To effectively process impulse noise for narrowband powerline communications (NB-PLCs) transceivers, capturing comprehensive statistics of nonperiodic asynchronous impulsive noise (APIN) is a critical task. However, existing mathematical noise generative models only capture part of the characteristics of noise. In this study, we propose a novel generative adversarial network (GAN) called noise generation GAN (NGGAN) that learns the complicated characteristics of practically measured noise samples for data synthesis. To closely match the statistics of complicated noise over the NB-PLC systems, we measured the NB-PLC noise via the analog coupling and bandpass filtering circuits of a commercial NB-PLC modem to build a realistic dataset. To train NGGAN, we adhere to the following principles: 1) we design the length of input signals that the NGGAN model can fit to facilitate cyclostationary noise generation; 2) the Wasserstein distance is used as a loss function to enhance the similarity between the generated noise and training data; and 3) to measure the similarity performances of GAN-based models based on the mathematical and practically measured datasets, we conduct both quantitative and qualitative analyses. The training datasets include: 1) a piecewise spectral cyclostationary Gaussian model (PSCGM); 2) a frequency-shift (FRESH) filter; and 3) practical measurements from NB-PLC systems. Simulation results demonstrate that the generated noise samples from the proposed NGGAN are highly close to the real noise samples. The principal component analysis (PCA) scatter plots and Fr\'echet inception distance (FID) analysis have shown that NGGAN outperforms other GAN-based models by generating noise samples with superior fidelity and higher diversity.

翻译：为有效处理窄带电力线通信（NB-PLC）收发机中的脉冲噪声，获取非周期性异步脉冲噪声（APIN）的全面统计特性是一项关键任务。然而，现有的数学噪声生成模型仅能捕捉噪声的部分特征。本研究提出了一种新颖的生成对抗网络（GAN），称为噪声生成GAN（NGGAN），该网络通过学习实际测量噪声样本的复杂特征进行数据合成。为紧密匹配NB-PLC系统中复杂噪声的统计特性，我们通过商用NB-PLC调制解调器的模拟耦合与带通滤波电路测量了NB-PLC噪声，构建了真实数据集。在训练NGGAN时，我们遵循以下原则：1）设计NGGAN模型可适配的输入信号长度，以促进循环平稳噪声的生成；2）使用Wasserstein距离作为损失函数，增强生成噪声与训练数据之间的相似性；3）为评估基于GAN的模型在数学数据集和实测数据集上的相似性表现，我们进行了定量与定性分析。训练数据集包括：1）分段谱循环平稳高斯模型（PSCGM）；2）频移（FRESH）滤波器；3）NB-PLC系统的实际测量数据。仿真结果表明，所提NGGAN生成的噪声样本与真实噪声样本高度接近。主成分分析（PCA）散点图与Fr\\'echet起始距离（FID）分析显示，NGGAN通过生成具有更高保真度和多样性的噪声样本，优于其他基于GAN的模型。