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  • Pjanić, Dino, et al. (author)
  • Learning-Based UE Classification in Millimeter-Wave Cellular Systems With Mobility
  • 2021
  • In: IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
  • Conference paper (peer-reviewed)abstract
    • Millimeter-wave cellular communication requires beamforming procedures that enable alignment of the transmitter and receiver beams as the user equipment (UE) moves. For efficient beam tracking it is advantageous to classify users according to their traffic and mobility patterns. Research to date has demonstrated efficient ways of machine learning based UE classification. Although different machine learning approaches have shown success, most of them are based on physical layer attributes of the received signal. This, however, imposes additional complexity and requires access to those lower layer signals. In this paper, we show that traditional supervised and even unsupervised machine learning methods can successfully be applied on higher layer channel measurement reports in order to perform UE classification, thereby reducing the complexity of the classification process.
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Type of publication
conference paper (1)
Type of content
peer-reviewed (1)
Author/Editor
Tufvesson, Fredrik (1)
Tataria, Harsh (1)
Sopasakis, Alexandro ... (1)
Pjanić, Dino (1)
Reial, Andres (1)
University
Lund University (1)
Language
English (1)
Research subject (UKÄ/SCB)
Engineering and Technology (1)
Year
2021 (1)

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