Adaptive equalization in magnetic-disk storage channels

Cioffi, J.M.; Abbott, W.L.; Thapar, H.K.; Melas, C.M.; Fisher, Kevin D.

doi:10.1109/35.46683

Cited by 125 publications

(20 citation statements)

References 30 publications

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“…Given a probabilistic model of with realizations in a suitable space and noting that, for any finite number of transmitted bits, an information lossless discretization of signal by expansion over an orthonormal finite-dimensional basis can be achieved, the detection strategy can be formulated as (6) where is the a priori probability of the information sequence and is the conditional probability density function (pdf) of the observation vector , given the information sequence . Under the assumption of statistical independence between and , the conditional probability density function in (6) can be expressed as (7) in which the integral is over the parameter space and is the pdf of vector . Given the sequences of random variables and , i.e., the parameter vector , and the data sequence , the observation vector is conditionally Gaussian.…”

Section: Sufficient Statisticsmentioning

confidence: 99%

“…2006.874096 was applied to the estimation of colored thermal noise and a modified Euclidean-distance branch metric computation in the Viterbi algorithm was proposed in order to incorporate linear prediction and enable maximum a posteriori probability (MAP) sequence detection. The performance improvement comes from an effective whitening of the noise samples, which exhibit correlation at the detector input due to partial response equalization [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

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Multidimensional signal processing and detection for storage systems with data-dependent transition noise

Pighi

Raheli

Amadei³

2006

IEEE Trans. Magn.

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In the last decade, significant research on detection algorithms capable of mitigating the effects of colored Gaussian thermal noise and transition noise in storage systems, has been performed. In this paper, we present a new detection scheme based on a multidimensional detector front end and multidimensional linear prediction, applied to maximum a posteriori probability (MAP) sequence detection. This method improves the bit-error-rate (BER) performance with respect to previous approaches and makes the detector quite insensitive to transition noise. We show that the gain in terms of BER versus signal-to-noise ratio with our detector increases with the user density. The results obtained for a magnetic storage channel are extendable to optical storage systems as well.Index Terms-Longitudinal and perpendicular recording, magnetic storage systems, multidimensional linear prediction, multidimensional signal processing, optical storage systems, transition noise.

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Section: Sufficient Statisticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multidimensional signal processing and detection for storage systems with data-dependent transition noise

Pighi

Raheli

Amadei³

2006

IEEE Trans. Magn.

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“…The output of the equalizer is a decision, either soft or hard that estimates the transmitted symbol. This is done in many applications besides underwater communications, for example in mobile radio communications [21,22] and magnetic disk storage channels [2].…”

Section: Need For Adaptive Equalizationmentioning

confidence: 99%

Evaluation of vector sensors for adaptive equalization in underwater acoustic communication

Lewis¹

2014

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Underwater acoustic communication is an extremely complex field that faces many challenges due to the time-varying nature of the ocean environment. Vector sensors are a proven technology that when utilizing their directional sensing capabilities allows us to minimize the effect of interfering noise sources. A traditional pressure sensor array has been the standard for years but suffers at degraded signal to noise ratios (SNR) and requires maneuvers or a lengthly array aperture to direction find. This thesis explores the effect of utilizing a vector sensor array to steer to the direction of signal arrival and the effect it has on equalization of the signal at degraded SNRs. It was demonstrated that utilizing a single vector sensor we were able steer to the direction of arrival and improve the ability of an equalizer to determine the transmitted signal. This improvement was most prominent when the SNR was degraded to levels of 0 and 10 dB where the performance of the vector sensor outperformed that of the pressure sensor in nearly 100% of cases. Finally, this performance improvement occured with a savings in computational expense.

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“…An equalizer is a system of filters which is used to invert the response of a noisy channel in order to recover the symbols transmitted through the channel without blowing up In a time-varying environment, the coefficients of the filter need to be varied with time. This is true also of a variety of environments including underwater communication, mobile radio [40] and magnetic disk recording technology [9], for instance. The adaptation of these filters in practical environments and understanding the behaviour of the adaptation algorithms is the subject of this thesis.…”

Section: The Need For Adaptive Equalizationmentioning

confidence: 99%

Direct-form adaptive equalization for underwater acoustic communication

Yellepeddi¹

2012

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Adaptive equalization is an important aspect of communication systems in various environments. It is particularly important in underwater acoustic communication systems, as the channel has a long delay spread and is subject to the effects of timevarying multipath fading and Doppler spreading.The design of the adaptation algorithm has a profound influence on the performance of the system. In this thesis, we explore this aspect of the system. The emphasis of the work presented is on applying concepts from inference and decision theory and information theory to provide an approach to deriving and analyzing adaptation algorithms. Limited work has been done so far on rigorously devising adaptation algorithms to suit a particular situation, and the aim of this thesis is to concretize such efforts and possibly to provide a mathematical basis for expanding it to other applications.We derive an algorithm for the adaptation of the coefficients of an equalizer when the receiver has limited or no information about the transmitted symbols, which we term the Soft-Decision Directed Recursive Least Squares algorithm. We will demonstrate connections between the Expectation-Maximization (EM) algorithm and the Recursive Least Squares algorithm, and show how to derive a computationally efficient, purely recursive algorithm from the optimal EM algorithm.Then, we use our understanding of Markov processes to analyze the performance of the RLS algorithm in hard-decision directed mode, as well as of the Soft-Decision Directed RLS algorithm. We demonstrate scenarios in which the adaptation procedures fail catastrophically, and discuss why this happens. The lessons from the analysis guide us on the choice of models for the adaptation procedure. We then demonstrate how to use the algorithm derived in a practical system for underwater communication using turbo equalization. As the algorithm naturally incorporates soft information into the adaptation process, it becomes easy to fit it into a turbo equalization framework. We thus provide an instance of how to use the information 3 of a turbo equalizer in an adaptation procedure, which has not been very well explored in the past. Experimental data is used to prove the value of the algorithm in a practical context.

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Adaptive equalization in magnetic-disk storage channels

Cited by 125 publications

References 30 publications

Multidimensional signal processing and detection for storage systems with data-dependent transition noise

Multidimensional signal processing and detection for storage systems with data-dependent transition noise

Evaluation of vector sensors for adaptive equalization in underwater acoustic communication

Direct-form adaptive equalization for underwater acoustic communication

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