GRADE-AO: Towards Near-Optimal Spatially-Coupled Codes With High Memories

Spatially-coupled (SC) codes, known for their threshold saturation phenomenon and low-latency windowed decoding algorithms, are ideal for streaming applications and data storage systems. SC codes are constructed by partitioning an underlying block code, followed by rearranging and concatenating the partitioned components in a convolutional manner. The number of partitioned components determines the memory of SC codes. While adopting higher memories results in improved SC code performance, obtaining optimal SC codes with high memory is known to be hard. In this paper, we investigate the relation between the performance of SC codes and the density distribution of partitioning matrices. We propose a probabilistic framework that obtains (locally) optimal density distributions via gradient descent. Starting from random partitioning matrices abiding by the obtained distribution, we perform low complexity optimization algorithms over the cycle properties to construct high memory, high performance quasi-cyclic SC codes. Simulation results show that codes obtained through our proposed method notably outperform state-of-the-art SC codes with the same constraint length and codes with uniform partitioning.

Automated summary

This paper investigates the relationship between the performance of SC codes and the density distribution of partitioning matrices, proposing a probabilistic framework that obtains (locally) optimal density distributions through gradient descent. The study shows that high memory, high performance quasi-cyclic SC codes can be constructed using low complexity optimization algorithms based on the obtained density distribution. Simulation results demonstrate that the codes obtained through this method outperform state-of-the-art SC codes with the same constraint length and codes with uniform partitioning.