Coding and Iterative Detection for Magnetic Recording Channels
Coding and Iterative Detection for Magnetic Recording Channels

Author: Zining Wu

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 165

ISBN-13: 146154565X

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The advent of the internet age has produced enormous demand for in creased storage capacity and for the consequent increases in the amount of information that can be stored in a small space. While physical and media improvements have driven the majority of improvement in modern storage systems, signal processing and coding methods have increasing ly been used to augment those improvements. Run-length-limited codes and partial-response detection methods have come to be the norm in an industry that once rejected any sophistication in the read or write pro cessing circuits. VLSI advances now enable increasingly sophisticated signal processing methods for negligible cost and complexity, a trend sure to continue even as disk access speeds progress to billions of bits per second and terabits per square inch in the new millennium of the in formation age. This new book representing the Ph. D. dissertation work of Stanford's recent graduate Dr. Zining Wu is an up-to-date and fo cused review of the area that should be of value to those just starting in this area and as well those with considerable expertise. The use of saturation recording, i. e. the mandated restriction of two-level inputs, creates interesting twists on the use of communica tion/transmission methods in recording.

Coding and Signal Processing for Magnetic Recording Systems
Coding and Signal Processing for Magnetic Recording Systems

Author: Bane Vasic

Publisher: CRC Press

Published: 2004-11-09

Total Pages: 742

ISBN-13: 0203490312

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Implementing new architectures and designs for the magnetic recording read channel have been pushed to the limits of modern integrated circuit manufacturing technology. This book reviews advanced coding and signal processing techniques and architectures for magnetic recording systems. Beginning with the basic principles, it examines read/write operations, data organization, head positioning, sensing, timing recovery, data detection, and error correction. It also provides an in-depth treatment of all recording channel subsystems inside a read channel and hard disk drive controller. The final section reviews new trends in coding, particularly emerging codes for recording channels.

Bandwidth Efficient Coding
Bandwidth Efficient Coding

Author: John B. Anderson

Publisher: John Wiley & Sons

Published: 2017-03-27

Total Pages: 208

ISBN-13: 1119345332

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This book addresses coding, a new solution to the major challenge of communicating more bits of information in the same radio spectrum. Explores concepts and new transmission methods that have arisen in the last 15 years Discusses the method of faster than Nyquist signaling Provides self-education resources by including design parameters and short MATLAB routines Bandwidth Efficient Coding takes a fresh look at classical information theory and introduces a different point of view for research and development engineers and graduate students in communication engineering and wireless communication.

Coding and Detection for 2-dimensional Channels
Coding and Detection for 2-dimensional Channels

Author: İsmail Demirkan

Publisher:

Published: 2006

Total Pages: 165

ISBN-13:

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Coding and detection techniques for one-dimensional (1-D) intersymbol interference (ISI) channels, particularly magnetic and optical recording channels, have been studied extensively for almost three decades. On the modulation coding side, the state-splitting algorithm has been developed to design efficient systematic modulation codes. On the detection side, Viterbi detector and decision-feedback equalization (DFE) have been well-understood. Two-dimensional (2-D) holographic data storage, has been developed to store the information page-wise instead of on 1-D tracks. This will signifficantly increase the storage density and read/write access of the information. However, most of the modulation coding and detection techniques for 1-D recording systems are unusable for 2-D holographic data storage. In this work, we present various methods for modelling and equalizing 2-D ISI channels. Some low complexity detectors, such as a threshold detector, have been proposed for certain 2-D ISI channels. One of the main problems of the holographic data storage is the misalignment between written and sampled data pages. This problem is addressed by using more detector pixels than data points, which is called oversampling. We also attempted to characterize the distance properties of certain 2-D ISI channels. An algorithm for finding error events is developed for any 2-D ISI channel. Unlike the 1-D constrained systems, the capacity of most 2-D constrained systems is not analytically known due to the lack of graph-based descriptions of such channels. This also complicates the design of efficient modulation codes. We propose algorithms for finding single-state and finite-state block codes for the hard-square constraint. The encoding and decoding of the modulation codes can be performed easily using codeword generating templates. We also propose an algorithm for finding single-state block codes for any 2-D constrained system represented by a set of forbidden patterns. For 1-D recording, we designed block codes satisfying the running digital sum (RDS) and time-varying maximum transition run (TMTR) constraints for perpendicular recording channels. The graphs of these constraints are combined to understand the design trade-off between the achievable coding rate and constraint parameters. The spectra of the combined constraints shows the properties of the constituent constraints. The modulations codes are designed by searching for all codewords satisfying certain constraint properties.

Detection and Decoding for Magnetic Storage Systems
Detection and Decoding for Magnetic Storage Systems

Author:

Publisher:

Published: 2009

Total Pages: 344

ISBN-13:

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The hard-disk storage industry is at a critical time as the current technologies are incapable of achieving densities beyond 500 Gb/in2, which will be reached in a few years. Many radically new storage architectures have been proposed, which along with advanced signal processing algorithms are expected to achieve much higher densities. In this dissertation, various signal processing algorithms are developed to improve the performance of current and next-generation magnetic storage systems. Low-density parity-check (LDPC) error correction codes are known to provide excellent performance in magnetic storage systems and are likely to replace or supplement currently used algebraic codes. Two methods are described to improve their performance in such systems. In the first method, the detector is modified to incorporate auxiliary LDPC parity checks. Using graph theoretical algorithms, a method to incorporate maximum number of such checks for a given complexity is provided. In the second method, a joint detection and decoding algorithm is developed that, unlike all other schemes, operates on the non-binary channel output symbols rather than input bits. Though sub-optimal, it is shown to provide the best known decoding performance for channels with memory more than 1, which are practically the most important. This dissertation also proposes a ternary magnetic recording system from a signal processing perspective. The advantage of this novel scheme is that it is capable of making magnetic transitions with two different but predetermined gradients. By developing optimal signal processing components like receivers, equalizers and detectors for this channel, the equivalence of this system to a two-track/two-head system is determined and its performance is analyzed. Consequently, it is shown that it is preferable to store information using this system, than to store using a binary system with inter-track interference. Finally, this dissertation provides a number of insights into the unique characteristics of heat-assisted magnetic recording (HAMR) and two-dimensional magnetic recording (TDMR) channels. For HAMR channels, the effects of laser spot on transition characteristics and non-linear transition shift are investigated. For TDMR channels, a suitable channel model is developed to investigate the two-dimensional nature of the noise.

Machine Learning Techniques for Turbo Equalization of Two-Dimensional Magnetic Recording
Machine Learning Techniques for Turbo Equalization of Two-Dimensional Magnetic Recording

Author: Jinlu Shen

Publisher:

Published: 2020

Total Pages: 0

ISBN-13:

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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.