Artificial Intelligence in Beyond 5G and 6G Reliable Communications
Ali Nauman, Tu N. Nguyen, Yazdan Ahmad Qadri, Zulqar Nain, Korhan Cengiz, Sung Won Kim
Abstract
The rapid increase in heterogeneous data traffic with the ongoing development of self-organizing and self-sustaining networks exposes the limitations of the fifth generation (5G) system, which was originally aimed at enabling the realization of the Internet of Everything. This study presents flexible design agreements of beyond 5G (B5G) from the current 3GPP study and proposes an intelligent network architecture for the 5G and |B5G paradigm to ensure that the network is self-sustained and self-organized. The key idea is to use machine learning (ML) to dynamically schedule flexible transmission time intervals at the slot level to optimize network performance. This study also provides an overview of the queuing model of the medium access control layer and presents how ML-enabled scheduling plays an important role in reducing queuing latency and providing reliable services of the B5G network.