Doubly Residual Neural Decoder: Towards Low-Complexity High-Performance Channel Decoding

TL;DR

This paper introduces the doubly residual neural (DRN) decoder, a novel neural network-based channel decoder that achieves high decoding performance with low complexity by combining residual input and residual learning techniques.

Contribution

The paper proposes the DRN decoder, which significantly improves decoding performance while maintaining low complexity, outperforming existing neural decoders across various channel codes.

Findings

DRN outperforms state-of-the-art decoders in decoding accuracy.

DRN achieves smaller model sizes compared to existing methods.

DRN reduces computational cost while maintaining high performance.

Abstract

Recently deep neural networks have been successfully applied in channel coding to improve the decoding performance. However, the state-of-the-art neural channel decoders cannot achieve high decoding performance and low complexity simultaneously. To overcome this challenge, in this paper we propose doubly residual neural (DRN) decoder. By integrating both the residual input and residual learning to the design of neural channel decoder, DRN enables significant decoding performance improvement while maintaining low complexity. Extensive experiment results show that on different types of channel codes, our DRN decoder consistently outperform the state-of-the-art decoders in terms of decoding performance, model sizes and computational cost.