Neural Dynamic Successive Cancellation Flip Decoding of Polar Codes

TL;DR

This paper proposes a new approximation scheme for neural DSCF decoding of polar codes that eliminates complex computations, maintaining high error correction performance with reduced implementation complexity.

Contribution

It introduces a training parameter and an approximation method that remove transcendental computations in DSCF decoding, preserving performance.

Findings

Achieves error correction comparable to SCL decoding

Reduces computational complexity of DSCF decoding

Maintains performance with no significant degradation

Abstract

Dynamic successive cancellation flip (DSCF) decoding of polar codes is a powerful algorithm that can achieve the error correction performance of successive cancellation list (SCL) decoding, with a complexity that is close to that of successive cancellation (SC) decoding at practical signal-to-noise ratio (SNR) regimes. However, DSCF decoding requires costly transcendental computations which adversely affect its implementation complexity. In this paper, we first show that a direct application of common approximation schemes on the conventional DSCF decoding results in significant error-correction performance loss. We then introduce a training parameter and propose an approximation scheme which completely removes the need to perform transcendental computations in DSCF decoding, with almost no error-correction performance degradation.