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Handover Management for Drones in Future Mobile Networks-A Survey.

Ibraheem Shayea1,2, Pabiola Dushi1, Mohammed Banafaa1

  • 1Department of Electronics and Communication Engineering, Istanbul Technical University (ITU), 34467 Istanbul, Turkey.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
Summary
This summary is machine-generated.

Intelligent handover schemes using machine learning can significantly improve drone network connectivity by addressing issues like packet loss and frequent handovers. This enhances the reliability of drones in 5G and 6G mobile networks.

Keywords:
Fifth Generation (5G)Sixth Generation (6G)Unmanned Aerial Vehicle (UAV)connected dronedronedrone networkhandover decision algorithmhandover managementmobile ad hoc networksmobile networksmobility management

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Area of Science:

  • Telecommunications Engineering
  • Wireless Networks
  • Robotics and Automation

Background:

  • Drones are increasingly vital for environmental, civil, and military applications, with significant potential in 5G and 6G mobile networks.
  • Their unique characteristics, like high-altitude propagation and mobility, present challenges for existing network mobility and handover models.
  • Current drone network deployments face issues such as limited power, packet loss, congestion, and high speeds, impacting connection stability.

Purpose of the Study:

  • To review existing literature on handover management techniques for connected drones in mobile communication networks.
  • To identify and detail the specific mobility management challenges faced by drones in future wireless networks.
  • To explore advanced solutions for maintaining stable and reliable drone connections.

Main Methods:

  • Comprehensive literature review focusing on drone networks, mobility management, and handover processes.
  • Analysis of challenges arising from drone characteristics in 5G and 6G environments.
  • Evaluation of intelligent handover schemes, including machine learning and deep learning approaches.

Main Results:

  • Existing mobility models are often inefficient for connected drone applications due to unique environmental factors.
  • Frequent handovers and connection instability are significant issues in current drone network deployments.
  • Intelligent handover schemes show potential for significantly reducing handover problems compared to traditional methods.

Conclusions:

  • Drone networks require specialized mobility and handover techniques for stable and reliable connectivity.
  • Machine learning and deep learning offer promising solutions for optimizing drone handover processes.
  • Further research into intelligent systems is crucial for the successful integration of drones into future mobile networks.