Information rates of multidimensional front ends for digital storage channels with data-dependent transition noise

Pighi, R.; Raheli, R.; Cappelletti, Fabio

doi:10.1109/tmag.2006.878127

Cited by 7 publications

(8 citation statements)

References 33 publications

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“…For this detector, the discrete time observable is corrupted by ISI and colored Gaussian thermal noise. The IR curve has been derived applying linear prediction to the colored thermal noise process [18], [19]. As a comparison, in Fig.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

“…Fig. 5 presents the IR curves obtained in [18] using (i) a mono-dimensional detector (1D curve), (ii) a bi-dimensional detector matched to position or width transition noise (2D t and 2D w curves, respectively), and (iii) a three-dimensional detector (3D curve). Note that the 1D curve is relative to the conventional front-end detector.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

“…The curve labeled by "2D t " shows the IR at the output of a bidimensional detector that uses only two filters in the front-end matched to h(t) and h t (t), respectively, and the curve labeled "2D w " presents the IR at the output of a bi-dimensional detector that uses two filters in the front-end matched to h(t) and h w (t), respectively. The curve labeled "3D" shows the IR achievable using a multidimensional front-end with filters matched to h(t), h t (t) and h w (t) [18]. Finally, the remaining curve in Fig.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

See 2 more Smart Citations

From Information Rate Computation to Communication System Design

Franceschini

Pighi

Ferrari

et al. 2007

2007 Information Theory and Applications Workshop

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Abstract-This paper investigates the application of information-theoretic techniques to the design of advanced communication and storage systems. We use the information rate (IR), evaluated using a recently proposed efficient numerical technique, as the key parameter characterizing the system performance. As case studies, we consider three different scenarios: (i) single-carrier (SC) and multi-carrier (MC) transmission systems over linear dispersive channels, (ii) digital storage systems impaired by media noise, and (iii) optical communication systems with direct photo-detection. The obtained results show that a clever IR analysis can provide significant insights into system performance and design in several scenarios of interest in current applications.

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Section: Numerical Results and Discussionmentioning

confidence: 99%

Section: Numerical Results and Discussionmentioning

confidence: 99%

Section: Numerical Results and Discussionmentioning

confidence: 99%

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From Information Rate Computation to Communication System Design

Franceschini

Pighi

Ferrari

et al. 2007

2007 Information Theory and Applications Workshop

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“…2, was demonstrated by bit error rate simulations. Furthermore, in [9], the SNR improvement of the proposed multidimensional detector was also confirmed by an information rate analysis.…”

Section: Sufficient Statistics and Detector Structurementioning

confidence: 60%

Oversampled Detector for Transition-Noise-Limited Digital Storage Systems

Pighi

Amadei

Raheli

2006

2006 IEEE International Conference on Communications

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Abstract-In the last decade, significant research has been performed on detection algorithms capable of mitigating the effects of colored Gaussian thermal noise and transition noise in digital storage systems. In this paper, we present a new Maximum A-Posteriori Probability (MAP) sequence detection scheme based on oversampling and linear prediction. The proposed solution improves the Bit Error Rate (BER) performance with respect to conventional systems and makes the detector more robust against transition noise. The results obtained for a magnetic channel can be also extended to optical storage systems.

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“…For example, Zhang et al investigate information rates both of magnetic recording 0018-9448/$20.00 © 2006 IEEE channels [48] and of fading multiple-input multiple-output (MIMO) channels with ISI [49]; magnetic recording is also considered by Ryan et al [34] as well as by Pighi et al [33]. Two-dimensional ISI channels are considered by Siegel et al [11], [40] and by Shental et al [39].…”

Section: Introductionmentioning

confidence: 99%

Simulation-Based Computation of Information Rates for Channels With Memory

Arnold

Loeliger

Vontobel

et al. 2006

IEEE Trans. Inform. Theory

490

526

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Abstract-The information rate of finite-state source/channel models can be accurately estimated by sampling both a long channel input sequence and the corresponding channel output sequence, followed by a forward sum-product recursion on the joint source/channel trellis. This method is extended to compute upper and lower bounds on the information rate of very general channels with memory by means of finite-state approximations. Further upper and lower bounds can be computed by reduced-state methods.

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Information rates of multidimensional front ends for digital storage channels with data-dependent transition noise

Cited by 7 publications

References 33 publications

From Information Rate Computation to Communication System Design

From Information Rate Computation to Communication System Design

Oversampled Detector for Transition-Noise-Limited Digital Storage Systems

Simulation-Based Computation of Information Rates for Channels With Memory

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