计算机工程 ›› 2019, Vol. 45 ›› Issue (11): 112-120.doi: 10.19678/j.issn.1000-3428.0054857

• 移动互联与通信技术 • 上一篇    下一篇

基于改进HSARSA(λ)算法的功率控制研究

谷静, 侯永平, 张雨轩, 张新   

  1. 西安邮电大学 电子工程学院, 西安 710121
  • 收稿日期:2019-05-08 修回日期:2019-06-12 发布日期:2019-06-25
  • 作者简介:谷静(1975-),女,副教授,主研方向为通信与信息系统;侯永平,硕士研究生;张雨轩,学士;张新,教授、博士。
  • 基金项目:
    国家自然科学基金(61272120);陕西省科技计划项目(2018JM6106)。

Research on Power Control Based on Improved HSARSA(λ) Algorithm

GU Jing, HOU Yongping, ZHANG Yuxuan, ZHANG Xin   

  1. School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
  • Received:2019-05-08 Revised:2019-06-12 Published:2019-06-25

摘要: 在MBS-PBS两层异构网络中,微微基站采用小区范围扩展技术对网络进行负载均衡时,pico小区边缘用户的通信受到MBS基站较大干扰。为此,提出一种基于启发函数的改进HSARSA(λ)算法。采用缩减功率的RP-ABS子帧技术,在保证宏基站自身通信性能的同时减小MBS基站对pico边缘用户的干扰,并运用基于启发函数的改进HSARSA(λ)算法与环境进行交互,以配置RP-ABS子帧密度与功率大小,达到干扰协调的目的。仿真结果表明,改进HSARSA算法与原始SARSA和Q-Learning等算法相比,pico边缘用户吞吐量分别提升12%和40%,系统用户吞吐量分别提升10.3%和20.2%,有效提高了pico边缘用户的通信性能。

关键词: 异构网络, 小区范围扩展, 负载均衡, RP-ABS技术, SARSA学习算法

Abstract: In a two-layer heterogeneous network,when the pico base station performs load balancing on the network by using the cell range extension technology,the communication of the edge users of the pico cell is greatly interfered by the MBS base station.Therefore,this paper proposes an improved HSARSA(λ) algorithm based on heuristic functions.The RP-ABS sub-frame technology with reduced power reduces the interference of the MBS on the pico edge users while ensuring the communication performance of the macro base station,and uses the HSARSA (λ) algorithm improved by the heuristic function to interact with the environment.The sub-frame density and power level are configured for interference coordination.Simulation results show that compared with the original SARSA and Q-Learning algorithms,the pico edge user throughput is increased by 12% and 40% respectively,and the system user throughput is increased by 10.3% and 20.2% respectively.The communication performance of the edge users of the pico cell is significantly improved.

Key words: heterogeneous network, Cell Range Extension(CRE), load balancing, RP-ABS technology, SARSA learning algorithm

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