Abstract: An improved Viterbi detector is disclosed in which each branch metric is calculated based on noise statistics that depend on the signal hypothesis corresponding to the branch. Also disclosed is a method of reducing the complexity of the branch metric calculations by clustering branches corresponding to signals with similar signal-dependent noise statistics. A feature of this architecture is that the branch metrics (and their corresponding square difference operators) are clustered into multiple groups, where all the members of each group draw input from a single, shared noise predictive filter corresponding to the group. In recording technologies as practiced today, physical imperfections in the representation of recorded user data in the recording medium itself are becoming the dominate source of noise in the read back data. This noise is highly dependent on what was (intended to be) written in the medium. The disclosed Viterbi detector exploits this statistical dependence of the noise on the signal.

Abstract: Disclosed herein is an apparatus and method of calibrating the parameters of a Viterbi detector 138 in which each branch metric is calculated based on noise statistics that depend on the signal hypothesis corresponding to the branch. An offline algorithm for calculating the parameters of data-dependent noise predictive filters 304A-D is presented which has two phases: a noise statistics estimation or training phase, and a filter calculation phase. During the training phase, products of pairs of noise samples are accumulated in order to estimate the noise correlations. Further, the results of the training phase are used to estimate how wide (in bits) the noise correlation accumulation registers need to be. The taps [t2[k], t1[k], t0[k]] of each FIR filter are calculated based on estimates of the entries of a 3-by-3 conditional noise correlation matrix C[k] defined by Cij[k]=E(ni-3nj-3|NRZ condition k).

Abstract: Disclosed herein is an apparatus and method of calibrating the parameters of a Viterbi detector 138 in which each branch metric is calculated based on noise statistics that depend on the signal hypothesis corresponding to the branch. An offline algorithm for calculating the parameters of data-dependent noise predictive filters 304A-D is presented which has two phases: a noise statistics estimation or training phase, and a filter calculation phase. During the training phase, products of pairs of noise samples are accumulated in order to estimate the noise correlations. Further, the results of the training phase are used to estimate how wide (in bits) the noise correlation accumulation registers need to be. The taps [t2[k], t1[k], t0[k]]of each FIR filter are calculated based on estimates of the entries of a 3-by-3 conditional noise correlation matrix C[k] defined by Cij[k]=E(ni-3nj-3|NRZ condition k).

Abstract: An improved sampled amplitude read/write channel is provided. The system is an integrated Generalized Partial Response Maximum Likelihood (GPRML) read channel incorporating Read, Write, and Servo modes of operation. One implementation includes a 32/34 rate parity code and matched Viterbi detector, a 32 state Viterbi detector optimal parity processor, robust frame synchronization, self-adaptive equalization, thermal asperity detection and compensation, adaptive magneto-resistive asymmetry compensation, low latency interpolated timing recovery and programmable write precompensation.

Abstract: An improved sampled amplitude read/write channel is provided. The system is an integrated Generalized Partial Response Maximum Likelihood (GPRML) read channel incorporating Read, Write, and Servo modes of operation. One implementation includes a 32/34 rate parity code and matched Viterbi detector, a 32 state Viterbi detector optimal parity processor, robust frame synchronization, self-adapive equalization, thermal asperity detection and compensation, adaptive magneto-resistive asymmetry compensation, low latency interpolated timing recovery and programmable write precompensation.