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Convex programming upper bounds on the capacity of 2-d constraints (2009).
| Content Provider | CiteSeerX |
|---|---|
| Author | Tal, Ido Roth, Ron M. |
| Abstract | 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 equalities and inequalities. 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 convex 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. |
| File Format | |
| Publisher Date | 2009-01-01 |
| Access Restriction | Open |
| Subject Keyword | 2-d Constraint Convex Programming Upper Bound Linear Equality Upper Bound Convex Program Certain Aspect Previous Upper Bound Key Step Independent Bit Constraint 1-d Constraint Maxentropic Stationary Probability Distribution Certain 2-d Rll Constraint Square Configuration Probability Distribution Corresponding Stationary Maxentropic Markov Chain |
| Content Type | Text |
