Adaptive Perturbation Enhanced SCL Decoder for Polar Codes

TL;DR

This paper introduces an adaptive perturbation method to enhance SCL decoding of polar codes, achieving stable, non-diminishing performance gains at large block lengths with minimal additional decoding attempts.

Contribution

The paper proposes a novel adaptive perturbation decoding algorithm that significantly improves SCL decoding performance for polar codes, especially at large code lengths, with minimal complexity increase.

Findings

Achieves non-diminishing performance gains at large block lengths.

Requires only one additional decoding attempt for doubled list size performance.

Demonstrates stable gains across various code rates and lengths.

Abstract

For polar codes, successive cancellation list (SCL) decoding algorithm significantly improves finite-length performance compared to SC decoding. SCL-flip decoding can further enhance the performance but the gain diminishes as code length increases, due to the difficulty in locating the first error bit position. In this work, we introduce an SCL-perturbation decoding algorithm to address this issue. A basic version of the algorithm introduces small random perturbations to the received symbols before each SCL decoding attempt, and exhibits non-diminishing gain at large block lengths. Its enhanced version adaptively performs random perturbations or directional perturbation on each received symbol according to previous decoding results, and managed to correct more errors with fewer decoding attempts. Extensive simulation results demonstrate stable gains across various code rates, lengths and list sizes. To the best of our knowledge, this is the first SCL enhancement with non-diminishing gains as code length increases, and achieves unprecedented efficiency. With only one additional SCL-$L$ decoding attempt (in total two), the proposed algorithm achieves SCL-$2L$-equivalent performance. Since the gain is obtained without increasing list size, the algorithm is best suited for hardware implementation.