Iterative Threshold Decoding of Spatially Coupled, Parallel-Concatenated Codes

TL;DR

This paper introduces a low-complexity, energy-efficient decoding method for spatially coupled, parallel concatenated codes that maintains high coding gain and is easier to implement than optimal iterative decoding strategies.

Contribution

It proposes a novel combination of convolutional self-orthogonal codes with suboptimal APP threshold decoders for SC-PCCs, reducing complexity while preserving performance.

Findings

Faster decoding with lower energy consumption.

Significant coding gain over existing threshold decodable codes.

Simpler implementation compared to optimal iterative methods.

Abstract

Spatially coupled, parallel concatenated codes (SC-PCCs) have been shown to approach channel capacity when decoded using optimal iterative methods. However, under complexity constraints such decoding strategies can result in unacceptable power and latency costs. In this work, we employ convolutional self-orthogonal component codes along with low-complexity, suboptimal a posteriori probability (APP) threshold decoders with SC-PCCs to reduce decoding complexity. The proposed code design is faster, more energy efficient, and easier to implement than optimal methods, while offering significant coding gain over existing threshold decodable, turbo-like constructions of similar latency and complexity. The design also serves to further illustrate the advantages spatial coupling can provide to existing code constructions and decoder implementations.