# Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networks

arXiv: https://arxiv.org/abs/2210.05874 · HTML: https://mlsanity.com/paper/2210.05874/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 58 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [A Transformer-based Framework for Multivariate Time Series Representation Learning](https://mlsanity.com/paper/2010.02803/) (2020)
- [Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/1901.07947/) (2019)
- [Edge-Computing-Enabled Smart Cities: A Comprehensive Survey](https://mlsanity.com/paper/1909.08747/) (2019)
- [Deep Learning Based Caching for Self-Driving Car in Multi-access Edge Computing](https://mlsanity.com/paper/1810.01548/) (2018)
- [Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching](https://mlsanity.com/paper/2110.10349/) (2021)
- [Spatial multi-LRU Caching for Wireless Networks with Coverage Overlaps](https://mlsanity.com/paper/1602.07623/) (2016)
- [TEDGE-Caching: Transformer-based Edge Caching Towards 6G Networks](https://mlsanity.com/paper/2112.00633/) (2021)
- [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)
- [Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile Edge Computing Networks](https://mlsanity.com/paper/2207.00755/) (2022)

## Built on by (most cited first, 4 of 4)

- [ViT-CAT: Parallel Vision Transformers with Cross Attention Fusion for Popularity Prediction in MEC Networks](https://mlsanity.com/paper/2210.15125/) (2022)
- [Federated Learning Assisted Edge Caching Scheme Based on Lightweight Architecture DDPM](https://mlsanity.com/paper/2506.04593/) (2025)
- [CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks](https://mlsanity.com/paper/2303.12097/) (2023)
- [CacheMamba: Popularity Prediction for Mobile Edge Caching Networks via Selective State Spaces](https://mlsanity.com/paper/2502.15746/) (2025)

## Abstract (arXiv)

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.