Reinforcement Learning for Adaptive Resource Allocation in Fog RAN for IoT With Heterogeneous Latency Requirements

In light of the quick proliferation of Internet of things (IoT) devices and applications, fog radio access network (Fog-RAN) has been recently proposed for fifth generation (5G) wireless communications to assure the requirements of ultra-reliable low-latency communication (URLLC) for the IoT applications which cannot accommodate large delays. To this end, fog nodes (FNs) are equipped with computing, signal processing and storage capabilities to extend the inherent operations and services of the cloud to the edge. We consider the problem of sequentially allocating the FN's limited resources to IoT applications of heterogeneous latency requirements. For each access request from an IoT user, the FN needs to decide whether to serve it locally at the edge utilizing its own resources or to refer it to the cloud to conserve its valuable resources for future users of potentially higher utility to the system (i.e., lower latency requirement). We formulate the Fog-RAN resource allocation problem in the form of a Markov decision process (MDP), and employ several reinforcement learning (RL) methods, namely Q-learning, SARSA, Expected SARSA, and Monte Carlo, for solving the MDP problem by learning the optimum decision-making policies. We verify the performance and adaptivity of the RL methods and compare it with the performance of the network slicing approach with various slicing thresholds. Extensive simulation results considering 19 IoT environments of heterogeneous latency requirements corroborate that RL methods always achieve the best possible performance regardless of the IoT environment.