Applying Machine Learning Technology to Optimize the Operational Cost of the Egyptian Optical Network

Rahouma, Kamel H.; Ali, Ayman A.

doi:10.1016/j.procs.2019.12.133

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…(9). The output of the model [22][23][24] was the predicted cost of repair, which accurately correlate with the pattern of the clustered input data. The predicted cost [25][26][27] of repairing an underground fiber cable cut, according to the model, can be determined when variables such as region and cause of cuts are known.…”

Section: Discussion and Evaluationmentioning

confidence: 94%

Using machine learning techniques to predict the cost of repairing hard failures in underground fiber optics networks

2020

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The two most common outdoor fiber optic cable installations are aerial installation and underground cable installation. Underground cable installation is buried directly underground or placed into a buried duct. Underground burial of fiber cable installations is mostly common for long-distance installations. The cables are usually buried in trenches. Underground duct installation also provides an opportunity for future expansion without the need to dig again. Preparation towards underground cable

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Section: Discussion and Evaluationmentioning

confidence: 94%

Using machine learning techniques to predict the cost of repairing hard failures in underground fiber optics networks

2020

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“…The first journal that was used as a reference was a research [7] This study uses the SWP (sliding window partitioning) and Random Forest machine learning methods to analyze the KPI and KQI relationships of the 5G cellular network. The research [8] applies the Neural Network machine learning method to optimize fiber optic network operating costs in telecommunications operators. Research [9] use ANN and Kmeans method.…”

Section: Relevant Researchmentioning

confidence: 99%

Analysis of Factors Affecting the Use of the 64QAM Modulation on the Long-Term Evolution Network by Using Random Forest Method

Jainul

Atok

2021

IJPS

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Nowadays internet traffic using cellular telecommunication network is increasing very rapidly. Good LTE (Long-Term Evolution) cellular network performance is very important for any telecommunication operator to maintain customer satisfaction. Poor network performance can also cause customers to switch to other operators. One of the indicator variables in observing the radio quality of the LTE cellular network is Penetration using 64QAM Modulation. 64QAM modulation can transmit higher bitrates with lower power usage. 64QAM modulation will be used if the Channel Quality Index (CQI) condition is very good. Network quality improvement can be done by adding new BTS or optimizing existing BTS. The addition of new BTS will increase coverage, quality, and capacity but cost is high, and the time required to build BTS is also long, while improving network quality by optimizing BTS can be done by purchasing LTE features and costs incurred still relatively low. In increasing the penetration of using 64QAM modulation, it is necessary to analyze the other variables. The traditional method to improve this Key Performance Indicator (KPI) requires an expert and professional but is often inaccurate and spends a lot of time finding the factors that cause it. To solve this problem, Random forest method is proposed. By knowing the variables that have a significant effect on network quality, the capital costs incurred by cellular operators for improving network quality will be more effective and efficient because the capital costs invested only focus on influencing variables such as purchasing LTE network features only done for those related to these variables. The results of this study, we make CQI improvement flow based on the classification of the random forest method that produces feature/variable importance.

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“…Fault localization in transparent optical networks is also addressed in [8] by using a Gaussian process classifier in a two-step scheme, using past data, for operation purposes and cost reduction. More recent works, such as [9], propose a universal platform to optimize operational costs in the Egyptian optical network, based on ML techniques, by deriving important operation factors towards the automation. Specifically, artificial neural network (ANN) technologies are proposed to manage energy consumption, fault localization, OSNR monitoring and configuration management for the best routes, leading to best response times, less number of complaints and best operational performance.…”

Section: Related Workmentioning

confidence: 99%

Applying cognitive dynamic learning strategies for margins reduction in operational optical networks

Calleja

Spadaro

Perelló

et al. 2020

Optical Switching and Networking

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Applying Machine Learning Technology to Optimize the Operational Cost of the Egyptian Optical Network

Cited by 5 publications

References 19 publications

Using machine learning techniques to predict the cost of repairing hard failures in underground fiber optics networks

Using machine learning techniques to predict the cost of repairing hard failures in underground fiber optics networks

Analysis of Factors Affecting the Use of the 64QAM Modulation on the Long-Term Evolution Network by Using Random Forest Method

Applying cognitive dynamic learning strategies for margins reduction in operational optical networks

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