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

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

2011-01-17

arxiv.org/abs/1101.3214v2

Computer Science

Information Theory

8 pages, 11 figures

Scientific paper

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.

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