Many blind channel equalization/identification algorithms are derived assuming the transmitted information sequence to be white. In practical communication systems, redundancy is added to the source sequence in order to detect and correct symbol errors in the receiver. It is not obvious how channel encoding will affect the assumption of whiteness. The autocorrelation function of some commonly used channel codes is analyzed in order to study the validity of assumptions used in blind equalization. The codes are presented in terms of a Markov model for which the autocorrelation is analytically expressed. The various encoded sequences are used in a prediction error based blind equalizer, and the performance is empirically compared with the case of unencoded data. A blind equalization example using a practical GSM speech encoder combined with a convolutional channel encoder is also given.
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