In order to improve transmission reliability and flexible cooperation in optical communication, rate-compatible punctured Polar codes have become a research hotspot. Aiming at the problem that the traditional decoding performance and transmission efficiency is limited, based on deep learning, a rate-compatible punctured Polar code decoding scheme is studied. We use convolutional neural network model as the basic structure of rate-compatible Polar code decoder. The log likelihood ratio values of the received sequence are input into the decoder for training. Simulation results show that the proposed decoder outperforms the traditional punctured Polar code decoder under high signal-to-noise ratio.
In order to improve transmission reliability and flexible cooperation in optical communication, rate-compatible punctured Polar codes have become a research hotspot. Aiming at the problem that the traditional decoding performance and transmission efficiency is limited, based on deep learning, a rate-compatible punctured Polar code decoding scheme is studied. We use convolutional neural network model as the basic structure of rate-compatible Polar code decoder. The log likelihood ratio values of the received sequence are input into the decoder for training. Simulation results show that the proposed decoder outperforms the traditional punctured Polar code decoder under high signal-to-noise ratio.
In order to overcome the problems of high bit error rate, delay and complexity, research on CNN polar decoding algorithm is proposed. This method uses CNN as the decoder, analyzes the difference between the traditional algorithm and the CNN decoder, adjusts the network parameters, obtains the best parameters, makes the decoder optimal, and in the channel coding scheme, the encoding and decoding complexity of polarized codes is low, and it has been strictly proved that they can reach the Shannon limit. However, there are a lot of shortcomings in the existing traditional decoding algorithms, With the maturity of deep learning, the application of deep learning to the field of communication, deep learning has advantage of powerful computing, and neural network after training is static, once only need data through the network, so the deep learning was applied to the polarization code decoding process, can effectively reduce the decoding delay and improve the efficiency of decoding, this paper mainly studies convolution decoder of neural networks in all aspects of performance, can get the conclusion by experiment, with the increase of the number of iterations network decoding ability improved, error performance is getting better and better, and CNN has obvious advantages.
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.
Polar code is the first type of constructive coding with achievable capacity, which has become the encoding scheme for 5G mobile communication systems and is a cutting-edge direction in channel coding research. However, traditional decoding algorithms for Polar codes still need to be improved in terms of errorcorrection performance and decoding delay. For Polar code short codes, due to the phenomenon of incomplete Polar, the traditional SC decoding algorithm has a problem of poor decoding performance. Algorithms that utilize deep learning for direct or auxiliary operations often yield more accurate computational results with lower computational complexity. Therefore, considering that applying deep learning to the communication field is a mainstream trend in the 6G era, a (16,8) short Polar code decoder based on recurrent neural network (RNN) is proposed. At the same time, a suitable dataset construction method was proposed, which achieved better performance than SC decoding algorithm under low signal-to-noise ratio by adjusting network parameters and training under the appropriate dataset.
In recent years, polar codes under deep learning have become a research hotspot because of their outstanding decoding performance. The neural network-based polar codes confidence propagation decoding algorithm can improve the decoding performance significantly, but the computational complexity and delay of the neural network-based polar codes confidence propagation decoding algorithm still need to be improved. In this paper, the theory of polar codes coding and the traditional polar codes confidence propagation decoding algorithm are introduced. The polar codes confidence propagation decoding algorithm based on deep neural network and recurrent neural network and the advantages and disadvantages of different decoding algorithms are mainly discussed, and the computational complexity of these algorithms is analyzed. According to the offset minimum sum approximation and scaling offset minimum sum approximation, the polar codes confidence propagation decoding algorithm based on recurrent neural network is optimized. Simulation results show that, compared with the traditional polar codes confidence propagation decoding algorithm, the decoding algorithm improves the decoding performance, reduces the computational complexity and reduces the iteration times of the algorithm. Compared with the polar codes confidence propagation decoding algorithm based on deep neural network and recurrent neural network, the multiplication and memory overhead of the algorithm are greatly reduced while the decoding performance of the algorithm is guaranteed.
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.
In order to solve the problem that the existing Polar code decoding algorithm based on neural network can only decode short codewords (codewords length N≤64), a new decoding algorithm using convolution neural network for long codewords (N≥512) was put forward. Instead of using a fixed drawn from the data sample proportion as training set and test set, the neural network parameters epoch and batch were used to control the neural network input, and the data explosion problem caused by the long codewords was solved. In addition, the influence of batch and epoch parameters on the decoding performance of convolution neural network was explored and the performance difference of neural network using different activation functions were investigated. Simulation results show that with the traditional SCL (successive cancellation list, L=2) decoding algorithm, convolution neural network in low signal-to-noise ratio on the performance is better than that of SCL (L=2), in high signal-to-noise ratio and SCL (L=2) algorithm of similar performance, and the larger the training data set, the better the decoding performance of neural network.