Deep reinforcement learning based resource allocation algorithm in cellular networks

被引：0
|
作者：
Liao X. [1 ,2 ]
Yan S. [3 ]
Shi J. [1 ]
Tan Z. [1 ]
Zhao Z. [1 ]
Li Z. [1 ]
机构：
[1] State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an
[2] School of Information and Communications, National University of Defense Technology, Xi'an
[3] The 29th Research Institute of China Electronics Technology Group Corporation, Chengdu
来源：
Tongxin Xuebao/Journal on Communications | 2019年 / 40卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Cellular networks; Deep reinforcement learning; Neural network; Resource allocation;
D O I：
10.11959/j.issn.1000-436x.2019002
中图分类号：
学科分类号：
摘要：
In order to solve multi-objective optimization problem, a resource allocation algorithm based on deep reinforcement learning in cellular networks was proposed. Firstly, deep neural network (DNN) was built to optimize the transmission rate of cellular system and to complete the forward transmission process of the algorithm. Then, the Q-learning mechanism was utilized to construct the error function, which used energy efficiency as the rewards. The gradient descent method was used to train the weights of DNN, and the reverse training process of the algorithm was completed. The simulation results show that the proposed algorithm can determine optimization extent of optimal resource allocation scheme with rapid convergence ability, it is obviously superior to the other algorithms in terms of transmission rate and system energy consumption optimization. © 2019, Editorial Board of Journal on Communications. All right reserved.
引用
收藏
页码：11 / 18
页数：7
相关论文
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