When Machine Learning Meets Wireless Cellular Networks: Deployment, Challenges, and Applications

TL;DR

This paper reviews how AI can transform wireless cellular networks by enhancing network management, security, and performance, emphasizing integration challenges, key factors, and practical use cases in 5G and beyond.

Contribution

It provides a comprehensive overview of AI integration in future wireless networks, highlighting key factors, types of network intelligence, and application examples across various network functions.

Findings

AI can significantly improve network efficiency and security.

Successful AI deployment requires addressing data, security, and explainability.

Use cases demonstrate AI's impact on physical layer, mobility, security, and localization.

Abstract

Artificial intelligence (AI) powered wireless networks promise to revolutionize the conventional operation and structure of current networks from network design to infrastructure management, cost reduction, and user performance improvement. Empowering future networks with AI functionalities will enable a shift from reactive/incident driven operations to proactive/data-driven operations. This paper provides an overview on the integration of AI functionalities in 5G and beyond networks. Key factors for successful AI integration such as data, security, and explainable AI are highlighted. We also summarize the various types of network intelligence as well as machine learning based air interface in future networks. Use case examples for the application of AI to the wireless domain are then summarized. We highlight on applications to the physical layer, mobility management, wireless security, and localization.