D3QN-Based Trajectory and Handover Management for UAVs Co-existing with Terrestrial Users

Yuhang Deng, Irshad A. Meer, Shuai Zhang, Mustafa Ozger, Cicek Cavdar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The ubiquitous cellular network is a strong candidate for providing UAVs’ wireless connectivity. Due to the maneuverability advantage and higher altitude, UAVs could have line-of-sight (LoS) connectivity with more base station (BS) candidates than terrestrial users. However, the LoS connectivity could also enhance the propagation of up-link interference caused by UAVs over co-existing terrestrial users. In addition, UAVs would perform more handovers than terrestrial users when moving due to the extensive overlap in the coverage areas of many BS candidates. The solution is to bypass the overlapping coverage areas by designing the UAVs’ trajectory and to reduce interference by optimizing radio resource allocation through handover management. This paper studies the joint optimization of a UAV’s trajectory design and handover management to minimize the weighted sum of three key performance indicators (KPIs): delay, up-link interference, and handover numbers. A dueling double deep Q-network (D3QN) based reinforcement learning algorithm is proposed to solve the optimization problem. Results show that the proposed approach can reduce the handover numbers by 90% and the interference by 18% at the cost of a small increment in transmission delay when compared with the benchmark scheme, which controls the UAV to move along the shortest path and perform handover based on received signal strength. Finally, we verify the advantage of introducing trajectory design, which can reduce the interference by 29% and eliminate the handover numbers by 33% when compared to the D3QN-based policy without trajectory design.

Original languageEnglish (US)
Title of host publication2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages103-110
Number of pages8
ISBN (Electronic)9783903176553
DOIs
StatePublished - 2023
Externally publishedYes
Event21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023 - Singapore, Singapore
Duration: Aug 24 2023Aug 27 2023

Publication series

NameProceedings of the International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt
ISSN (Print)2690-3334
ISSN (Electronic)2690-3342

Conference

Conference21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023
Country/TerritorySingapore
CitySingapore
Period8/24/238/27/23

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems and Management
  • Control and Optimization
  • Modeling and Simulation

Keywords

  • cellular-connected UAVs
  • handover management
  • machine learning
  • radio resource allocation
  • reinforcement learning
  • trajectory design

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