Semantics-Enhanced Temporal Graph Networks for Content Caching and Energy Saving

Jianhang Zhu*, Rongpeng Li*, Xianfu Chen, Shiwen Mao, Jianjun Wu, Zhifeng Zhao*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

1 Citation (Scopus)

Abstract

The enormous amount of network equipment and users implies a tremendous growth of Internet traffic for multi-media services. To mitigate the traffic pressure, architectures with in-network storage have been proposed to cache popular content at devices in close proximity to users in order to decrease the number of backhaul hops. Meanwhile, the reduced transmission distance also contributes to energy saving. However, due to limited storage, only a fraction of the content can be cached, while caching the most popular content is cost-effective. Correspondingly, it becomes essential to devise an effective popularity prediction method. In this regard, some existing efforts manifest the effectiveness of dynamic graph neural network (DGNN) models, but it remains challenging to tackle sparse datasets. Herein, we first propose a reformative temporal graph network, named STGN, to address the challenge and improve prediction performance. Specifically, the STGN model leverages extra semantic messages to help establish implicit paths within the sparse interaction graph and enhance the temporal and structural learning of a DGNN model. Furthermore, we devise a user-specific attention mechanism to aggregate various semantics in a fine-grained manner. Finally, extensive simulations verify the superiority of our STGN models and demonstrate the potential in terms of energy-saving.

Original languageEnglish
Title of host publicationICC 2023 - IEEE International Conference on Communications
Subtitle of host publicationSustainable Communications for Renaissance
EditorsMichele Zorzi, Meixia Tao, Walid Saad
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages1724-1729
Number of pages6
ISBN (Electronic)9781538674628
DOIs
Publication statusPublished - 2023
MoE publication typeA4 Article in a conference publication
Event2023 IEEE International Conference on Communications, ICC 2023 - Rome, Italy
Duration: 28 May 20231 Jun 2023

Conference

Conference2023 IEEE International Conference on Communications, ICC 2023
Country/TerritoryItaly
CityRome
Period28/05/231/06/23

Keywords

  • Content caching
  • dynamic graph neural network
  • energy saving
  • popularity prediction
  • semantics

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