TEDGE-Caching: Transformer-based Edge Caching Towards 6G Networks
Zohreh Hajiakhondi Meybodi, Arash Mohammadi, Elahe Rahimian, Shahin, Heidarian, Jamshid Abouei, Konstantinos N. Plataniotis

TL;DR
This paper introduces TEDGE-Caching, a novel transformer-based framework for edge content caching in 6G networks, improving prediction of content popularity without pre-processing, to enhance mobile data delivery.
Contribution
It proposes the first use of Vision Transformer neural networks for proactive edge caching, addressing limitations of previous DNN models in 6G networks.
Findings
Outperforms existing caching methods in simulations
Requires no data pre-processing or additional context
Effectively predicts content popularity for dynamic environments
Abstract
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…
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Taxonomy
TopicsCaching and Content Delivery · Advanced Wireless Communication Technologies
MethodsAttention Is All You Need · Linear Layer · Byte Pair Encoding · Label Smoothing · Absolute Position Encodings · Multi-Head Attention · Residual Connection · Softmax · Adam · Dropout
