Predictive Modeling of Delay in an LTE Network by Optimizing the Number of Predictors Using Dimensionality Reduction Techniques

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dc.citation.spage8511
dc.citation.volume13
dc.contributor.authorStojčić, Mirko
dc.contributor.authorBanjanin, Milorad K.
dc.contributor.authorVasiljević, Milan
dc.contributor.authorNedić, Dragana
dc.contributor.authorStjepanović, Aleksandar
dc.contributor.authorDanilović, Dejan
dc.contributor.authorPuzić, Goran
dc.date.accessioned2024-11-27T11:39:04Z
dc.date.available2024-11-27T11:39:04Z
dc.date.issued2023
dc.description.abstractDelay in data transmission is one of the key performance indicators (KPIs) of a network. The planning and design value of delay in network management is of crucial importance for the optimal allocation of network resources and their performance focuses. To create optimal solutions, predictive models, which are currently most often based on machine learning (ML), are used. This paper aims to investigate the training, testing and selection of the best predictive delay model for a VoIP service in a Long Term Evolution (LTE) network using three ML techniques: Multilayer Perceptron (MLP), Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). The space of model input variables is optimized by dimensionality reduction techniques: RReliefF algorithm, Backward selection via the recursive feature elimination algorithm and the Pareto 80/20 rule. A three-segment road in the geo-space between the cities of Banja Luka (BL) and Doboj (Db) in the Republic of Srpska (RS), Bosnia and Herzegovina (BiH), covered by the cellular network (LTE) of the M:tel BL operator was chosen for the case study. The results show that the k-NN model has been selected as the best solution in all three optimization approaches. For the RReliefF optimization algorithm, the best model has six inputs and the minimum relative error (RE) RE = 0.109. For the Backward selection via the recursive feature elimination algorithm, the best model has four inputs and RE = 0.041. Finally, for the Pareto 80/20 rule, the best model has 11 inputs and RE = 0.049. The comparative analysis of the results concludes that, according to observed criteria for the selection of the final model, the best solution is an approach to optimizing the number of predictors based on the Backward selection via the recursive feature elimination algorithm.
dc.identifier.doi10.3390/app13148511
dc.identifier.urihttps://vaseljena.ues.rs.ba/handle/123456789/1354
dc.language.isoen
dc.publisherMDPI
dc.sourceApplied Sciences
dc.subjectdelay; dimensionality reduction; LTE; VoIP; Multilayer Perceptron; Support Vector Machines; k-nearest neighbors; Feature Selection; Pareto 80/20 rule
dc.titlePredictive Modeling of Delay in an LTE Network by Optimizing the Number of Predictors Using Dimensionality Reduction Techniques
dc.typeArticle
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