SSP'05 -- Proceedings

	IEEE/SP 13th workshop on Statistical Signal Processing July, 17-20, 2005 - Bordeaux - France

Information regarding the paper

Title

Improved Synchronisation For Superimposed Training Based Channel Estimation

Author(s)

Enrique Alameda-Hernandez	The University of Leeds. School of EE Eng. LS2 9JT. UK
Desmond C. McLernon	The University of Leeds. School of EE Eng. LS2 9JT. UK
Mounir Ghogho	The University of Leeds. School of EE Eng. LS2 9JT. UK
Aldo G. Orozco-Lugo	CINVESTAV-IPN. Sec Comunicaciones. CP 07360. Mexico City. Mexico
Manuel M. Lara	CINVESTAV-IPN. Sec Comunicaciones. CP 07360. Mexico City. Mexico

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Abstract

This paper introduces a synchronisation method for superimposed training (ST) based channel estimation, using periodic ST sequences. The method exploits the particular structure, occurring when the ST sequence period is larger than the channel length, of the vector containing the received signal's first-order cyclostationary statistics. After synchronisation, any DC-offset can be removed and an unbiased channel estimate can be obtained. Necessary and sufficient conditions for synchronisation are provided. The problem of training sequence design for an improved synchronisation is also addressed. An expression for the variance of the channel estimate is obtained as well, assuming perfect synchronisation and using the designed training sequences. The proposed synchronisation method is computationally more efficient than existing methods, and yet its performance, in term of channel estimation MSE and BER, is not diminished as shown by simulations.