A Low Density Lattice Decoder via Non-Parametric Belief Propagation

TL;DR

This paper establishes a theoretical connection between low density lattice code decoding and non-parametric belief propagation, enabling improved convergence analysis and extensions to more general matrices.

Contribution

It shows that LDLC decoding is an instance of non-parametric belief propagation, allowing transfer of knowledge and new convergence conditions.

Findings

LDLC decoder is an instance of non-parametric belief propagation

New convergence conditions for LDLC decoder are derived

Proposed efficient construction for sparse generator matrices

Abstract

The recent work of Sommer, Feder and Shalvi presented a new family of codes called low density lattice codes (LDLC) that can be decoded efficiently and approach the capacity of the AWGN channel. A linear time iterative decoding scheme which is based on a message-passing formulation on a factor graph is given. In the current work we report our theoretical findings regarding the relation between the LDLC decoder and belief propagation. We show that the LDLC decoder is an instance of non-parametric belief propagation and further connect it to the Gaussian belief propagation algorithm. Our new results enable borrowing knowledge from the non-parametric and Gaussian belief propagation domains into the LDLC domain. Specifically, we give more general convergence conditions for convergence of the LDLC decoder (under the same assumptions of the original LDLC convergence analysis). We discuss how to extend the LDLC decoder from Latin square to full rank, non-square matrices. We propose an efficient construction of sparse generator matrix and its matching decoder. We report preliminary experimental results which show our decoder has comparable symbol to error rate compared to the original LDLC decoder.%