Paper data
Title:
Bayesian Estimation of Clipped Gaussian Processes with Application to OFDM
Author(s):
Banelli Paolo, University of Perugia, Dept. of Elec. and Inform. Engr., Perugia, Italy
Leus Geert, Katholieke Universiteit Leuven, Dept. of Elec. Engr., Leuven, Belgium
Giannakis Georgios, University of Minnesota, Dept. of Elec. and Comp. Engr., Minneapolis, MN, USA
Page numbers in the proceedings:
Volume I pp 181-184
Session:
Parameter Estimation and Statistical Signal Analysis
Bayesian Estimation of Clipped Gaussian Processes with Application to OFDM
Author(s):
Banelli Paolo, University of Perugia, Dept. of Elec. and Inform. Engr., Perugia, Italy
Leus Geert, Katholieke Universiteit Leuven, Dept. of Elec. Engr., Leuven, Belgium
Giannakis Georgios, University of Minnesota, Dept. of Elec. and Comp. Engr., Minneapolis, MN, USA
Page numbers in the proceedings:
Volume I pp 181-184
Session:
Parameter Estimation and Statistical Signal Analysis
Paper abstract
Several engineering applications ranging from control to communications have to deal with a clipped Gaussian process, observed in the presence of Additive White Gaussian Noise (AWGN). For such a scenario, we derive in this paper a closed form expression of a Bayesian estimator, which recovers the original undistorted Gaussian process by minimizing the mean square estimation error. In addition, we use the obtained closed form expression to show that the Bayesian estimator results in a Bit-Error Rate (BER) improvement compared to existing receivers for an Orthogonal Frequency Division Multiplexing (OFDM) system in an AWGN channel that is impaired by a clipping device at the transmitter.
Paper
