Improved Generalized Automorphism Belief Propagation Decoding

TL;DR

This paper enhances generalized automorphism ensemble decoding for short block length LDPC codes by integrating preprocessing with the Tanner graph, leading to improved decoding performance and ensemble gains.

Contribution

The authors propose merging the preprocessing step with the Tanner graph in GAED, reducing information loss and improving overall decoding performance.

Findings

Improved performance of constituent paths in GAED.

Enhanced ensemble decoding performance.

Better trade-offs between latency, throughput, and complexity.

Abstract

With the increasing demands on future wireless systems, new design objectives become eminent. Low-density parity-check codes together with belief propagation (BP) decoding have outstanding performance for large block lengths. Yet, for future wireless systems, good decoding performance for short block lengths is mandatory, a regime in which BP decoding typically shows a significant gap to maximum likelihood decoding. Automorphism ensemble decoding (AED) is known to reduce this gap effectively and, in addition, enables an easy trade-off between latency, throughput, and complexity. Recently, generalized AED (GAED) was proposed to increase the set of feasible automorphisms suitable for ensemble decoding. By construction, GAED requires a preprocessing step within its constituent paths that results in information loss and potentially limits the gains of GAED. In this work, we show that the preprocessing step can be merged with the Tanner graph of BP decoding, thereby improving the performance of the constituent paths. Finally, we show that the improvement of the individual paths also enhances the overall performance of the ensemble.