  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.
