TP Decoding

TL;DR

This paper generalizes the Tree Pruning algorithm for probabilistic inference to decoding linear codes, demonstrating through simulations that it bridges belief propagation and MAP decoding, with implications for code decoding methods.

Contribution

The paper extends Tree Pruning to linear code decoding and explores various pruning schemes, connecting belief propagation and MAP decoding.

Findings

Tree Pruning interpolates between belief propagation and MAP decoding.

Numerical simulations show improved decoding performance.

Theoretical discussion on implications of the generalized method.

Abstract

`Tree pruning' (TP) is an algorithm for probabilistic inference on binary Markov random fields. It has been recently derived by Dror Weitz and used to construct the first fully polynomial approximation scheme for counting independent sets up to the `tree uniqueness threshold.' It can be regarded as a clever method for pruning the belief propagation computation tree, in such a way to exactly account for the effect of loops. In this paper we generalize the original algorithm to make it suitable for decoding linear codes, and discuss various schemes for pruning the computation tree. Further, we present the outcomes of numerical simulations on several linear codes, showing that tree pruning allows to interpolate continuously between belief propagation and maximum a posteriori decoding. Finally, we discuss theoretical implications of the new method.