Design and experimental validation of a software-defined radio access network testbed with slicing support

Network slicing is a fundamental feature of 5G systems to partition a single network into a number of segregated logical networks, each optimized for a particular type of service or dedicated to a particular customer or application. The realization of network slicing is particularly challenging in the Radio Access Network (RAN) part, where multiple slices can be multiplexed over the same radio channel and Radio Resource Management (RRM) functions shall be used to split the cell radio resources and achieve the expected behaviour per slice. In this context, this paper describes the key design and implementation aspects of a Software-Defined RAN (SD-RAN) experimental testbed with slicing support. The testbed has been designed consistently with the slicing capabilities and related management framework established by 3GPP in Release 15. The testbed is used to demonstrate the provisioning of RAN slices (e.g., preparation, commissioning, and activation phases) and the operation of the implemented RRM functionality for slice-aware admission control and scheduling.Peer ReviewedPostprint (published version

Design and Experimental Validation of a Software-Defined Radio Access Network Testbed with Slicing Support

Network slicing is a fundamental feature of 5G systems to partition a single network into a number of segregated logical networks, each optimized for a particular type of service, or dedicated to a particular customer or application. The realization of network slicing is particularly challenging in the Radio Access Network (RAN) part, where multiple slices can be multiplexed over the same radio channel and Radio Resource Management (RRM) functions shall be used to split the cell radio resources and achieve the expected behaviour per slice. In this context, this paper describes the key design and implementation aspects of a Software-Defined RAN (SD-RAN) experimental testbed with slicing support. The testbed has been designed consistently with the slicing capabilities and related management framework established by 3GPP in Release 15. The testbed is used to demonstrate the provisioning of RAN slices (e.g. preparation, commissioning and activation phases) and the operation of the implemented RRM functionality for slice-aware admission control and scheduling

End-to-end elasticity control of cloud-network slices

The design of efficient elasticity control mechanisms for dynamic resource allocation is crucial to increase the efficiency of future cloud-network slice-defined systems. Current elasticity control mechanisms proposed for cloud- or network-slicing, only consider cloud- or network-type resources respectively. In this paper, we introduce the elaSticity in cLOud-neTwork Slices (SLOTS) which aims to extend the horizontal elasticity control to multi-providers scenarios in an end-to-end fashion, as well as to provide a novel vertical elasticity mechanism to deal with critical insufficiency of resources by harvesting underused resources on other slices. Finally, we present a preliminary assessment of the SLOTS prototype in a real testbed, revealing outcomes that suggest the viability of the proposal.Peer ReviewedPostprint (published version

Deep Reinforcement Learning for Resource Management in Network Slicing

Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201

Secure and Privacy-preserving Network Slicing in 3GPP 5G System Architecture

Network slicing in 3GPP 5G system architecture has introduced significant improvements in the flexibility and efficiency of mobile communication. However, this new functionality poses challenges in maintaining the privacy of mobile users, especially in multi-hop environments. In this paper, we propose a secure and privacy-preserving network slicing protocol (SPNS) that combines 5G network slicing and onion routing to address these challenges and provide secure and efficient communication. Our approach enables mobile users to select network slices while incorporating measures to prevent curious RAN nodes or external attackers from accessing full slice information. Additionally, we ensure that the 5G core network can authenticate all RANs, while avoiding reliance on a single RAN for service provision. Besides, SPNS implements end-to-end encryption for data transmission within the network slices, providing an extra layer of privacy and security. Finally, we conducted extensive experiments to evaluate the time cost of establishing network slice links under varying conditions. SPNS provides a promising solution for enhancing the privacy and security of communication in 5G networks