Abstract
Content caching offers an effective solution to reduce the traffic load and alleviate the burden on backhaul links in future wireless networks. In this paper, we study the converged networks to push and cache the popular services. The popular services are delivered by the broadcasting networks, and cached in the router nodes in a distributed cache network. Due to the limited storage capacity of the router node, we formulate the service scheduling problem as a Markov Decision Process (MDP), aiming to maximize the equivalent throughput. Considering the large state space involved in the distributed cache network, it is great challenge to obtain a tractable solution by the classical optimization algorithm. To tackle this problem, we propose a service scheduling strategy based on deep Q-learning. Simulation results demonstrate that the proposed scheme can significantly improve the equivalent throughput of the converged networks.
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