# DIR-Net: Deep Residual Polar Decoding Network Based on Information Refinement
## 5 Conclusions  
In this paper, a novel polar decoding algorithm, DIR-Net, based on deep learning was proposed. To achieve the goal of separating effective information from interference information, we carefully designed several information optimization modules based on the attention mechanism. Furthermore, we constructed a high-performance decoding network based on the proposed attention modules. The proposed network contains two subnetworks with different functions, i.e., the denoising subnetwork to remove the noise brought in the transmission channel and the decoding subnetwork to decode the denoised information. Benefiting from the cascaded attention module, the information can be refined step-by-step, thus obtaining a low error rate. Furthermore, the residual connections in the denoising subnetwork facilitate the convergence of this network. In the end, a series of experiments were conducted to verify the effectiveness of the DIR-Net algorithm. The experimental results show that our method had a low BER in the presence of both noise  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **16-8** & **32-16** & **64-32** & **128-64** & **256-16** \\ \hline Denoiser & \(8.310\times 10^{-4}\) & \(8.386\times 10^{-4}\) & \(8.397\times 10^{-4}\) & \(8.414\times 10^{-4}\) & \(8.456\times 10^{-4}\) \\ Decoder & \(3.371\times 10^{-4}\) & \(3.386\times 10^{-4}\) & \(3.409\times 10^{-4}\) & \(3.423\times 10^{-4}\) & \(3.431\times 10^{-4}\) \\ DIR-Net & \(1.168\times 10^{-3}\) & \(1.180\times 10^{-3}\) & \(1.181\times 10^{-3}\) & \(1.183\times 10^{-3}\) & \(1.187\times 10^{-3}\) \\ res\_dec & \(7.089\times 10^{-4}\) & \(7.115\times 10^{-4}\) & \(7.120\times 10^{-4}\) & \(7.145\times 10^{-4}\) & \(7.469\times 10^{-4}\) \\ only\_fc\_dec & \(1.844\times 10^{-4}\) & \(1.856\times 10^{-4}\) & \(1.857\times 10^{-4}\) & \(1.864\times 10^{-4}\) & \(1.868\times 10^{-4}\) \\ FC\_Decoder & \(1.818\times 10^{-4}\) & \(1.819\times 10^{-4}\) & \(1.836\times 10^{-4}\) & \(1.840\times 10^{-4}\) & \(1.848\times 10^{-4}\) \\ SC & \(3.039\times 10^{-3}\) & \(9.498\times 10^{-3}\) & \(3.122\times 10^{-2}\) & \(1.106\times 10^{-1}\) & \(1.408\times 10^{-1}\) \\ BP & \(3.727\times 10^{-2}\) & \(9.214\times 10^{-2}\) & \(2.189\times 10^{-1}\) & \(5.047\times 10^{-1}\) & \(1.1512\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of DIR-Net Decoding Time and Conventional Techniques with Different Code Lengths.  
and fading in the channel. Compared with existing decoding methods, DIR-Net achieved better performance.  
Conceptualization, Y.F. and B.S.; methodology, Y.F.; software, B.S. and Y.W.; validation, B.S. and Y.W.; formal analysis, Y.W.; data curation, B.S.; writing--original draft preparation, B.S.; writing--review and editing, B.S. and Y.W.; supervision, Y.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant Number 61971291), the central government leads local science and technology development projects (2022020128-JH6/1001), the science and technology funds from Liaoning Education Department (serial number: IJKZ0242), and Shenyang Natural Science Foundation (Grant No. 22-315-6-10).  
Not applicable.  
The authors declare no conflict of interest.