RT Conference Proceedings
T1 Parametric model and estimator classifier for optimal averaging in mobile OFDM systems with superimposed training
A1 Piqué Muntané, Ignasi
A1 Fernández-Getino García, María Julia
AB 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.
PB IEEE
SN 979-8-3503-2641-3
YR 2023
FD 2023
LK https://hdl.handle.net/10016/38374
UL https://hdl.handle.net/10016/38374
LA eng
NO Proceedings of: 2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC), 25-28 June 2023, Jeju, Republic of Korea.
DS e-Archivo
RD 1 sept. 2024