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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

A Novel Decoding Scheme for Polar Code Using Convolutional Neural Network

Chen Wen, Jian Xiong, Lin Gui, Zhiping Shi, Yuwei Wang

Published Jun 1, 2019• 7 citations
Publisher

Abstract

Polar code is the first capacity-achieving channel coding scheme which has been selected for the next generation of wireless communication standard(5G). However, the traditional decoding schemes such as CA-SCL and BP algorithms cannot take into account both BER and latency performance. Inspired by the deep learning technology, we propose a scheme that combine convolutional neural network (CNN) with traditional Belief Propagation (BP) decoding. By exploiting noise correlation for channel decoding under colored noise environment, the proposed scheme can obtain a more accurate estimation of channel noise. We simulate the performance of the proposed scheme under BPSK and 4QAM modulation. For QAM modulation, a scheme that extend the conventional real-valued CNN technique to the complex-valued one is given. The results demonstrate that bit error rates (BER) performance can be improved after removing the estimated noise. Compare to the traditional Polar decoding schemes, the proposed scheme has better trade-off between BER and latency performance under colored noise environment.

DOI
10.1109/bmsb47279.2019.8971888

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Polar code is the first capacity-achieving channel coding scheme which has been selected for the next generation of wireless communication standard(5G). However, the traditional decoding schemes such as CA-SCL and BP algorithms cannot take into account both BER and latency performance. Inspired by the deep learning technology, we propose a scheme that combine convolutional neural network (CNN) with traditional Belief Propagation (BP) decoding. By exploiting noise correlation for channel decoding under colored noise environment, the proposed scheme can obtain a more accurate estimation of channel noise. We simulate the performance of the proposed scheme under BPSK and 4QAM modulation. For QAM modulation, a scheme that extend the conventional real-valued CNN technique to the complex-valued one is given. The results demonstrate that bit error rates (BER) performance can be improved after removing the estimated noise. Compare to the traditional Polar decoding schemes, the proposed scheme has better trade-off between BER and latency performance under colored noise environment.
Known for their capacity-achieving abilities, polar codes have been selected as the control channel coding scheme for 5G communications. To satisfy the needs of high throughput and low latency, belief propagation (BP) is chosen as the decoding algorithm. However, in general, the error performance of BP is worse than that of enhanced successive cancellation (SC). Recently, critical-set bit-flipping (CS-BF) is applied to BP decoding to lower the error rate. However, its trial and error process result in even longer latency. In this work, we propose a convolutional neural network-assisted bit-flipping (CNN-BF) mechanism to further enhance BP decoding of polar codes. With carefully designed input data and model architecture, our proposed CNN-BF can achieve much higher prediction accuracy and better error correction capability than CS-BF but with only half latency. It also achieves a lower block error rate (BLER) than SC list (CA-SCL).
Polar code is selected for the 5-th generation of wireless communication standard. Meanwhile, with the demand of low latency in 5G and great success in deep learning area, neural network decoder (NND) becomes a promising candidate for future decoder. However, NND is currently only feasible for very short code lengths. In this paper, we proposed a belief propagation-neural network (BP-NN) algorithm for polar codes based on partitioning. The core of our algorithm is replacing subblock of belief propagation (BP) decoder with NNDs, and using BP decoding framework to connect them. In addition, we also introduce a weight function to complete the message propagation process. Simulation results show the proposed algorithm reduces the decoding delay compared to the traditional BP algorithm and have a well bit error rate (BER) performance. At the same time, it also suitable for long polar codes.
Known for their capacity-achieving abilities, polar codes have been selected as the control channel coding scheme for 5G communications. To satisfy high throughput and low latency, belief propagation (BP) is ideal as the decoding algorithm due to its nature of parallel processing. However, the error performance of BP is in general worse than that of enhanced successive cancellation (SC). Recently, bit-flipping (BF) mechanism is applied to BP decoding to lower the error rate. However, its trial-and-error process results in longer latency. In this work, we propose a convolutional neural network-aided bit-flipping (CNN-BF) mechanism to further enhance BP decoding. With carefully designed input data and model architecture, our proposed CNN-BF can achieve better error correction capability with less flipping attempts than prior works. It also achieves a lower block error rate (BLER) than SC list (SCL).
Polar code has been selected as the control channel coding scheme for the 5th generation mobile communication technology (5G), and the performance of short polar codes is receiving intensive attention. However, belief propagation (BP) algorithm suffers from performance loss under short codes. In order to improve decoding performance, this paper proposes a BP flip decoding algorithm based on convolutional neural network (CNN-BP flip). Cyclic redundancy check (CRC) is used to check the results after BP decoding, and extracts the left messages in the factor graph of the last iteration that have not passed the CRC. The extracted messages are mapped into an image as the input of the convolutional neural network (CNN), the network model is adopted to classify the flipping bits. Compared with the critical set-based BP flip (CS-BP flip) algorithm, the CNN-BP flip algorithm achieves 0.23 dB gain at the number of flip attempts T1=5 and the block error rate (BLER)=10-2for polar code with N=64.
In 5G mobile communication standard, polar codes have been used as coding on control channels. Although the polar code has good performance, the performance of its short code is still facing huge challenges. Moreover, the size limitation of using the deep learning model as the decoder of the polar code has always been the focus of researchers seeking a breakthrough. In this paper, the convolutional neural network (CNN) model in deep learning is cascaded with a successive cancellation (SC) decoder to achieve the decoding of polar codes under additive correlated Gaussian noise (ACGN) channels, thereby improving the decoding performance of short polar codes under ACGN and alleviating the code length limitation of a single deep learning model as a decoder. The cascaded decoding structure is represented as a CNN-SC decoder, which can effectively estimate colored noise. The simulation shows that the CNN-SC decoder improves the performance of polar codes under ACGN channels significantly.
Deep learning recently shows strong competitiveness to improve polar code decoding. However, suffering from prohibitive training and computation complexity, the conventional deep neural network (DNN) is only possible for very short code length. In this paper, the main problems of deep learning in decoding are well solved. We first present the multiple scaled belief propagation (BP) algorithm, aiming at obtaining faster convergence and better performance. Based on this, deep neural network decoder (NND) with low complexity and latency, is proposed for any code length. The training only requires a small set of zero codewords. Besides, its computation complexity is close to the original BP. Experiment results show that the proposed (64,32) NND with 5 iterations achieves even lower bit error rate (BER) than the 30-iteration conventional BP and (512, 256) NND also outperforms conventional BP decoder with same iterations. The hardware architecture of basic computation block is given and folding technique is also considered, saving about 50% hardware cost.