Swarm intelligence-inspired localization and power control for terahertz (THz) UAV-vehicle networks

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dc.authorid0000-0002-9650-6350
dc.authorid0000-0001-8271-9059
dc.contributor.authorKorpe, Enis
dc.contributor.authorAkkas, Mustafa Alper
dc.contributor.authorOzturk, Yavuz
dc.date.accessioned2026-01-24T12:31:03Z
dc.date.available2026-01-24T12:31:03Z
dc.date.issued2025
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractIn recent years, terahertz (THz) communication has gained significant attention as a transformative technology for high-speed wireless networks, addressing the limitations of conventional frequency bands in meeting the escalating demand for data transmission. THz communication is particularly critical in vehicle-to-everything (V2X) and unmanned aerial vehicle (UAV)-based communication networks, where ultra-low latency, high bandwidth, and reliable connectivity are essential. Operating in the frequency spectrum between the microwave and infrared bands, THz communication offers the potential for multi-gigabit data transmission rates, rendering it a promising enabler for next-generation intelligent transportation systems, autonomous vehicles, and UAV-supported applications. Furthermore, artificial intelligence (AI) emerges as a pivotal tool to enhance the reliability and efficiency of THz-based V2X and UAV communication networks by enabling the prediction of network traffic patterns and mobility dynamics. This study introduces a swarm intelligence-based AI approach designed to optimize system performance by minimizing latency and transmission power requirements while ensuring the required signal-to-noise ratio (SNR) within a UAV-assisted vehicular network operating in the THz band. The proposed methodology employs a dual-objective optimization framework that balances latency and transmission power within a predefined communication time frame. Comparative analysis is conducted between a baseline network with randomly distributed UAVs and a network employing UAV deployment guided by the proposed AI scheme. Also, the performance of proposed method is compared with existing swarm intelligence algorithms. Performance metrics, including SNR and latency, are evaluated to assess the system's efficacy. The channel modeling process leverages the Line-by-Line Radiative Transfer Model (LBLRTM) to characterize the propagation environment in the UAV-assisted vehicular network.
dc.identifier.doi10.1016/j.adhoc.2025.103892
dc.identifier.issn1570-8705
dc.identifier.issn1570-8713
dc.identifier.scopus2-s2.0-105004220775
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.adhoc.2025.103892
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5624
dc.identifier.volume176
dc.identifier.wosWOS:001488755800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofAd Hoc Networks
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260121
dc.subjectTerahertz
dc.subjectVehicular communication
dc.subjectUAV
dc.subjectArtificial intelligence
dc.subjectSNR
dc.subjectLBLRTM
dc.titleSwarm intelligence-inspired localization and power control for terahertz (THz) UAV-vehicle networks
dc.typeArticle

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