Approximately around each decade, there is a new generation of mobile technology being designed, developed, and ultimately deployed to facilitate new or more efficient digital services in many areas from transportation to communication. As we approach 2030, the target for initial deployments of 6G networks, research on 6G has been accelerating. The
6G FNS project, supported by the Dutch National Growth Fund, aims at developing hardware and software technologies that will realize the 6G vision in a sustainable, efficient, and secure way. Two Ph.D. candidates will start working at the UT, EDGE Research Center as part of 6G FNS.
Project Description: 6G networks will bring together sensing, communication, and computing functions in a single infrastructure, paving the way toward many novel applications such as ubiquitous healthcare or context-aware intelligent network management. The massive sensing information with extremely high spatial and temporal granularity can be used to make processes in, for example, agriculture, mobility and industry many times more efficient and enable new applications in those areas. To realize this vision, both radio signals transmitted by the network nodes and physical sensors (e.g., Internet of medical devices as body implants or environmental monitoring devices) will play a key role by providing information about the physical world on a massive scale. For reasons of scalability, energy consumption, privacy and security, the processing of this data will not be done in a central location, but distributed across the network, in the device, the edge, and in the cloud. Because of privacy and ownership perspectives, it is important to keep this data local, owned, and within local jurisdiction as much as possible, and to learn from the data without sharing the data itself (as, for example, with federated learning). Moreover, as sustainability is one of the ten design principles of 6G, how the sensing data is collected, processed, and communicated must be determined with sustainability concerns in mind.
The goal of this PhD project is to:
- Design and develop mechanisms for on-sensor and in-network compression of sensor information to decrease the amount of data that need to be stored, communicated, and computed;
- Design and develop distributed privacy-preserving AI solutions (e.g., hierarchical Deep Reinforcement Learning solutions) for the considered sensing use cases in cooperation with industry partners (e.g., sensing for user applications or sensing for intelligent 6G network management);
- Design and develop distributed control algorithms for network management, leveraging sensing and distributed AI.
Methodology: The methodology of this Ph.D. project includes both theoretical approaches (e.g., optimization schemes, design of heuristics) and experimental development and testing in the Edge Centre’s research infrastructure. The candidate will work in close collaboration with other researchers (e.g., other PhD candidates) in the 6G FNS project at the University of Twente and other partners. There is a possibility of short-term research visit(s) or internship at 6G FNS industry partners.
This Ph.D. position is part of the recently established
EDGE research centre, and specifically in the
DACS and
PS research groups The candidate will work under the supervision of dr. Suzan Bayhan and dr. Alex Chiumento and will have guidance from the promotors prof.dr.ir. Geert Heijenk and prof.dr.ing. Paul Havinga.