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Neural embedded smart link generation scheme for heterogeneous network
The Long Term Evolution (LTE) network is a very much popular network in heterogeneous network. Heterogeneous networks provide maximum data rate by integrating various technologies and channels, based on appropriate network selection. For the sensible data transmission in the LTE network, noise plays...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6310772/ https://www.ncbi.nlm.nih.gov/pubmed/30603717 http://dx.doi.org/10.1016/j.heliyon.2018.e01089 |
_version_ | 1783383493094408192 |
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author | Padaganur, Satyanarayan K. Mallapur, Jayashree D. |
author_facet | Padaganur, Satyanarayan K. Mallapur, Jayashree D. |
author_sort | Padaganur, Satyanarayan K. |
collection | PubMed |
description | The Long Term Evolution (LTE) network is a very much popular network in heterogeneous network. Heterogeneous networks provide maximum data rate by integrating various technologies and channels, based on appropriate network selection. For the sensible data transmission in the LTE network, noise plays an vital role as channel is a free space. The minimum noise channel selection is a decision of present and previous status of network channel. Proposed scheme develop neural network model, which will act as a smart link generation scheme for computing minimum noise channel path for sensible data transmission. Hence, proposed scheme will improve performance of the network. Result indicates that, proposed scheme improves throughput and system reliability. Proposed scheme is also reduces packet loss rate and energy consumption in contrast with conventional techniques. |
format | Online Article Text |
id | pubmed-6310772 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-63107722019-01-02 Neural embedded smart link generation scheme for heterogeneous network Padaganur, Satyanarayan K. Mallapur, Jayashree D. Heliyon Article The Long Term Evolution (LTE) network is a very much popular network in heterogeneous network. Heterogeneous networks provide maximum data rate by integrating various technologies and channels, based on appropriate network selection. For the sensible data transmission in the LTE network, noise plays an vital role as channel is a free space. The minimum noise channel selection is a decision of present and previous status of network channel. Proposed scheme develop neural network model, which will act as a smart link generation scheme for computing minimum noise channel path for sensible data transmission. Hence, proposed scheme will improve performance of the network. Result indicates that, proposed scheme improves throughput and system reliability. Proposed scheme is also reduces packet loss rate and energy consumption in contrast with conventional techniques. Elsevier 2018-12-27 /pmc/articles/PMC6310772/ /pubmed/30603717 http://dx.doi.org/10.1016/j.heliyon.2018.e01089 Text en © 2018 Published by Elsevier Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Padaganur, Satyanarayan K. Mallapur, Jayashree D. Neural embedded smart link generation scheme for heterogeneous network |
title | Neural embedded smart link generation scheme for heterogeneous network |
title_full | Neural embedded smart link generation scheme for heterogeneous network |
title_fullStr | Neural embedded smart link generation scheme for heterogeneous network |
title_full_unstemmed | Neural embedded smart link generation scheme for heterogeneous network |
title_short | Neural embedded smart link generation scheme for heterogeneous network |
title_sort | neural embedded smart link generation scheme for heterogeneous network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6310772/ https://www.ncbi.nlm.nih.gov/pubmed/30603717 http://dx.doi.org/10.1016/j.heliyon.2018.e01089 |
work_keys_str_mv | AT padaganursatyanarayank neuralembeddedsmartlinkgenerationschemeforheterogeneousnetwork AT mallapurjayashreed neuralembeddedsmartlinkgenerationschemeforheterogeneousnetwork |