Periodic sequences with optimal properties for channel estimation and fast start-up equalization
IBM Journal of Research and Development
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In this paper, we consider joint iterative equalization and channel estimation for TDMA systems over a frequency selective, quasi-static fading channel. We assume a slowly varying channel impulse response from one burst to the following one. We modify the maximum a posteriori (MAP) criterion by taking into account an a priori channel estimate obtained from the burst preceding the one processed. Assuming that the receiver knows the channel statistics, we propose to use the EM (Expectation-Maximization) algorithm for a joint iterative equalization and channel estimation. During each iteration of the receiver, the EM algorithm reestimates the channel by using the a posteriori probabilities (APP) available at the output of the equalizer. The algorithm we propose has linear-time complexity per iteration. Simulations show that our MAP receiver reaches the performance of the MAP equalizer with perfect channel knowledge after a few iterations, for high SNR and for small values of normalized Doppler spread. They show also that our receiver outperforms the MAP equalizer that uses a channel estimate obtained by using a training sequence.