Learning Based Joint Cache and Power Allocation in Fog Radio Access Networks

The growing demand for rich content services and developments of industrial internet of things and vehicle-to-everything communications pose challenging requirements for the next-generation fog radio access networks (F-RANs). Though F-RANs are promising to support these enabling technologies by leveraging edge caching and edge computing, delay performance is still straightforward and should be optimized. A latency optimization problem for F-RANs is formulated, and to solve the problem, a deep reinforcement learning (DRL) based joint proactive cache placement and power allocation strategy is proposed in this paper. Furthermore, to enhance the content serving capability at the edge, we rigorously consider that a set of F-RAN nodes cooperatively serve the content request. The user's demand can be adaptively satisfied either through fog access point mode at the network edge or by centralized cloud computing mode at the cloud tier. The key idea of the proposal is to learn the user's demand and make an intelligent decision for caching appropriate content and allocating a significant amount of power resources. Simulation results show the effectiveness and performance gains of the proposal under maintaining throughput compared with other baselines.