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Low-complexity power allocation in pilot-pouring superimposed training over CB-FMT

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Comunicacioneses
dc.contributor.authorChen Hu, Kun
dc.contributor.authorFernández-Getino García, María Julia
dc.contributor.authorTonello, Andrea M.
dc.contributor.authorGarcía-Armada, Ana
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España)es
dc.date.accessioned2022-01-12T12:00:21Z
dc.date.available2022-01-12T12:00:21Z
dc.date.issued2021-12
dc.description.abstractPilot-pouring superimposed training (PPST) is a novel channel estimation technique specially designed for cyclic block filtered multi-tone (CB-FMT), where the pilot symbols are poured into the subcarriers taking advantage of the power left unused by the data symbols. Hence, since this technique is based on superimposed training (ST) principles, the data rate is not reduced, unlike the pilot symbol assisted modulation (PSAM). Besides, it exploits a weighted average at the receiver side that is capable of minimizing the mean squared error (MSE) of the channel estimation, and then enhancing the performance of the system. However, the existing proposal on PPST is limited to the minimization of the MSE to improve channel estimation for a given power allocation factor, without solving the joint optimization of channel estimation and data detection procedures. With this aim, this work addresses the whole problem to reach the best performance for both tasks, thus taking into account also the power allocation factor in the opt where the pilot symbols are poured into the subcarriers taking advantage of the power left unused by the data symbols. Hence, since this technique is based on superimposed training (ST) principles, the data rate is not reduced, unlike the pilot symbol assisted modulation (PSAM). Besides, it exploits a weighted average at the receiver side that is capable of minimizing the mean squared error (MSE) of the channel estimation, and then enhancing the performance of the system. However, the existing proposal on PPST is limited to the minimization of the MSE to improve channel estimation for a given power allocation factor, without solving the joint optimization of channel estimation and data detection procedures. With this aim, this work addresses the whole problem to reach the best performance for both tasks, thus taking into account also the power allocation factor in the optimization process, where the spectral efficiency must be maximized through the signal-to-interference plus noise ratio (SINR). Two optimization approaches are proposed, where the first one, referred as pilot-pouring optimization (PPO), is focused on performance at the expense of a high complexity, while the second one, denoted as low-complexity PPO (LPPO), is able to trade-off between performance and execution time. Numerical results are provided in order to show the validity of our proposal, where the different optimization problems are compared in terms of SINR and execution time.en
dc.description.sponsorshipThis work was supported by Spanish National Projects TERESA-ADA (TEC2017-90093-C3-2-R) (MINECO/AEI/FEDER, UE), and IRENE-EARTH (PID2020-115323RB-C33 / AEI / 10.13039/501100011033). The work of A. M. Tonello was supported by the Chair of Excellence Program of the Universidad Carlos III de Madrid.en
dc.format.extent12
dc.identifier.bibliographicCitationChen-Hu, K., Fernandez-Getino, M. J. , Tonello, A. M. & Garcia, A. (2021). Low-Complexity Power Allocation in Pilot-Pouring Superimposed-Training Over CB-FMT. IEEE Transactions on Vehicular Technology, 70(12), 13010–13021.en
dc.identifier.doihttps://doi.org/10.1109/TVT.2021.3123389
dc.identifier.issn0018-9545
dc.identifier.publicationfirstpage13010
dc.identifier.publicationissue12
dc.identifier.publicationlastpage13021
dc.identifier.publicationtitleIEEE Transactions on Vehicular Technologyen
dc.identifier.publicationvolume70
dc.identifier.urihttps://hdl.handle.net/10016/33868
dc.identifier.uxxiAR/0000028987
dc.language.isoengen
dc.publisherIEEEen
dc.relation.projectIDGobierno de España. TEC2017-90093-C3-2-Res
dc.relation.projectIDGobierno de España. PID2020-115323RB-C33es
dc.rights© 2021, IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.other5Gen
dc.subject.otherFMTen
dc.subject.otherChannel estimationen
dc.subject.otherMulticarrier modulationen
dc.subject.otherSuperimposed trainingen
dc.subject.otherPilot-pouringen
dc.titleLow-complexity power allocation in pilot-pouring superimposed training over CB-FMTen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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