Cooperative Hybrid-Caching for Long-Tail Distribution Request with Deep Reinforcement Learning

Wireless caching is regarded for alleviating network congestion in next-generation communications. In this work, we focus on the impact of input data non-uniformity on neural network training when using deep learning to solve the wireless cache strategy. In particular, in wireless caching, the assumed user request is generally a classical long-tailed distribution: Zipf law. We address this problem from cache models and deep reinforcement learning models. On the one hand, base station will prefetch partial most popular contents on the user side to reduce the partiality of caching strategies. On the other hand, the trick of deep longtail learning is added to prevent the neural network from been over-fitting caused by inputs are concentrated in the most popular files. The performance for different reinforcement learning methods is analyzed, which show that our method can achieve better performance and latency than the existing content caching.