Analysis and design of raptor codes for joint decoding using Information Content evolution

TL;DR

This paper analyzes the convergence of raptor codes under joint decoding over BIAWGNC using Information Content evolution, proposing an optimized decoding scheme that improves efficiency over traditional tandem decoding.

Contribution

It introduces an analytical framework for joint decoding of raptor codes using Information Content evolution and proposes an optimized decoding scheme that enhances decoding efficiency.

Findings

Joint decoding outperforms tandem decoding in efficiency.

Analytical convergence analysis guides code design.

Optimization method improves decoding performance.

Abstract

In this paper, we present an analytical analysis of the convergence of raptor codes under joint decoding over the binary input additive white noise channel (BIAWGNC), and derive an optimization method. We use Information Content evolution under Gaussian approximation, and focus on a new decoding scheme that proves to be more efficient: the joint decoding of the two code components of the raptor code. In our general model, the classical tandem decoding scheme appears to be a subcase, and thus, the design of LT codes is also possible.