Deep learning (DL) has been gradually applied to wireless communication and has achieved remarkable results. However, training a DL model requires numerous data, and an insufficient training dataset will cause serious overfitting problem and reduce model accuracy. Data augmentation (DA) is one of the commonly used techniques to solve the above problem. It is widely used to improve the performance of image and text classification tasks, but its impact on DL-based wireless communication has not been fully explored. Inspired by the emerging deep autoencoder (AE) generative model, we propose an AE-based DA method to improve the generalization performance of the DL-based wireless communication. We perform experimental verification on the two tasks of channel estimation model of wireless communication physical layer and massive multiple-input multiple-output (MIMO) power allocation optimization model. Experimental results show that the AE-based DA method can improve the generalization performance of the wireless communication, but this improvement is related to the training dataset size. When the training dataset size is smaller than a threshold, AE can improve model performance by increasing the relevant training data, but when it is larger than this threshold, it reduces model performance. We also propose that the straight line intersection method can be used directly to roughly determine this threshold. Furthermore, we propose a mixup-based method to solve the problem that the AE cannot improve model performance when the training dataset size is larger than the threshold.

中文翻译：

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基于自动编码器的数据增强对基于深度学习的无线通信的影响


深度学习（DL）已逐渐应用于无线通信并取得了显着的成果。然而，训练深度学习模型需要大量数据，训练数据集不足会导致严重的过拟合问题并降低模型精度。数据增强（DA）是解决上述问题的常用技术之一。它被广泛用于提高图像和文本分类任务的性能，但其对基于深度学习的无线通信的影响尚未得到充分探讨。受新兴的深度自动编码器（AE）生成模型的启发，我们提出了一种基于AE的DA方法来提高基于DL的无线通信的泛化性能。我们对无线通信物理层信道估计模型和大规模多输入多输出（MIMO）功率分配优化模型两个任务进行了实验验证。实验结果表明，基于AE的DA方法可以提高无线通信的泛化性能，但这种提高与训练数据集的大小有关。当训练数据集大小小于某个阈值时，AE可以通过增加相关训练数据来提高模型性能，但当训练数据集大小大于该阈值时，则会降低模型性能。我们还提出可以直接使用直线相交法来粗略地确定这个阈值。此外，我们提出了一种基于mixup的方法来解决当训练数据集大小大于阈值时AE无法提高模型性能的问题。