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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. Hence, 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 the IoT applications of heterogeneous latency requirements. For each access request from an IoT user, the FN needs to decide whether to serve it locally 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 a fixed-thresholdbased algorithm. 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.

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This paper presents radio frequency (RF) capacity estimation for millimeter wave (mm-wave) based fifth-generation (5G) cellular networks using field-level simulations. It is shown that, by reducing antenna beamwidth from 65∘ to 30 ∘ , we can enhance the capacity of mm-wave cellular networks roughly by 3.0 times at a distance of 220 m from the base station (BS). This enhancement is far much higher than the corresponding enhancement of 1.2 times observed for 900 MHz and 2.6 GHz microwave networks at the same distance from the BS. Thus the use of narrow beamwidth transmitting antennas has more pronounced benefits in mm-wave networks. Deployment trials performed on an LTE TDD site operating on 2.6 GHz show that 6-sector site with 27 ∘ antenna beamwidth enhances the quality of service (QoS) roughly by 40% and more than doubles the overall BS throughput (while enhancing the per sector throughput 1.1 times on average) compared to a 3-sector site using 65 ∘ antenna beamwidth. This agrees well with our capacity simulations. Since mm-wave 5G networks will use arbitrary number of beams, with beamwidth much less than 30 ∘ , the capacity enhancement expected in 5G system when using narrow beamwidth antennas would be much more than three times observed in our simulations.