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CORR
2008
Springer

Concave Programming Upper Bounds on the Capacity of 2-D Constraints

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Concave Programming Upper Bounds on the Capacity of 2-D Constraints
The capacity of 1-D constraints is given by the entropy of a corresponding stationary maxentropic Markov chain. Namely, the entropy is maximized over a set of probability distributions, which is defined by some linear requirements. In this paper, certain aspects of this characterization are extended to 2-D constraints. The result is a method for calculating an upper bound on the capacity of 2-D constraints. The key steps are: The maxentropic stationary probability distribution on square configurations is considered. A set of linear equalities and inequalities is derived from this stationarity. The result is a concave program, which can be easily solved numerically. Our method improves upon previous upper bounds for the capacity of the 2-D "no independent bits" constraint, as well as certain 2-D RLL constraints.
Ido Tal, Ron M. Roth
Added 24 Jan 2011
Updated 24 Jan 2011
Type Journal
Year 2008
Where CORR
Authors Ido Tal, Ron M. Roth
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