Support for Edge Computing in the 5G Network

Choi, Young-Seok; Park, Noik

doi:10.1109/icufn.2018.8436806

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E2e Network Slicing and The Latest Developments1

Introduction1

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2020

2023

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Cited by 3 publications

(2 citation statements)

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“…In [14], authors presented network slice architecture for 5G CN and provide working mechanisms for slice selection, NAS routing and slice reselection to support multiple logical networks over a single physical network. In particular, the mechanisms considered mobility of UE, the reselection approach by UE is designed to minimise the control processing in the 5G CN.…”

Section: E2e Network Slicing and The Latest Developmentsmentioning

confidence: 99%

Survey on health care applications in 5G networks

2020

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Section: E2e Network Slicing and The Latest Developmentsmentioning

confidence: 99%

Survey on health care applications in 5G networks

2020

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“…Functionality is divided between a centralized pool and locations closer to the cell sites via a midhaul link. Joint computation and communication [6] and multi-access edge computing (MEC) [7] concepts considered for beyond 5G (B5G) networks also favor software-based implementations due to the possiblity of integration with non-communicationrelated software.…”

Section: Introductionmentioning

confidence: 99%

Scheduling Latency of Midhaul-based Commodity Hardware C-RAN

Malm

Tirkkonen

2021

2021 IEEE 4th 5G World Forum (5GWF)

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The impact of midhaul-induced latency on the performance of a wireless communication system must be understood in order to be appropriately accounted for. To this end, we study the impact of midhaul delay and jitter on the scheduling of a cloud radio access network. A soft-real-time commodity hardware testbed is used to gather data using up to 16 virtualized distributed units. The empirical data is used to develop models to analyze the midhaul latency's impact on spectral efficiency and outage probability. Modeling takes into account the burstiness of deadline misses in order to replicate outage duration in addition to frequency. The recorded data is then used to derive an estimate of system scaling in terms of shortest transmission time interval supported for a set number of distributed units for a given deadline miss rate. Results show that scaling behavior depends on the target rate. More stringent targets emphasize the impact of tail latencies. The testbed suffers less than 0.1 % spectral efficiency loss from deadline misses at a transmission time interval duration of 500 µs with 16 distributed units.

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