Publication:
Vehicular Network Spectrum Allocation Using Hybrid NOMA and Multi-agent Reinforcement Learning

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dc.citedby6
dc.contributor.authorAlatabani L.E.en_US
dc.contributor.authorSaeed R.A.en_US
dc.contributor.authorAli E.S.en_US
dc.contributor.authorMokhtar R.A.en_US
dc.contributor.authorKhalifa O.O.en_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorid57224509526en_US
dc.contributor.authorid16022855100en_US
dc.contributor.authorid57221716104en_US
dc.contributor.authorid16022551600en_US
dc.contributor.authorid9942198800en_US
dc.contributor.authorid56239664100en_US
dc.date.accessioned2024-10-14T03:20:41Z
dc.date.available2024-10-14T03:20:41Z
dc.date.issued2023
dc.description.abstractThe recent years have seen a proven impact of the reinforcement learning use in many applications which showed tremendous success in solving many decision-making paradigms in machine learning. Most of the successful applications involves the existence of more than one agent, which makes it fall into the multi-agent category, taking autonomous driving as an example of these applications. We know that today�s Internet of Vehicles (IoVs) consists of multi-communication patterns which work efficiently in keeping all the IoV network components connected. With regards to sharing the frequency spectrum, applying Non-Orthogonal Multiple Access (NOMA) communication built over deep deterministic policies gradients (DDPG) scheme to cope with the rabid erratic channels conditions due to fast mobility nature of vehicles network has proven promising results. In this paper the framework of NOMA communication-based DDPG and multiple agent reinforcement learning approach (MARL) are discussed in brief, and then, the performance evaluation of DDPG scheme compared with MARL and random spectrum allocation approaches for vehicular network spectrum and resources allocation is analysed. � 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-031-26580-8_23
dc.identifier.epage158
dc.identifier.scopus2-s2.0-85161558042
dc.identifier.spage151
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85161558042&doi=10.1007%2f978-3-031-26580-8_23&partnerID=40&md5=e1bedc6c43f20b2b97ce40f8179aafd5
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34563
dc.pagecount7
dc.publisherSpringer Natureen_US
dc.sourceScopus
dc.sourcetitleAdvances in Science, Technology and Innovation
dc.subjectDDPG
dc.subjectHybrid NOMA
dc.subjectMARL
dc.subjectRandom allocation
dc.subjectReinforcement Learning
dc.subjectSpectrum allocation
dc.subjectV2V communications
dc.titleVehicular Network Spectrum Allocation Using Hybrid NOMA and Multi-agent Reinforcement Learningen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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