Time-Varying Channel Estimation Using Two-Dimensional Channel Orthogonalization and Superimposed Training

• Published in: IEEE transactions on signal processing Vol. 60; no. 8; pp. 4439 - 4443
• Main Authors: Carrasco-Alvarez, Roberto, Parra-Michel, R., Orozco-Lugo, Aldo G., Tugnait, Jitendra K.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.08.2012; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Channel estimation; Channels; Delay; Detection, estimation, filtering, equalization, prediction; Discrete prolate spheroidal basis; Estimation; Estimators; Exact sciences and technology; Information, signal and communications theory; Kernel; Mathematical analysis; orthogonal basis expansion; Orthogonal functions; Signal and communications theory; Signal, noise; Statistics; superimposed training; Telecommunications and information theory; time-varying channel estimation; Time-varying channels; Training; Transaction processing; universal basis; Vectors; Vectors (mathematics); Performance evaluation; Time variable channel; Parameter estimation; State of the art; time-varying channel estimation; Two channel system; Discrete prolate spheroidal basis; Subspace method; universal basis; Two dimensional model; Algorithm; Learning; Simulation; Orthogonal function; Channel estimation; Signal processing; Delay time; Delayed time; orthogonal basis expansion; superimposed training
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2012.2195658

In this correspondence, a method is presented for estimating double-selective channels using superimposed training (ST). The estimator is based on a subspace projection of the time-varying channel onto a set of two dimensional orthogonal functions. These functions are formed via the outer product of the discrete prolate spheroidal basis vectors and the universal basis vectors. This approach allows the channel to be expanded in both the time-delay and time dimensions with the fewest parameters when incomplete channel statistics are given. This correspondence also provides a theoretical performance analysis of the estimation algorithm and its corroboration via simulations. It is shown that this new method provides an enhancement in channel estimation when compared with state-of-the-art approaches.