Prediction-based adaptive blind equalization: a performance study

Blind equalization of a communication channel using a prediction-based lattice blind equalizer (LBE) is considered. Second order cyclostationary statistics and a single-input multiple-output model arising from fractional sampling of the received data are used. The performance of the LBE algorithm is studied in extensive simulations where commonly used example channels are employed. Convergence in the mean square error (MSE) and symbol error rate (SER) as well as the number of symbols required to open the eye are studied at different SNRs. Robustness in the face of channel order mismatch and channels with common subchannel zeros is considered. The simulation results are compared to the results obtained by the fractionally spaced constant modulus algorithm, the cyclic-RLS algorithm and the subspace method by Moulines et al. (see IEEE Trans. Signal Proc., vol.43, no.2, p.516-25, 1995).

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