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Determining optimal zone radius of zone routing protocol based on deep recurrent neural networks in the next generation wireless backhaul networks

Next-generation wireless networks are becoming more popular and rely on reliable backhaul networks to work properly. Wireless backhaul networks also adopt various innovative technologies to improve capacity and provide more flexible deployments to meet networks' high-quality requirements. One o...

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Main Authors: Sirait, Fadli, Jusoh, Mohd Taufik Bin, Dimyati, Kaharudin, Din, Muhammad Faiz Bin Md
Format: Article
Published: Insight Society 2022
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.um.edu.my/43861/
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spelling my.um.eprints.438612023-12-29T03:53:17Z http://eprints.um.edu.my/43861/ Determining optimal zone radius of zone routing protocol based on deep recurrent neural networks in the next generation wireless backhaul networks Sirait, Fadli Jusoh, Mohd Taufik Bin Dimyati, Kaharudin Din, Muhammad Faiz Bin Md TK Electrical engineering. Electronics Nuclear engineering Next-generation wireless networks are becoming more popular and rely on reliable backhaul networks to work properly. Wireless backhaul networks also adopt various innovative technologies to improve capacity and provide more flexible deployments to meet networks' high-quality requirements. One of the essential innovations to maintain the wireless backhaul performance is combining the existing routing protocol technology and the deep learning concept. The concept of deep learning is gaining traction as a powerful way to add intelligence to wireless networks with complex topologies and radio environments. This is because conventional routing protocols do not learn from their previous experiences with various network anomalies. This paper proposed a predictive model of zone radius value using the deep recurrent neural network variant, namely the long short-term memory recurrent neural network (LSTMRNN) algorithm. Determination of zone radius value conducted by measuring the whole of nodes routing zone using various network performance as input parameters such as Routing Overhead, Energy Consumption, Throughput, and User Usage. Performance measurements such as mean square error (MSE), error distribution histogram, training state, regression, correlation, and time series response are gauged and compared for static and mobile node environments. Results showed that the proposed algorithm can accurately predict zone radius for both environments. However, the accuracy of the proposed algorithm is higher when implemented in a static node environment. © 2022, International Journal on Advanced Science, Engineering and Information Technology. All Rights Reserved. Insight Society 2022 Article PeerReviewed Sirait, Fadli and Jusoh, Mohd Taufik Bin and Dimyati, Kaharudin and Din, Muhammad Faiz Bin Md (2022) Determining optimal zone radius of zone routing protocol based on deep recurrent neural networks in the next generation wireless backhaul networks. International Journal on Advanced Science, Engineering and Information Technology, 12 (5). 2147 – 2155. ISSN 20885334, DOI https://doi.org/10.18517/ijaseit.12.5.15747 <https://doi.org/10.18517/ijaseit.12.5.15747>. 10.18517/ijaseit.12.5.15747
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Sirait, Fadli
Jusoh, Mohd Taufik Bin
Dimyati, Kaharudin
Din, Muhammad Faiz Bin Md
Determining optimal zone radius of zone routing protocol based on deep recurrent neural networks in the next generation wireless backhaul networks
description Next-generation wireless networks are becoming more popular and rely on reliable backhaul networks to work properly. Wireless backhaul networks also adopt various innovative technologies to improve capacity and provide more flexible deployments to meet networks' high-quality requirements. One of the essential innovations to maintain the wireless backhaul performance is combining the existing routing protocol technology and the deep learning concept. The concept of deep learning is gaining traction as a powerful way to add intelligence to wireless networks with complex topologies and radio environments. This is because conventional routing protocols do not learn from their previous experiences with various network anomalies. This paper proposed a predictive model of zone radius value using the deep recurrent neural network variant, namely the long short-term memory recurrent neural network (LSTMRNN) algorithm. Determination of zone radius value conducted by measuring the whole of nodes routing zone using various network performance as input parameters such as Routing Overhead, Energy Consumption, Throughput, and User Usage. Performance measurements such as mean square error (MSE), error distribution histogram, training state, regression, correlation, and time series response are gauged and compared for static and mobile node environments. Results showed that the proposed algorithm can accurately predict zone radius for both environments. However, the accuracy of the proposed algorithm is higher when implemented in a static node environment. © 2022, International Journal on Advanced Science, Engineering and Information Technology. All Rights Reserved.
format Article
author Sirait, Fadli
Jusoh, Mohd Taufik Bin
Dimyati, Kaharudin
Din, Muhammad Faiz Bin Md
author_facet Sirait, Fadli
Jusoh, Mohd Taufik Bin
Dimyati, Kaharudin
Din, Muhammad Faiz Bin Md
author_sort Sirait, Fadli
title Determining optimal zone radius of zone routing protocol based on deep recurrent neural networks in the next generation wireless backhaul networks
title_short Determining optimal zone radius of zone routing protocol based on deep recurrent neural networks in the next generation wireless backhaul networks
title_full Determining optimal zone radius of zone routing protocol based on deep recurrent neural networks in the next generation wireless backhaul networks
title_fullStr Determining optimal zone radius of zone routing protocol based on deep recurrent neural networks in the next generation wireless backhaul networks
title_full_unstemmed Determining optimal zone radius of zone routing protocol based on deep recurrent neural networks in the next generation wireless backhaul networks
title_sort determining optimal zone radius of zone routing protocol based on deep recurrent neural networks in the next generation wireless backhaul networks
publisher Insight Society
publishDate 2022
url http://eprints.um.edu.my/43861/
_version_ 1787133830491013120
score 13.211869

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