Deep Feature Learning for Wireless Spectrum Data
Ljupcho Milosheski Gregor Cerar Blaž Bertalanič Carolina Fortuna Mihael Mohorčič
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.