Deep reinforcement learning based resource allocation algorithm in cellular networks

Xiaomin LIAO; Shaohu YAN; Jia SHI; Zhenyu TAN; Zhongling ZHAO; Zan LI

doi:10.11959/j.issn.1000-436x.2019002

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Deep reinforcement learning based resource allocation algorithm in cellular networks

Topics:5G and AI | 更新时间：2024-06-05

- Deep reinforcement learning based resource allocation algorithm in cellular networks
- Journal on Communications Vol. 40, Issue 2, Pages: 11-18(2019)
- 作者机构：
  
  1. 西安电子科技大学综合业务网理论及关键技术国家重点实验室，陕西西安 710071
  2. 国防科技大学信息通信学院，陕西西安 710106
  3. 中国电子科技集团公司第二十九研究所，四川成都 610036
- 作者简介：
- 基金信息：
  
  The Key Project of National Natural Science Foundation of China(61631015)
- DOI：10.11959/j.issn.1000-436x.2019002
  CLC： TN929.5
- Online First：2019-02，
  
  Published：25 February 2019
- 稿件说明：
移动端阅览
Xiaomin LIAO, Shaohu YAN, Jia SHI, et al. Deep reinforcement learning based resource allocation algorithm in cellular networks[J]. Journal on Communications, 2019, 40(2): 11-18.
DOI：

Xiaomin LIAO, Shaohu YAN, Jia SHI, et al. Deep reinforcement learning based resource allocation algorithm in cellular networks[J]. Journal on Communications, 2019, 40(2): 11-18. DOI： 10.11959/j.issn.1000-436x.2019002.

摘要

针对蜂窝网资源分配多目标优化问题，提出了一种基于深度强化学习的蜂窝网资源分配算法。首先构建深度神经网络（DNN），优化蜂窝系统的传输速率，完成算法的前向传输过程；然后将能量效率作为奖惩值，采用Q-learning机制来构建误差函数，利用梯度下降法来训练DNN的权值，完成算法的反向训练过程。仿真结果表明，所提出的算法可以自主设置资源分配方案的偏重程度，收敛速度快，在传输速率和系统能耗的优化方面明显优于其他算法。

Abstract

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.

关键词

Keywords

references

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