LTE core network testing using generated traffic based on models from real-life data

Varga, Pál; Olaszi, Péter

doi:10.1109/ants.2013.6802892

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…After verification and fine-tuning of the model, the traffic generator can be deployed in order to reveal the performance limitations of mobile core networks. Figure 5 illustrates our methodology through the general cycle of modeling: observation, analysis, model creation, implementation and finally verification and deployment-according to [35]. 1.…”

Section: Traffic Modeling Methodologymentioning

confidence: 99%

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Towards Traffic Identification and Modeling for 5G Application Use-Cases

2020

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To analyze next-generation mobile networks properly, there is a need to define key performance indicators (KPIs). Testing signaling only or just partial domains of the network have been replaced with end-to-end testing methodologies. With the appearing of machine-to-machine (M2M) applications, this question became even harder, since there is no direct user feedback. Quality of experience cannot be measured accurately in M2M applications, even if the network operates correctly and without failures. There are dozens of new—but theoretical—use-cases for 5G; however, these are not tested on a live network. The modeling methodology used throughout the paper follows the steps of observation, analysis, model creation, implementation, and verification. The first part of the paper examines the three application-types: enhanced mobile broadband (eMBB), critical Internet of Things (cIoT), and mass Internet of Things (mIoT). Afterwards, we introduce the main traffic characteristics based on current mobile networks’ traffic patterns and measurements. Considering the measurement results, we introduce a methodology and define traffic models for the simulation of different application-types. To validate these models, we compare the generated artificial traffic with real traffic patterns. In the second part of the paper, we examine what the main effects of these traffic patterns on a domestic 5G test-network are. Finally, we suggest some considerations on the possible main impacts regarding 5G network design.

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Section: Traffic Modeling Methodologymentioning

confidence: 99%

“…Tasks and their results in the methodology. Shaded gray rectangles illustrate procedures; parallelograms represent intermediate data[35].…”

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confidence: 99%

Towards Traffic Identification and Modeling for 5G Application Use-Cases

2020

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“…Although there are several kinds of research on WiFi coverage in industrial areas, unfortunately, these researches hardly include any case studies on cellular coverage. Measurements in the industrial sited, under live conditions, provide great feedback to standardization bodies [135]. It is clear that in a manufacturer's environment, different drilling, cutting, pressing and welding machines operate, and radio interference is difficult to model accurately through theoretical methods.…”

Section: Research Gaps For Private Campus Networkmentioning

confidence: 99%

5G support for Industrial IoT Applications— Challenges, Solutions, and Research gaps

Varga

Pető

Frankó

et al. 2020

Sensors

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201

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Industrial IoT has special communication requirements, including high reliability, low latency, flexibility, and security. These are instinctively provided by the 5G mobile technology, making it a successful candidate for supporting Industrial IoT (IIoT) scenarios. The aim of this paper is to identify current research challenges and solutions in relation to 5G-enabled Industrial IoT, based on the initial requirements and promises of both domains. The methodology of the paper follows the steps of surveying state-of-the art, comparing results to identify further challenges, and drawing conclusions as lessons learned for each research domain. These areas include IIoT applications and their requirements; mobile edge cloud; back-end performance tuning; network function virtualization; and security, blockchains for IIoT, Artificial Intelligence support for 5G, and private campus networks. Beside surveying the current challenges and solutions, the paper aims to provide meaningful comparisons for each of these areas (in relation to 5G-enabled IIoT) to draw conclusions on current research gaps.

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Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0

et al. 2022

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The Industry 4.0 initiative has been showing the way for industrial production to optimize operations based on collecting, processing, and sharing data. There are new requirements on the production floor: flexible but ultra-reliable, low latency wireless communications through interoperable systems can share data. Further challenges of data sharing and storage arise when diverse systems come into play at the Manufacturing Operations Management and Business Planning & Logistics levels. The emerging complex cyber-physical systems of systems need to be engineered with care. Regarding industrial requirements, the telecommunication industry has many similarities to production—including ultra-reliability, high complexity, and having humans “in-the-loop”. The current paper aims to provide an overview of converging telco-grade solutions that can be successfully applied in the wide sense of industrial production. These toolsets range from model-driven engineering through system interoperability frameworks, 5G- and 6G-supported manufacturing, and the telco-cloud to speech recognition in noisy environments.

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LTE core network testing using generated traffic based on models from real-life data

Cited by 3 publications

References 7 publications

Towards Traffic Identification and Modeling for 5G Application Use-Cases

Towards Traffic Identification and Modeling for 5G Application Use-Cases

5G support for Industrial IoT Applications— Challenges, Solutions, and Research gaps

Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0

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