On the capacity of the noisy runlength channel

Heegard, C.; Duel-Hallan, A.; Krishnamoorthy, R.

doi:10.1109/18.79941

Cited by 5 publications

(6 citation statements)

References 10 publications

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“…The filter coefficients in Example 1 are often compactly represented by the formal sum The signal-to-noise ratio (SNR) will be defined as (19) (It is clear that this SNR definition is inadequate for some applications, but this qualification seems to apply also to alternative definitions including that of [44].) For channels, as in Example 1, is known analytically, which means that the algorithm of Section III is only needed to compute .…”

Section: Numerical Examplesmentioning

confidence: 99%

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Simulation-Based Computation of Information Rates for Channels With Memory

Arnold

Loeliger

Vontobel

et al. 2006

IEEE Trans. Inform. Theory

494

529

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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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Section: Numerical Examplesmentioning

confidence: 99%

“…information rate [35]. A related topic is the continuous-time additive white Gaussian noise (AWGN) channel with peak-amplitude-constrained input, which was addressed by Heegard et al [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Simulation-Based Computation of Information Rates for Channels With Memory

Arnold

Loeliger

Vontobel

et al. 2006

IEEE Trans. Inform. Theory

494

529

View full text Add to dashboard Cite

show abstract

“…The above equations give a recipe for computing the channel capacity of the model in (6). Fix a set of parameters {W, H, u 2 , p 2 } .…”

Section: )mentioning

confidence: 99%

“…Fix a set of parameters {W, H, u 2 , p 2 } . Then, for a given P,, the probability density functions f(y) and f(ylx0) can be numerically computed according to (6). Equations (10-13) give the mutual information I(c0; y).…”

Section: )mentioning

confidence: 99%

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Achievable radial information densities in magnetic recording systems

Voois¹,

Cioffi²

[Conference Record] GLOBECOM '92 - Communications for Global Users: IEEE

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Misalignment of recording head position results in intertrack interference (ITI), which is a primary factor limiting radial density in current digital magnetic recording systems. This paper examines achievable radial information density by modeling the magnetic recording system as a communication channel and then computing its capacity as a function of track width. The model includes the effects of IT1 but ignores intersymbol interference (13). Mechanical servo error is modeled as a Gaussian random variable independent of head or track width. The results show that for a fixed servo error variance, narrowing track width can significantly increase the overall information storage density. The results also reveal the surprising fact that the optimal head width can be greater than or equal to the track width.

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Recording medium properties and capacity bounds

O’Sullivan¹,

Porter²,

Indeck³

et al. 1994

Journal of Applied Physics

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The effect of such medium properties as magnetic unit size, film thickness, and strength of anisotropy, dipolar, and exchange interactions on the storage capacity limit of a thin-film recording medium is investigated. Relationships are derived for media with different properties that have equivalent capacity. The physically realizable range of material properties and their effect on the capacity bound are explored. Results on edge effects for capacity bound computations are presented.The results indicate that media for which anisotropy is the dominant source of magnetic energy have the greatest potential for information storage.

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On the capacity of the noisy runlength channel

Cited by 5 publications

References 10 publications

Simulation-Based Computation of Information Rates for Channels With Memory

Simulation-Based Computation of Information Rates for Channels With Memory

Achievable radial information densities in magnetic recording systems

Recording medium properties and capacity bounds

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