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Open AccessJournal ArticleDOI

Coding for white-efficient memory

R. Ahlswede, +1 more
- 01 Oct 1989 - 
- Vol. 83, Iss: 1, pp 80-97
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TLDR
This is the most basic quantity (the storage capacity) of a WEM ( X n, ϕn)n = 1∞ and a characterization of this and related quantities is given.
Abstract
We introduce write-efficient memories (WEM) as a new model for storing and updating information on a rewritable medium. There is a cost ϕ: X × X → R ∞ assigned to changes of letters. A collection of subsets C = {Ci: 1 ≤ i ≤ M} of X n is an (n, M, D) WEM code, if C i ∩ C j = ⊘ for all i ≠ j and if D max = max l⩽i,j⩽Mx n ϵC j Y n ϵC 1 max min ∑ j=1 n ϕ(x t , y t )⩽D . Dmax is called the maximal correction cost with respect to the given cost function. The performance of a code C can also be measured by two parameters, namely, the maximal cost per letter d C = n−1Dmax and the rate of the size r C = n−1 log M. The rate achievable with a maximal per letter cost d is thus R(d)= sup c:d c ⩽d r c . This is the most basic quantity (the storage capacity) of a WEM ( X n, ϕn)n = 1∞. We give a characterization of this and related quantities.

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Citations
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Constrained Codes that Mitigate Inter-Cell Interference in Read/Write Cycles for Flash Memories

TL;DR: In this article, the authors consider ICI-free write-once-memory (WOM) codes that support multiple writes of a WOM while mitigating ICI effects and show that the asymptotic rate loss due to the imposition of the balanced property is approximately 2%.
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On the capacity and error-correcting codes of write-efficient memories

TL;DR: It is interesting to note that the deterministic WEM behaves like the discrete noiseless channels of Shannon (1948), and some interesting properties for the maximization problem of information functions with multiple variables which are difficult to obtain otherwise are obtained.
References
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Journal ArticleDOI

Writing on dirty paper (Corresp.)

TL;DR: It is shown that the optimal transmitter adapts its signal to the state S rather than attempting to cancel it, which is also the capacity of a standard Gaussian channel with signal-to-noise power ratio P/N.
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On the capacity of computer memory with defects

TL;DR: A computer memory with defects is modeled as a discrete memoryless channel with states that are statistically determined, and Arimoto-Blahut type algorithms are used to compute the storage capacity.
Journal ArticleDOI

How to reuse a “write-once≓ memory*

TL;DR: It is demonstrated that such “write-once memories” can be “rewritten to a surprising degree” and an n-wit WOM is shown to have a “capacity” of up to n · log(n) bits.
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