Error correction in data storage systems using polar codes

Abstract

This paper investigates error correction in two-dimensional (2-D) intersymbol interference (ISI) channels using polar codes. 2-D channels offer high data storage capacity compared to traditional one-dimensional (1-D) channels but suffer from greater ISI effects. Error correction codes can be used to mitigate the effects of ISI in 2-D data storage systems and improve their performance and reliability. Polar codes achieve the symmetric capacity for the class of binary-input discrete memoryless channels (B-DMCs). A post-processing technique is proposed to improve the performance of polar codes in the 2-D ISI channel. The simplified successive cancellation (SSC) polar decoder is useful for data storage systems owing to its good waterfall region performance, low error floors and good throughput rates and is therefore adopted for the study. Simulation results indicate that the proposed technique has an error correction performance comparable to some existing decoders and has better performance than other decoding techniques.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Improving Decodability of Polar Codes by Adding Noise

“…However, its application in polar decoding is still in the early stages. The authors in [33,35] employed the GA to generate perturbation noise, so as to perturb the received signal that failed to decode. Inspired by the GA of AI technology, the authors in [45] used the mean of the log-likelihood ratio (LLR) distribution as the fitness function of each individual to update the successive cancellation flip (SCF) decoding algorithm of the population.…”

“…In this section, we first provide the perturbation decoding principle. Then, we briefly summarize the recent related works, including the PB-SSC decoding algorithm [33] and the PP-SSC decoding algorithm [35].…”

“…The PP-SSC decoding algorithm [35] has been proposed to improve the performance of polar codes in data storage systems, where the GA procedure is employed to enhance the iteration process by producing perturbation vectors that are inherently better than the directed perturbed ones.…”

“…A decoding algorithm for polar codes based on the perturbation with a convolution neural network (CNN) was proposed in [34]. In [35], a post-processing technique was proposed to improve the performance of the SSC polar decoder in the 2D intersymbol interference (ISI) data storage system, namely the post-processing SSC (PP-SSC) decoding algorithm, in which the perturbation algorithm and the genetic algorithm (GA) successively generate perturbation vectors that accelerate the convergence of the decoder. Unfortunately, the secondary generation of perturbation noise by the GA is performed online, which greatly increases the delay and complexity of the decoder.…”

An improved algorithm of generating shortening patterns for polar codes

“…Polar codes have been subject to investigation for the purpose of error correction within storage systems, notably in flash memory, where errors may arise due to physical limitations. This exploration is motivated by the capacity-achieving attributes inherent to Polar codes, which render them an appealing choice for mitigating errors within storage devices [17]. Furthermore, within the field of quantum communication, Polar codes have garnered attention for their potential applications in error correction and the transmission of information through quantum channels [18].…”