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As a consequence of the COVID-19 pandemic, the demand for telecommunicationfor remote learning/working and telemedicine has significantly increased.Mobile Edge Caching (MEC) in the 6G networks has been evolved as an efficientsolution to meet the phenomenal growth of the global mobile data traffic bybringing multimedia content closer to the users. Although massive connectivityenabled by MEC networks will significantly increase the quality ofcommunications, there are several key challenges ahead. The limited storage ofedge nodes, the large size of multimedia content, and the time-variant users'preferences make it critical to efficiently and dynamically predict thepopularity of content to store the most upcoming requested ones before beingrequested. Recent advancements in Deep Neural Networks (DNNs) have drawn muchresearch attention to predict the content popularity in proactive cachingschemes. Existing DNN models in this context, however, suffer from longtermdependencies, computational complexity, and unsuitability for parallelcomputing. To tackle these challenges, we propose an edge caching frameworkincorporated with the attention-based Vision Transformer (ViT) neural network,referred to as the Transformer-based Edge (TEDGE) caching, which to the best ofour knowledge, is being studied for the first time. Moreover, the TEDGE cachingframework requires no data pre-processing and additional contextualinformation. Simulation results corroborate the effectiveness of the proposedTEDGE caching framework in comparison to its counterparts.

TEDGE-Caching: Transformer-based Edge Caching Towards 6G Networks