AI-Driven OTA Update Optimization for Fixed Wireless Access Devices: A Technical Deep Dive

Author(s)	Arun Sugumar
Country	United States
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.
Keywords	Fixed Wireless Access, Artificial Intelligence, Network Automation, Over-The-Air Updates, Intent-Based Networking
Field	Computer
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-04-02
Cite This	AI-Driven OTA Update Optimization for Fixed Wireless Access Devices: A Technical Deep Dive - Arun Sugumar - IJSAT Volume 16, Issue 2, April-June 2025.

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