Deep-Learning-Aided Successive-Cancellation Decoding of Polar Codes

TL;DR

This paper introduces a deep-learning-enhanced decoding algorithm for polar codes that reduces computational complexity while maintaining high error-correction performance.

Contribution

It proposes a novel deep-learning-aided SCL decoding method with a new bit-flipping metric optimized via deep learning, improving efficiency over existing techniques.

Findings

Requires up to 66% fewer multiplications.

Requires up to 36% fewer additions.

Achieves comparable error-correction performance to state-of-the-art methods.

Abstract

A deep-learning-aided successive-cancellation list (DL-SCL) decoding algorithm for polar codes is introduced with deep-learning-aided successive-cancellation (DL-SC) decoding being a specific case of it. The DL-SCL decoder works by allowing additional rounds of SCL decoding when the first SCL decoding attempt fails, using a novel bit-flipping metric. The proposed bit-flipping metric exploits the inherent relations between the information bits in polar codes that are represented by a correlation matrix. The correlation matrix is then optimized using emerging deep-learning techniques. Performance results on a polar code of length 128 with 64 information bits concatenated with a 24-bit cyclic redundancy check show that the proposed bit-flipping metric in the proposed DL-SCL decoder requires up to 66% fewer multiplications and up to 36% fewer additions, without any need to perform transcendental functions, and by providing almost the same error-correction performance in comparison with the state of the art.