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Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm

A cognitive radio network (CRN) is an intelligent network that can detect unoccupied spectrum space without interfering with the primary user (PU). Spectrum scarcity arises due to the stable channel allocation, which the CRN handles. Spectrum handoff management is a critical problem that must be add...

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Autores principales: Srivastava, Vikas, Singh, Parulpreet, Malik, Praveen Kumar, Singh, Rajesh, Tanwar, Sudeep, Alqahtani, Fayez, Tolba, Amr, Marina, Verdes, Raboaca, Maria Simona
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9962354/
https://www.ncbi.nlm.nih.gov/pubmed/36850606
http://dx.doi.org/10.3390/s23042011
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author Srivastava, Vikas
Singh, Parulpreet
Malik, Praveen Kumar
Singh, Rajesh
Tanwar, Sudeep
Alqahtani, Fayez
Tolba, Amr
Marina, Verdes
Raboaca, Maria Simona
author_facet Srivastava, Vikas
Singh, Parulpreet
Malik, Praveen Kumar
Singh, Rajesh
Tanwar, Sudeep
Alqahtani, Fayez
Tolba, Amr
Marina, Verdes
Raboaca, Maria Simona
author_sort Srivastava, Vikas
collection PubMed
description A cognitive radio network (CRN) is an intelligent network that can detect unoccupied spectrum space without interfering with the primary user (PU). Spectrum scarcity arises due to the stable channel allocation, which the CRN handles. Spectrum handoff management is a critical problem that must be addressed in the CRN to ensure indefinite connection and profitable use of unallocated spectrum space for secondary users (SUs). Spectrum handoff (SHO) has some disadvantages, i.e., communication delay and power consumption. To overcome these drawbacks, a reduction in handoff should be a priority. This study proposes the use of dynamic spectrum access (DSA) to check for available channels for SU during handoff using a metaheuristic algorithm depending on machine learning. The simulation results show that the proposed “support vector machine-based red deer algorithm” (SVM-RDA) is resilient and has low complexity. The suggested algorithm’s experimental setup offers several handoffs, unsuccessful handoffs, handoff delay, throughput, signal-to-noise ratio (SNR), SU bandwidth, and total spectrum bandwidth. This study provides an improved system performance during SHO. The inferred technique anticipates handoff delay and minimizes the handoff numbers. The results show that the recommended method is better at making predictions with fewer handoffs compared to the other three.
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spelling pubmed-99623542023-02-26 Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm Srivastava, Vikas Singh, Parulpreet Malik, Praveen Kumar Singh, Rajesh Tanwar, Sudeep Alqahtani, Fayez Tolba, Amr Marina, Verdes Raboaca, Maria Simona Sensors (Basel) Article A cognitive radio network (CRN) is an intelligent network that can detect unoccupied spectrum space without interfering with the primary user (PU). Spectrum scarcity arises due to the stable channel allocation, which the CRN handles. Spectrum handoff management is a critical problem that must be addressed in the CRN to ensure indefinite connection and profitable use of unallocated spectrum space for secondary users (SUs). Spectrum handoff (SHO) has some disadvantages, i.e., communication delay and power consumption. To overcome these drawbacks, a reduction in handoff should be a priority. This study proposes the use of dynamic spectrum access (DSA) to check for available channels for SU during handoff using a metaheuristic algorithm depending on machine learning. The simulation results show that the proposed “support vector machine-based red deer algorithm” (SVM-RDA) is resilient and has low complexity. The suggested algorithm’s experimental setup offers several handoffs, unsuccessful handoffs, handoff delay, throughput, signal-to-noise ratio (SNR), SU bandwidth, and total spectrum bandwidth. This study provides an improved system performance during SHO. The inferred technique anticipates handoff delay and minimizes the handoff numbers. The results show that the recommended method is better at making predictions with fewer handoffs compared to the other three. MDPI 2023-02-10 /pmc/articles/PMC9962354/ /pubmed/36850606 http://dx.doi.org/10.3390/s23042011 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Srivastava, Vikas
Singh, Parulpreet
Malik, Praveen Kumar
Singh, Rajesh
Tanwar, Sudeep
Alqahtani, Fayez
Tolba, Amr
Marina, Verdes
Raboaca, Maria Simona
Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm
title Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm
title_full Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm
title_fullStr Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm
title_full_unstemmed Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm
title_short Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm
title_sort innovative spectrum handoff process using a machine learning-based metaheuristic algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9962354/
https://www.ncbi.nlm.nih.gov/pubmed/36850606
http://dx.doi.org/10.3390/s23042011
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