The Error Probability of Spatially Coupled Sparse Regression Codes over Memoryless Channels

TL;DR

This paper rigorously analyzes the non-asymptotic error probability of GAMP decoding for spatially coupled sparse regression codes over memoryless channels, demonstrating exponential decay with code length.

Contribution

It provides the first rigorous non-asymptotic analysis of GAMP decoding performance for SC-SPARCs over memoryless channels.

Findings

Error probability decays exponentially with code length.

GAMP decoder achieves reliable communication over memoryless channels.

The analysis confirms the effectiveness of SC-SPARCs with GAMP decoding.

Abstract

Sparse Regression Codes (SPARCs) are capacity-achieving codes introduced for communication over the Additive White Gaussian Noise (AWGN) channels and were later extended to general memoryless channels. In particular it was shown via threshold saturation that Spatially Coupled Sparse Regression Codes (SC-SPARCs) are capacity-achieving over general memoryless channels when using an Approximate Message Passing decoder (AMP). This paper, for the first time rigorously, analyzes the non-asymptotic performance of the Generalized Approximate Message Passing (GAMP) decoder of SC-SPARCs over memoryless channels, and proves exponential decaying error probability with respect to the code length.