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  • Learning to detect wireless spectrum occupancy using clustering approaches [Elektronski vir]
    Cerar, Gregor, telekomunikacije, 1990- ...
    Driven by various academic, standardization and regulatory initiatives, recent research on spectrum resource utilisation has focused also on technology and transmission classification using various ... deep learning (DL) architectures. However, especially in unlicensed bands it is often hard to obtain labelled data of sufficient quality for training DL for all transmissions that may occur. Therefore in this paper we investigate clustering techniques that require no labelled data or prior knowledge on continuous spectrum sensing sweeps over a 200 kHz wide band in the unlicensed European 868 MHz frequency band. Using several clusterability tests we show that the sweeps can be clustered, however the number of clusters is not clear. By analyzing the sweeps with three state-of-theart techniques, K-Means, Agglomerative Hierarchical Clustering, and Hierarchical Density-Based Spatial Clustering (HDBSCAN) 9-10 clusters are discovered. The quantitative evaluation shows that HDBSCAN outperforms the other two and the qualitative analysis shows that HDBSCAN seems to be able to better discriminate between the same technology transmitting at/from different power levels/distance, however it has a relatively poorer class balance compared to K-Means and tends to group more transmissions than needed in the majority no transmission cluster.
    Source: 2023 19th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) [Elektronski vir] : 21-23 June 2023, Montreal, Canada (str. 143-148)
    Type of material - conference contribution ; adult, serious
    Publish date - 2023
    Language - english
    COBISS.SI-ID - 160390147

    Link(s):

    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10187869

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