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Cluster based Deep Reinforcement Learning for Wireless Caching with Social Connection Awareness

Coded caching can improve the robustness of wireless caching networks. This paper investigates the joint caching and communication optimization in terminal based wireless coded caching networks. Social characteristics of private terminals are considered to further harvest more performance gain in terms of hit ratio. To tackle the problem of unknown popularity distribution, we adopt deep reinforcement learning. Since the joint caching and communication optimization of all Content Providers (CP) results in larger-scale action and state space, we propose a novel cluster based deep reinforcement learning (CB-DRL) scheme. We present simulation results to demonstrate complexity reduction and effectiveness of the proposed algorithm.