Publication: Parametric model and estimator classifier for optimal averaging in mobile OFDM systems with superimposed training
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2023
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IEEE
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Abstract
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.
Description
Proceedings of: 2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC), 25-28 June 2023, Jeju, Republic of Korea.
Keywords
OFDM, Superimposed training, Time-variant channel, Channel estimation, Minimum mean squared error, Least squares, Averaging, Regression, Classifier
Bibliographic citation
Piqué 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.