The integration of positioning/tracking with wireless communications is a feasible approach to achieving unmanned flying in advanced urban air mobility (A-UAM) with autonomous aerial vehicle per se. Thus, our contribution is an air route design to define the traffic of airborne vehicles, and of future fully automated aircraft under the constraints of transmission delay and throughput reliability using machine learning algorithms.
The command-and-control requirement presents an approach to autonomous flying with the assistance of the cellular network that interacts with the aircraft by sending waypoints and velocity commands and receiving position and velocity reports.
Navigation for a UAV-swarm
Challenges
Defining connectivity under wireless channel uncertainty 1
Distributed positioning/tracking with regards to latency
Maintaining the swarm visibility via monitoring
Energy efficiency 2
Autonomous flight
Path for UAV-swarm
How robust/reliable are the links:
1)UAV-to-UAV (formation control)
2)UAV-to-I* (autonomous flying/navigation)
* I: Infrastructure such as Wireless base station/vertiport
Science and technology inquiries
Variance and CRLB performance metrics for latency and positioning
Variance reduction via machine learning
Vertiports networking for the sensing required to track the position of the swarm of UAVs
Air route design under energy consumption and latency constraints
Delay spread is the difference between the propagation delays along two signals 3. What is the best response to wireless channel delay spread?
Sources:
1 T. Zeng, M. Mozaffari, O. Semiari, W. Saad, M. Bennis and M. Debbah, "Wireless Communications and Control for Swarms of Cellular-Connected UAVs," 2018 52nd Asilomar, pp. 719-723
2 M. Hayajneh, M. Ndong, N. A. Ali and H. Tembine, "Non-asymptotic linear growth of energy efficiency in distributed autonomous D2D MIMO wireless communications", IEEE Access, vol. 8, pp. 105914-105921, 2020.
3 D. Tse and P. Viswanath, Fundamentals of Wireless Communications, Cambridge, U.K:Cambridge Univ. Press, 2005.
4 B. Salamat, G. Elsbacher, A. M. Tonello and L. Belzner, "Model-Free Distributed Reinforcement Learning State Estimation of a Dynamical System Using Integral Value Functions," in IEEE Open Journal of Control Systems, vol. 2, pp. 70-78, 2023
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