Artificial intelligence-based fair allocation in NOMA technique: A review

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dc.authorid0000-0003-2415-9131
dc.authorid0000-0001-6999-1410
dc.authorid0000-0003-4559-7173
dc.contributor.authorKırtay, Seda
dc.contributor.authorYıldız, Kazım
dc.contributor.authorBöcekçi, Veysel Gökhan
dc.date.accessioned2024-09-04T06:45:49Z
dc.date.available2024-09-04T06:45:49Z
dc.date.issued2024
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractNon-Orthogonal Multiple Access (NOMA) is an innovation that has great potential in wireless communication. It permits multiple users to efficiently allot a frequency band by adjusting their power allocations. Nevertheless, attaining fair power allocation in NOMA structures presents complex challenges that require specific models, extensive training data, and addressing issues of generalization. This review aims to explore the applications of Artificial Intelligence (AI) and Deep Learning (DL) methods to tackle the challenges associated with fair power allocation in NOMA systems. The focus is on developing strong AI-DL models and creative optimization methods specifically designed for dynamic environments to improve transparency and interpretability. This study explores a wide range of techniques, including Reinforcement Learning, Convolutional Neural Networks (CNN) for power allocation, Generative Adversarial Networks, Deep Reinforcement Learning, and Transfer Learning. The goal is to enhance various aspects, such as power allocation, user coupling, scheduling strategies, interference cancellation, user mobility, security, and deeplearning- based NOMA. Despite the difficulties, impartial power allocation algorithms based on AI and DL show promise in improving user performance and promoting fair power distribution in NOMA systems. This study emphasizes the significance of continuous research efforts to overcome current obstacles, enhance efficiency, and strengthen the dependability of wireless communication systems. This highlights the significance of NOMA as an advanced innovation for upcoming wireless generations that go beyond 5G. Future areas of study involve investigating federated learning and novel techniques for gathering data and utilizing interpretable AI-DL models to address existing constraints. Overall, this review highlights the potential of AI and DL techniques in achieving fair power distribution in NOMA systems. However, further investigation is crucial to addressing obstacles and fully exploring the capabilities of NOMA technology.
dc.identifier.citationKırtay, S., Yıldız, K., & Böcekçi, V. G. (2024). Artificial intelligence-based fair allocation in NOMA technique: A review. International Journal of Sensors, Wireless Communications and Control, 14(3), pp. 161-174.
dc.identifier.doi10.2174/0122103279288496240121074942
dc.identifier.endpage174
dc.identifier.issn2210-3279
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85201587447
dc.identifier.scopusqualityQ3
dc.identifier.startpage161
dc.identifier.urihttps://doi.org/10.2174/0122103279288496240121074942
dc.identifier.urihttps://hdl.handle.net/20.500.13055/769
dc.identifier.volume14
dc.indekslendigikaynakScopus
dc.institutionauthorKırtay, Seda
dc.institutionauthorid0000-0003-2415-9131
dc.language.isoen
dc.publisherBentham Science Publishers
dc.relation.ispartofInternational Journal of Sensors, Wireless Communications and Control
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAI NOMA
dc.subjectConvolutional Neural Networks
dc.subjectDL
dc.subjectFair Allocation
dc.subjectNOMA
dc.subjectNon-Orthogonal Multiple Access
dc.titleArtificial intelligence-based fair allocation in NOMA technique: A review
dc.typeReview Article
dspace.entity.typePublication

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