Fairness aware deep reinforcement learning for grant-free NOMA-IoT networks

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dc.contributor.authorBalci, Abdullah
dc.contributor.authorSokullu, Radosveta
dc.date.accessioned2024-08-31T07:49:26Z
dc.date.available2024-08-31T07:49:26Z
dc.date.issued2024
dc.departmentEge Üniversitesien_US
dc.description.abstractNext generation networks have special areas related with the Internet of Things (IoT) to improve the performance of cellular networks in terms of throughput. Grant -free non -orthogonal multiple access (GF-NOMA) seems a feasible solution, letting machine type communication (MTC) devices transmits their packets when they ready to transmit. GF-NOMA increases the spectral efficiency by using the superimposing signals with different power levels over the same time and frequency resources. However, the main drawbacks of GF-NOMA are randomness and the management of power level selection of MTC devices. In 6G-IoT networks, the intelligence should be met to random access. It is time to design new access methods to solve the GFNOMA issues that should be between the randomness and fully coordinated medium access. Deep -Q -Network (DQN) has become a very hot research topic in recent years that let the MTC devices to make a smart decision in an intelligent way to improve the throughput. Selfishness is an undesirable behavior of DQN for GF-NOMA system where the resources have different cost. In this study, we develop a novel learning framework for power domain GF-NOMA. The goal of our learning framework is to maximize the throughput considering fairness in power consumption which provides long -life to the IoT network. The learning algorithm push the MTC devices to exchange the resources between each other over time. The results show that the proposed method outperform the NOMA scheme with random selection in terms of throughput and increase the fairness index when the DQN with selfish behavior is employed.en_US
dc.description.sponsorshipEge University Scientific Research Projects Coordination Unit., Turkey [FDK-2020-21820]en_US
dc.description.sponsorshipThis study is supported by Ege University Scientific Research Projects Coordination Unit., Turkey Project Number: FDK-2020-21820.en_US
dc.identifier.doi10.1016/j.iot.2024.101079
dc.identifier.issn2543-1536
dc.identifier.issn2542-6605
dc.identifier.scopus2-s2.0-85185178811en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.iot.2024.101079
dc.identifier.urihttps://hdl.handle.net/11454/104870
dc.identifier.volume25en_US
dc.identifier.wosWOS:001175452200001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofInternet of Thingsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240831_Uen_US
dc.subjectInternet Of Thingsen_US
dc.subjectRandom Accessen_US
dc.subjectGrant-Free Accessen_US
dc.subjectAlohaen_US
dc.subjectNomaen_US
dc.subjectMachine Learningen_US
dc.titleFairness aware deep reinforcement learning for grant-free NOMA-IoT networksen_US
dc.typeArticleen_US

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