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
Learning-based trajectory optimization for 5G mmWave uplink UAVs
|Author:||Susarla, Praneeth1; Deng, Yansha2; Destino, Giuseppe1,2;|
1University of Oulu, Finland
2King’s College London, United Kingdom
|Online Access:||PDF Full Text (PDF, 0.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020110288921
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-11-02
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.
IEEE/CIC International Conference on Communications in China - Workshops
|Pages:||1 - 7|
2020 IEEE International Conference on Communications Workshops (ICC Workshops)
IEEE International Conference on Communications Workshops
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
The research leading to these results has received funding from European-Union project Primo-5G, Academy of Finland projects 6Genesis Flagship (Grant No. 318927), IIoT CONnectivity for mechanICAL systems (ICONICAL) and Positioning-aided Reliably-connected Industrial Systems with Mobile mmWave Access (PRISMA).
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
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