# DIR-Net: Deep Residual Polar Decoding Network Based on Information Refinement
## 1 Introduction  
Polar codes are a forward error correction coding method applied in communication systems based on the theory of channel polarization and are closer to the Shannon limit than other channel coding methods [1]. Researchers have paid attention to them for their lower complexity of coding and decoding. In recent years, they have emerged as a hot topic for academic research, especially for their successful application in fifth-generation mobile communication (5G) [2; 3]. As a result, this study focuses on how to further enhance the performance of the decoder. The conventional decoding techniques can be generally classified as successive elimination (SC) [1] and belief propagation (BP) algorithms [2], in addition to enhanced techniques based on the abovementioned schemes. One of them, the SC algorithm, has low computational complexity but suffers from high latency and low throughput. To address this issue, some studies concentrated on low-latency SC decoding techniques [4; 5], which combine the stability of recognition-based techniques with the dynamism of check-based techniques. In addition, some studies have been presented to improve error correction performance [6; 7; 8; 9; 10; 11; 12]. While all of these are serial techniques, which suffer from random node errors, some parallel decoding techniques, including the BP algorithm [13; 14], have been proposed to address this issue. Despite the improved error performance of the BP algorithm, it has high complexity due to iterative computation. Deep learning has been demonstrated to be a potent technique for enhancing the speed and performance of algorithms in the domains of computer vision and naturallanguage processing. Recently, the technology has also been applied to communication systems, including channel coding. Deep neural networks have been proposed to assist conventional decoding in the area of polar decoding, such as lowering latency [15; 16; 17; 18; 19; 20], extracting sequence features [21; 22; 23], reducing the memory overhead [24], and learning the noise correlation [25]. The construction of deep learning decoding networks has recently caught the attention of some researchers. A deep feed-forward neural network was introduced in [26] to directly decode the input polar codes. A fully connected network was proposed in [27] to decode polar-coded short packets over a flat fading channel. [PERSON] et al. demonstrated that the neural network could learn the entire coding structure by comparing the three network structures of MLP, CNN, and RNN under the same conditions [28]. To improve the SNR of received symbols, a denoiser based on residual learning was introduced before the neural network decoder (NND) in [29], which was characterized as a residual neural network decoder (RNND). In [30], a double long short-term memory (DLSTM) neural network was proposed to improve the performance of short block length by clipping frozen bits to enhance prediction accuracy. Deep learning-based decoders perform better than traditional decoders [31]. However, most decoding technologies treat deep neural networks as black boxes, where the decoding network consists of only a few fully connected or convolutional layers that take polar codes as inputs to the network and output decoded symbols directly. These techniques assume that all information, including noise and some other information, positively contributes to the decoding. In this paper, a novel deep-learning framework is proposed for polar decoding to improve accuracy. Unlike traditional deep learning decoders which use all input information indiscriminately for decoding, a deep residual network based on information refinement (DIR-Net) with attention mechanism is designed to refine the information step-by-step, and it achieves high-quality decoding. We utilize a large number of attention modules in the designed decoding network in order to better distinguish between valid and disturbing information. The proposed DIR-Net is divided into two stages, the denoising stage and the decoding stage.