Deep Feature Learning for Wireless Spectrum Data

TL;DR

This paper introduces an unsupervised deep learning approach using CNNs to automatically learn feature representations for wireless spectrum data, significantly reducing dimensionality and capturing detailed transmission burst patterns.

Contribution

It presents a novel unsupervised CNN-based model that learns compact, fine-grained features from wireless spectrum data without labels, outperforming PCA in capturing transmission burst shapes.

Findings

Achieved 99.3% reduction in feature components compared to PCA.

Successfully extracted fine-grained transmission burst shapes.

Outperformed baseline PCA in clustering wireless transmission data.

Abstract

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.