SECS: A Soft Ensemble-Combining Stage for Low-Latency Decoding

2026-08-10

AI Summary

Traditional ensemble decoding fails to meet the dual requirements of ultra-reliability and low latency when all parallel constituent decoders fail simultaneously. To address this limitation, this work proposes a Soft Ensemble Combination Stage (SECS), which replaces hard selection with soft information fusion. The approach leverages shallow, diverse belief propagation (BP) decoders that, after only a few iterations, produce highly reliable bit reliability estimates. These are then integrated with ordered statistics decoding (OSD) for re-encoding, closely approximating maximum-likelihood (ML) performance. By significantly reducing the number of required BP iterations, the proposed method achieves substantial latency reduction while markedly narrowing the performance gap to ML decoding across three short block codes.

Abstract

Ensemble decoding is a promising technique for ultra-reliable low-latency communication, as it trades hardware parallelism for decoding latency by running M diverse belief propagation (BP) decoders in parallel. Conventional ensembles select their output from the candidates. Hence, decoding fails whenever no member finds the correct codeword. In this work, we show that shallow, diverse BP members are poor correctors, but excellent sorters. Based on this observation, we propose a soft ensemble-combining stage (SECS) that combines extrinsic messages after only a few iterations, yielding a reliability ordering whose most reliable positions are nearly error-free. A subsequent re-encoding stage (like ordered-statistics decoding (OSD)) converts this ordering into a near-maximum likelihood (ML) candidate codeword, at a fraction of the BP latency of a fully converged ensemble. We demonstrate the proposed scheme on three short codes: a (63,30) Bose-Ray-Chaudhuri-Hocquenghem code, an overcomplete PG(2,8) code, and a search-designed (105,53) cyclic code. In all cases, the SECS with OSD post-processing closes most of the gap to ML decoding, while significantly reducing the number of required BP iterations.

Problem

• ensemble decoding
• low-latency communication
• belief propagation
• decoding failure
• ultra-reliable communication

Innovation

• soft ensemble-combining
• low-latency decoding
• belief propagation
• ordered-statistics decoding
• maximum likelihood approximation