Abstract 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.

中文翻译：

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LDPC码MWC编码光CDMA系统深度多层感知器神经网络分析

摘要 在本文中，将深度多层感知器神经网络 (MLPNN) 应用于和积算法 (SPA) 以解码具有低密度奇偶校验的 Modified Welch-Costas (MWC) 编码光码分多址 (OCDMA) 系统。校验（LDPC）码被分析。目标是通过随机梯度下降 (SGD) 训练 MLPNN-SPA，以学习和优化神经网络解码器每个边缘的权重。一旦训练了这些参数，MLPNN-SPA 的解码复杂度与 SPA 的相似。此外，仿真结果表明，与 SPA 相比，MLNN-SPA 可以在不增加解码复杂度的情况下提高系统性能。