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

Abstract

An expected plethora of demanding services and use cases mandates a revolutionary shift in the way future wireless network resources are managed. Indeed, when application requirements for tight quality of service (QoS) are combined with increased network complexity, legacy network-management routines will become untenable in 6G. Artificial intelligence (AI) is emerging as a fundamental enabler to orchestrate network resources from bottom to top. AIenabled radio access and core will open up new opportunities for automated 6G configurations. At the same time, many challenges in AI-enabled networks need to be addressed. Long convergence times, memory complexity, and the intricate behavior of machine-learning algorithms under uncertainty and the network's highly dynamic channel, traffic, and mobility conditions contribute to the challenges. In this article, we survey state-of-the-art research on using machine-learning techniques to improve the performance of wireless networks. In addition, we identify challenges and open issues to provide a roadmap for researchers.