|Table of Contents|

Power Control in Ultra Dense Network:A DeepReinforcement Learning Based Method(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2022年01期
Page:
16-23
Research Field:
机器学习
Publishing date:

Info

Title:
Power Control in Ultra Dense Network:A DeepReinforcement Learning Based Method
Author(s):
Mao Jin1; 2; Xiong Ke1; 2; Wei Ning3; 4; Zhang Yu5; Zhang Ruichen1; 2
(1.School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China)(2.Beijing Key Laboratory of Traffic Data Analysis and Mining,Beijing 100044,China)(3.ZTE Corporation,Shenzhen 518057,China)(4.State Key Laboratory of Mobile Network and Mobile Multimedia Technology,Shenzhen 518055,China)(5.State Grid Energy Research Institute Co.,Ltd.,Beijing 102209,China)
Keywords:
ultra-dense networks; power control; information capacity; QoS; deep reinforcement learning
PACS:
TP391
DOI:
10.3969/j.issn.1672-1292.2022.01.003
Abstract:
For ultra-dense networks,in view of the problem of low spectrum utilization due to excessive users and large interference,an optimization problem is formulated to increase the system information capacity and satisfy the number of users with the quality of service(QoS)by optimizing the transmission power. Since the problem is non convex and the power control is a discrete variable,it is modeled as a Markov decision policy process. To this end,a power control algorithm based on deep reinforcement learning is proposed,and the corresponding action space,state space and reward function are designed. Simulation results show that compared with the maximum transmit power strategy and random transmit power strategy,the proposed algorithm improves the information capacity by at least 15.9% and the satisfaction of users’ QoS by at least 10.7%. Moreover,compared with the algorithm without considering the improvement of user’s QoS,the proposed algorithm improves the user’s QoS by appropriately reducing the information capacity.

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Last Update: 2022-03-15