  Non-terrestrial networks (NTNs) are a critical enabler of the persistent
connectivity vision of sixth-generation networks, as they can service areas
where terrestrial infrastructure falls short. However, the integration of these
networks with the terrestrial network is laden with obstacles. The dynamic
nature of NTN communication scenarios and numerous variables render
conventional model-based solutions computationally costly and impracticable for
resource allocation, parameter optimization, and other problems. Machine
learning (ML)-based solutions, thus, can perform a pivotal role due to their
inherent ability to uncover the hidden patterns in time-varying,
multi-dimensional data with superior performance and less complexity.
Centralized ML (CML) and decentralized ML (DML), named so based on the
distribution of the data and computational load, are two classes of ML that are
being studied as solutions for the various complications of terrestrial and
non-terrestrial networks (TNTN) integration. Both have their benefits and
drawbacks under different circumstances, and it is integral to choose the
appropriate ML approach for each TNTN integration issue. To this end, this
paper goes over the TNTN integration architectures as given in the 3rd
generation partnership project standard releases, proposing possible scenarios.
Then, the capabilities and challenges of CML and DML are explored from the
vantage point of these scenarios.
