Sequential Automorphism Ensemble Decoding with Early Stopping

2026-04-30

AI Summary

This work addresses the high computational complexity of the Automatic Isomorphism Ensemble Decoder (AED) by proposing a low-complexity sequential activation strategy for sub-decoders. The method introduces, for the first time, an early termination mechanism into the AED framework, leveraging the strong correlation between successive cancellation (SC) path metrics and final decoding outcomes to enable efficient stopping decisions via a pre-optimized threshold. Under block error rate (BLER) conditions below $10^{-3}$, the proposed approach achieves a 6× to 22× reduction in average decoding complexity compared to the original AED, with negligible degradation in error-correction performance.

Abstract

In this paper, a low-complexity approach for the automorphism ensemble decoder (AED) using successive cancellation (SC) as constituent decoders is proposed. The approach sequentially activates sub-decoders and terminates the decoding process based on pre-optimized parameters, derived from the strong correlation observed between the decoding outcome and the SC path metric. An algorithm is proposed to find a list of early termination thresholds that minimize average decoding complexity subject to a block-error rate (BLER) constraint. For various code parameters and a BLER below $10^{-3}$, simulation results show that average decoding complexity is reduced by a factor of at least $6 \times$, and up to $22 \times$, compared to the original AED complexity, with a negligible degradation in BLER.

Problem

• automorphism ensemble decoding
• decoding complexity
• block-error rate
• early stopping
• successive cancellation

Innovation

• automorphism ensemble decoding
• successive cancellation
• early stopping
• decoding complexity
• path metric