Artificial neural network based antenna sensitivity assignments for chaotic Internet service provider network architecture
The connectivity and grade of service of an Internet Service Provider (ISP) in the Philippines is observed and analysed in this research. Traditionally, the sensitivity of the antennas for wireless access points are done manually by monitoring the signal levels onsite during the installation process...
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oai:animorepository.dlsu.edu.ph:faculty_research-30222023-01-12T07:18:07Z Artificial neural network based antenna sensitivity assignments for chaotic Internet service provider network architecture Villanueva, Lolita E. Gustilo, Reggie C. The connectivity and grade of service of an Internet Service Provider (ISP) in the Philippines is observed and analysed in this research. Traditionally, the sensitivity of the antennas for wireless access points are done manually by monitoring the signal levels onsite during the installation process. Ten subscriber locations are randomly selected as test points. The connectivity of these subscribers is observed given that their sensitivities are set manually. Finally, a proposed artificial neural network algorithm is presented to improve the availability of the internet link. The proposed algorithm incorporates the random variations of the received signal levels of the internet access points and possible degradation of signals from attenuation due to rain. Experiment results show that at least 75% increase in availability is observed using the proposed algorithm during rainy events. © 2018 Authors. 2018-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/2023 Faculty Research Work Animo Repository Internet service providers--Philippines Wireless communication systems--Philippines Neural networks (Computer science) Electrical and Computer Engineering |
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Internet service providers--Philippines Wireless communication systems--Philippines Neural networks (Computer science) Electrical and Computer Engineering Villanueva, Lolita E. Gustilo, Reggie C. Artificial neural network based antenna sensitivity assignments for chaotic Internet service provider network architecture |
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The connectivity and grade of service of an Internet Service Provider (ISP) in the Philippines is observed and analysed in this research. Traditionally, the sensitivity of the antennas for wireless access points are done manually by monitoring the signal levels onsite during the installation process. Ten subscriber locations are randomly selected as test points. The connectivity of these subscribers is observed given that their sensitivities are set manually. Finally, a proposed artificial neural network algorithm is presented to improve the availability of the internet link. The proposed algorithm incorporates the random variations of the received signal levels of the internet access points and possible degradation of signals from attenuation due to rain. Experiment results show that at least 75% increase in availability is observed using the proposed algorithm during rainy events. © 2018 Authors. |
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text |
author |
Villanueva, Lolita E. Gustilo, Reggie C. |
author_facet |
Villanueva, Lolita E. Gustilo, Reggie C. |
author_sort |
Villanueva, Lolita E. |
title |
Artificial neural network based antenna sensitivity assignments for chaotic Internet service provider network architecture |
title_short |
Artificial neural network based antenna sensitivity assignments for chaotic Internet service provider network architecture |
title_full |
Artificial neural network based antenna sensitivity assignments for chaotic Internet service provider network architecture |
title_fullStr |
Artificial neural network based antenna sensitivity assignments for chaotic Internet service provider network architecture |
title_full_unstemmed |
Artificial neural network based antenna sensitivity assignments for chaotic Internet service provider network architecture |
title_sort |
artificial neural network based antenna sensitivity assignments for chaotic internet service provider network architecture |
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Animo Repository |
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2018 |
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https://animorepository.dlsu.edu.ph/faculty_research/2023 |
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