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Modeling Network Slicing in 5G and Beyond Networks

ABG-105832 Thesis topic
2022-05-23 Public funding alone (i.e. government, region, European, international organization research grant)
ETIS - ENSEA - Université de Cergy Pontoise
Cergy - Ile-de-France - France
Modeling Network Slicing in 5G and Beyond Networks
  • Telecommunications
  • Computer science

Topic description

One of the key goals of 5G and beyond networks is to incorporate a wide variety of services with distinct performance requirements into a single physical network, where each service has its own logical network isolated from other networks. Indeed, 5G is envisioned as a network to support multiple services with specific performance requirements in highly heterogeneous environments. In this context, network slicing is considered as the key technology for meeting the service requirements of diverse application domains and ensure that the slices can be isolated from each other. This PhD project will provide innovative slicing frameworks that allow network operators to instantiate heterogeneous slice services adaptively according to the time-varying requirements, the services’ demand, the trajectory of high mobility users and the available resources. Moreover, we will investigate machine learning to achieve autonomous behavior of slices. In addition, we will address some promising emerging services that can have diverse and conflicting computing, storage, latency, reliability, and throughput requirements.

Funding category

Public funding alone (i.e. government, region, European, international organization research grant)

Funding further details

Ecole doctorale

Presentation of host institution and host laboratory

ETIS - ENSEA - Université de Cergy Pontoise

ETIS is a joint research department between CYU Cergy Paris University, ENSEA Graduate School of Electrical Engineering and CNRS/INS2I.

ETIS develops research in the field of the theory of information with both theoretical and experimental activities in order to allow information processing systems to acquire capacities of autonomy. Autonomy is considered both in terms of learning and adaptation to the environment (including users) as well as making decision that includes low energy consumption and computing power for example.

ETIS designed systems perform intelligent processing which is adaptable to increasing complexity. The concerned areas are reconfigurable chip systems, data analysis, image indexing, developmental robotics, information theory and telecommunications. Learning and adaptation algorithms based on data constitue the core of the developed systems.

The ETIS laboratory is at the heart of the current AI revolutionLearning and adaptation algorithms based on data constitue the core of the developed systems.

Candidate's profile

1. The applicant must have a Master’s degree (or equivalent) in wireless communications, or any related discipline.
2. Good programming skills in MATLAB and/or Python.
3. Knowledge of machine learning and optimization tools.

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