New Bounds on the Capacity of Multidimensional Run-Length Constraints

We examine the well-known problem of determining the capacity of multidimensional run-length-limited constrained systems. By recasting the problem, which is essentially a combinatorial counting problem, into a probabilistic setting, we are able to derive new lower and upper bounds on the capacity of -RLL systems. These bounds are better than all previously-known ana-lytical bounds for , and are tight asymptotically. Thus, we settle the open question: what is the rate at which the capacity of -RLL systems converges to 1 as ? We also provide the first nontrivial upper bound on the capacity of general -RLL systems