Empirical Evaluation of Approximation Algorithms for Probabilistic Decoding
This work addresses decoding efficiency in communication systems, but it is incremental as it builds on existing frameworks like mini-bucket elimination.
The paper tackles the problem of decoding messages transmitted through noisy channels by comparing approximation algorithms for probabilistic decoding, finding that the approx-mpe algorithm outperforms iterative belief propagation on coding networks with bounded induced width.
It was recently shown that the problem of decoding messages transmitted through a noisy channel can be formulated as a belief updating task over a probabilistic network [McEliece]. Moreover, it was observed that iterative application of the (linear time) Pearl's belief propagation algorithm designed for polytrees outperformed state of the art decoding algorithms, even though the corresponding networks may have many cycles. This paper demonstrates empirically that an approximation algorithm approx-mpe for solving the most probable explanation (MPE) problem, developed within the recently proposed mini-bucket elimination framework [Dechter96], outperforms iterative belief propagation on classes of coding networks that have bounded induced width. Our experiments suggest that approximate MPE decoders can be good competitors to the approximate belief updating decoders.