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Optimum Averaging of Superimposed Training Schemes in OFDM under Realistic Time-Variant Channels

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Comunicacioneses
dc.contributor.authorPiqué Muntané, Ignasi
dc.contributor.authorFernández-Getino García, María Julia
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2021-09-13T07:26:27Z
dc.date.available2021-09-13T07:26:27Z
dc.date.issued2021-08-16
dc.description.abstractThe current global bandwidth shortage in orthogonal frequency division multiplexing (OFDM)-based systems motivates the use of more spectrally efficient techniques. Superimposed training (ST) is a candidate in this regard because it exhibits no information rate loss. Additionally, it is very flexible to deploy and it requires low computational cost. However, data symbols sent together with training sequences cause an intrinsic interference. Previous studies, based on an oversimplified channel (a quasi-static channel model) have solved this interference by averaging the received signal over the coherence time. In this paper, the mean square error (MSE) of the channel estimation is minimized in a realistic time-variant scenario. The optimization problem is stated and theoretical derivations are presented to attain the optimum amount of OFDM symbols to be averaged. The derived optimal value for averaging is dependent on the signal-to-noise ratio (SNR) and it provides a better MSE, of up to two orders of magnitude, than the amount given by the coherence time. Moreover, in most cases, the optimal number of OFDM symbols for averaging is much shorter, about 90% reduction of the coherence time, thus it provides a decrease of the system delay. Therefore, these results match the goal of improving performance in terms of channel estimation error while getting even better energy efficiency, and reducing delays.en
dc.description.sponsorshipThis work was supported by the Spanish National Project Hybrid Terrestrial/Satellite Air Interface for 5G and Beyond - Areas of Dif-cult Access (TERESA-ADA) [Ministerio de Economía y Competitividad (MINECO)/Agencia Estatal de Investigación (AEI)/Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea (UE)] under Grant TEC2017-90093-C3-2-R.en
dc.format.extent12
dc.identifier.bibliographicCitationPique Muntane, I. & Fernandez-Getino Garcia, M. J. (2021b). Optimum Averaging of Superimposed Training Schemes in OFDM Under Realistic Time-Variant Channels. IEEE Access, 9, pp. 115620–115631.en
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2021.3104997
dc.identifier.issn2169-3536
dc.identifier.publicationfirstpage115620
dc.identifier.publicationlastpage115631
dc.identifier.publicationtitleIEEE Accessen
dc.identifier.publicationvolume9
dc.identifier.urihttps://hdl.handle.net/10016/33260
dc.identifier.uxxiAR/0000028301
dc.language.isoeng
dc.publisherIEEE
dc.relation.projectIDGobierno de España. TEC2017-90093-C3-2-Res
dc.relation.projectIDAT-2021
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherOFDMen
dc.subject.otherSuperimposed trainingen
dc.subject.otherTime-variant channelen
dc.subject.otherChannel estimationen
dc.subject.otherLeast squaresen
dc.subject.otherOptimizationen
dc.subject.otherAveragingen
dc.titleOptimum Averaging of Superimposed Training Schemes in OFDM under Realistic Time-Variant Channelsen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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