On Parameter Optimization of Product Codes for Iterative Bounded Distance Decoding with Scaled Reliability

TL;DR

This paper uses density evolution to optimize parameters of binary product codes for iterative bounded distance decoding with scaled reliability, identifying that 3-bit error correcting component codes offer the best performance-complexity balance.

Contribution

It introduces an optimization method for product code parameters using density evolution and highlights the optimal component code error correction capability.

Findings

Binary PCs with 3-bit error correction are optimal for performance and complexity.

Density evolution effectively guides parameter optimization.

The proposed approach improves decoding efficiency.

Abstract

We use density evolution to optimize the parameters of binary product codes (PCs) decoded based on the recently introduced iterative bounded distance decoding with scaled reliability. We show that binary PCs with component codes of 3-bit error correcting capability provide the best performance-complexity trade-off.