Robust Channel Assignment for Hybrid NOMA Systems with Condition Number Constrainted DRL

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dc.contributor.authorZheng, J.
dc.contributor.authorTang, X.
dc.contributor.authorWei, X.
dc.contributor.authorSong, L.
dc.contributor.authorMuhsen, H.
dc.contributor.authorHabbal, A.
dc.date.accessioned2024-09-29T16:20:53Z
dc.date.available2024-09-29T16:20:53Z
dc.date.issued2021
dc.departmentKarabük Üniversitesien_US
dc.descriptionInstitute on Networking Systems of AI (INSAI)en_US
dc.description2021 International Conference on Networking Systems of AI, INSAI 2021 -- 19 November 2021 through 20 November 2021 -- Shanghai -- 178906en_US
dc.description.abstractThe Hybrid Non-Orthogonal Multiple Access (NOMA) is an alternative solution for future multiple access techniques, and the performance of hybrid NOMA systems relies on the quality of channel assignment. Conventional optimization approaches rely on the perfect Channel State Information (CSI), which hinders the deployment of the Hybrid systems. Deep Reinforcement Learning (DRL) approaches are robust to uncertain environments, and have been applied to deal with the dynamic channel assignment in hybrid NOMA systems. In this paper, a novel DRL approach based on condition number constraint is proposed to further enhance the robustness of the model. The simulation results show that the proposed approach achieves higher average spectral efficiency under imperfect CSI, compared to unconstrained DRL approaches and conventional approaches. This is useful for critical infrastructure systems such as base stations that require a high degree of robustness. © 2021 IEEE.en_US
dc.description.sponsorshipFujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, (2021ZZ120); National Science Found for Young Scholars, (61806186)en_US
dc.identifier.doi10.1109/INSAI54028.2021.00025
dc.identifier.endpage81en_US
dc.identifier.isbn978-166540859-2
dc.identifier.scopus2-s2.0-85129828593en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage77en_US
dc.identifier.urihttps://doi.org/10.1109/INSAI54028.2021.00025
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9376
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 2021 International Conference on Networking Systems of AI, INSAI 2021en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectChannel assignmenten_US
dc.subjectCondition numberen_US
dc.subjectDeep reinforcement learningen_US
dc.subjectHybrid noma systemsen_US
dc.titleRobust Channel Assignment for Hybrid NOMA Systems with Condition Number Constrainted DRLen_US
dc.typeConference Objecten_US

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