Resource Allocation in Integrated Terrestrial and Non-terrestrial Networks

Due to increasing demands for novel services, advancements in non-terrestrial networks (NTNs) are seen as an effective solution to complement terrestrial networks (TNs) in under-served areas. However, the high mobility of new users, e.g., autonomous cars, in addition to that of NTN platforms, will cause extreme signal fluctuations. The latter will lead to radio link failures caused, for instance, by late executed handovers, or inefficient uplink/downlink resource allocation. Moreover, the integrated NTN-TN infrastructure is dynamic in terms of resource availability and loitering time. This makes resource allocation in the integrated NTN-TN a challenging task. Consequently, we target in this project the development of novel and intelligent (machine learning-based) resource allocation schemes across the integrated TN-NTN, to maximize the capacity, energy efficiency, and loitering time, while accounting for the NTN-TN dynamicity and limited resources. Developed methods would enable the seamless integration of NTNs to TNs, identified as a key 6G paradigm.

Faculty Supervisor:

Wael Jaafar

Student:

Partner:

Universitat Pompeu Fabra;Université Badji Mokhtar - Annaba

Discipline:

Engineering

Sector:

Technology; Aerospace; Information and Communications Technology

University:

École de technologie supérieure

Program: