SCALABLE AI-POWERED FRAMEWORK FOR NEXT-GENERATION BROADBAND NETWORKS

Authors

  • Rajesh Babu Ahirwar Author

DOI:

https://doi.org/10.29121/gjaets.2025.3.1

Keywords:

AI, Autoencoders, Deep Q-Learning, Edge AI, Federated Learning, Multi-Agent Systems, Proximal Policy Optimization, Reinforcement Learning, Transfer Learning.

Abstract

The increasing demands of next-generation broadband networks, particularly with the advent of 5G and beyond, necessitate advanced, scalable AI-driven frameworks for dynamic network management, resource allocation, and predictive maintenance. This paper introduces a scalable AI-powered framework that integrates deep learning models for optimizing network performance in the context of broadband networks, particularly 5G. The proposed framework includes the use of deep learning models such as Deep Q-Learning (DQN), Proximal Policy Optimization (PPO), Autoencoders, and Multi-Agent Systems (MAS) to handle complex data, detect anomalies, predict failures, and optimize traffic routing. Additionally, edge AI is incorporated to reduce latency, enhance real-time decision-making, and optimize network responsiveness. We discuss the implementation workflow, key deep learning models, and future directions like Federated Learning and Transfer Learning that will further enhance the adaptability and scalability of broadband networks.

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Published

2025-03-31

Issue

Section

Articles

How to Cite

SCALABLE AI-POWERED FRAMEWORK FOR NEXT-GENERATION BROADBAND NETWORKS. (2025). Global Journal of Advanced Engineering Technologies and Sciences, 12(3), 9-16. https://doi.org/10.29121/gjaets.2025.3.1

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