Throughput measurement in 4G wireless data networks: Performance evaluation and validation

Abolfazli, Saeid; Sanaei, Zohreh; Wong, Shen Yuong; Tabassi, Ali; Rosen, Steven

doi:10.1109/iscaie.2015.7298322

Cited by 11 publications

(9 citation statements)

References 7 publications

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“…Network throughput is one of the key network performance metrics for QoS among end-users. However, demonstrating reliability and validity of network throughput measurement deployed in vendor end-point is a critical task [31].…”

Section: • Simulation Scenario and Resultsmentioning

confidence: 99%

A New Communication Protocol for Drones Cooperative Network: 5G Site Survey Case Study

Othman¹,

Megahed²,

Rezka³

et al. 2021

IJACSA

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Directing the antennas of the 5th generation mobile network optimally became a hard and tedious process due to the abundance of the antennas that the 5th generation networks relay on. Also, due to the traditional way of measuring the signal strength of the 5th generation networks. The process that could take weeks of working until it is done. So, the solution is to make an automated process to measure signal strength and to direct antennas using "Drones" rather than human power. This way will ease the process of directing antennas in a quite shorter amount of time which will be some hours instead of weeks. Plus the low cost and the high accuracy achieved. So, a cooperative network between "Drones" and a new communication protocol to support that network will be designed. "Drones" will communicate with each others and with antennas through exchanging messages by "MQTT cloud" using new designed communication protocol. "Raspberry pi" platform will be used as a server to control the direction of antennas. "Drones" will carry a "4G mobile" and a "Raspberry pi" with a "Building Identification System (BIS)" setuped on it. The "(BIS)" gives every building a number and will recognize the entrance of buildings to measure signal strength at every floor. Then, performance analysis metrics (Throughput) will be measured. OMNeT++ "will be used for simulation", and "Raspberry Pi" platform will be used to implement the system and measure the performance of the new Communication Protocol.

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Section: • Simulation Scenario and Resultsmentioning

confidence: 99%

A New Communication Protocol for Drones Cooperative Network: 5G Site Survey Case Study

Othman¹,

Megahed²,

Rezka³

et al. 2021

IJACSA

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“…In other hand, in (Hernandez & Insuasty, 2015), presents a new protocol approach to test active tools for av_bw in wireless networks environments. In addition, in (Abolfazli, Sanaei, Wong, Tabassi, & Rosen, 2015) introduces a methodology to accurately measure throughput of the 4G WiMAX data network in real wireless environment from end-users perspective; they suggest an intelligent monitoring system in corporation of both (A. Tirumala & Gibbs, 2006) and OOKLA (Abolfazli, Sanaei, Wong, Tabassi, & Rosen, 2015) that can measure the throughput, but also can predict the future throughput based on previous throughput data. Finally, in (Dely, et al, 2014) proposes BEST-AP as a new mechanism for selecting the based on a novel av_bw estimation scheme.…”

Section: Related Workmentioning

confidence: 99%

“…However, the studies do not show a real analysis of the effects that cause the performance of the traffic flow in a network infrastructure with wireless trunk segments; such as, the real capacity of the link, lost packets, latency, av_bw, among others; because they only focus on evaluating the av_bw estimation tools in wireless network infrastructures based on IEEE 802.11, and 4G/LTE architectures, among others (Abolfazli, Sanaei, Wong, Tabassi, & Rosen, 2015), (Paul, Tachibana, & Hasegawa, 2016).…”

Section: Introductionmentioning

confidence: 99%

A novel available bandwidth estimation method for mobile networks using a train of packet groups

Shiobara

Okamawari

2017

Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication

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Nowadays the platform par excellence for the development of all telecommunication activities is the Internet; and its infrastructure is facing new challenges every day due to the growth in demand for more content, such as streaming video, storage, and cloud processing. Also, to maintain optimal levels of service quality, network applications demand more telecommunication resources. Similarly, the network infrastructures that support these applications have evolved, and demand greater and more efficient management of the trunk links, which play a primary role in sustaining services. Therefore, this paper presents the performance evaluation of trunk, wired and wireless links in a heterogeneous computer network infrastructure, using available bandwidth estimation tools such as IGI, Pathload, and Traceband. Thus, for the experimental evaluation of the trunk links, two real network scenarios were implemented, where crosstraffic was generated in a synthetic way using the Mgen tool. Consequently, this study allowed verifying in other aspects; that the metrics of the estimation tools can be used to evaluate and know the performance of wired and wireless trunk links, which can be reliable up to 96% for network administrative tasks.

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“…Arti cial intelligence (AI) has become the revolutionary technology of the 20th century and has found multiple applications in elds ranging from weather forecasting, astronomical exploration to autonomous systems [7].…”

Section: Introductionmentioning

confidence: 99%

Finding Hidden Links among Variables in a Large-Scale 4G Mobile Traffic Network Dataset with Deep Learning

Diouf

Ndong²,

Diop

et al. 2022

Preprint

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Using a mobile dataset from Orange Senegal small cell 4G network, we study the effect of variables such as data traffic at the downlink level, data traffic at the uplink level, total data traffic, maximum number of active users, signaling protocol, uplink user rate, downlink user rate, physical resource block rate for downlink, block rate physical resources for uplink, load data logging, channel quality indicator, downlink radio delay average, over the perceived rate at the downlink. We are looking to find the variable that most affects the perceived flow. We do this by using machine learning to find the variable that closely explains the variation in perceived flow and helps predict flow with greater accuracy. We observe that correlation analysis is unable to find a hidden relationship between throughput and other variables. With models such as linear regression, decision tree, random forest, multi-layered perceptron and deep neural network, the channel quality indicator (CQI_Avg) turns out to be the variable that more closely explains the variation in perceived flow and more accurately contributes to the prediction compared to others variables.

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Throughput measurement in 4G wireless data networks: Performance evaluation and validation

Cited by 11 publications

References 7 publications

A New Communication Protocol for Drones Cooperative Network: 5G Site Survey Case Study

A New Communication Protocol for Drones Cooperative Network: 5G Site Survey Case Study

A novel available bandwidth estimation method for mobile networks using a train of packet groups

Finding Hidden Links among Variables in a Large-Scale 4G Mobile Traffic Network Dataset with Deep Learning

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