Western Digital Technologies, Inc.

Yiming Chen

Anantha Raman Krishnan

Shayan Srinivasa Garani

Guoxiao Guo

Jie Yu

Wei Guo

Jianguo Zhou

Ning Zheng

A data storage device is disclosed comprising a storage medium. First data is encoded into a first codeword comprising a plurality of i-bit symbols, and second data is encoded into a second codeword comprising a plurality of j-bit symbols, wherein i is different than j and a first code rate of the first codeword is less than a second code rate of the second codeword. The first codeword and the second codeword are symbol interleaved to generate an interleaved codeword, and the interleaved codeword is written to the storage medium.

Data storage device encoding and interleaving codewords to improve trellis sequence detection

A data storage device is disclosed comprising a storage medium. Input data is encoded according to at least one channel code constraint to generate first data and second data. The first data is encoded into a first codeword, and the second data is encoded into a second codeword, wherein a first code rate of the first codeword is less than a second code rate of the second codeword. The first codeword and the second codeword are interleaved to generate an interleaved codeword, and the interleaved codeword is written to the storage medium.

A data storage device is disclosed comprising a storage medium. First data is encoded into a first codeword, and second data is encoded into a second codeword, wherein a first code rate of the first codeword is less than a second code rate of the second codeword. The first codeword and the second codeword are interleaved to generate an interleaved codeword that is written to the storage medium.

A data storage device is disclosed wherein a first 2D data dependent noise whitening (DDNW) filter is configured to perform 2D DDNW of first and second 2D equalized samples to generate first 2D noise whitened samples. A second 2D DDNW filter is configured to perform 2D DDNW of the first and second 2D equalized samples to generate second 2D noise whitened samples. A 2D sequence detector is configured to detect a first data sequence recorded in a first data track from the first and second 2D noise whitened samples and to detect a second data sequence recorded in a second data track from the first and second 2D noise whitened samples.

Data storage device comprising two-dimensional data dependent noise whitening filters for two-dimensional recording

A data storage device is disclosed comprising a disk comprising data, and a head actuated over the disk. A timing signal is generated by reading the data from the disk, and a frequency generator is adjusted based on the timing signal to generate a disk locked clock substantially synchronized to a rotation speed of the disk. A free fall condition is detected based on the timing signal, and when the free fall condition is detected the head is parked.

Data storage device detecting free fall condition from disk speed variations

A data storage device is disclosed comprising a non-volatile memory (NVM), wherein data is read from the NVM to generate a two dimension matrix of signal samples, including a first dimension and a second dimension. The matrix of signal samples is first equalized to reduce intersymbol interference (ISI) in the first dimension to generate second dimension signal samples, and second equalized to reduce ISI in the second dimension to generate first dimension signal samples. A first data sequence is detected in response to the first dimension signal samples, and a second data sequence is detected in response to the second dimension signal samples.

Data storage device employing orthogonal equalization and sequence detection to compensate for two-dimensional intersymbol interference

A data storage device is disclosed comprising a disk comprising a plurality of data tracks. A first data track is read to generate a first read signal that is sampled to generate first signal samples. A second data track adjacent the first data track is read to generate a second read signal that is sampled to generate second signal samples. The first signal samples and the second signal samples are processed by a first two-dimensional (2D) to one-dimensional (1D) equalizer to generate a first 1D sequence of equalized samples, and by a 2D-to-2D equalizer to generate a 2D sequence of equalized samples. The first 1D sequence of equalized samples are processed using a first 1D detector to detect a first estimated data sequence, and the 2D sequence of equalized samples are processed using a 2D detector to detect a second estimated data sequence.

Data storage device employing one-dimensional and two-dimensional channels

A disk drive is configured to decode data written over a plurality of data tracks using joint self-iterating soft equalization and an iterative turbo 2-D MAP-based detection, by performing soft self-iterating linear 2-D MMSE equalization on received input samples; computing MMSE estimates for coefficients of a 2-D equalization filter based on a-priori values, mean of the a-priori values, the 2-D filter and the received input samples; updating the coefficients of the 2-D filter using a variance based on the a-priori values when carrying out a first iteration and based on an extrinsic LLR when carrying out subsequent iterations; determining values of the individual bits in the input samples using the updated coefficients; computing an output LLR of each individual received sample based on the computed MMSE estimate, the mean and the variance, and computing the extrinsic LLR by subtracting a priori LLR from the output LLR.

Methods and devices for joint two-dimensional self-iterating equalization and detection

Decoding data written over a plurality of tracks includes selecting a mask that accounts for Inter-Symbol-Interference, the mask including channel coefficients and defining a trellis defining a plurality of states; receiving a signal corresponding to an input pixel from each of the plurality of tracks and generating equalized samples therefrom; computing transition branch probabilities for each input vector based on a Gaussian noise distribution using the equalized samples, ideal samples and a priori probabilities of the input vector; computing forward and backward probabilities via recursions using the computed transition branch probabilities; combining the forward, backward and transition branch probabilities to generate a-posteriori probabilities for the input vector; marginalizing the a-posteriori probabilities over values of neighboring pixels to generate an a-posteriori probability for a pixel of the input vector in a given state, and decoding the pixel as a first or second logical state from the marginalized a-posteriori probabilities.

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Irvine, CA, United States of America

Yiming Chen