LAUSR

With the deployment of 5G networks, standards organizations have started working on the design phase for 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 by 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 layer (PHY) and link layer 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 a future timeline on the readiness of standardization bodies to adapt to these changes. Finally, we highlight major issues in ML use in wireless technology, and provide potential directions to mitigate some of them in 6G wireless networks.