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Journals / Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2026 / Cilt: 16 Sayı: 2

Artificial Intelligence Based Approach for Efficient Slice Selection in Next Generation Wireless Networks

Yeni Nesil Kablosuz Ağlarda Etkin Dilim Seçimi için Yapay Zeka Tabanlı Yaklaşım

Sümeyye Bayrakdar(DÜZCE ÜNİVERSİTESİ)0000-0002-8148-1090
Pages
393–407
Publication Date
2026-06-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results demonstrate that this approach offers a low-complexity, computationally efficient, and effective solution for selecting the appropriate network slice in cognitive radio networks.

📌 3 Key Takeaways

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Objective & Hypothesis:With the advent of the fifth-generation wireless technology, now known as 5G, a new era in communications has begun.
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Key Findings & Data:This study proposes an effective AI-based network slice selection approach for slice selection in 5G-based cognitive radio networks.
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Conclusion & Impact:The results demonstrate that this approach offers a low-complexity, computationally efficient, and effective solution for selecting the appropriate network slice in cognitive radio networks.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Ağ dilimleme · Dilim seçimi · Bilişsel radyo

Abstract

With the advent of the fifth-generation wireless technology, now known as 5G, a new era in communications has begun. Customizing network services to meet the diverse and personalized demands of various users has become crucial. Network slicing is a key technology for achieving this customization. Cognitive radio technologies, which enable efficient spectrum utilization, play a crucial role in providing customized services, as do network slicing. Cognitive radio technology, a dynamic spectrum access technique, is one of the most widely used access techniques for opportunistically exploiting unoccupied spectrum channels. In cognitive radio networks that aim for efficient spectrum utilization, selecting the most suitable network slice for the service provided by secondary users is a crucial optimization problem. This study proposes an effective AI-based network slice selection approach for slice selection in 5G-based cognitive radio networks. The proposed approach successfully implemented network slice selection using fuzzy logic-based network slicing. The results demonstrate that this approach offers a low-complexity, computationally efficient, and effective solution for selecting the appropriate network slice in cognitive radio networks.

Özet

Günümüzde 5G olarak adlandırılan beşinci nesil kablosuz teknolojinin ortaya çıkışı ile birlikte, iletişimde yeni bir dönem başlamış oldu. Bu dönemde çeşitli kullanıcıların farklı ve kişisel taleplerini karşılamak için ağ hizmetlerinin özelleştirilmesi büyük bir önem kazanmıştır. Ağ dilimleme, bu özelleştirmeyi sağlamak için temel bir teknoloji olarak karşımıza çıkmaktadır. Özelleştirilmiş hizmetlerin sunulmasında ağ dilimleme kadar, spektrumun verimli kullanımını sağlayan bilişsel radyo teknolojileri de önemli bir rol üstlenmektedir. Dinamik spektrum erişim tekniklerinden olan bilişsel radyo teknolojisi, boş spektrum kanallarını fırsatçı bir şekilde kullanmak için en çok kullanılan erişim tekniklerinden biridir. Spektrumun etkin kullanımını hedefleyen bilişsel radyo ağlarda, ikincil kullanıcılar için hizmete en uygun ağ dilimini seçmek önemli bir optimizasyon problemidir. Bu çalışmada 5G tabanlı bilişsel radyo ağlarda dilim seçimi için yapay zeka tabanlı etkili bir ağ dilimle yaklaşımı önerilmiştir. Önerilen yaklaşım ile ağ dilim seçimi bulanık mantık tabanlı ağ dilimleme kullanılarak başarıyla gerçekleştirilmiştir. Elde edilen sonuçlar, bu yaklaşımın bilişsel radyo ağlarda uygun ağ dilim seçimi yapmada düşük karmaşıklıkta, hesaplama açısından verimli ve etkili bir çözüm sunduğunu göstermektedir

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Cite This Article

Bayrakdar, S. (2026). Artificial Intelligence Based Approach for Efficient Slice Selection in Next Generation Wireless Networks. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 16 Sayı: 2, 393–407. https://doi.org/10.21597/jist.1755914

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