Time-varying Channel Estimation and Symbol Detection Using Superimposed Training in OFDM Systems

Abstract

In mobile orthogonal frequency division multiplexing (OFDM) systems, time-varying channels result in severe intercarrier interference (ICI), and greatly degrade the system performance. So, it is necessary to estimate the accurate channel for equalization of received symbols. But, the conventional pilot-assisted channel estimation scheme consumes valuable bandwidth. In this paper, we adopt superimposed training approach for OFDM systems to estimate the time-varying channel, which is approximated by a basis expansion model (BEM). The proposed scheme is an extension of the superimposed training approach previously proposed for time-invariant channels in OFDM systems. At the same time, we employ an iterative best linear unbiased estimator (BLUE) to minimize the mean square error (MSE) of the coefficient estimates and improve the system performance. Simulation results prove the effectiveness of the proposed scheme in fast time-varying scenario.