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Analysis of deep multilayer perceptron neural network in MWC coded optical CDMA system with LDPC code

机译:利用LDPC码分析MWC编码光CDMA系统中的深层多层射击性神经网络

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摘要

In this paper, applying the Deep Multilayer Perceptron Neural Network (MLPNN) to the Sum-Product Algorithm (SPA) for decoding the Modified Welch-Costas (MWC) coded Optical Code Division Multiple Access (OCDMA) system with Low-Density Parity-Check (LDPC) code is analyzed. The goal is to train the MLPNN-SPA through the stochastic gradient descent (SGD) to learn and optimize the weights to each edge of the neural network decoder. Once these parameters have been trained, the decoding complexity of the MLPNN-SPA is similar to that of the SPA. Furthermore, from the simulation results, it has been shown that the MLNN-SPA can improve the system performance without additional decoding complexity as compared to the SPA.
机译:在本文中,将深层多层的Perceptron神经网络(MLPNN)应用于具有低密度奇偶校验的修改的WELCH-COSTAS(MWC)编码的光码划分多址(OCDMA)系统的SUM - 产品算法(SPA)。 (LDPC)代码被分析。目标是通过随机梯度下降(SGD)训练MLPNN-SPA来学习和优化神经网络解码器的每个边缘的权重。一旦训练这些参数,MLPNN-SPA的解码复杂度类似于SPA的解码复杂性。此外,从仿真结果中,已经表明,与水疗中心相比,MLNN-SPA可以改善系统性能而无需额外的解码复杂性。

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