  Coded/uncoded content placement in Mobile Edge Caching (MEC) has evolved as
an efficient solution to meet the significant growth of global mobile data
traffic by boosting the content diversity in the storage of caching nodes. To
meet the dynamic nature of the historical request pattern of multimedia
contents, the main focus of recent researches has been shifted to develop
data-driven and real-time caching schemes. In this regard and with the
assumption that users' preferences remain unchanged over a short horizon, the
Top-K popular contents are identified as the output of the learning model. Most
existing datadriven popularity prediction models, however, are not suitable for
the coded/uncoded content placement frameworks. On the one hand, in
coded/uncoded content placement, in addition to classifying contents into two
groups, i.e., popular and nonpopular, the probability of content request is
required to identify which content should be stored partially/completely, where
this information is not provided by existing data-driven popularity prediction
models. On the other hand, the assumption that users' preferences remain
unchanged over a short horizon only works for content with a smooth request
pattern. To tackle these challenges, we develop a Multiple-model (hybrid)
Transformer-based Edge Caching (MTEC) framework with higher generalization
ability, suitable for various types of content with different time-varying
behavior, that can be adapted with coded/uncoded content placement frameworks.
Simulation results corroborate the effectiveness of the proposed MTEC caching
framework in comparison to its counterparts in terms of the cache-hit ratio,
classification accuracy, and the transferred byte volume.
