# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1907.11563