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Intelligent vehicular systems and smart city applications are the fastestgrowing Internet of things (IoT) implementations at a compound annual growthrate of 30%. In view of the recent advances in IoT devices and the emerging newbreed of IoT applications driven by artificial intelligence (AI), fog radioaccess network (F-RAN) has been recently introduced for the fifth generation(5G) wireless communications to overcome the latency limitations of cloud-RAN(C-RAN). We consider the network slicing problem of allocating the limitedresources at the network edge (fog nodes) to vehicular and smart city userswith heterogeneous latency and computing demands in dynamic environments. Wedevelop a network slicing model based on a cluster of fog nodes (FNs)coordinated with an edge controller (EC) to efficiently utilize the limitedresources at the network edge. For each service request in a cluster, the ECdecides which FN to execute the task, i.e., locally serve the request at theedge, or to reject the task and refer it to the cloud. We formulate the problemas infinite-horizon Markov decision process (MDP) and propose a deepreinforcement learning (DRL) solution to adaptively learn the optimal slicingpolicy. The performance of the proposed DRL-based slicing method is evaluatedby comparing it with other slicing approaches in dynamic environments and fordifferent scenarios of design objectives. Comprehensive simulation resultscorroborate that the proposed DRL-based EC quickly learns the optimal policythrough interaction with the environment, which enables adaptive and automatednetwork slicing for efficient resource allocation in dynamic vehicular andsmart city environments.

Deep Reinforcement Learning for Adaptive Network Slicing in 5G for Intelligent Vehicular Systems and Smart Cities