Enhanced Successive Cancellation List Decoder for Long Polar Codes Targeting Air Interface
Jiajie Li, Sihui Shen, Warren J. Gross

TL;DR
This paper introduces advanced decoding algorithms for long polar codes, significantly reducing memory and computational requirements while maintaining near-optimal performance for air interface applications.
Contribution
It proposes novel bias-enhanced and partitioned SCL decoders that improve efficiency and scalability for long polar codes, with theoretical performance analysis.
Findings
67% reduction in memory usage with similar decoding performance
Up to 5.4 times reduction in computational complexity
Negligible performance loss of 0.05 dB compared to larger list decoders
Abstract
Polar codes are the first codes with a proven capacity-achieving capability, but their decoding faces several challenges, especially under long code lengths. In this paper, we target algorithmic improvements and analyses to enable the implementation of long polar codes (e.g., length 8K bits) by addressing key challenges in memory usage and computational complexity presented by successive cancellation list (SCL) polar decoding. Perturbation-enhanced (PE) SCL decoders with a list size of reach the decoding performance of the SCL decoder with a list size of . The proposed bias-enhanced (BE) SCL decoders, which simplify the PE SCL decoder based on insights gained by an ablation study, return similar decoding performance to PE SCL decoders. Also, proposed BE generalized partitioned SCL (GPSCL) decoders with a list size of have a reduction in the memory usage and similar…
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