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We demonstrate how the 5G network slicing model can be extended to address data security requirements. In this work we demonstrate two different slice configurations, with different encryption requirements, representing two diverse use-cases for 5G networking: namely, an enterprise application hosted at a metro network site, and a content delivery network. We create a modified software-defined networking (SDN) orchestrator which calculates and provisions network slices according to the requirements, including encryption backed by quantum key distribution (QKD), or other methods. Slices are automatically provisioned by SDN orchestration of network resources, allowing selection of encrypted links as appropriate, including those which use standard Diffie-Hellman key exchange, QKD and quantum-resistant algorithms (QRAs), as well as no encryption at all. We show that the set-up and tear-down times of the network slices takes of the order of 1-2 minutes, which is an order of magnitude improvement over manually provisioning a link today.
Reinforcement learning (RL) for network slicing is considered in the 5G radio access network, where the base station, gNodeB, allocates resource blocks (RBs) to the requests of user equipments and maximizes the total reward of accepted requests over
In 5G networks, slicing allows partitioning of network resources to meet stringent end-to-end service requirements across multiple network segments, from access to transport. These requirements are shaping technical evolution in each of these segment
The paper presents a reinforcement learning solution to dynamic resource allocation for 5G radio access network slicing. Available communication resources (frequency-time blocks and transmit powers) and computational resources (processor usage) are a
In this paper, we provide a comprehensive review and updated solutions related to 5G network slicing using SDN and NFV. Firstly, we present 5G service quality and business requirements followed by a description of 5G network softwarization and slicin
Cellular-Vehicle to Everything (C-V2X) aims at resolving issues pertaining to the traditional usability of Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) networking. Specifically, C-V2X lowers the number of entities involved in vehicula