  As the services and requirements of next-generation wireless networks become
increasingly diversified, it is estimated that the current frequency bands of
mobile network operators (MNOs) will be unable to cope with the immensity of
anticipated demands. Due to spectrum scarcity, there has been a growing trend
among stakeholders toward identifying practical solutions to make the most
productive use of the exclusively allocated bands on a shared basis through
spectrum sharing mechanisms. However, due to the technical complexities of
these mechanisms, their design presents challenges, as it requires coordination
among multiple entities. To address this challenge, in this paper, we begin
with a detailed review of the recent literature on spectrum sharing methods,
classifying them on the basis of their operational frequency regime that is,
whether they are implemented to operate in licensed bands (e.g., licensed
shared access (LSA), spectrum access system (SAS), and dynamic spectrum sharing
(DSS)) or unlicensed bands (e.g., LTE-unlicensed (LTE-U), licensed assisted
access (LAA), MulteFire, and new radio-unlicensed (NR-U)). Then, in order to
narrow the gap between the standardization and vendor-specific implementations,
we provide a detailed review of the potential implementation scenarios and
necessary amendments to legacy cellular networks from the perspective of
telecom vendors and regulatory bodies. Next, we analyze applications of
artificial intelligence (AI) and machine learning (ML) techniques for
facilitating spectrum sharing mechanisms and leveraging the full potential of
autonomous sharing scenarios. Finally, we conclude the paper by presenting open
research challenges, which aim to provide insights into prospective research
endeavors.
