  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.
