Download or read book Machine Learning Techniques for Turbo Equalization of Two-Dimensional Magnetic Recording written by Jinlu Shen and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: To reach higher areal density for next generation hard disk drives, two-dimensional magnetic recording (TDMR) is a promising technology that emphasizes signal processing techniques for data recovery without requiring radical changes of the recording media. In TDMR, an array of read heads captures effects from multiple tracks. This helps mitigate inter track interference, which becomes severe at higher recording densities, through two-dimensional signal processing. This work proposes machine learning approaches to address common challenges in TDMR.First, a 2D decoding system that uses readback data from three tracks to detect data on the center track is developed. This system consists of a linear minimum mean square error (MMSE) equalizer with partial response (PR) target, a Bahl0́3Cocke0́3Jelinek0́3Raviv (BCJR) detector, and an irregular repeat-accumulate (IRA) low-density parity-check decoder. A joint design method of PR target and MMSE filter coefficients to minimize detector bit error rate (BER) at the BCJR output is presented.Next, a separate signal processing path consisting of a linear PR equalizer, a trained local area influence probabilistic (LAIP) detector and an IRA decoder is added to a 2D version of the above system for passing soft information to the BCJR for simultaneous three-track detection. This triples the system throughput. The LAIP training methods are tailored for PR equalizer outputs.Furthermore, a deep neural network (DNN) based a posteriori probability (APP) system that consists of an MMSE equalizer, a DNN detector, and an IRA decoder is presented. Three different types of DNNs are investigated 0́3 convolutional neural networks, long short-term memory, and fully-connected neural networks. Turbo loops between the DNN detector and the IRA decoder are explored, and a novel DNN training-per-iteration approach for iterative decoding with the IRA is proposed. A 30.47% reduction in detector BER, 21.72% increase in areal density and three times throughput gain are achieved.Lastly, an alternative system is developed in which the linear MMSE equalizer is replaced by a neural network based nonlinear equalizer adapted using cross entropy between the true probability of the bit and the detector's estimate of it. Simulation results show that cross entropy is a superior criterion to MSE, and nonlinear equalizer structure is better than linear structure.