Several researchers are interested in polar codes for the sake of their capacity which nearly catches memoryless channels capacity. They have been used as part of the 5G technology. Polar decoders like belief propagation decoders showed unfavorable performance during limited iterations. Nowadays, Machine-learning (ML) techniques are used to enhance the efficiency of polar codes decoders. Recurrent neural network belief propagation (RNN-BP) decoders proved better efficiency in a lower number of cycles. In this paper, a performance scheduling learning technique is proposed. This technique achieves a coding gain of 0.2 dB for the RNN-BP decoder at BER of 2*10−4. A new approach is also introduced in the quantization step. In this approach, the quantization is done after the learning process is finished instead of after each iteration. This approach fixes the issue of model convergence. These combined approaches show up to 0.45 dB improvement at BER of 2*10−4.
Several researchers are interested in polar codes for the sake of their capacity which nearly catches memoryless channels capacity. They have been used as part of the 5G technology. Polar decoders like belief propagation decoders showed unfavorable performance during limited iterations. Nowadays, Machine-learning (ML) techniques are used to enhance the efficiency of polar codes decoders. Recurrent neural network belief propagation (RNN-BP) decoders proved better efficiency in a lower number of cycles. In this paper, a performance scheduling learning technique is proposed. This technique achieves a coding gain of 0.2 dB for the RNN-BP decoder at BER of 210−4. A new approach is also introduced in the quantization step. In this approach, the quantization is done after the learning process is finished instead of after each iteration. This approach fixes the issue of model convergence. These combined approaches show up to 0.45 dB improvement at BER of 210−4.
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%.
Polar codes are part of the 5G technology enablers as they nearly achieve memoryless channels capacity. Recently, many researchers explored Machine-learning (ML) techniques to increase the effectiveness of polar codes decoders. Recurrent Neural Network Belief Propagation decoders (RNN-BP) demonstrated superior efficiency in fewer cycles than standard Polar decoders, as traditional Belief propagation decoders performed poorly throughout a limited number of iterations. In this paper, we present the first implementation of the RNN-BP on Field Programmable Gate Arrays (FPGA). Two implementations are presented in this paper. The first one uses a single processing unit, and the second one is enhanced by multiple processing units with a pipeline register. The multiple processing units’ design of RNN-BP shows much higher throughput which is 17x of belief propagation decoder implementation and 3x of the Soft-output CANcellation (SCAN) decoder implementation. We also achieved better BER than the two mentioned implementations 7. 08x than the original BP and 4. 5x better than the SCAN implementation. The combined memory and registers resource consumption in our design is less than the compared two implementations while consuming a larger number of look-up tables (LUTs).
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.
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.
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.
In recent years, neural network decoding of polar codes, such as neural belief propagation (BP), has been intro-duced. These methods use deep learning to transform the factor graph into a neural network model by unfolding the decoding iterations, thereby enhancing the accuracy of traditional decoding processes. However, current prevalent methodologies for loss function calculation only take into account the output of the final layer. In our analysis, we found that when calculating the loss function using only the output from the last layer, the convergence speed of the decoder significantly decreases, especially when the number of unfolded iterations is higher. In this paper, we incorporate the output of all iterations into the loss function in the original neural BP structure. Additionally, we optimize the loss function by assigning different weights to losses at different iterations. As a result, the weighted loss function not only provides a lower Bit Error Rate (BER) compared to the original neural BP decoder at lower SNR, but also accelerates convergence speed.