Partitioned Successive-Cancellation List Decoding of Polar Codes

TL;DR

This paper introduces a partitioned SCL decoding algorithm for polar codes that reduces memory requirements and can outperform traditional SCL decoding through strategic partitioning and list size selection.

Contribution

A novel partitioned SCL decoding method that decreases memory usage and enhances performance compared to conventional SCL decoding.

Findings

Reduces memory requirements for SCL decoding

Achieves better error-correction performance with partitioning

Requires careful selection of list sizes and partitions

Abstract

Successive-cancellation list (SCL) decoding is an algorithm that provides very good error-correction performance for polar codes. However, its hardware implementation requires a large amount of memory, mainly to store intermediate results. In this paper, a partitioned SCL algorithm is proposed to reduce the large memory requirements of the conventional SCL algorithm. The decoder tree is broken into partitions that are decoded separately. We show that with careful selection of list sizes and number of partitions, the proposed algorithm can outperform conventional SCL while requiring less memory.