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Generalized belief propagation algorithm for the capacity of multi-dimensional run-length limited constraints

The performance of the generalized belief propagation algorithm for computing the noiseless capacity of finite-sized two-dimensional and three-dimensional run-length limited constraints is investigated. For each constraint, a method is proposed to choose a set of clusters. Simulation results for different sizes of channels with different constraints are reported. Convergence to the Shannon capacity is also discussed.