ENHANCING 5G NETWORK EFFICIENCY: AN AI-BASED APPROACH TO DYNAMIC RESOURCE ALLOCATION, ANOMALY DETECTION, AND LATENCY REDUCTION

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 Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-Learning (DQN), 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.