Enhanced Channel Estimation Using Superimposed Training Based on Universal Basis Expansion

Roberto Carrasco-Alvarez, Ramón Parra-Michel, Aldo G. Orozco-Lugo, Jitendra K. Tugnait

Abstract: In this correspondence, an approach to enhance the quality of superimposed training (ST) based channel estimation procedures is proposed. The approach is based on postprocessing the estimated channel. This postprocessing is performed with the projection of the estimated channel onto a set of orthogonal functions known as the Universal Basis (UB), that were defined in [A. G. Orozco-Lugo, R. Parra-Michel, D. McLernon, and V. Kontorovitch, ldquoEnhancing the Performance of the CR Blind Channel Estimation Algorithm Using the Karhunen-Loeve Expansion,rdquo Proceedings of the ICASSP, May 2002, pp. III-2653-III-2656 ]. The projection leads to improved channel estimation when compared to raw ST methods. We demonstrate the enhanced performance of the proposed technique by means of both theoretical formulas and simulation results, focusing on data dependent ST.