Learning-based trajectory optimization for 5G mmWave uplink UAVs
Susarla, Praneeth; Deng, Yansha; Destino, Giuseppe; Saloranta, Jani; Mahmoodi, Toktam; Juntti, Markku; Sílven, Olli (2020-07-21)
P. Susarla et al., "Learning-Based Trajectory Optimization for 5G mmWave Uplink UAVs," 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Dublin, Ireland, 2020, pp. 1-7, doi: 10.1109/ICCWorkshops49005.2020.9145194
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https://urn.fi/URN:NBN:fi-fe2020110288921
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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.
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