Learning-Based UE Classification in Millimeter-Wave Cellular Systems With Mobility
This work addresses efficient beam tracking for mobile users in millimeter-wave networks, but it is incremental as it adapts existing machine learning methods to a different data source.
The paper tackled the problem of user equipment classification in millimeter-wave cellular systems by applying supervised and unsupervised machine learning methods to higher layer channel measurement reports, resulting in reduced complexity compared to existing approaches that rely on physical layer signals.
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.