  As a consequence of the COVID-19 pandemic, the demand for telecommunication
for remote learning/working and telemedicine has significantly increased.
Mobile Edge Caching (MEC) in the 6G networks has been evolved as an efficient
solution to meet the phenomenal growth of the global mobile data traffic by
bringing multimedia content closer to the users. Although massive connectivity
enabled by MEC networks will significantly increase the quality of
communications, there are several key challenges ahead. The limited storage of
edge nodes, the large size of multimedia content, and the time-variant users'
preferences make it critical to efficiently and dynamically predict the
popularity of content to store the most upcoming requested ones before being
requested. Recent advancements in Deep Neural Networks (DNNs) have drawn much
research attention to predict the content popularity in proactive caching
schemes. Existing DNN models in this context, however, suffer from longterm
dependencies, computational complexity, and unsuitability for parallel
computing. To tackle these challenges, we propose an edge caching framework
incorporated with the attention-based Vision Transformer (ViT) neural network,
referred to as the Transformer-based Edge (TEDGE) caching, which to the best of
our knowledge, is being studied for the first time. Moreover, the TEDGE caching
framework requires no data pre-processing and additional contextual
information. Simulation results corroborate the effectiveness of the proposed
TEDGE caching framework in comparison to its counterparts.
