Threshold Saturation via Spatial Coupling: Why Convolutional LDPC Ensembles Perform so well over the BEC

TL;DR

This paper explains why spatially coupled convolutional LDPC codes achieve near-capacity performance by demonstrating that their belief-propagation thresholds saturate to the MAP thresholds, significantly improving decoding efficiency.

Contribution

The paper introduces the concept of threshold saturation in spatially coupled codes, providing a theoretical explanation and proof for their superior performance over the BEC.

Findings

Threshold saturation causes BP thresholds to reach MAP thresholds.

Spatial coupling increases the error-correcting radius with blocklength.

Empirical evidence suggests similar phenomena occur for various ensembles and channels.

Abstract

Convolutional LDPC ensembles, introduced by Felstrom and Zigangirov, have excellent thresholds and these thresholds are rapidly increasing as a function of the average degree. Several variations on the basic theme have been proposed to date, all of which share the good performance characteristics of convolutional LDPC ensembles. We describe the fundamental mechanism which explains why "convolutional-like" or "spatially coupled" codes perform so well. In essence, the spatial coupling of the individual code structure has the effect of increasing the belief-propagation (BP) threshold of the new ensemble to its maximum possible value, namely the maximum-a-posteriori (MAP) threshold of the underlying ensemble. For this reason we call this phenomenon "threshold saturation." This gives an entirely new way of approaching capacity. One significant advantage of such a construction is that one can create capacity-approaching ensembles with an error correcting radius which is increasing in the blocklength. Our proof makes use of the area theorem of the BP-EXIT curve and the connection between the MAP and BP threshold recently pointed out by Measson, Montanari, Richardson, and Urbanke. Although we prove the connection between the MAP and the BP threshold only for a very specific ensemble and only for the binary erasure channel, empirically a threshold saturation phenomenon occurs for a wide class of ensembles and channels. More generally, we conjecture that for a large range of graphical systems a similar saturation of the "dynamical" threshold occurs once individual components are coupled sufficiently strongly. This might give rise to improved algorithms as well as to new techniques for analysis.