Neural Dynamic Successive Cancellation Flip Decoding of Polar Codes
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.
Keywords
Cite
@article{arxiv.1907.11563,
title = {Neural Dynamic Successive Cancellation Flip Decoding of Polar Codes},
author = {Nghia Doan and Seyyed Ali Hashemi and Furkan Ercan and Thibaud Tonnellier and Warren Gross},
journal= {arXiv preprint arXiv:1907.11563},
year = {2019}
}