ADAPTIVE SOFT-OUTPUT DETECTOR FOR MAGNETIC TAPE READ CHANNELS

摘要

In one embodiment, a method includes passing a signal through a noise whitening filter, passing the signal through a soft detector to calculate first soft information, passing the signal through the soft decoder to calculate second soft information based on the first soft information, and sending the second soft information to the soft detector, wherein the noise whitening filter is configured to process the signal according to the following transfer polynomial: W(D)=1−(p1D+ . . . . p1Dλ), where p1 . . . pλ are noise whitening coefficients, D is delay corresponding to bit duration, and a transfer polynomial of the tape channel is F(D)=1+f1D+ . . . +fLDL, wherein L represents a memory length of the tape channel, and wherein λ represents a memory length of the noise whitening filter. Other methods, systems, and computer program products are described in more embodiments.

主权项

1. A data storage system, comprising:
a ape channel for reading data from a magnetic tape medium to produce a signal; a noise whitening filter positioned subsequent to the tape channel configured to receive the signal, wherein the noise whitening filter is configured to minimize variance of its output signal; a soft detector configured to receive outp f.o the noise whitening filter, the soft detector configured to calculate first soft information about each bit of the signal and sending the first soft infomration to a soft decoder; and the soft decoder positioned subsequent to the soft detector, the soft decoder being configured to calculate second soft information about each bit of the signal and sending the second soft information to the soft detector; wherein the soft detector applies a Dual-MAX (DMAX) algorithm, comprising:αk(Sk)≅maxSk-1{γk(Sk-1,Sk)+αk-1(Sk-1)}βk-1(Sk-1)≅maxSk{γk(Sk-1,Sk)+βk(Sk)}LLR(ak)≅maxSk-1→Skαk=+1{αk-1(Sk-1)+γk(Sk-1,Sk)+βk(Sk)}-maxSk-1→Skαk=-1{αk-1(Sk-1)+γk(Sk-1,Sk)+βk(Sk)}, wherein yk is the signal, ak denotes a bit in a bit sequence of the signal, ak(Sk) is an alpha term for a current state (Sk) in a forward recursion, ak(Sk−1) is an alpha term for a previous state (Sk−1) in the forward recursion βk(Sk) is a beta term for the current state in a backward recursion, βk−1(Sk−1) is a beta term for the previous state in the backward recursion, and LLR(ak) is an approximation of a log-likelihood term that calculates a posteriori probabilities.