Obálka

Federated Learning System for Dynamic Radio/MEC Resource Allocation and Slicing Control in Open Radio Access Network

Uloženo v:
Vydáno v:Future Internet vol. 17, no. 3 (2025), p. 106
Hlavní autor: Martínez-Morfa, Mario
Další autoři: Ruiz de Mendoza, Carlos, Cervelló-Pastor, Cristina, Sallent-Ribes, Sebastia
Vydáno:
MDPI AG
Témata:
Linear programming
Deep learning
Collaboration
Adaptability
Integer programming
5G mobile communication
Bandwidths
Edge computing
Resource allocation
Mobile computing
Synchronism
Automation
Privacy
Resource management
Performance evaluation
Cellular communication
Efficiency
Case studies
Optimization
Flexibility
Network latency
Quality of service
Resource utilization
Portable computers
Real time
Federated learning
On-line přístup:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstrakt:The evolution of cellular networks from fifth-generation (5G) architectures to beyond 5G (B5G) and sixth-generation (6G) systems necessitates innovative solutions to overcome the limitations of traditional Radio Access Network (RAN) infrastructures. Existing monolithic and proprietary RAN components restrict adaptability, interoperability, and optimal resource utilization, posing challenges in meeting the stringent requirements of next-generation applications. The Open Radio Access Network (O-RAN) and Multi-Access Edge Computing (MEC) have emerged as transformative paradigms, enabling disaggregation, virtualization, and real-time adaptability—which are key to achieving ultra-low latency, enhanced bandwidth efficiency, and intelligent resource management in future cellular systems. This paper presents a Federated Deep Reinforcement Learning (FDRL) framework for dynamic radio and edge computing resource allocation and slicing management in O-RAN environments. An Integer Linear Programming (ILP) model has also been developed, resulting in the proposed FDRL solution drastically reducing the system response time. On the other hand, unlike centralized Reinforcement Learning (RL) approaches, the proposed FDRL solution leverages Federated Learning (FL) to optimize performance while preserving data privacy and reducing communication overhead. Comparative evaluations against centralized models demonstrate that the federated approach improves learning efficiency and reduces bandwidth consumption. The system has been rigorously tested across multiple scenarios, including multi-client O-RAN environments and loss-of-synchronization conditions, confirming its resilience in distributed deployments. Additionally, a case study simulating realistic traffic profiles validates the proposed framework’s ability to dynamically manage radio and computational resources, ensuring efficient and adaptive O-RAN slicing for diverse and high-mobility scenarios.
ISSN:1999-5903
DOI:10.3390/fi17030106
Zdroj:ABI/INFORM Global