Open AccessPerformance Comparison of ANN Training Algorithms for Hysteresis Determination in LTE networks
Author(s) -
E. E. Ekong,
Adeyinka A. Adewale,
A. Ben-Obaje,
A. M. Alalade,
C N Ndujiuba
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1378/4/042094
Subject(s) - computer science , algorithm , 3rd generation partnership project 2 , handover , margin (machine learning) , regularization (linguistics) , rss , reduction (mathematics) , quality of service , machine learning , artificial intelligence , mathematics , telecommunications , telecommunications link , geometry , operating system
Long-Term Evolution (LTE) network is an improved standard for mobile telecommunication system developed by the 3 rd 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.