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

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

doi:10.1007/s11277-023-10201-0

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

Hitesh Singh, Vivek Kumar^*, 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 case of future telecommunication technologies, extremely high data rates above 100 Gbps is the expectations, which can be fulfilled by utilizing higher spectrum bands. Higher frequency bands like terahertz wave bands are expected to have far broader bandwidths then 5G, therefore 6G will need to encourage R&D activities to utilize so called terahertz waves with frequencies ranging from 100 to 200 GHz. The challenge in utilizing these higher frequency bands is their sensitive nature toward outdoor environmental conditions like cloud, Fog, dust and Rain. To address these issues, this paper proposes a Machine Learning Model based on Artificial Neural Network to predict the attenuation caused due to Clouds and Fog at D and G bands. The model was trained using AMSER–2 Satellite data. The trained model is further optimized using different optimizing techniques. Obtained results was compared with the different existing models.

Original language	English
Journal	Wireless Personal Communications
Volume	129
Issue	3
Pages (from-to)	1669-1692
Number of pages	24
ISSN	0929-6212
DOIs	https://doi.org/10.1007/s11277-023-10201-0
Publication status	Published - Apr 2023

Keywords

Artificial neural network
Cloud and fog attenuation
Machine learning
Satellite communication 6G technology
Terahertz waves

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10.1007/s11277-023-10201-0

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

abstract = "In case of future telecommunication technologies, extremely high data rates above 100 Gbps is the expectations, which can be fulfilled by utilizing higher spectrum bands. Higher frequency bands like terahertz wave bands are expected to have far broader bandwidths then 5G, therefore 6G will need to encourage R&D activities to utilize so called terahertz waves with frequencies ranging from 100 to 200 GHz. The challenge in utilizing these higher frequency bands is their sensitive nature toward outdoor environmental conditions like cloud, Fog, dust and Rain. To address these issues, this paper proposes a Machine Learning Model based on Artificial Neural Network to predict the attenuation caused due to Clouds and Fog at D and G bands. The model was trained using AMSER–2 Satellite data. The trained model is further optimized using different optimizing techniques. Obtained results was compared with the different existing models.",

keywords = "Artificial neural network, Cloud and fog attenuation, Machine learning, Satellite communication 6G technology, Terahertz waves",

author = "Hitesh Singh and Vivek Kumar 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 = apr,

doi = "10.1007/s11277-023-10201-0",

language = "English",

volume = "129",

pages = "1669--1692",

journal = "Wireless Personal Communications",

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Smart Channel Modelling for Cloud and Fog Attenuation Using ML for Designing of 6G Networks at D and G Bands. / Singh, Hitesh; Kumar, Vivek; Saxena, Kumud et al.
In: Wireless Personal Communications, Vol. 129, No. 3, 04.2023, p. 1669-1692.

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

TY - JOUR

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

AU - Singh, Hitesh

AU - Kumar, Vivek

AU - Saxena, Kumud

AU - Kapse, Vinod M.

AU - Bonev, Boncho

AU - Prasad, Ramjee

PY - 2023/4

Y1 - 2023/4

N2 - In case of future telecommunication technologies, extremely high data rates above 100 Gbps is the expectations, which can be fulfilled by utilizing higher spectrum bands. Higher frequency bands like terahertz wave bands are expected to have far broader bandwidths then 5G, therefore 6G will need to encourage R&D activities to utilize so called terahertz waves with frequencies ranging from 100 to 200 GHz. The challenge in utilizing these higher frequency bands is their sensitive nature toward outdoor environmental conditions like cloud, Fog, dust and Rain. To address these issues, this paper proposes a Machine Learning Model based on Artificial Neural Network to predict the attenuation caused due to Clouds and Fog at D and G bands. The model was trained using AMSER–2 Satellite data. The trained model is further optimized using different optimizing techniques. Obtained results was compared with the different existing models.

AB - In case of future telecommunication technologies, extremely high data rates above 100 Gbps is the expectations, which can be fulfilled by utilizing higher spectrum bands. Higher frequency bands like terahertz wave bands are expected to have far broader bandwidths then 5G, therefore 6G will need to encourage R&D activities to utilize so called terahertz waves with frequencies ranging from 100 to 200 GHz. The challenge in utilizing these higher frequency bands is their sensitive nature toward outdoor environmental conditions like cloud, Fog, dust and Rain. To address these issues, this paper proposes a Machine Learning Model based on Artificial Neural Network to predict the attenuation caused due to Clouds and Fog at D and G bands. The model was trained using AMSER–2 Satellite data. The trained model is further optimized using different optimizing techniques. Obtained results was compared with the different existing models.

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KW - Cloud and fog attenuation

KW - Machine learning

KW - Satellite communication 6G technology

KW - Terahertz waves

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DO - 10.1007/s11277-023-10201-0

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AN - SCOPUS:85149241904

SN - 0929-6212

VL - 129

SP - 1669

EP - 1692

JO - Wireless Personal Communications

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IS - 3

ER -

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

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Keywords

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