Marvell Asia Pte., Ltd.

Washington State University

Jinlu Shen

Nitin Nangare

Benjamin Joseph Belzer

Ahmed Aboutaleb

Amirhossein Sayyafan

Krishnamoorthy Sivakumar

A deep neural network (DNN) media noise predictor configured for one-dimensional-magnetic (1DMR) recording or two-dimensional-magnetic (TDMR) is introduced. Such architectures are often combined with a trellis-based intersymbol interference (ISI) detection component in a turbo architecture to avoid the state explosion problem by separating the inter-symbol interference (ISI) detection and media noise estimation into two separate detectors and uses the turbo-principle to exchange information between them so as to address the modeling problem by way of training a DNN-based media noise estimators. Thus, beneficial aspects include a reduced bit-error rate (BER), an increased areal density, and a reduction in computational complexity and computational time.

Deep neural network a posteriori probability detectors and media noise predictors for one- and two-dimensional magnetic recording

Systems and methods for adaptation of a two-dimensional magnetic recording (TDMR) channel are provided. Read-back signals from respective read sensors of a TDMR channel are received at an equalizer, the read-back signals corresponding to a digital signal value. A log-likelihood ratio (LLR) signal is generated based at least in part on the read-back signals. A cross-entropy value is generated indicative of a mismatch between a probability of detected bit and a probability of the true recorded bit. The equalizer is adapted by setting an equalizer parameter to a value that corresponds to a minimum cross-entropy value from among the computed cross-entropy value and one or more previously computed cross-entropy values, to decrease a read-back bit error rate for the TDMR channel.

Systems and methods for adaptation of a two-dimensional magnetic recording channel

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Pullman, WA, United States of America

Jinlu Shen