Doubly Residual Neural Decoder: Towards Low-Complexity High-Performance Channel Decoding
This work provides a more efficient and effective channel decoding solution for communication systems, which is an incremental improvement over existing neural decoders.
This paper introduces the Doubly Residual Neural (DRN) decoder, which integrates residual input and residual learning to address the challenge of achieving high decoding performance and low complexity simultaneously in neural channel decoders. The DRN decoder consistently outperforms state-of-the-art decoders across various channel codes in terms of decoding performance, model size, and computational cost.
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.