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Approximations for ITV Rain Model Using Machine Learning

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Standard

Approximations for ITV Rain Model Using Machine Learning. / Kumar, Vivek; Singh, Hitesh; Saxena, Kumud; Bonev, Boncho; Prasad, Ramjee.

2021 56th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2021 - Proceedings. IEEE, 2021. p. 159-162 ( International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), Vol. 56th).

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Harvard

Kumar, V, Singh, H, Saxena, K, Bonev, B & Prasad, R 2021, Approximations for ITV Rain Model Using Machine Learning. in 2021 56th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2021 - Proceedings. IEEE, International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), vol. 56th, pp. 159-162, 56th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2021, Sozopol, Bulgaria, 16/06/2021. https://doi.org/10.1109/ICEST52640.2021.9483552

APA

Kumar, V., Singh, H., Saxena, K., Bonev, B., & Prasad, R. (2021). Approximations for ITV Rain Model Using Machine Learning. In 2021 56th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2021 - Proceedings (pp. 159-162). IEEE. International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST) Vol. 56th https://doi.org/10.1109/ICEST52640.2021.9483552

CBE

Kumar V, Singh H, Saxena K, Bonev B, Prasad R. 2021. Approximations for ITV Rain Model Using Machine Learning. In 2021 56th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2021 - Proceedings. IEEE. pp. 159-162. ( International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), Vol. 56th). https://doi.org/10.1109/ICEST52640.2021.9483552

MLA

Kumar, Vivek et al. "Approximations for ITV Rain Model Using Machine Learning". 2021 56th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2021 - Proceedings. IEEE. ( International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), Vol. 56th). 2021, 159-162. https://doi.org/10.1109/ICEST52640.2021.9483552

Vancouver

Kumar V, Singh H, Saxena K, Bonev B, Prasad R. Approximations for ITV Rain Model Using Machine Learning. In 2021 56th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2021 - Proceedings. IEEE. 2021. p. 159-162. ( International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), Vol. 56th). https://doi.org/10.1109/ICEST52640.2021.9483552

Author

Kumar, Vivek ; Singh, Hitesh ; Saxena, Kumud ; Bonev, Boncho ; Prasad, Ramjee. / Approximations for ITV Rain Model Using Machine Learning. 2021 56th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2021 - Proceedings. IEEE, 2021. pp. 159-162 ( International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), Vol. 56th).

Bibtex

@inproceedings{126e89c2c5804ef8b2a5c008fc4ffa65,
title = "Approximations for ITV Rain Model Using Machine Learning",
abstract = "In communication technologies, availability is the key performance matrix. Different factors which affect the availability of links are hardware reliability, finding interference etc. In radio wave propagation studies, attenuation caused by hydrometeors like rain plays an important role especially for higher frequency bands. Different models are there for the prediction of attenuation caused by rain out of which ITU-R model is one of the widely acceptable models. In this paper, K-Means algorithm is used to propose an improved ITU-R model. Proposed model can make up the shortcoming of ITU-R model to determine the break-up points in frequency range and obtained soft clusters have been trained by machine learning algorithms then proposes a mathematical model for prediction of radio wave attenuation due to rain. Results from proposed model compared with ITU-R model.",
keywords = "Clustering, ITU model, Millimeter waves, Rain Attenuation, Regression analysis",
author = "Vivek Kumar and Hitesh Singh and Kumud Saxena and Boncho Bonev and Ramjee Prasad",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 56th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2021 ; Conference date: 16-06-2021 Through 18-06-2021",
year = "2021",
doi = "10.1109/ICEST52640.2021.9483552",
language = "English",
series = " International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST)",
publisher = "IEEE",
pages = "159--162",
booktitle = "2021 56th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2021 - Proceedings",

}

RIS

TY - GEN

T1 - Approximations for ITV Rain Model Using Machine Learning

AU - Kumar, Vivek

AU - Singh, Hitesh

AU - Saxena, Kumud

AU - Bonev, Boncho

AU - Prasad, Ramjee

N1 - Publisher Copyright: © 2021 IEEE.

PY - 2021

Y1 - 2021

N2 - In communication technologies, availability is the key performance matrix. Different factors which affect the availability of links are hardware reliability, finding interference etc. In radio wave propagation studies, attenuation caused by hydrometeors like rain plays an important role especially for higher frequency bands. Different models are there for the prediction of attenuation caused by rain out of which ITU-R model is one of the widely acceptable models. In this paper, K-Means algorithm is used to propose an improved ITU-R model. Proposed model can make up the shortcoming of ITU-R model to determine the break-up points in frequency range and obtained soft clusters have been trained by machine learning algorithms then proposes a mathematical model for prediction of radio wave attenuation due to rain. Results from proposed model compared with ITU-R model.

AB - In communication technologies, availability is the key performance matrix. Different factors which affect the availability of links are hardware reliability, finding interference etc. In radio wave propagation studies, attenuation caused by hydrometeors like rain plays an important role especially for higher frequency bands. Different models are there for the prediction of attenuation caused by rain out of which ITU-R model is one of the widely acceptable models. In this paper, K-Means algorithm is used to propose an improved ITU-R model. Proposed model can make up the shortcoming of ITU-R model to determine the break-up points in frequency range and obtained soft clusters have been trained by machine learning algorithms then proposes a mathematical model for prediction of radio wave attenuation due to rain. Results from proposed model compared with ITU-R model.

KW - Clustering

KW - ITU model

KW - Millimeter waves

KW - Rain Attenuation

KW - Regression analysis

UR - http://www.scopus.com/inward/record.url?scp=85112283491&partnerID=8YFLogxK

U2 - 10.1109/ICEST52640.2021.9483552

DO - 10.1109/ICEST52640.2021.9483552

M3 - Article in proceedings

AN - SCOPUS:85112283491

T3 - International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST)

SP - 159

EP - 162

BT - 2021 56th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2021 - Proceedings

PB - IEEE

T2 - 56th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2021

Y2 - 16 June 2021 through 18 June 2021

ER -