Performance Comparison of ANN Training Algorithms for Hysteresis Determination in LTE networks

Long-Term Evolution (LTE) network is an improved standard for mobile telecommunication system developed by the 3rd Generation Partnership Project (3GPP) requires an efficient handover framework which would reduce hysteresis and improve quality of service (QoS) of subscribers by maximizing scarce rad...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1378; no. 4; pp. 42094 - 42107
Main Authors Ekong, E E, Adewale, A A, Ben-Obaje, A, Alalade, A M, Ndujiuba, C N
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.12.2019
Subjects
Algorithms
Bayesian analysis
Electric communication systems
Hysteresis
Physics
Reduction
Regularization
Signal strength
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Summary:Long-Term Evolution (LTE) network is an improved standard for mobile telecommunication system developed by the 3rd Generation Partnership Project (3GPP) requires an efficient handover framework which would reduce hysteresis and improve quality of service (QoS) of subscribers by maximizing scarce radio resources. This paper compares the performance of two ANN prediction algorithms (LevenbergMarquadt and Bayesian regularization) based on received signal strength (RSS) and the hysteresis margin parameters for neuro-adaptive hysteresis margin reduction algorithm. The Bayesian regularization algorithm had a lower mean error when compared with the Levenberg-Marquadt (LM) prediction algorithm and as such a better option for neuro-adaptive hysteresis margin reduction algorithm.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1378/4/042094