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.
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.
In recent years, the emerging Deep Learning (DL) technology has made progress in the field of decoding. Current polar code neural network decoder has faster convergence speed and better Bit Error Rate (BER) performance than Belief Propagation (BP) decoding. However, it still has the problem of high computational complexity. Therefore, in order to improve this problem, this paper adopts the idea of improving information update in the iterative process, and proposes a Recurrent Neural Network (RNN) Offset Min-Sum (OMS) BP decoding algorithm that improves Left information update (RNN-OMSBP-L) . The simulation results show that, compared with the Deep Neural Network (DNN) BP(DNN-BP) decoding algorithm, this algorithm replaces all multiplications with a cost of 6.25% addition. Compared with the current optimized RNN OMS and approximate BP(RNN-OMS-BP) decoding algorithm, the decoding algorithm in this paper uses improved information to reduce 25% of the addition operations with almost no loss in BER performance, while saving part of the storage space overhead. Under the same BER performance, it reduces the number of iterations by 37.5%.
With the development of communication technology, it is urgent to design channel decoding algorithms to maximize the high throughput and minimize the latency. As the capacity achieving polar codes are adopted by 5-th generation wireless system (5G), it is required to design efficient the decoding algorithms for polar codes with high performance and low complexity. Deep neural network (DNN)-based algorithms which have shown superiority in other fields, can obtain better performance using multi-scale belief propagation (BP) algorithm, whereas the decoding complexity needs to be decreased. We propose a residual neural network based belief propagation (ResNet- BP) algorithm to solve the inherent drawbacks in the existing methods, by reducing the number of variables in the network and reducing the complexity in terms of multiplication. Simulation results demonstrate that the proposed method can achieve the approximating performance of recurrent neural network-based belief propagation (RNN-BP) with a lower number of iterations.
Abstract Compared with SC decoding, BP decoding with the parallel mechanism has higher throughput and lower latency, which is more suitable for the demand of 5G scene. To further improve its FER performance and reduce the memory overhead, a recurrent neural network-aided bit-flipping BP decoding of polar codes is proposed. Firstly, it uses bit flip to correct the wrong decoded bits during the decoding iteration. And then, the offset min-sum approximation is used to replace multiplication operation. Lastly the improved recurrent neural network architecture is adopted to realize parameter sharing. The simulation shows that the proposed scheme has a better error correction ability with fewer flipping times, and can effectively reduce the computational resource consumption and extra memory overhead of BP decoding.
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.
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.
With the wide application of Deep Neural Network (DNN) in the field of communication, this technique has also been applied in the coding and decoding technology, but there are few neural network structures with good performance in the decoder. This paper proposes a new polar code decoding network structure, which uses a modular network structure to train the empirical parameters in the iterative belief propagation (BP) algorithm model. The experiment part uses the new structure to implement scaling min-sum (SMS) algorithm and offset min-sum (OMS) algorithm. Through comparing the performance of the above algorithms with the traditional structure, it is found that the new structure has the advantages of better bit error rate (BER) and faster convergence performance compared with the traditional network structure.