A Path-Survival Analytical Framework for SCL Decoding of Polar Code
It provides a theoretical tool for polar code performance prediction, addressing a gap compared to LDPC codes, but the framework is domain-specific and incremental.
The paper proposes an analytical framework for predicting CA-SCL decoding performance of polar codes without exhaustive simulations, validated across various code lengths, rates, list sizes, and channel models.
A theoretical analysis of CRC-aided successive cancellation list (CA-SCL) decoding for polar codes remains an open problem, despite its widespread practical adoption. While low-density parity-check (LDPC) codes benefit from mature analytical tools, such as density evolution (DE), for predicting the performance of belief-propagation (BP) decoding, similar techniques are not directly applicable to CA-SCL decoding. This limitation stems from the complex path-pruning mechanism inherent in CA-SCL decoding. In this paper, we propose an analytical framework based on a novel path-survival model that captures the evolution of the correct path's rank during decoding. The proposed framework enables efficient prediction of CA-SCL decoding performance without requiring exhaustive list-specific Monte Carlo simulations. Extensive numerical evaluations demonstrate its effectiveness across a wide range of code lengths, code rates, list sizes, and channel models.