基于跳过机制的低复杂度顺序统计译码算法

Objective Ultra-Reliable Low-Latency Communication (URLLC) in 5G and the emerging Hyper-Reliable LowLatency Communication (HRLLC) in 6G impose exceptionally stringent requirements on both reliability and
end-to-end delay. These requirements create opportunities and challenges for short-length channel codes,
particularly in scenarios where Maximum-Likelihood (ML) or near-ML decoding is desirable but computational
complexity and latency are prohibitive. Ordered Statistic Decoding (OSD) is a universal near-ML decoding
technique that can closely approach finite-length performance bounds. However, its re-encoding step suffers
from combinatorial explosion, resulting in impractical complexity in high-throughput and low-latency systems.
The excessive number of Test-Error-Pattern (TEP) re-encodings fundamentally restricts the deployment of
OSD in URLLC and HRLLC contexts. To address this bottleneck, we design multiple efficient skip mechanisms
that substantially reduce re-encoding operations while maintaining negligible performance degradation.
Methods Three complementary skipping mechanisms are developed to prune the OSD re-encoding search:
(1) Soft-information based skipping. Two criteria are introduced—Trivial and Dynamic Approximate Ideal
(DAI), to compare the soft metric of each TEP against the minimum soft weight in the current list. Candidates