Abstract
Factor graphs are bipartite graphs used for expressing how a global function factors into a product of local functions. In this thesis, we focus on factor graphs of codes. Some decoding algorithms, such as the BCJR algorithm, the Viterbi algorithm, and the Pearl's belief propagation algorithm, can be treated as instances of the sum-product algorithm applied to factor graphs of codes. The sum-product algorithm can be applied to an arbitrary factor graph, whether it is cycle-free or not. When a factor graph of a code is cycle-free, the sum-product algorithm will be terminated and accurately compute all marginal functions of the global function. But if the graph is loopy, the sum-product algorithm can be run endlessly and results in an iterative decoding algorithm. The traditional iterative decoding algorithm is an instance of the sum-product algorithm with parallel scheduling. Unfortunately, it sometimes diverges. In this thesis, we propose a stochastic sequential scheduling scheme, called Markovian scheduling, to avoid the divergence behavior. Simulation-study shows that this Markovian scheduling is successful.