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Deep Reinforcement Learning Based Content Caching in Wireless Networks
GAO Huihui 1,ZHAO Zhongyuan 2 *
1.the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing;the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing
2.
*Correspondence author
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Funding: none
Opened online:30 December 2020
Accepted by: none
Citation: GAO Huihui,ZHAO Zhongyuan.Deep Reinforcement Learning Based Content Caching in Wireless Networks[OL]. [30 December 2020] http://en.paper.edu.cn/en_releasepaper/content/4753285
 
 
Content caching is considered as an integral part of content management in wireless networks. In this paper, we studied the Deep Reinforcement Learning (DRL) based content caching scheme in wireless networks. First, an optimization problem that aims to minimize the total cost of content delivery is formulated. Second, a DRL-based algorithm is proposed to implement real time management of content caching and delivery, which can approach the optimal solution without iterations during each decision epoch. Finally, the simulation results are provided to evaluate the performance of our proposed scheme, which show that it can achieve lower cost than the existing content caching schemes.
Keywords:wireless networks; content caching; deep reinforcement learning; resource management
 
 
 

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