A Decentralized Framework for AI-based Resource Allocation in Open RAN
Mrs. Gyara Monika *1 Assistant Professor, Department of CSE (Artificial Intelligence & Machine Learning), ACE Engineering College, Ankushapur, Hyderabad monika.gyara@aceec.ac.in (Corresponding Author)
Kosana HarshaVardhan *2 Student of ACE Engineering College, Department of CSE (Artificial Intelligence & Machine Learning) kosana.harshavardhan0705@gmail.com
Yash Vinay Chamle *3 Student of ACE Engineering College, Department of CSE (Artificial Intelligence& Machine Learning) chamleyash@gmail.com
Vangoori Harsha Vardhan *4 Student of ACE Engineering College, Department of CSE (Artificial Intelligence &Machine Learning)vangooriharsha17@gmail.com
Abstract: Modern mobile networks, particularly within the Open-RAN (O-RAN) architecture, increasingly rely on Artificial Intelligence for dynamic network slicing. However, conventional approaches often depend on a centralized AI model, which introduces significant challenges related to scalability, reliability as a single point of failure, and data privacy. This project proposes and implements a novel decentralized framework, to address these limitations using Federated Learning and Deep Learning. The system features autonomous DL agents, implemented in Pyt1orch, that reside at the network edge, making real-time resource allocation decisions based on local data. These agents collaboratively train a global intelligence model, orchestrated by a central FL server, by sharing only their learned model parameters, thus preserving data privacy. Through a custom-built simulation environment, this work demonstrates that the federated system successfully learns to manage network resources, achieving performance comparable to a centralized model while exhibiting graceful degradation and enhanced scalability.
Keywords: O-RAN, Federated Learning, Deep Learning, Resource Allocation, Decentralized AI, Edge Intelligence, Network Slicing, PyTorch, Privacy Preservation, Scalability.
