Self-training adaptive equalization for multilevel partial-response transmission systems

We present a new self-training method for adjusting the coefficients of a transversal equalizer with T-spaced taps in a multilevel partial response class-IV (PRIV) system. Self-training equalization from distorted random data signals is inherently more difficult to achieve for partial-response systems than for full-response systems. An equalizer with T-spaced taps is sufficient to obtain equalized output signals for arbitrary sampling phase. Following Sato (1975), in the described self-training equalization algorithm we first transform the equalizer output into full-response form, then compute a pseudo-error signal, and finally translate the pseudo-error signal into an error signal for the desired partial-response equalizer output. This error signal is used to adjust the equalizer coefficients according to the LMS algorithm. The new method differs from the Sato algorithm in two ways. First, the channel inversion for obtaining full-response signals is accomplished exactly by mixed linear feedback and decision feedback equalization, whereas in the case of the Sato algorithm the inversion is only achieved approximately. Secondly, for the derivation of pseudo-error signals more knowledge of the statistical properties of ideal, but noisy full-response signals is exploited. We present simulation results illustrating the superior convergence properties of the new self-training method