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Massive MIMO for Aerial Highways: Enhancing Cell Selection via SSB Beams Optimization

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Massive MIMO for Aerial Highways: Enhancing Cell Selection via SSB Beams Optimization

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dc.contributor.author Bernabè, Matteo es_ES
dc.contributor.author López-Pérez, David es_ES
dc.contributor.author Piovesan, Nicola es_ES
dc.contributor.author Geraci, Giovanni es_ES
dc.contributor.author Gesbert, David es_ES
dc.date.accessioned 2024-09-09T18:09:36Z
dc.date.available 2024-09-09T18:09:36Z
dc.date.issued 2024 es_ES
dc.identifier.uri http://hdl.handle.net/10251/207820
dc.description.abstract [EN] In this article, we introduce a novel approach for enhancing cellular connectivity for unmanned aerial vehicles (UAVs) on aerial highways via terrestrial 5G networks. Owing to their ability to navigate 3D space, UAVs may experience favourable channel gains across multiple network cells; thus, from a network operator's perspective, selecting serving cells that maximize UAV capacity is not straightforward. Merely considering conventional metrics like reference signal received power (RSRP) may lead to selecting cells that offer high power but are inefficient in multiplexing UAVs, leading to possible reduced data rate performance. To tackle this problem, we propose a novel 5G synchronization signal block (SSB) beams planning solution to strategically control the UAVs cell association and maximize UAVs capacity without affecting terrestrial users. To solve the associated NP-hard problem, we propose a heuristic solution based on a novel metric that captures the multiplexing capability, average channel quality gain, and interference. Leveraging our proposed metric, we first optimally split the aerial highway and define serving cells, and then optimally select SSB beams and their transmitting power to ensure coverage from the previously defined serving cells set. Results indicate that our solution significantly improves the UAV data rate performance on aerial highways across different network scenarios and traffic conditions without impacting terrestrial users. es_ES
dc.description.sponsorship This work was supported in part by the Generalitat Valenciana, Spain, through CIDEGENT PlaGenT under Grant CIDEXG/2022/17 and Project iTENTE; in part by the Spanish State Research Agency under Grant PID2021-123999OB-I00, Grant CEX2021-001195-M, and Grant CNS2023-145384; in part by the UPF-Fractus Chair on Tech Transfer and 6G; and in part by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union NextGenerationEU. es_ES
dc.language Inglés es_ES
dc.publisher IEEE Communications Society es_ES
dc.relation.ispartof IEEE Open Journal of the Communications Society es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject 3D network es_ES
dc.subject 5G es_ES
dc.subject SSB beam planning es_ES
dc.subject UAV, Aerial highways es_ES
dc.subject Cell association es_ES
dc.subject Drone corridors es_ES
dc.subject MMIMO es_ES
dc.title Massive MIMO for Aerial Highways: Enhancing Cell Selection via SSB Beams Optimization es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1109/OJCOMS.2024.3418339 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-123999OB-I00/ES/HACIA EL DESPLIEGUE DE REDES NO TERRESTRES/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//CIDEXG%2F2022%2F17//iTENTE/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//CEX2021-001195-M/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//CNS2023-145384/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Telecomunicación y Aplicaciones Multimedia - Institut Universitari de Telecomunicacions i Aplicacions Multimèdia es_ES
dc.description.bibliographicCitation Bernabè, M.; López-Pérez, D.; Piovesan, N.; Geraci, G.; Gesbert, D. (2024). Massive MIMO for Aerial Highways: Enhancing Cell Selection via SSB Beams Optimization. IEEE Open Journal of the Communications Society. 5:3975-3996. https://doi.org/10.1109/OJCOMS.2024.3418339 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1109/OJCOMS.2024.3418339 es_ES
dc.description.upvformatpinicio 3975 es_ES
dc.description.upvformatpfin 3996 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 5 es_ES
dc.identifier.eissn 2644-125X es_ES
dc.relation.pasarela S\525056 es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Universitat Pompeu Fabra es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES


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