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Multi-User Sparse Vector Coding for eXtreme Ultra-Reliable Low-Latency Communication in beyond 5G

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dc.contributor.authorSabapathy, Sundaresan
dc.contributor.authorMaruthu, Surendar
dc.contributor.authorJayakody, Dushantha Nalin K.
dc.contributor.institutionCTS - Centro de Tecnologia e Sistemas
dc.contributor.pblInstitute of Electrical and Electronics Engineers (IEEE)
dc.date.accessioned2025-07-29T21:22:54Z
dc.date.available2025-07-29T21:22:54Z
dc.date.issued2025
dc.descriptionThis work was supported in part by the Sri Lanka Institute of Information Technology through the grant PVC(R&I)/RG/2024/12, by the European Commission via Marie Sk\u0142odowska-Curie Actions (MSCA) as part of the project REMARKABLE (No. 101086387), by the COFAC - Cooperativa de Forma\u00E7\u00E3o e Anima\u00E7\u00E3o Cultural, C.R.L. (University of Lus\u00F3fona University), via the project PortuLight (COFAC/ILIND/COPELABS/2/2023), by the national funds through FCT - Funda\u00E7\u00E3o para a Ci\u00EAncia e a Tecnologia - as part of the projects URLLC-UAV (2023.08191.CEECIND) and by the Scheme for Promotion of Academic & Research Collaboration (SPARC), Government of India, via grant no. SPARC/2024-2025/NXTG/P3524. Publisher Copyright: © 2013 IEEE.
dc.description.abstractShort A short packet transmission scheme, such as Sparse Vector Coding (SVC), is a primary candidate for achieving ultra-low latency and high-reliability communication (URLLC). This paper proposes a spectral-efficient multi-user SVC (MU-SVC) scheme for achieving next-generation URLLC or eXtreme URLLC (xURLLC) in beyond 5G (B5G) communications. The key idea is to transmit multiple user information within a single sparse vector where the users are segregated into far users (FU) and near users (NU) depending on the distance from the base station. The classification into FU and NU paves way to optimize resource allocation, user fairness, manage interference, ensure reliable communication and quality of service requirements. Firstly, the FU binary data is converted into a sparse vector and secondly, the NU data is modulated and embedded into the non-zero positions of the sparse vector to form an MU-SVC. On transmission, the FU data is obtained through sparse demapping, while the NU adopts symbol detection techniques like the maximum likelihood detector. A new performance metric, called position error rate (PoER), is introduced to study the performance of the FU since it is based on the correct identification of the non-zero positions. Theoretical analyses of PoER and symbol error rate (SER) were carried out for FU and NU, respectively and the results are also validated through Monte-Carlo simulations. Further, the bit error rate, complexity, spectral and latency analyses are performed for MU-SVC and compared with the SVC and enhanced SVC schemes. The simulation results demonstrate an improved spectral efficiency and low latency with high reliability for the proposed MU-SVC scheme, thus, achieving xURLLC with reduced complexity in the multi-user scenario for B5G.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent13
dc.format.extent1329226
dc.identifier.doi10.1109/ACCESS.2025.3551398
dc.identifier.issn2169-3536
dc.identifier.otherPURE: 123174826
dc.identifier.otherPURE UUID: a552e641-3e88-43cf-95a6-07c8f1d57689
dc.identifier.otherScopus: 105003040357
dc.identifier.otherWOS: 001459554100010
dc.identifier.urihttp://hdl.handle.net/10362/185709
dc.identifier.urlhttps://www.scopus.com/pages/publications/105003040357
dc.language.isoeng
dc.peerreviewedyes
dc.relationFunding Information: info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00066%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04111%2F2020/PT
dc.relationCOPELABS - Cognitive and People-centric Computing R&D Unit
dc.subjectMulti-user
dc.subjectposition error rate
dc.subjectsparse vector coding
dc.subjectsuperimposed transmission
dc.subjectsymbol error rate
dc.subjectURLLC
dc.subjectGeneral Computer Science
dc.subjectGeneral Materials Science
dc.subjectGeneral Engineering
dc.titleMulti-User Sparse Vector Coding for eXtreme Ultra-Reliable Low-Latency Communication in beyond 5Gen
dc.typejournal article
degois.publication.firstPage56780
degois.publication.lastPage56792
degois.publication.titleIEEE Access
degois.publication.volume13
dspace.entity.typePublication
oaire.awardNumberUIDB/04111/2020
oaire.awardTitleCOPELABS - Cognitive and People-centric Computing R&D Unit
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04111%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication67639a80-0e51-4a24-9895-28ed2e7ea6e2
relation.isProjectOfPublication.latestForDiscovery67639a80-0e51-4a24-9895-28ed2e7ea6e2

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