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.
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.
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).
Known for their capacity-achieving abilities and low complexity for both encoding and decoding, 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, it suffers from worse error performance than that of cyclic redundancy check (CRC)-aided successive cancellation list (CA-SCL). Recently, convolutional neural network-aided bit-flipping (CNN-BF) is applied to BP decoding, which can accurately identify the erroneous bits to achieve a better error rate and lower decoding latency than prior critical-set bit-flipping (CS-BF) mechanism. However, successive BF, having better error correction capability, has not been explored in CNN-BF since the more complicated flipping strategy is out of the scope of supervised learning. In this work, by using imitation learning, a convolutional neural network-aided tree-based multiple-bits BF (CNN-Tree-MBF) mechanism is proposed to explore the benefits of multiple-bits BF. With the CRC information as additional input data, the proposed CNN-BF model can further reduce 5 flipping attempts. Besides, a tree-based flipping strategy is proposed to avoid useless flipping attempts caused by wrongly flipped bits. From the simulation results, our approach can outperform CS-BF and reduce flipping attempts by 89% when code length is 64, code rate is 0.5 and SNR is 1 dB. It also achieves a comparable block error rate (BLER) as CA-SCL.
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 codes are the first class of error correcting codes that provably achieve the channel capacity at infinite code length. They were selected for use in the fifth generation of cellular mobile communications (5G). In practical scenarios such as 5G, a cyclic redundancy check (CRC) is concatenated with polar codes to improve their finite length performance. This is mostly beneficial for sequential successive-cancellation list decoders. However, for parallel iterative belief propagation (BP) decoders, CRC is only used as an early stopping criterion with incremental error-correction performance improvement. In this paper, we first propose a CRC-polar BP (CPBP) decoder by exchanging the extrinsic information between the factor graph of the polar code and that of the CRC. We then propose a neural CPBP (NCPBP) algorithm which improves the CPBP decoder by introducing trainable normalizing weights on the concatenated factor graph. Our results on a 5G polar code of length 128 show that at the frame error rate of 10-5and with a maximum of 30 iterations, the error-correction performance of CPBP and NCPBP are approximately 0.25 dB and 0.5 dB better than that of the conventional CRC-aided BP decoder, respectively, while introducing almost no latency overhead.
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.
The bit-flip method has been successfully applied to the successive cancellation (SC) decoder to improve the block error rate (BLER) performance for polar codes in the finite code length region. However, due to the sequential decoding, the SC decoder inherently suffers from longer decoding latency than that of the belief propagation (BP) decoder with efficient early stopping criterion. It is natural to ask how to perform bit-flip in a polar BP decoder. In this paper, bit-flip is introduced into the BP decoder for polar codes. The idea of critical set (CS), that is, originally proposed by Zhang et al. for identifying unreliable bits in a SC bit-flip decoder, is extended to the BP decoder here. After revealing the relationship between CS and the incorrect BP decoding results, critical set with order ω (CS-ω) is constructed to identify unreliable bit decisions in polar BP decoding. The simulation results demonstrate that compared with the conventional BP decoder, the BLER of the proposed bit-flip decoder can achieve significant signal-to-noise ratio (SNR) gain which is comparable to that of a cyclic redundancy check-aided SC list decoder with a moderate list size. In addition, the decoding latency of the proposed BP bit-flip decoder is only slightly higher than that of the conventional BP decoder in the medium and high SNR regions.