CEDoc – UM6P – CC: Quantum AI for UAVs Communications-Aware Planning Full-time

Mohammed VI Polytechnic University is an institution dedicated to research and innovation in Africa and aims to position itself among world-renowned universities in its fields

The University is engaged in economic and human development and puts research and innovation at the forefront of African development. A mechanism that enables it to consolidate Morocco’s frontline position in these fields, in a unique partnership-based approach and boosting skills training relevant for the future of Africa.

Located in the municipality of Benguerir, in the very heart of the Green City, Mohammed VI Polytechnic University aspires to leave its mark nationally, continentally, and globally.

Context:

Recently there has been a growing interest in the integration of unmanned aerial vehicles (UAVs) into the communications network. This has been boosted due to the development of 6G cellular technology. It is expected that UAVs will form part of the network to extend and improve the network capabilities. In addition, it is also expected that UAVs will be clients of the cellular network. This integration of UAVs into cellular networks demands the development of smart and complex communications-aware trajectory planning techniques [1-2].On the other hand, the introduction of Quantum engineering in the field of machine learning (ML) is creating new and interesting opportunities. One recent trend in the field of communications-aware planning is the use of Quantum AI and machine learning algorithms to deal with the complexity that the problems of this field exhibit [3-5].

This PhD project will be conducted in collaboration with the Department of Automatic Control from the Center for Research and Advanced Studies of the National Polytechnic Institute in Mexico, the University of Sydney-Australia a leader in the field of Quantum engineering and technologies. The project will be supervised by Prof Daniel Bonilla Licea and co-supervised by Profs Lamiae Azizi and Hajar el Hammouti.

Research Objectives:

The objective of this project is to develop Quantum AI algorithms to solve complex communications-aware trajectory planning problems involving UAVs and 6G technologies with applications in maintenance of manufacturing sites. The final aim of the project is to develop a friendly user set of tools; and such development will be enabled by the most powerful High Performance Computing supercomputer in Africa, Toubkal, and carried out in collaboration with Prof Imad Kissami.

Admission Criteria:

Mandatory:

-Knowledge of classical ML and RL

-Knowledge of Python

-Solid mathematical background

-Good communications skills in English

Convenient:

-Knowledge of DRL (optional)

-Knowledge of Qiskit

-Notions of communications theory and/or signal processing

-Notions of control theory

-Notions of robotics

References:

[1] D. Bonilla Licea, M. Ghogho and M. Saska, “When Robotics Meets Wireless Communications: An Introductory Tutorial,” in Proceedings of the IEEE, vol. 112, no. 2, pp. 140-177, Feb. 2024, doi: 10.1109/JPROC.2024.3380373.

[2] D. Bonilla Licea, G. Silano, M. Ghogho and M. Saska, “Omnidirectional Multi-Rotor Aerial Vehicle Pose Optimization: A Novel Approach to Physical Layer Security,” ICASSP 2024 – 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea, Republic of, 2024, pp. 9021-9025, doi:

[3] Y. Li, A. H. Aghvami and D. Dong, “Intelligent Trajectory Planning in UAV-Mounted Wireless Networks: A Quantum-Inspired Reinforcement Learning Perspective,” in IEEE Wireless Communications Letters, vol. 10, no. 9, pp. 1994-1998, Sept. 2021, doi: 10.1109/LWC.2021.3089876.

[4] Silvirianti, B. Narottama and S. Y. Shin, “Layerwise Quantum Deep Reinforcement Learning for Joint Optimization of UAV Trajectory and Resource Allocation,” in IEEE Internet of Things Journal, vol. 11, no. 1, pp. 430-443, 1 Jan.1, 2024, doi: 10.1109/JIOT.2023.3285968.

[5] C. Park et al., “Quantum Multiagent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems,” in IEEE Internet of Things Journal, vol. 10, no. 22, pp. 20033-20048, 15 Nov.15, 2023, doi: 10.1109/JIOT.2023.3282908.

[6] Simon Luo, Lamiae Azizi, Mahito Sugiyama“Hierarchical probabilistic model for blind source separation via Legendre transformation” in Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:312-321, 2021.

To inquire or submit application: Daniel.BONILLA@um6p.ma

Important documents for applying. CV, transcripts of Bachelor and Masters, cover letter

UM6P.