Fast Sparse Superposition Codes have Exponentially Small Error Probability for R < C
Information Theory
2012-07-11 v1 math.IT
Statistics Theory
Statistics Theory
Abstract
For the additive white Gaussian noise channel with average codeword power constraint, sparse superposition codes are developed. These codes are based on the statistical high-dimensional regression framework. The paper [IEEE Trans. Inform. Theory 55 (2012), 2541 - 2557] investigated decoding using the optimal maximum-likelihood decoding scheme. Here a fast decoding algorithm, called adaptive successive decoder, is developed. For any rate R less than the capacity C communication is shown to be reliable with exponentially small error probability.
Cite
@article{arxiv.1207.2406,
title = {Fast Sparse Superposition Codes have Exponentially Small Error Probability for R < C},
author = {Antony Joseph and Andrew Barron},
journal= {arXiv preprint arXiv:1207.2406},
year = {2012}
}
Comments
23 pages, 7 figures