Simeon Furrer, Mark A. Lantz, et al.
IEEE Transactions on Magnetics
In this paper we 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 non-linearity of the system during the adaptation process. Here we 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. © 1994 IEEE.
Simeon Furrer, Mark A. Lantz, et al.
IEEE Transactions on Magnetics
Sanaz Kazemi, Paul Hurley, et al.
CoSeRa 2015
Mark A. Lantz, Giovanni Cherubini, et al.
IEEE TCST
Manuel Le Gallo, Abu Sebastian, et al.
IEDM 2017