GenPolar: Generative AI-Aided Complexity Reduction for Polar SCL Decoding

The CRC-aided successive cancellation list (CA-SCL) decoding algorithm for polar codes has gained widespread adoption thanks to its outstanding performance. However, with the evolution of 6G technologies, the high complexity of CA-SCL decoding poses a challenge in meeting growing performance requirements. Consequently, it is crucial to devise strategies that reduce this complexity without compromising error rates. Current efforts to mitigate the complexity mainly depend on harnessing special nodes associated with the code construction sequences, such as Fast-SCL decoding. However, these strategies suffer from redundant complexity due to ill-suited construction sequences and unnecessary sorting operations within special nodes. Addressing this issue, this paper proposes a hardware-friendly and GenAI-aided complexity reduction approach for Fast-SCL decoding, named GenPolar. This approach involves two-step optimization techniques: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Transformer encoder models</i> for generating polar construction sequences, and 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a sorting entropy based method</i> for sorting reduction. These two-step techniques result in reduced complexity with negligible performance loss. For polar codes of length-1024 with code rates of 0.25, 0.50, and 0.75, Gen-Polar achieves latency reductions of 20.6%, 29.8%, and 40.6%, respectively. Even benchmarking against the reduced-complexity version of Fast-SCL decoding, the relative gains are 14.0%, 17.8%, and 22.3%, respectively. It should be noted that the immediate application is not limited to Fast-SCL decoding but also extends to other node-based SCL decoding algorithms like SSCL-SPC and SR-SCL.