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Computer Engineering ›› 2020, Vol. 46 ›› Issue (12): 185-192. doi: 10.19678/j.issn.1000-3428.0056421

• Mobile Internet and Communication Technology • Previous Articles     Next Articles

Block-aware Power Allocation Based on Q-Learning in Millimeter-Wave Network

SHI Zhao, SUN Changyin, JIANG Fan   

  1. School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
  • Received:2019-10-28 Revised:2019-12-19 Published:2019-12-24

毫米波网络中基于Q-Learning的阻塞感知功率分配

施钊, 孙长印, 江帆   

  1. 西安邮电大学 通信与信息工程学院, 西安 710121
  • 作者简介:施钊(1993-),男,硕士研究生,主研方向为5G移动通信系统功率控制;孙长印,副教授;江帆,教授。
  • 基金资助:
    国家自然科学基金(61801382,61871321);国家科技重大专项(2017ZX03001012-005);陕西省自然科学基金重点项目(2019JZ-06);陕西省重点研发计划"重点产业创新链(群)-工业领域"项目(2019ZDLGY07-06)。

Abstract: millimeter-Wave(mm-Wave) communication is expected to provide significant capacity gains in ultra-dense network scenarios of the 5G wireless communication system.To address the complex interference in the mm-Wave communication scenario and the interruption caused by the high block rate of the dynamic links of the cell edge users,this paper proposes a power allocation strategy scheme based on Q-Learning algorithm considering the high intermission rate of mm-Wave communication.Poisson Cluster Process(PCP) is used in the modelling of randomly deployed base station user systems,and the different influences of link block on the useful signals and interference signals are analyzed.Then the egoistic and altruistic strategy is introduced in the design of state and reward function of the Q-Learning algorithm,and the machine learning strategy is used to get the optimal solution to power allocation.Simulation results show that,compared with the CDP-Q scheme that does not consider the link block rate,the proposed algorithm significantly improves the total capacity of the system due to the optimal power allocation based on the dynamic status of links.

Key words: millimeter-Wave(mm-Wave) communication, link block, Q-Learning algorithm, power allocation, Poisson Cluster Process(PCP), egoistic and altruistic strategy

摘要: 毫米波通信可在5G无线通信系统超密集网络场景中提供显著的系统容量增益,但毫米波通信场景中干扰复杂多变,并且小区边缘用户动态链路的高阻塞率会引起中断问题。为此,基于Q-Learning算法,提出一种考虑毫米波链路高间歇性概率的功率分配方案。基于泊松簇过程对随机部署的基站用户系统进行建模,分析链路阻断对有用信号和干扰信号带来的不同影响,并将利己利他策略引入Q-Learning算法的状态和回报函数设计中,通过机器学习策略得到功率分配最优解。仿真结果表明,与未考虑链路阻塞概率的CDP-Q方案相比,该方案由于根据链路动态链接状况进行最优功率分配,显著提升了系统总容量。

关键词: 毫米波通信, 链路阻塞, Q-Learning算法, 功率分配, 泊松簇过程, 利己利他策略

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