A denoiser for correlated noise channel decoding: Gated-neural network

Xiao Li, Ling Zhao, Zhen Dai, Yonggang Lei
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Abstract

This letter proposes a sliced-gated-convolutional neural network with belief propagation (SGCNN-BP) architecture for decoding long codes under correlated noise. The basic idea of SGCNNBP is using Neural Networks (NN) to transform the correlated noise into white noise, setting up the optimal condition for a standard BP decoder that takes the output from the NN. A gate-controlled neuron is used to regulate information flow and an optional operation—slicing is adopted to reduce parameters and lower training complexity. Simulation results show that SGCNN-BP has much better performance (with the largest gap being 5dB improvement) than a single BP decoder and achieves a nearly 1dB improvement compared to Fully Convolutional Networks (FCN).
用于相关噪声信道解码的去噪器门控神经网络
本文提出了一种具有信念传播功能的切片门控卷积神经网络(SGCNN-BP)架构,用于解码相关噪声下的长码。SGCNNBP 的基本思想是利用神经网络(NN)将相关噪声转化为白噪声,为标准 BP 解码器设定最佳条件,该解码器采用 NN 的输出。使用门控神经元来调节信息流,并采用可选操作--切片来减少参数和降低训练复杂度。仿真结果表明,SGCNN-BP 的性能比单一 BP 解码器好得多(最大差距为 5dB),与全卷积网络(FCN)相比提高了近 1dB。
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