Learning-based trajectory optimization for 5G mmWave uplink UAVs

Praneeth Susarla, Yansha Deng, Giuseppe Destino, Jani Saloranta, Toktam Mahmoodi, Markku Juntti, Olli Silven

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

14 Citations (Scopus)

Abstract

A Connectivity-constrained based path planning for unmanned aerial vehicles (UAVs) is proposed within the coverage area of a 5G NR Base Station (BS) that uses mmWave technology. We consider an uplink communication between UAV and BS under multipath channel conditions for this problem. The objective is to guide a UAV, starting from a random location and reaching its destination within the BS coverage area, by learning a trajectory alongside achieving better connectivity. We propose simultaneous learning-based path planning of UAV and beam tracking at the BS side under urban macro-cellular(UMa) pathloss conditions, to reduce its sweeping time with apriori computational overhead using the deep reinforcement learning method such as Deep Q-Network (DQN). Our results show that our proposed learning-based joint path planning and beam tracking method is on par with the learning-based shortest path planning, besides beam tracking comparable to heuristic exhaustive beam searching method.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728174402
DOIs
Publication statusPublished - Jun 2020
Event2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Dublin, Ireland
Duration: 7 Jun 202011 Jun 2020

Publication series

Name2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings

Conference

Conference2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020
Country/TerritoryIreland
CityDublin
Period7/06/202011/06/2020

Keywords

  • 5G
  • Beamforming
  • MmWave
  • Path planning
  • Reinforcement learning
  • UAVs

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