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

Autor:	A. M. Alalade, E. E. Ekong, A. Ben-Obaje, Adeyinka A. Adewale, Charles N. Ndujiuba
Rok vydání:	2019
Předmět:	History Hysteresis (economics) Computer science Performance comparison Training (meteorology) Simulation Computer Science Applications Education
Zdroj:	Journal of Physics: Conference Series. 1378:042094
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/1378/4/042094
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6974dbed52ded645957d90bd0378f240 https://doi.org/10.1088/1742-6596/1378/4/042094 Zobrazit plný text záznamu