Nonlinear self-training adaptive equalization for partial-response systems

The authors discuss a new self-training algorithm for adaptive equalization of multilevel partial-response class-IV (PRIV) systems. Adaptive distributed-arithmetic equalizers are considered, where the process of multiplying the tap signals with tap gains and summing the resulting products is replaced by a procedure involving only table look-up and shift-and-add operations. Classical self-training adaptation schemes for linear adaptive equalizers do not converge if applied to distributed-arithmetic equalizers, because of the inherent nonlinearity of the system during the adaptation process. The authors show that, by adopting a generalized stochastic gradient to adjust the look-up tables, the mean-square error converges to a value which depends on the system parameters. For practical system implementation, a two-step modified algorithm is proposed to closely approach in the steady state the minimum achievable mean-square error. Numerical results are presented for multilevel PRIV systems for high-rate data transmission over twisted-pair cables