Deep learning-based cell-level and beam-level mobility management system

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1 Citation (Scopus)
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The deployment with beamforming-capable base stations in 5G New Radio (NR) requires an efficient mobility management system to reliably operate with minimum effort and interruption. In this work, we propose two artificial neural network models to optimize the cell-level and beam-level mobility management. Both models consist of convolutional, as well as dense, layer blocks. Based on current and past received power measurements, as well as positioning information, they choose the optimum serving cell and serving beam, respectively. The obtained results show that the proposed cell-level mobility model is able to sustain a strong serving cell and reduce the number of handovers by up to 94.4% compared to the benchmark solution when the uncertainty (representing shadowing, interference, etc.) is introduced to the received signal strength measurements. The proposed beam-level mobility management model is able to proactively choose and sustain the strongest serving beam, even when high uncertainty is introduced to the measurements.

Original languageEnglish
Article number7124
Pages (from-to)1-17
Number of pages17
JournalSensors (Switzerland)
Issue number24
Publication statusPublished - 2 Dec 2020
Publication typeA1 Journal article-refereed


  • 5G New Radio
  • Artificial neural network
  • Beam-level mobility
  • Handover
  • Mobility management
  • Supervised learning

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering


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