributed online power allocation in femtocell networks[J].Communications and Mobile Computing,2015,15:1929-1944.
[8] SHIN D,CHOI S.Power control for data load balancing with coverage in dynamic femtocell networks[J].Wireless Networks,2016,22(4):1145-1159.
[9] WANG C,KUO W H,CHU C Y,et al.QoS-aware cooperative power control and resource allocation scheme in LTE femtocell networks[J].Computer Communications,2017,110:164-174.
[10] LIU Dongyu.Research on routing and resource allocation algorithm based on reinforcement learning in D2D network[D].Beijing:Beijing University of Posts and Telecommunications,2019.(in Chinese)刘东煜.D2D网络中基于强化学习的路由选择与资源分配算法研究[D].北京:北京邮电大学,2019.
[11] LI Han.Resource allocation scheme under self-powered UDN network based on deep reinforcement learning[D].Beijing:Beijing University of Posts and Telecommunications,2019.(in Chinese)李瀚.基于深度强化学习的自供能UDN网络下资源分配方案[D].北京:北京邮电大学,2019.
[12] CHENG Baichuan.Research on MEC computing unloading and resource allocation based on deep reinforcement learning[D].Beijing:Beijing University of Posts and Telecommunications,2019.(in Chinese)程百川.基于深度强化学习的MEC计算卸载和资源分配研究[D].北京:北京邮电大学,2019.
[13] JIANG N,DENG Y,NALLANATHAN A,et al.Reinforcement learning for real-time optimization in NB-IoT networks[J].IEEE Journal on Selected Areas in Communications,2019,37(6):1424-1440.
[14] ZHENG Xiangquan,ZHANG Xianlu,HE Xiang.Aresource allocation algorithm to maximize throughput gain in D2D communications[J].Mobile Communications,2018,42(6):26-33.(in Chinese)郑相全,张先禄,何香.一种最大化吞吐量增益的D2D通信资源分配算法[J].移动通信,2018,42(6):26-33.
[15] TANG Zhen,TAO Shaokun,ZHAO Dongbin,et al.Deep reinforcement learning progress:from AlphaGo to AlphaGo Zero[J].Control Theory and Applications,2017,34(12):1529-1546.(in Chinese)唐振,韬邵坤,赵冬斌,等.深度强化学习进展:从AlphaGo到AlphaGo Zero[J].控制理论与应用,2017,34(12):1529-1546.
[16] CHENG Siyu,LIN Feng.The influence of computer AlphaGo algorithm on human go algorithm[J].China Science and Technology Information,2019(2):40-41.(in Chinese)程思雨,林锋.计算机围棋AlphaGo算法对人类围棋算法的影响[J].中国科技信息,2019(2):40-41.
[17] ZHOU Yi,MA Xiaoyong,GAO Fuxiao,et al.Autonomous deployment and energy efficiency optimization strategy of UAV based on deep reinforcement learning[J].Chinese Journal on Internet of Things,2019,3(2):47-55.(in Chinese)周毅,马晓勇,郜富晓,等.基于深度强化学习的无人机自主部署及能效优化策略[J].物联网学报,2019,3(2):47-55.
[18] LIAO Xiaomin,YAN Shaohu,SHI Jia,et al.Deep reinforcement learning based resource allocation algorithm in cellular networks[J].Journal on Communications,2019,40(2):11-18.(in Chinese)廖晓闽,严少虎,石嘉,等.基于深度强化学习的蜂窝网资源分配算法[J].通信学报,2019,40(2):11-18.
[19] ZHANG Liangliang,FENG Jing,HU Guyu.Deep learning method for community structure based on compressive sensing[J].Computer Engineering,2014,40(9):190-195.(in Chinese)张梁梁,冯径,胡谷雨.基于压缩感知的社团结构深度学习方法[J].计算机工程,2014,40(9):190-195.
[20] XIE Mangang,JIA Xiangdong,ZHOU Meng,et al.Analysis for outage probability of two-hop relay cooperative heterogeneous networks[J].Computer Engineering,2019,45(4):61-65.(in Chinese)颉满刚,贾向东,周猛,等.双跳中继协作异构网络中断概率分析[J].计算机工程,2019,45(4):61-65.

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