In recent years, the traditional feature engineering process for training machine learning models is being automated by the feature extraction layers integrated in deep learning architectures. In wireless networks, many studies were conducted in automatic learning of feature representations for domain-related challenges. However, most of the existing works assume some supervision along the learning process by using labels to optimize the model. In this paper, we investigate an approach to learning feature representations for wireless transmission clustering in a completely unsupervised manner, i.e. requiring no labels in the process. We propose a model based on convolutional neural networks that automatically learns a reduced dimensionality representation of the input data with 99.3% less components compared to a baseline principal component analysis (PCA). We show that the automatic representation learning is able to extract fine-grained clusters containing the shapes of the wireless transmission bursts, while the baseline enables only general separability of the data based on the background noise.

翻译：近年来，深度学习架构中集成的特征提取层正逐步自动化传统机器学习模型中的特征工程过程。在无线网络领域，已有许多研究针对领域相关挑战开展了特征表示的自动学习。然而，现有工作大多假设学习过程需要借助标签来优化模型，即需要某种程度的监督。本文研究了一种完全无监督的无线传输聚类特征表示学习方法，即在学习过程中无需任何标签。我们提出了一种基于卷积神经网络的模型，该模型能够自动学习输入数据的降维表示，与传统主成分分析（PCA）基线相比，其分量数量减少了99.3%。我们证明，这种自动表示学习能够提取包含无线传输脉冲形状的细粒度聚类，而基线方法仅能基于背景噪声实现对数据的常规分离。