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IEEE Communications Letters

A UnetNND-BP Architecture for Channel Decoding Under Correlated Noise

Huiying Chen, Pengchao Zhao, Chengju Li

Published Feb 5, 2024• 0 citations
Publisher

Abstract

In this letter, we introduce a Unet-based neural network denoiser-belief propagation (UnetNND-BP) architecture with two training modes to improve the decoding performance of low-density parity-check (LDPC) codes. In the supervised learning mode, UnetNND-BP achieves better performance than benchmark schemes, while in the self-supervised learning mode, it achieves comparable performance.

DOI
10.1109/lcomm.2024.3362509

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In this letter, we introduce a Unet-based neural network denoiser-belief propagation (UnetNND-BP) architecture with two training modes to improve the decoding performance of low-density parity-check (LDPC) codes. In the supervised learning mode, UnetNND-BP achieves better performance than benchmark schemes, while in the self-supervised learning mode, it achieves comparable performance.
To combat the correlated noise and utilize the non-equiprobability of the source, we propose a novel Low-density parity-check (LDPC) decoder design based on a priori information and neural network, which includes two neural networks, namely Correlated-Denoising Convolutional Neural Network (Correlated-DnCNN) and Belief Propagation-Deep Neural Network (BP-DNN), respectively. Correlated-DnCNN is used to remove the correlation of channel noise and reduce the noise power. BP-DNN is used to eliminate the harmful effect of short cycles of the Tanner graph and explore the priori information of the source. With the priori information to assist in initializing the input nodes of the BP-DNN network, we introduce compression capability to the LDPC, thus the proposed scheme can improve efficiency significantly when there is a large amount of compressible data in the physical layer. To train the Correlated-DnCNN and BP-DNN jointly, we design a new three-objective joint denoising-decoding loss function, which can not only guarantee the decoding performance, but also constrain the noise to conform to the normal distribution as much as possible, and improve the network convergence performance. Experiments show that the proposed decoding framework can remove the noise correlation, reduce the noise power, and improve the decoding performance effectively.
In this paper, we analyze the performance of neural belief propagation (BP) decoding on the additive white Gaussian noise (AWGN) channel, compared to the traditional BP algorithm. Previous investigations have shown that assigning pre-trained weights to BP messages can significantly improve the decoding performance in case of high-density parity-check (HDPC) codes, by reducing the negative impact of short cycles. These weights are trained by a neural network whose structure matches the trellis of the decoder. Specifically, we show that medium-density paritycheck (MDPC) codes decoded with neural BP algorithm can achieve lower bit error rate versus HDPC codes with the same codeword length and the same code rate.
For the Belief Propagation (BP) algorithm of low-density parity-check (LDPC) codes, existing deep learning methods have a limited performance improvement and it is difficult to train deep-level networks. In this paper, a model-driven quasi-residual network (Quasi-ResNet) BP decoding architecture is proposed for LDPC codes to further improve the performance of standard BP decoding. This method feeds the reliable messages calculated in current iteration into the next iteration based on the shortcut connection, and adjusts the weight of shortcut connection based on the error Back Propagation algorithm of neural network to determine the optimal genetic proportion of reliable messages. The decoding architecture is composed of a model-driven deep neural network (DNN) and shortcut connection. Simulation results show that the decoder can not only unfold more layers quickly compared with the DNN-based BP decoder, but also further improve the decoding performance.
Today's modern wireless communication systems demand extensive quality and computational power with an increase in day-to-day data-rate standards. The latest com-munication standards focus on low latency, high throughput, and reliable error correction capabilities which inspired us to explore the 5G New Generation (NR) technology. Low-Density Parity-Check Codes (LDPC) are known to have error correction capabilities that can achieve Shannon's maximum channel ca-pacity. Encoding of LDPC Codes features a sparse property that enables effortless decoding at the receiver end. Advancements in deep learning have helped in estimating the channel noise more accurately. The Iterative Belief Propagation-Convolutional Neural Network (BP-CNN) architecture involves a CNN block which is trained with a quadratic loss function concatenated to a standard BP Decoder. The proposed Iterative BP-CNN architecture with a Batch Normalization layer results in a better performance compared to the Iterative BP-CNN architecture as it standardizes the input to the CNN block and also solves the problem of vanishing gradients during backpropagation.
In long wave communication scenarios, channel interferences lead to noise correlation. Since traditional belief propagation (BP) decoding algorithm is designed based on additive white Gaussian noise (AWGN), a performance cost will appear when it comes to correlated noise. In this paper, a new LDPC decoding scheme that applies to correlated Gaussian noise is proposed. We design a BP-gated neural network-BP structure to carry on two rounds of BP decoding with the second round based on optimized noise. By adopting gated neurons in typical NN, CNN, training performance is improved. Simulation shows that compared with BP decoding algorithm, the new decoding scheme outperforms traditional method by 0.5~0.61 dB when bit error rate is 10-6. This decoding scheme also works on pectinate noise with performance gain of 1.5 dB.