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

arXiv: https://arxiv.org/abs/2112.00633 · HTML: https://mlsanity.com/paper/2112.00633/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Deep Learning Based Caching for Self-Driving Car in Multi-access Edge Computing](https://mlsanity.com/paper/1810.01548/) (2018)
- [Spatial multi-LRU Caching for Wireless Networks with Coverage Overlaps](https://mlsanity.com/paper/1602.07623/) (2016)
- [Deep Learning for Wireless Coded Caching with Unknown and Time-Variant Content Popularity](https://mlsanity.com/paper/2008.09422/) (2020)
- [Cooperative Caching and Transmission Design in Cluster-Centric Small Cell Networks](https://mlsanity.com/paper/1601.00321/) (2016)
- [Joint Transmission Scheme and Coded Content Placement in Cluster-centric UAV-aided Cellular Networks](https://mlsanity.com/paper/2101.11787/) (2021)

## Built on by (most cited first, 7 of 7)

- [A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks](https://mlsanity.com/paper/2306.07303/) (2023)
- [Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networks](https://mlsanity.com/paper/2210.05874/) (2022)
- [Predictive Edge Caching through Deep Mining of Sequential Patterns in User Content Retrievals](https://mlsanity.com/paper/2210.02657/) (2022)
- [ViT-CAT: Parallel Vision Transformers with Cross Attention Fusion for Popularity Prediction in MEC Networks](https://mlsanity.com/paper/2210.15125/) (2022)
- [Semantics-enhanced Temporal Graph Networks for Content Popularity Prediction](https://mlsanity.com/paper/2301.12355/) (2023)
- [CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks](https://mlsanity.com/paper/2303.12097/) (2023)
- [Learning to Remember: Attentive Reinforcement Learning for Edge Serverless Autoscaling](https://mlsanity.com/paper/2603.28790/) (2026)

## Abstract (arXiv)

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.