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Cooperative and heterogeneous multi-agent learning for 6G network orchestration

Technological challenge: Communication networks, IOT, radiofrequencies and antennas (learn more)

Department: Département Systèmes (LETI)

Laboratory: Laboratoire Sans fils Haut Débit

Start Date: 01-09-2022

Location: Grenoble

CEA Code: SL-DRT-22-0250

Contact: mickael.maman@cea.fr

In beyond 5G/6G networks , it is imperative to easily deploy and manage a private/ad-hoc network of mobile users such as a fleet of vehicles or drones. The objective of this thesis is to define strategies and associated protocols (control and resource allocation) to self-organize "mesh" networks of mobile users. The research questions are: (i) How to manage a cooperative multi-agent system for the orchestration and self-organization of a 6G network? (ii) How to orchestrate a distributed multi-objective network? (iii) Are the multi-agent approach and network reconfiguration compatible with the dynamics of the environment? While existing studies focus on problems aiming at optimizing a single objective function with homogeneous agents, we are interested in local/distributed multi-agent cooperative learning between heterogeneous users/moving agents (with different optimization functions). The first step of this thesis will be to optimize heterogeneous multi-objective functions for a 6G network with a central orchestrator. The second step of this thesis will concern cooperative heterogeneous multi-agent systems and interactions between agents (concurrent learning, team learning, ...) to jointly solve tasks and maximize utility. The last step of this thesis will concern a Hybrid approach (Centralized and Distributed)

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