  The growth in wireless broadband users, devices, and novel applications has
led to a significant increase in the demand for new radio frequency spectrum.
This is expected to grow even further given the projection that the global
traffic per year will reach 4.8 zettabytes by 2022. Moreover, it is projected
that the number of Internet users will reach 4.8 billion and the number of
connected devices will be close 28.5 billion devices. However, due to the
spectrum being mostly allocated and divided, providing more spectrum to expand
existing services or offer new ones has become more challenging. To address
this, spectrum sharing has been proposed as a potential solution to improve
spectrum utilization efficiency. Adopting effective and efficient spectrum
sharing mechanisms is in itself a challenging task given the multitude of
levels and techniques that can be integrated to enable it. To that end, this
paper provides an overview of the different spectrum sharing levels and
techniques that have been proposed in the literature. Moreover, it discusses
the potential of adopting dynamic sharing mechanisms by offering
Spectrum-as-a-Service architecture. Furthermore, it describes the potential
role of machine learning models in facilitating the automated and efficient
dynamic sharing of the spectrum and offering Spectrum-as-a-Service.
