Intenship | Network Management for Spectrum Sharing in 6G Terrestrial and n at TNO
Den Haag, , Netherlands -
Full Time


Start Date

Immediate

Expiry Date

01 Dec, 25

Salary

0.0

Posted On

02 Sep, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

It, Professional Development, Case

Industry

Information Technology/IT

Description

ABOUT THIS POSITION

Future/6G mobile network are expected to provide ubiquitous coverage, resilient services and uniform customer experience by integrating terrestrial (TN) and non-terrestrial networks (NTN). The coexistence of TN and NTNs necessitates careful interference studies and criteria for optimal spectrum management, usage, and operation between heterogeneous networks.

Responsibilities

Key activities of the proposed graduation project are:

  • To conduct a thorough literature review concerning spectrum sharing or coexistence in the context of 6G TN-NTN integration, in order to provide a solid research basis, aid in modelling and potentially further sharpen the problem formulation to ensure novelty.
  • To define a limited set of challenging scenarios with gradually increasing complexity, which will form the basis for the solution development and assessment. We aim to consider the use of the FR3 spectrum region.
  • To model all relevant scenario aspects related to e.g. network layout, key BS/UE/traffic characteristics and the propagation environment.
  • To develop one or more potentially AI/ML-radio resource management algorithms for spectrum sharing in the context of 6G TN-NTN integration. Formulate one or more heuristic solutions that can be used as a baseline.
  • To develop a system-level simulator incorporating all model and solution aspects.
  • To utilise aforementioned simulator to conduct an extensive quantitative assessment of the proposed resource management algorithms for a range of relevant scenarios and possibly distinct optimisation timescales.
  • To derive key insights and conclusions w.r.t. the optimisation solution, the attainable spectrum efficiency gain and coverage/performance effects, the sensitivity thereof to selected scenario aspects and complexity issues related to the algorithm execution itself, implementational feasibility and (for AI/ML) training aspects.
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