Smart Network Slicing for Vehicular Fog-RANs

Abstract

Modern transportation systems are facing a sharp alteration since the Internet of Vehicles (IoV) has activated intense information exchange among vehicles, infrastructure, and pedestrians. Existing approaches fail in efficiently handling the heterogeneous network traffic because of the complicated network environment and dynamic vehicle density. Recently, the fog-radio access network with network slicing has emerged as a promising solution to fulfill the demands of the maldistributed network traffic. However, available fog resources as well as network traffic are all dynamic and unpredictable due to high mobility of vehicles, which results in weak resource utilization. To address this problem, we propose a smart slice scheduling scheme in vehicular fog radio access networks. This scheduling scheme is formed as a Markov decision process. Accordingly, an intelligent algorithm for network slices is proposed based on the Monte Carlo tree search in terms of a new metric cross entropy, which is able to allocate the resource allocation for the match of traffic load in the time-space domain. This slice scheduling algorithm does not require any prior knowledge of the network traffic. Furthermore, this paper first reveals the relationship between road traffic and the IoV resource based on the metric perception-reaction time. A collaborative scheduling scheme is proposed to tune the road traffic speed to further release available IoV resource under the heavy traffic load. Simulation results indicate that the proposed algorithm outperforms several baselines in terms of throughput and delay with low complexity.