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TS-SCLF: Threshold-Learning-Based SCLF Polar Decoder With Segmented CRC
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DOI：10.1109/LWC.2025.3627410.png)
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Abstract
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Successive cancellation list flip (SCLF) polar decoding has garnered extensive research attention thanks to its outstanding error correction performance. However, it suffers from high complexity and long worst-case decoding latency, particularly at low signal-to-noise ratios (SNRs). To address these issues, this letter proposes TS-SCLF, an SCLF decoder that employs a dual-threshold strategy and segmented CRC to reduce latency. The proposed dual-threshold strategy dynamically skips redundant flipping attempts through historical failure pattern analysis, while employing Gaussian distribution-inspired path metric thresholds to enable early successive cancellation list (SCL) termination. Furthermore, a deep Q-network (DQN) framework is applied to optimize the threshold parameters, achieving a good balance between complexity and error performance. Experimental results show that for a (1024, 512) polar code, TS-SCLF reduces the average number of decoding attempts by up to 55.3% at low SNRs compared with state-of-the-art counterparts, while preserving error correction performance.
Keywords:
Successive cancelation list flip
polar codes
reinforcement learning
early termination
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