  With the deployment of 5G networks, standards organizations have started
working on the design phase for sixth-generation (6G) networks. 6G networks
will be immensely complex, requiring more deployment time, cost and management
efforts. On the other hand, mobile network operators demand these networks to
be intelligent, self-organizing, and cost-effective to reduce operating
expenses (OPEX). Machine learning (ML), a branch of artificial intelligence
(AI), is the answer to many of these challenges providing pragmatic solutions,
which can entirely change the future of wireless network technologies. By using
some case study examples, we briefly examine the most compelling problems,
particularly at the physical (PHY) and link layers in cellular networks where
ML can bring significant gains. We also review standardization activities in
relation to the use of ML in wireless networks and future timeline on readiness
of standardization bodies to adapt to these changes. Finally, we highlight
major issues in ML use in the wireless technology, and provide potential
directions to mitigate some of them in 6G wireless networks.
