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Joint Channel and Nonlinearity Estimation for Memoryless Nonlinear Systems

2025Artigo científico Acesso aberto
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dc.contributor.authorMokhtari, Zahra
dc.contributor.authorDinis, Rui
dc.contributor.authorHu, Sha
dc.contributor.authorKapetanovic, Dzevdan
dc.contributor.institutionDEE - Departamento de Engenharia Electrotécnica e de Computadores
dc.contributor.pblInstitute of Electrical and Electronics Engineers (IEEE)
dc.date.accessioned2025-06-05T21:20:09Z
dc.date.available2025-06-05T21:20:09Z
dc.date.issued2025
dc.descriptionPublisher Copyright: © 2025 The Authors.
dc.description.abstractSystem nonlinearity due to hardware impairments has always been a challenging issue. Distortion cancellation and iterative detection based receivers such as the Bussgang Noise Cancelling (BNC) receiver are used to detect the original data in the presence of strong nonlinear (NL) effects. However, these receivers require knowledge of the system nonlinearity which is usually unknown in practical systems. Bussgang decomposition and its general form denoted Generalized Bussgang decomposition (GBD), have been commonly used to model system nonlinearity. In GBD the nonlinearity output is decomposed as the sum of uncorrelated terms of increased orders and provides spectral characteristics of the useful and distortion terms. In this paper we consider nonlinearity at the transmitter side and model it with GBD. We aim to estimate the scalar weights in the GBD to later use them at the BNC receiver. However, knowledge of the channel is required to make a reliable estimate of the NL parameters. On the other hand the pilots for channel estimation are affected by the system nonlinearity, which can preclude reliable channel estimation. Therefore, in this paper we propose a joint channel and NL parameter estimation technique by designing appropriate training signals for each estimation phase (i.e. channel estimation and NL parameter estimation). We also derive a closed form expression for the average power of residual distortion in GBD with estimated parameters to see how well this model can characterize the nonlinearity. The results show that the proposed estimation technique has good accuracy and enables quasi-ideal performance for a BNC receiver.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent13
dc.format.extent1271155
dc.identifier.doi10.1109/ACCESS.2025.3530817
dc.identifier.issn2169-3536
dc.identifier.otherPURE: 117946708
dc.identifier.otherPURE UUID: b8601e2a-bd43-4674-96f8-9f5721e38415
dc.identifier.otherScopus: 85216002503
dc.identifier.otherWOS: 001405925700011
dc.identifier.otherORCID: /0000-0002-8520-7267/work/185461686
dc.identifier.urihttp://hdl.handle.net/10362/183932
dc.identifier.urlhttps://www.scopus.com/pages/publications/85216002503
dc.language.isoeng
dc.peerreviewedyes
dc.relationFunding Information: info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/Concurso de Projetos de I&D em Todos os Domínios Científicos - 2022/2022.08786.PTDC/PT
dc.subjectGeneralized Bussgang decomposition
dc.subjectnonlinear effects
dc.subjectnonlinear parameter estimation
dc.subjectOFDM
dc.subjectGeneral Computer Science
dc.subjectGeneral Materials Science
dc.subjectGeneral Engineering
dc.titleJoint Channel and Nonlinearity Estimation for Memoryless Nonlinear Systemsen
dc.typejournal article
degois.publication.firstPage13143
degois.publication.lastPage13155
degois.publication.titleIEEE Access
degois.publication.volume13
dspace.entity.typePublication
oaire.awardNumber2022.08786.PTDC
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/Concurso de Projetos de I&D em Todos os Domínios Científicos - 2022/2022.08786.PTDC/PT
oaire.fundingStreamConcurso de Projetos de I&D em Todos os Domínios Científicos - 2022
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication40f847e1-36b9-4565-8da4-bbfc596b54b8
relation.isProjectOfPublication.latestForDiscovery40f847e1-36b9-4565-8da4-bbfc596b54b8

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