5G/5G+ network management employing AI-based continuous deployment

Panek, Michał; Pomykała, Adam; Jablonski, Ireneusz; Woźniak, Michał

doi:10.1016/j.asoc.2023.109984

Cited by 5 publications

(3 citation statements)

References 36 publications

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“…The "elementIp" indicates the IP address of the network element, while the "elementType" denotes the specific type of network element. Within the dataset, eight types of network functions are present: "PSBC," "S-CSCF," "EDS," "LDRA," "I-CSCF," "UDM," "SMF," and "AMF" [17]. Lastly, the "measObjLdn" refers to the link code that establishes connectivity between network functions.…”

Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Time Series Prediction Algorithms on Multiple Network Function Data of NWDAF

Chen,

Song,

Zhang

et al. 2024

International Journal of Distributed Sensor Networks

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With the emergence and vigorous development of 5G technology, there is a significant surge in network usage and traffic, resulting in heightened complexity within network and IT environments. This exponential increase in activity produces a plethora of events, making conventional systems inadequate for the efficient management of 5G networks. In comparison to 4G technology, 5G technology brings forth a host of new features, one of which is the network data analytics function (NWDAF). This function grants network operators the flexibility to either employ their own data analytics methodologies based on machine learning (ML) and deep learning (DL) into their networks. In this paper, we present a dataset named “NWDAF-NFPP” for network function performance time series prediction, collected from a laboratory at China Telecom. The dataset is carefully anonymized to ensure maximum realism and comprehensiveness, while safeguarding sensitive information. It encompasses eight categories of network functions, with data collected at five-minute intervals. The availability of this dataset provides valuable resources for researchers to conduct time series prediction research on network element performance. Following data collection, a total of six models were employed for network element performance prediction, encompassing both machine learning and deep learning approaches. This diverse set of models was carefully chosen to ensure comprehensive coverage of different techniques and algorithms. Through the comparison and analysis of these models, we aim to evaluate their predictive capabilities and identify the most effective approach for network element performance prediction. This comparative analysis will provide valuable insights into the strengths and limitations of each model, aiding in informed decision-making for network optimization and management strategies in the future.

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Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Time Series Prediction Algorithms on Multiple Network Function Data of NWDAF

Chen,

Song,

Zhang

et al. 2024

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…and performance measurements (KPIs calculated from counters). Counters describe the events in a mobile network on low level [34] (like the average downlink Radio Link Control (RLC) delay in gNodeB Distributed Unit per slice). Subscribers in this network cluster have been divided into four groups:…”

Section: Datasetmentioning

confidence: 99%

“…Therefore, it has to be taken into account how the connection between the physical and the digital network should be maintained. In our case the plan is to incorporate it into a continuous delivery cluster similar to the one described in [34].…”

Section: Forecasting and Dimensioning Frameworkmentioning

confidence: 99%