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PhD Opening at the LORIA Lab, Université de Lorraine, France on Exploiting synergies of UAV networks and 5G

Country/Region : France

Website :


A PhD position is available at the Loria Lab of the University of Lorraine,
France. The candidate will join the SIMBIOT research team. The position is
available from October 2019.
Graduate students at the M.Sc. or B.Sc. level with background in
Electrical and Computer Engineering or Computer Science are encouraged
to apply.
Context and objectives
Unmanned aerial vehicles (UAVs) are quickly spreading into several fields of civilian and commercial applications, such as infrastructure inspection, event filming, disaster management, precision-agriculture. To realize UAV applications, it is necessary to design and develop innovative wireless communication technologies and networked cooperative schemes, not only to support the UAV operations and ensure their safe operations, but also to enable the high-capacity mission-related data transmissions for rate-demanding applications. As 5G communications promise astonishing improvements in performance (e.g., 100x data rates, 1000x connected devices, 1/10x energy consumption, 1/5x data latency), in addition to service management and orchestration, and network function virtualization, the integration of UAVs into 5G (and beyond) systems is a promising technology to achieve such goals. The usage of UAV fleets in 5G can, on one hand, assist the terrestrial communications in 5G, which are referred to as UAV-assisted 5G communications, and on the other hand, it can boost incredibly the Internet of Things (IoT) paradigm, leading to an Internet of Intelligent Things, comprising not only objects but also UAV and other intelligent devices. In order to manage UAV fleets in 5G, two crucial scientific questions must be addressed: the design and development of network mechanisms to handle UAV “virtualized” capabilities, the formulation of services management and mapping with UAV fleets capabilities as mathematical optimization problems under energy constraints.
Position description
By leveraging the experience of the SIMBIOT team on UAV networks, the PhD student will design and evaluate new algorithms for managing UAV fleets in order to exploit 5G communications architecture and satisfy 5G communication requirements. In this sense, the UAV fleets can be seen as either a “tool” to accomplish a specific mission requested by the end user, and/or as a 5G core network extension to increase coverage. In both case, networking and optimization schemes are needed. Therefore, the student will focus on the following research directions:
• Definition of network-aware algorithms of movement for accomplishing the UAV missions and/or extending the network coverage by leveraging our experience with controlled mobility in wireless networks.
• Integration of UAV fleet control into the reference 5G communications architecture,
• Development of innovative models and algorithms for managing UAV services and designing UAV-based 5G networks, extending the current state-of-the-art.
Candidate's profile
- Knowledge of UAV communication architectures/protocols as well as of 5G communication architecture.
- Practical skills in designing and implementing networking protocols for controlled mobility devices.
- Advanced programming skills in C++/Java and Matlab, knowledge of network simulators.
- High proficiency in English
Application process
The following documents are required:
- CV,
- motivation letter,
- statement of research experience and interests,
- University transcripts
as web links (and not as attachments) contained into an email, whose subject will be "Application for PhD position at the Loria Lab", which must be addressed to Enrico Natalizio (at Web links of research articles authored by the applicant are welcome to be included, too.
- April 30th, 2019: application submission deadline
- May 9th, 2019: notification of shortlisted candidates for interview
- second week of May, 2019: interviews
- July 7th, 2019: notification of the selected candidate
- October 2019: position start date

Last modified: 2019-03-23 23:27:12