DC9: Adaptive algorithms for object detection in JCAS

Task: Adaptive algorithms for object detection in JCAS (WP3)

Host institution: UNITN

Country: Italy

Supervisor: Prof. P. Casari [UNITN]

Co-supervisors: Prof F. Granelli [UNITN]; Dr. Mikko Uusitalo [Nokia];

Objectives: To optimise object detection and sensing by minimising the signalling overhead of sensing through intelligent allocation of sensing signals; To exploit multi-agent reinforcement learning techniques to provide adaptive and near-optimal strategies in highly dynamic scenarios; To implement the proposed solutions in COTS sub-6GHz testbed at UNITN for performance evaluation.

Expected Results: Effective object detection and sensing is achieved with low signalling overhead; Proposed reinforcement learning techniques can adapt well in highly dynamic scenarios; Proposed techniques is implemented in sub-6GHz testbed.

PhD enrolment: Doctoral School of the University of Trento

Planned secondments: 

  • TU Delft (3 months, M17-M19): Adaptive object sensing using embedded platforms, with M. Zuniga (KPI: joint journal paper)

  • Nokia-FI (4 months, M24-M27): Effective JCAS scheduling in dense cellular networks, with M. Uusitalo (KPI: joint conference paper)

Candidate profile: Electrical Engineering, Telecommunications, Computer Science

Desirable skills/interests: Wireless networking, 4G/5G protocols, signal/array processing, machine learning, optimization, hands-on experience with hardware and systems (the applicant should be proficient in at least one or two of the skills).

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Shortlisted applicants for this position will be prompted to complete the application on the University of Trento’s official web site, in order to comply with formal University rules.