Piqué Muntané, IgnasiFernández-Getino García, María Julia2023-09-192023-09-192023Piqué Muntané, P. & Fernández-Getino García, M. J. (25-28 June 2023). Parametric model and estimator classifier for optimal averaging in mobile OFDM systems with superimposed training [proceedings]. In 2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC), Jeju, Republic of Korea.979-8-3503-2641-3https://hdl.handle.net/10016/38374Proceedings of: 2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC), 25-28 June 2023, Jeju, Republic of Korea.Superimposed training (ST) is an attractive technique for channel estimation in orthogonal frequency division multiplexing (OFDM) modulation. However, its main challenge is the intrinsic interference due to the joint transmission of pilot and data symbols, which can be mitigated by averaging the received signal. Previous works analyzed the mean square error (MSE) of the channel estimation, for both least squares (LS) and minimum MSE (MMSE) estimators, and showed that, under realistic channel models, the optimum number of averaged symbols could be computed by solving a transcendental equation. In this paper, as a practical implementation proposal, these optimum averaging values are parametrically approximated with a multilinear regression model. Also, it is proposed an accurate classifier that, under delay and performance tolerances, is able to select the most suitable estimator between LS and MMSE.6eng© 2023, IEEE.OFDMSuperimposed trainingTime-variant channelChannel estimationMinimum mean squared errorLeast squaresAveragingRegressionClassifierconference outputElectrónicaTelecomunicacioneshttps://doi.org/10.1109/ITC-CSCC58803.2023.10212534open access16Proceedings of 2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)CC/0000034506