5G Network Slicing for Mission-critical use cases

Roddy, Mark; Truong, Thuy; Walsh, Paul; Bado, Mustafa Al; Wu, Yanxin; Michael, Healy; Ahearne, Sean

doi:10.1109/5gwf.2019.8911651

Cited by 16 publications

(4 citation statements)

References 5 publications

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“…The result shown that the end to end delay of their uRLLC slice was 30 ms. The authors in [35] proposed the service orchestration model SliceNet and implemented the end to end cognitive network slicing and slice management framework. Through the edge computing, they shorten the delay of end to end slices to provide better service in the medical scenario.…”

Section: B Cls For Network Managementmentioning

confidence: 99%

Intent-Driven Closed-Loop Control and Management Framework for 6G Open RAN

Zhang,

Yang,

Dong

et al. 2024

IEEE Internet Things J.

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Future mobile networks should provide on-demand services for various industries and applications with the stringent guarantees of quality of experience (QoE), which highly challenge the flexibility of network management. However, the diverse requirements of QoE and the management of heterogeneous networks create significant pressure towards communication service providers (CSPs). In the 6th generation mobile networks, the CSPs should guarantee resilient performance for the communication service consumers with less human involvement. In this work, we turn to intent-driven network and on-demand slice management, and to decrease the complexity and cost in full life cycle slice management, we first present an intentdriven closed-loop (CL) control and management framework that automates the deployment of network slices and manages resources intelligently based on the extended CL architecture. And then, we explore and exploit the deep reinforcement learning algorithm to address the problem of resource allocation, which is formulated as a Markov decision process. Finally, we demonstrate the feasibility of the proposed framework by deploying the open radio access network (RAN) infrastructure in OpenAirInterface platform and realizing the CL control and management with near real-time RAN intelligent controller. The emulation results demonstrate the effectiveness of slicing performance, measured in terms of delay and rate.

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Section: B Cls For Network Managementmentioning

confidence: 99%

Intent-Driven Closed-Loop Control and Management Framework for 6G Open RAN

Zhang,

Yang,

Dong

et al. 2024

IEEE Internet Things J.

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“…Network slicing has now become a powerful approach to ensuring certain levels of well-performing services. Nowadays, there is a lot of research and developments in the state of the art that leverage network slicing to guarantee QoS levels among heterogeneous use cases such as the Internet of things (IoT) [17]- [19], smart grids [20], [21], smart cities [22], eHealth (e.g., telemedicine, critical services) [23], [24], or intelligent transport, education and media and entertainment [25], among others.…”

Section: Related Workmentioning

confidence: 99%

“…FCA sends also periodic metrics (20). All such metrics are directly stored in the inventory (21), shared with metric aggregator engine (22) and they are then indexed (23). Moreover, FCA makes the subscription to actions and intents (24); all intents messages received are directly stored in the inventory (25), shared with the provisioning engine (26) and then indexed (27).…”

Section: Bootstrappingmentioning

confidence: 99%

E2E network slice management framework for 5G multi-tenant networks

et al. 2023

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“…Among the 5G features that can enable connected ambulances is network slicing, where logical network resources can be provisioned to accommodate specific application demands. A study conducted in network slicing environment using facilities at the 5G Prototyping Lab at Dell EMC facilities Ireland and SliceNet reported an average round trip latency of 296.91 ms from client to core, an average round trip time of 50.68 ms from client to edge, and an average packet loss of 7.2% for the core and 0.1% at the edge [ 65 ]. Another study was carried out in [ 64 ] using the same experimental tools with the added features like QoS control based on the data plane programmability and low-latency cloud-based mobile edge computing (MEC) platform.…”

Section: Kpis For Specific 5g-healthcare Use Casesmentioning

confidence: 99%

Communication Requirements in 5G-Enabled Healthcare Applications: Review and Considerations

et al. 2022

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Fifth generation (5G) mobile communication technology can enable novel healthcare applications and augment existing ones. However, 5G-enabled healthcare applications demand diverse technical requirements for radio communication. Knowledge of these requirements is important for developers, network providers, and regulatory authorities in the healthcare sector to facilitate safe and effective healthcare. In this paper, we review, identify, describe, and compare the requirements for communication key performance indicators in relevant healthcare use cases, including remote robotic-assisted surgery, connected ambulance, wearable and implantable devices, and service robotics for assisted living, with a focus on quantitative requirements. We also compare 5G-healthcare requirements with the current state of 5G capabilities. Finally, we identify gaps in the existing literature and highlight considerations for this space.

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5G Network Slicing for Mission-critical use cases

Cited by 16 publications

References 5 publications

Intent-Driven Closed-Loop Control and Management Framework for 6G Open RAN

Intent-Driven Closed-Loop Control and Management Framework for 6G Open RAN

E2E network slice management framework for 5G multi-tenant networks

Communication Requirements in 5G-Enabled Healthcare Applications: Review and Considerations

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