AI-enabled Future Wireless Networks: Challenges, Opportunities and Open Issues

TL;DR

This paper surveys how AI and machine learning can revolutionize future wireless networks like 6G by enabling automation and addressing key challenges such as complexity and dynamic conditions.

Contribution

It provides a comprehensive overview of current AI applications in wireless networks, identifies challenges, and outlines open issues for future research.

Findings

AI enables automated network resource management.

Challenges include convergence time and complexity of ML algorithms.

Open issues highlight future research directions.

Abstract

A plethora of demanding services and use cases mandate a revolutionary shift in the management of future wireless network resources. Indeed, when tight quality of service demands of applications are combined with increased complexity of the network, legacy network management routines will become unfeasible in 6G. Artificial Intelligence (AI) is emerging as a fundamental enabler to orchestrate the network resources from bottom to top. AI-enabled radio access and AI-enabled core will open up new opportunities for automated configuration of 6G. On the other hand, there are many challenges in AI-enabled networks that need to be addressed. Long convergence time, memory complexity, and complex behaviour of machine learning algorithms under uncertainty as well as highly dynamic channel, traffic and mobility conditions of the network contribute to the challenges. In this paper, we survey the state-of-art research in utilizing machine learning techniques in improving the performance of wireless networks. In addition, we identify challenges and open issues to provide a roadmap for the researchers.