When to Stop? Dynamic Early Termination of Sequential Ensembles

Abstract

Ordered-statistics decoding (OSD) post-processing substantially improves the performance of belief propagation (BP) ensembles, but it also removes their natural syndrome-based stopping criterion. The high computational complexity of ensemble decoding and OSD can be reduced by sequential activation of ensemble members paired with an early termination. Instead of a syndrome-based termination we therefore apply dynamic ensemble termination (DET) to sequential ensembles: members are evaluated one at a time and decoding stops when the estimated risk that an unseen member would correct the current decision falls below an offline-fitted threshold. With hardware-oriented design in mind, we instantiate the framework with layered normalized min-sum component decoders, a row-boosted ensemble, conditional low-order OSD on the dual code, and a conventional low-cost decoder gate. A decision tree estimates the residual risk from statistics of the current candidate list. Member-dependent thresholds then target a prescribed frame error rate (FER) loss. We validate this framework on mutliple IEEE 802.11 low-density parity-check (LDPC) codes with blocklengths \(648,1296\), and \(1944\) at rates \(1/2\) and \(5/6\). At each code's design point, where the full \(64\)-member list reaches an FER of \(10^{-3}\), the DET decoder processes \(1.04\) to \(1.15\) members on average. On the \((648,540)\) code, it reduces BP work by \(39\times\) and OSD activations by \(18\times\) against the full list while retaining an signal-to-noise ratio (SNR) gain of about \(0.4\,\mathrm{dB}\).