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Analysis of back propagation and radial basis function neural networks for handover decisions in wireless communication

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 10 No. 5 (2020)

DOI
10.11591/ijece.v10i5.pp4835-4843
ISSN
2722-2578, 2088-8708
Published
2020-10-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

In mobile systems, handoff is a vital process, referring to a process of allocating an ongoing call from one BS to another BS. The handover technique is very important to maintain the Quality of service. Handover algorithms, based on neural networks, fuzzy logic etc. can be used for the same purpose to keep Quality of service as high as possible. In this paper, it is proposed that back propagation networks and radial basis functions may be used for taking handover decision in wireless communication networks. The performance of these classifiers is evaluated on the basis of neurons in hidden layer, training time and classification accuracy. The proposed approach shows that radial basis function neural network give better results for making handover decisions in wireless heterogeneous networks with classification accuracy of 90%.

Collaboration network of the authors

2 authors · 1 co-authorship link

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  • Jamia Millia Islamia
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
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