Efficient Resource Allocation Utilizing Q-Learning in Multiple UA Communications

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


In recent years, unmanned aircraft systems (UASs) have garnered significant attention, and the demand for communication utilizing unmanned aircrafts (UAs) has increased. However, the limited available frequency for UA communication, despite the increasing utilization, poses a problem. Moreover, for the practical application of UA communication, the changes and differences in the propagation environment and communication demand of each UA communication must be considered because of the variety of supposed services, and the considerable interference caused by obstacles and reflected waves, due to high UA mobility. In this research, we construct a UA communication management system at the UAS operating frequency, with reference to the physical channel configuration of the LTE and LTE Sidelink, utilizing Q-Learning. In addition, we determine the effectiveness of the proposed method by evaluating various propagation-environment scenarios and communication demands.

Original languageEnglish
JournalIEEE Transactions on Network Science and Engineering
Publication statusAccepted/In press - 2018 May 30


  • Aircraft
  • Device-to-device communication
  • Interference
  • Long Term Evolution
  • Modulation
  • Q-Learning
  • Resource management
  • UAS
  • physical channel configuration
  • reinforcement learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Networks and Communications

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