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Adaptive Network Slicing In Fog Ran For Iot With Heterogeneous Latency And Computing Requirements: A Deep Reinforcement Learning Approach, Almuthanna Nassar
Adaptive Network Slicing In Fog Ran For Iot With Heterogeneous Latency And Computing Requirements: A Deep Reinforcement Learning Approach, Almuthanna Nassar
USF Tampa Graduate Theses and Dissertations
In view of the recent advances in Internet of Things (IoT) devices and the emerging new breed of smart city applications and intelligent vehicular systems driven by artificial intelligence, fog radio access network (F-RAN) has been recently introduced for the next generation wireless communications. The capability of F-RAN has emerged to overcome the latency limitations of cloud-RAN (C-RAN) and assure the quality-of-service (QoS) requirements of the ultra-reliable-low-latency-communication (URLLC) for IoT applications. 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. However, …