Year: 2025
Type: article
Abstract: This article presents a comprehensive analysis of AI-driven optimization strategies for Over-The-Air(OTA) updates in Fixed Wireless Access (FWA) devices, addressing the growing challenges in managingsoftware updates across expanding 5G networks. The article explores the limitations of traditional updatemechanisms and proposes an advanced AI-based solution architecture that leverages machine learningtechniques for dynamic update scheduling. The article examines the implementation of reinforcementlearning, federated learning, and edge-based prediction capabilities to enhance update managementefficiency while maintaining network stability and security. Through analysis of real-world deploymentsand experimental data, the article demonstrates how AI-driven approaches can significantly improve updatesuccess rates, reduce network congestion, and accelerate security patch deployments while minimizingservice disruptions.
Author: Arun Sugumar -
Institution: Anna University, Chennai
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409218733