Fast Sparse Superposition Codes have Exponentially Small Error Probability for R < C

preprint2012arXivOpen access

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.

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