The neural network (NN) has been widely used as a promising technique in fiber optical communication owing to its powerful learning capabilities. The NN-based equalizer is qualified to mitigate mixed linear and nonlinear impairments, providing better performance than conventional algorithms. Many demonstrations employ a traditional pseudo-random bit sequence (PRBS) as the training and test data. However, it has been revealed that the NN can learn the generation rules of the PRBS during training, degrading the equalization performance. In this work, to address this problem, we propose a combination strategy to construct a strong random sequence that will not be learned by the NN or other advanced algorithms. The simulation and experimental results based on data over an additive white Gaussian noise channel and a real intensity modulation and direct detection system validate the effectiveness of the proposed scheme.

中文翻译：

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基于神经网络的均衡器的训练数据生成和验证。

由于其强大的学习能力，神经网络（NN）已被广泛用作光纤通信中的一种有前途的技术。基于NN的均衡器具有减轻线性和非线性混合损伤的资格，比传统算法具有更好的性能。许多演示都采用传统的伪随机比特序列（PRBS）作为训练和测试数据。然而，已经揭示出，NN可以在训练期间学习PRBS的生成规则，从而降低了均衡性能。在这项工作中，为了解决这个问题，我们提出了一种组合策略，以构造一个不会被NN或其他高级算法学习的强随机序列。