Emergency communication network planning method based on deep reinforcement learning

被引：1

作者：

Yin C. ^{[1
]}

Yang R. ^{[1
]}

Zhu W. ^{[1
]}

Zou X. ^{[1
]}

机构：

[1] School of Information and Communication, National University of Defense Technology, Wuhan

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2020年 / 42卷 / 09期

关键词：

Emergency communication; Intelligence; Network planning; Reinforcement learning;

D O I：

10.3969/j.issn.1001-506X.2020.09.27

中图分类号：

学科分类号：

摘要：

Focus on the problem of high demand on prior knowledge and weak timeliness of traditional algorithm for emergency communication network planning, a toplogy planning method for emergency communication network based on deep reinforcement learning is proposed. Developing a method of sample data generation using Monte Carlo tree search and self-game, the policy network and value network based on residual network is designed. On this basis, Tensorflow is used to build and train the model. Simulation results show that the proposed planning method can effctively realize the intelligent planning of network topology, and has high timeliness and feasibility. © 2020, Editorial Office of Systems Engineering and Electronics. All right reserved.

引用

页码：2091 / 2097

页数：6

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