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Projecte llegit

Títol: A set of DRL-based xApps for joint RAN/MEC Resource Allocation and Slicing management in O-RAN

Estudiants que han llegit aquest projecte:

Director/a: CERVELLÓ PASTOR, CRISTINA

Departament: ENTEL

Títol: A set of DRL-based xApps for joint RAN/MEC Resource Allocation and Slicing management in O-RAN

Data inici oferta: 20-07-2023     Data finalització oferta: 20-03-2024


Estudis d'assignació del projecte:
    MU MASTEAM 2015
Tipus: Individual
 
Lloc de realització: EETAC
 
Paraules clau:
O-RAN, DRL, 5G, Resource Allocation, Slicing, MEC
 
Descripció del contingut i pla d'activitats:
Objective:
Design, develop and validate a set of techniques based on the
application of Federated Learning, which, combined with Deep
Reinforcement Learning, will allow the 6G MEC to be integrated
with the O-RAN architecture. The solution will be deployed in an
automated distributed management use case with minimal human
intervention in massive and heterogeneous communications of real-
time services.

Tasks:
- Design and evaluate AI/ML algorithms to build and apply local
and global models in distributed environments with hundreds of
agents located at the end users, the access network, and the core
in dynamic scenarios.
 
Overview (resum en anglès):
The evolution of wireless communication technologies, moving from the established fifth-generation (5G) to beyond 5G and ultimately the sixth-generation (6G), highlights the need for a significant shift in architectural approach. This transformation is essential to effectively accommodate the significant increase in multi-connectivity and on-demand services that is expected in the near future.

Machine Learning (ML) and more specifically Deep Reinforcement Learning (DRL) are a promising approach to solving the aforementioned challenges. In this project, an approach for dynamic resource allocation and management of both the Radio Acess Network (RAN) and Multi-Access Edge Computing (MEC) leveraging Deep Q-Network (DQN) is proposed. Two DQN models are implemented for admission control and maintenance of RAN-level slicing, in order to be deployed as Extended Applications (xApps) within the O-RAN architectural framework. This methodology ensures effective resource allocation while maintaining the Quality of Services (QoS).

The proposed solution is validated through simulation results, demonstrating its effectiveness in improving network efficiency and performance in future 5G and 6G networks. Further stages include the implementation of Federated Learning to deploy the proposed models in multiple mobile scenarios and the correspondent emulation in real-scale frameworks.

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