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Multicriteria Oppositional-Learnt Dragonfly Resource-Optimized QoS Driven Channel Selection for CRNs, Journal of Telecommunications and Information Technology, 2022, nr 4
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Tytuł: Multicriteria Oppositional-Learnt Dragonfly Resource-Optimized QoS Driven Channel Selection for CRNs, Journal of Telecommunications and Information Technology, 2022, nr 4

Autor:

Maloj, Suman ; Devi, Ch.S.N. Sirisha

Data wydania:

2022, nr 4

Typ zasobu:

artykuł

Opis:

Cognitive radio networks (CRNs) allow their users to achieve adequate QoS while communicating. The major concern related to CRN is linked to guaranteeing free channel selection to secondary users (SUs) in order to maintain the network’s throughput. Many techniques have been designed in the literature for channel selection in CRNs, but the throughput of the network has not been enhanced yet. Here, an efficient technique, known as multicriteria oppositional-learnt dragonfly resource optimized QoS-driven channel selection (MOLDRO-QoSDCS) is proposed to select the best available channel with the expected QoS metrics. The MOLDRO-QoSDCS technique is designed to improve energy efficiency and throughput, simultaneously reducing the sensing time. By relying on oppositional-learnt multiobjective dragonfly optimization, the optimal available channel is selected depending on signal-to-noise ratio, power consumption, and spectrum utilization. In the optimization process, the population of the available channels is initialized. Then, using multiple criteria, the fitness function is determined and the available channel with the best resource availability is selected. Using the selected optimal channel, data transmission is effectively performed to increase the network’s throughput and to minimize the sensing time. The simulated outputs obtained with the use of Matlab are compared with conventional algorithms in order to verify the performance of the solution. The MOLDRO-QoSDCS technique performs better than other methods in terms of throughput, sensing time, and energy efficiency.

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Format:

application/pdf

Identyfikator zasobu:

oai:bc.itl.waw.pl:2252 ; ISSN 1509-4553, on-line: ISSN 1899-8852

DOI:

10.26636/jtit.2022.164722

eISSN:

1899-8852

Źródło:

Journal of Telecommunications and Information Technology

Język:

ang

Prawa:

Biblioteka Naukowa Instytutu Łączności

Temat i słowa kluczowe:

multicriteria dragonfly optimization QoS metric oppositional learning optimal available channel cognitive ratio network (CRN)

Kolekcje, do których przypisany jest obiekt:

Data ostatniej modyfikacji:

6 cze 2024

Data dodania obiektu:

7 lut 2023

Liczba wyświetleń treści obiektu:

28

Wszystkie dostępne wersje tego obiektu:

https://bc.itl.waw.pl/publication/2561

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Nazwa wydania Data
Devi, Ch.S.N. Sirisha, Multicriteria Oppositional-Learnt Dragonfly Resource-Optimized QoS Driven Channel Selection for CRNs, Journal of Telecommunications and Information Technology, 2022, nr 4 6 cze 2024

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Instytut Łączności – Państwowy Instytut Badawczy ul. Szachowa 1 04-894 Warszawa

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(+48) 5128 696

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biblioteka@il-pib.pl

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