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  • Deep feature learning for wireless spectrum data [Elektronski vir]
    Milosheski, Ljupcho ...
    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.
    Source: IEEE International Mediterranean Conference on Communications and Networking (IEEE MEDITCOM 2023) [Elektronski vir] : 4-7 September 2023 in Dubrovnik, Croatia (1-6 str.)
    Type of material - conference contribution ; adult, serious
    Publish date - 2023
    Language - english
    COBISS.SI-ID - 166887171

    Link(s):

    https://arxiv.org/pdf/2308.03530.pdf
    DOI

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