@inbook{6d60401354a84eb484987649c9f63fe1,
title = "Learning-based trajectory optimization for 5G mmWave uplink UAVs",
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.",
keywords = "5G, Beamforming, MmWave, Path planning, Reinforcement learning, UAVs",
author = "Praneeth Susarla and Yansha Deng and Giuseppe Destino and Jani Saloranta and Toktam Mahmoodi and Markku Juntti and Olli Silven",
year = "2020",
month = jun,
doi = "10.1109/ICCWorkshops49005.2020.9145194",
language = "English",
series = "2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings",
address = "United States",
note = "2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 ; Conference date: 07-06-2020 Through 11-06-2020",
}