Measurement and comparison of data rate and time delay of end-devices in licensed sub-6 GHz 5G standalone non-public networks

Lackner, Thorge; Hermann, Julian; Dietrich, Fabian; Kühn, Christian; Angos, Mario; Jooste, Johannes L.; Palm, Daniel

doi:10.1016/j.procir.2022.05.120

Cited by 14 publications

(3 citation statements)

References 14 publications

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“…In more detail, Rischke et al [23] perform a wide set of DL and Uplink (UL) measurement to characterize packet delay and losses in a SA campus networks. Lackner et al [24] measure data rate and round trip delay in a standalone testbed facility. Makino et al [25] evaluate throughput and delay performance in a private deployment.…”

Section: Performance Assessments Of 5g Sa Deploymentsmentioning

confidence: 99%

Catch the Pitch of 5G FWA: EMF and Throughput Measurements of 3.5-GHz Standalone Deployment in a Baseball Stadium

Chiaraviglio

Lodovisi

Franci

et al. 2023

IEEE Open J. Commun. Soc.

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The provisioning of 5G technology does not only involve mobile terminals, but also new services such as Fixed Wireless Access (FWA). The aim of this study is to examine the ElectroMagnetic Field (EMF) and throughput performance of a FWA deployment utilizing Standalone technology operating at 3.5 GHz. To address the unique characteristics of 5G FWA signals, an innovative framework has been designed based on the measurement of 5G FWA spectrum using four independent chains and an additional traffic generation chain to saturate the radio link capacity at the measurement location. Methodologies for evaluating 5G FWA exposure under conservative conditions, such as measurements of exposure during active traffic generation and maximum power extrapolations, are also introduced. Results from real measurements taken at a baseball stadium show that 5G FWA exposure is consistently low, typically below 0.4 [V/m], with an upper bound of 0.59 [V/m], while the achieved throughput is up to 250 [Mbps]. Additionally, the measured 5G exposure levels are a small fraction compared to those emitted by other technologies such as 4G. Furthermore, the values estimated by simulation from the output power counters of the base station are found to be in close agreement with the measured exposure levels.

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Section: Performance Assessments Of 5g Sa Deploymentsmentioning

confidence: 99%

Catch the Pitch of 5G FWA: EMF and Throughput Measurements of 3.5-GHz Standalone Deployment in a Baseball Stadium

Chiaraviglio

Lodovisi

Franci

et al. 2023

IEEE Open J. Commun. Soc.

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show abstract

“…Turkka et al conclude that both 5G devices and networks need to be further optimised to achieve the industrial suitability of 5G. A study by Lackner et al [9] presents data rate and RTT measurements of five different 5G devices in a private 5G network using licensed frequencies. Their results show that 5G currently lacks behind its targeted performance, especially for low latency-based industrial applications.…”

Section: Rationale Of the Papermentioning

confidence: 99%

Supporting Digital Transformation: Real-Time Monitoring of Private 5g Networks to Educate Future Connectivity Experts by Means of Learning Factories

et al. 2023

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“…Recently, the objective of obtaining an efficient ITS is closer to reality due to the advancements in data transmission by fifth Generation (5G) wireless technology [8,9]. This technology transmits information with a speed of between 15 to 20 Gbps (Giga bytes per second), with a latency 10 times better than that of 4G [10]. In addition, advancements in cloud computing systems and Graphic Processing Units (GPUs) attract researchers' attention and enable them to monitor urban dynamics in real time [11].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Multi-Label Deep Learning Classifiers for Real-Time Vehicle Detection to Support Intelligent Transportation Systems

Shokri,

Larouche,

Homayouni

2023

Smart Cities

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An Intelligent Transportation System (ITS) is a vital component of smart cities due to the growing number of vehicles year after year. In the last decade, vehicle detection, as a primary component of ITS, has attracted scientific attention because by knowing vehicle information (i.e., type, size, numbers, location speed, etc.), the ITS parameters can be acquired. This has led to developing and deploying numerous deep learning algorithms for vehicle detection. Single Shot Detector (SSD), Region Convolutional Neural Network (RCNN), and You Only Look Once (YOLO) are three popular deep structures for object detection, including vehicles. This study evaluated these methodologies on nine fully challenging datasets to see their performance in diverse environments. Generally, YOLO versions had the best performance in detecting and localizing vehicles compared to SSD and RCNN. Between YOLO versions (YOLOv8, v7, v6, and v5), YOLOv7 has shown better detection and classification (car, truck, bus) procedures, while slower response in computation time. The YOLO versions have achieved more than 95% accuracy in detection and 90% in Overall Accuracy (OA) for the classification of vehicles, including cars, trucks and buses. The computation time on the CPU processor was between 150 milliseconds (YOLOv8, v6, and v5) and around 800 milliseconds (YOLOv7).

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Measurement and comparison of data rate and time delay of end-devices in licensed sub-6 GHz 5G standalone non-public networks

Cited by 14 publications

References 14 publications

Catch the Pitch of 5G FWA: EMF and Throughput Measurements of 3.5-GHz Standalone Deployment in a Baseball Stadium

Catch the Pitch of 5G FWA: EMF and Throughput Measurements of 3.5-GHz Standalone Deployment in a Baseball Stadium

Supporting Digital Transformation: Real-Time Monitoring of Private 5g Networks to Educate Future Connectivity Experts by Means of Learning Factories

A Comparative Analysis of Multi-Label Deep Learning Classifiers for Real-Time Vehicle Detection to Support Intelligent Transportation Systems

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