AoI-Based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching

Zhu, Jianhang; Li, Rongpeng; Ding, Guoru; Wang, Chan; Wu, Jianjun; Zhao, Zhifeng; Zhang, Honggang

doi:10.1109/tccn.2022.3227920

Cited by 12 publications

(2 citation statements)

References 29 publications

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“…Local edge servers, with their limited content storage and processing capacity, need precise coordination between communication resources and processing/storage resources within specific local areas. The management of how content is loaded onto and removed from a local server becomes important, involving techniques such as ML [158] or more frequent visualization strategies (Zipf's law). An additional consideration is the coordination of radio resources within a given area with the latency requirements of certain applications.…”

Section: Convergence Of Sensing Positioning Computing and Communicationsmentioning

confidence: 99%

From 5G-Advanced to 6G in 2030: New Services, 3GPP Advances, and Enabling Technologies

Giuliano

2024

IEEE Access

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The telecommunications systems are in continuous evolution. After voice, video, mobile internet, and Internet of Things, what services will be supported in the near future? In the paper, three envisioned services are highlighted, which will be provided in the coming years by new telecommunication systems: immersive communications, everything connected, and high-positioning. The author provides a comprehensive description of their characteristics and investigates the developments that will be implemented in 3GPP Releases 17, Release 18, and Release 19, including technologies that could be integrated for supporting the three new services. In order to evaluate the performance of the new technologies and services, it is important to define appropriate Key Performance Indicators (KPIs). The paper reports and proposes new KPIs for network evaluation to support specific new services such as virtual/mixed reality, smart sensors, and gesture recognition, then facilitating the effective design of the next-generation network and its performance assessment optimally. Requirements of the major application fields that will see widespread adoption in the next 3-8 years due to these developments are also investigated. Finally, the paper further outlines the most promising enabling technologies, supporting the three bearer services.

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Section: Convergence Of Sensing Positioning Computing and Communicationsmentioning

confidence: 99%

From 5G-Advanced to 6G in 2030: New Services, 3GPP Advances, and Enabling Technologies

Giuliano

2024

IEEE Access

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“…Tang et al [19] modeled user request behavior and user preferences using MDP and Zipf distribution, and proposed a new reinforcement learning-based algorithm to reveal file prevalence and user preferences. Zhu et al [20] developed an AoI-based time attention graph neural network to maximize the accuracy of user interest prediction. Liu et al [21] designed a context-aware prevalence learning algorithm to adapt to the changing trend of content prevalence.…”

Section: Introductionmentioning

confidence: 99%

Epidemic dynamics edge caching strategy for 6G networks

Wang,

Zhang,

Zhang

2024

Front. Phys.

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By caching popular content on edge servers closer to users to respond to users’ content requests in 6G networks, the transmission load of backhaul links can be reduced. However, the time-varying characteristics of content prevalence leads to the issue that the cache content may not match the user’s needs, resulting in a decrease in cache success ratio. To solve these issues, we proposed a cache distribution strategy based on epidemic dynamics (CDSED) for 6G edge network. First, a 6G edge caching content model (6G ECCM) is constructed to establish the process of cache content propagation among users as an infectious disease propagation process, analyze the distribution of users’ interest in cache content and obtain the cache content state probability prediction equation, and use the cache content state probability prediction equation to predict the cache content prevalence. Second, based on the predicted prevalence results, a prevalence predictive genetic-annealing cache content algorithm (PGAC) is proposed with the optimization objective of maximizing the cache success ratio. The algorithm designs the selection function of the traditional genetic algorithm as a simulated annealing selection function based on the cache content success ratio, which avoids the defect of the genetic algorithm that converges to the locally optimum cache strategy too early and enhances the cache success ratio. Finally, the optimum cache content decision is solved by iterative alternation. Simulation results demonstrate that CDSED strategy can enhance cache success ratio than the LRU strategy, the LFU strategy, and the MPC strategy.

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Football trajectory prediction and real-time feedback mechanism based on Temporal Convolutional Network

Zhang,

Xi,

Wang

et al. 2025

Alexandria Engineering Journal

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AoI-Based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching

Cited by 12 publications

References 29 publications

From 5G-Advanced to 6G in 2030: New Services, 3GPP Advances, and Enabling Technologies

From 5G-Advanced to 6G in 2030: New Services, 3GPP Advances, and Enabling Technologies

Epidemic dynamics edge caching strategy for 6G networks

Football trajectory prediction and real-time feedback mechanism based on Temporal Convolutional Network

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