Irregularly Clipped Sparse Regression Codes

TL;DR

This paper introduces irregularly clipped sparse regression codes with optimized clipping thresholds, achieving improved error performance and robustness over traditional clipped codes through state evolution and OAMP decoding.

Contribution

It proposes a novel irregular clipping scheme with optimized thresholds for SR codes, enhancing SER performance and robustness compared to regular clipping methods.

Findings

Achieves 0.4 dB SNR gain over regular clipping at SER 10^{-5}

Optimized irregular clipping improves the tradeoff between clipping and noise distortion

Demonstrates robustness across various code rates

Abstract

Recently, it was found that clipping can significantly improve the section error rate (SER) performance of sparse regression (SR) codes if an optimal clipping threshold is chosen. In this paper, we propose irregularly clipped SR codes, where multiple clipping thresholds are applied to symbols according to a distribution, to further improve the SER performance of SR codes. Orthogonal approximate message passing (OAMP) algorithm is used for decoding. Using state evolution, the distribution of irregular clipping thresholds is optimized to minimize the SER of OAMP decoding. As a result, optimized irregularly clipped SR codes achieve a better tradeoff between clipping distortion and noise distortion than regularly clipped SR codes. Numerical results demonstrate that irregularly clipped SR codes achieve 0.4 dB gain in signal-to-noise-ratio (SNR) over regularly clipped SR codes at code length$\,\approx2.5\!\times\! 10^4$ and SER$\,\approx10^{-5}$. We further show that irregularly clipped SR codes are robust over a wide range of code rates.