Generalized Belief Propagation for the Noiseless Capacity and Information Rates of Run-Length Limited Constraints

The performance of the generalized belief propagation algorithm for computing the noiseless capacity and mutual information rates of finite-size two-dimensional and three-dimensional run-length limited constraints is investigated. For each constraint, a method is proposed to choose the basic regions and to construct the region graph. Simulation results for the capacity of different constraints as a function of the size of the channel and mutual information rates of different constraints as a function of signal-to-noise ratio are reported. Convergence to the Shannon capacity is also discussed.Comment: 8 pages, 11 figure

Capacity estimation of two-dimensional channels using Sequential Monte Carlo

We derive a new Sequential-Monte-Carlo-based algorithm to estimate the capacity of two-dimensional channel models. The focus is on computing the noiseless capacity of the 2-D one-infinity run-length limited constrained channel, but the underlying idea is generally applicable. The proposed algorithm is profiled against a state-of-the-art method, yielding more than an order of magnitude improvement in estimation accuracy for a given computation time