Smart Channel Modelling for Rain Attenuation Using ML for Designing of 6G Networks at D and G Bands

Vivek Kumar; Hitesh Singh; Kumud Saxena; Vinod M. Kapse; Boncho Bonev; Ramjee Prasad

doi:10.1007/s11277-023-10701-z

Smart Channel Modelling for Rain Attenuation Using ML for Designing of 6G Networks at D and G Bands

Vivek Kumar^*, Hitesh Singh, Kumud Saxena, Vinod M. Kapse, Boncho Bonev, Ramjee Prasad

^*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Abstract

In the future, communication technologies are expected to achieve data rates exceeding 100 Gbps by utilizing higher spectrum, like terahertz wavebands, which have broader bandwidth than 5G. In order to accomplish this goal, the research and development efforts for 6G will concentrate on the utilization of terahertz waves, which have frequencies having range of 100–200 GHz. Nonetheless, application of these bands faces challenges due to their vulnerability to external environmental factors, such as cloud cover, fog, dust, and rain. This research proposes a Machine Learning Models to estimate the Rain induced attenuation at the D and G bands to address these challenges. AMSER-2 Satellite data was used to train the model. Different optimization strategies are used to improve the training model. The obtained results were compared to various related state of art.

Original language	English
Journal	Wireless Personal Communications
Volume	132
Issue	3
Pages (from-to)	2069-2096
Number of pages	28
ISSN	0929-6212
DOIs	https://doi.org/10.1007/s11277-023-10701-z
Publication status	Published - Oct 2023

Keywords

Artificial neural network (ANN)
Machine learning
Rain attenuation
Satellite communication 6G technology
Terahertz waves

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10.1007/s11277-023-10701-z

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title = "Smart Channel Modelling for Rain Attenuation Using ML for Designing of 6G Networks at D and G Bands",

abstract = "In the future, communication technologies are expected to achieve data rates exceeding 100 Gbps by utilizing higher spectrum, like terahertz wavebands, which have broader bandwidth than 5G. In order to accomplish this goal, the research and development efforts for 6G will concentrate on the utilization of terahertz waves, which have frequencies having range of 100–200 GHz. Nonetheless, application of these bands faces challenges due to their vulnerability to external environmental factors, such as cloud cover, fog, dust, and rain. This research proposes a Machine Learning Models to estimate the Rain induced attenuation at the D and G bands to address these challenges. AMSER-2 Satellite data was used to train the model. Different optimization strategies are used to improve the training model. The obtained results were compared to various related state of art.",

keywords = "Artificial neural network (ANN), Machine learning, Rain attenuation, Satellite communication 6G technology, Terahertz waves",

author = "Vivek Kumar and Hitesh Singh and Kumud Saxena and Kapse, {Vinod M.} and Boncho Bonev and Ramjee Prasad",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.",

year = "2023",

month = oct,

doi = "10.1007/s11277-023-10701-z",

language = "English",

volume = "132",

pages = "2069--2096",

journal = "Wireless Personal Communications",

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Smart Channel Modelling for Rain Attenuation Using ML for Designing of 6G Networks at D and G Bands. / Kumar, Vivek; Singh, Hitesh; Saxena, Kumud et al.
In: Wireless Personal Communications, Vol. 132, No. 3, 10.2023, p. 2069-2096.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

TY - JOUR

T1 - Smart Channel Modelling for Rain Attenuation Using ML for Designing of 6G Networks at D and G Bands

AU - Kumar, Vivek

AU - Singh, Hitesh

AU - Saxena, Kumud

AU - Kapse, Vinod M.

AU - Bonev, Boncho

AU - Prasad, Ramjee

PY - 2023/10

Y1 - 2023/10

N2 - In the future, communication technologies are expected to achieve data rates exceeding 100 Gbps by utilizing higher spectrum, like terahertz wavebands, which have broader bandwidth than 5G. In order to accomplish this goal, the research and development efforts for 6G will concentrate on the utilization of terahertz waves, which have frequencies having range of 100–200 GHz. Nonetheless, application of these bands faces challenges due to their vulnerability to external environmental factors, such as cloud cover, fog, dust, and rain. This research proposes a Machine Learning Models to estimate the Rain induced attenuation at the D and G bands to address these challenges. AMSER-2 Satellite data was used to train the model. Different optimization strategies are used to improve the training model. The obtained results were compared to various related state of art.

AB - In the future, communication technologies are expected to achieve data rates exceeding 100 Gbps by utilizing higher spectrum, like terahertz wavebands, which have broader bandwidth than 5G. In order to accomplish this goal, the research and development efforts for 6G will concentrate on the utilization of terahertz waves, which have frequencies having range of 100–200 GHz. Nonetheless, application of these bands faces challenges due to their vulnerability to external environmental factors, such as cloud cover, fog, dust, and rain. This research proposes a Machine Learning Models to estimate the Rain induced attenuation at the D and G bands to address these challenges. AMSER-2 Satellite data was used to train the model. Different optimization strategies are used to improve the training model. The obtained results were compared to various related state of art.

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KW - Machine learning

KW - Rain attenuation

KW - Satellite communication 6G technology

KW - Terahertz waves

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EP - 2096

JO - Wireless Personal Communications

JF - Wireless Personal Communications

IS - 3

ER -

Smart Channel Modelling for Rain Attenuation Using ML for Designing of 6G Networks at D and G Bands

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