Description
Date depot: 5 avril 2024
Titre: Next Generation Network: Deep learning approach
Directeur de thèse:
Hassine MOUNGLA (LIPADE)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Systèmes et réseaux
Resumé: In the upcoming sixth generation (6G), machine learning and mobile edge computing are expected to play a crucial role due to their ability to provide flexible deployment, strong adaptability, and high-quality services. They can efficiently offer communication and computing services to ground intelligence devices, thus accelerating the development of wireless communication.
They can be playing the role of device which is expected to improve the services and quality of life of users for next generation networks.
Nonetheless, as the number of mobile edges grows, challenges such as mobility and service continuity will need to be addressed. Mobile edge computing (MEC) is a promising technology to support mission-critical mobile communication applications, such as intelligent path planning and safety applications. The central cloud processing schemes have exhibited high latency and scalability related problems. In this context, a collaborative mobile edge computing paradigm is emerging to reduce the computing service latency and improve service reliability for operator networks. It also enables sharing resources.
In this thesis, we will find out that machine learning, big data analytics, and artificial intelligence will help in making the “next generation network” self-adaptive, self-aware, prescriptive, and proactive providing that future network operators cannot work without shifting their operational framework to AI and machine learning technologies.