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. 
Giovanni Sabato and Mehdi Molkaraie
cs.IT math.IT stat.CO