Wireless Edge Caching and Content Popularity Prediction Using Machine Learning

Krishnendu, S.; Bharath, B. N.; Bhatia, Vimal; Nebhen, Jamel; Dobrovolny, Michal; Ratnarajah, Tharmalingam

doi:10.1109/mce.2022.3160585

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Although the existing caching strategy improves the cache hit rate, there are still many problems in practice (Serhane et al, 2021;Ren et al, 2020;Krishnendu et al, 2022). Serhane et al (2021) propose a cache-optimization algorithm based on chemical reaction.…”

Section: Introductionmentioning

confidence: 99%

“…This strategy is combined with UAV base station scheduling to optimize the average cache hit rate of the system, but does not consider the user distribution problem. Krishnendu et al (2022) propose a strategy for wireless edge caching and content popularity prediction using machine learning. However, the popularity of content cached in the network lags behind the change of user preferences, resulting in a decrease in cache hit rate.…”

Section: Introductionmentioning

confidence: 99%

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Echo State Network-Based Content Prediction for Mobile Edge Caching Networks

Cai

Chen

Zhang

et al. 2023

International Journal of Information Technology and Web Engineering

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With the rapid development of internet communication and the wide application of intelligent terminal, moving the cache to the edge of the network is an effective solution to shorten the delay of users accessing content. However, the existing cache work lacks the comprehensive consideration of users and content, resulting in low cache hit ratio and low accuracy of the whole system. In this paper, the authors propose a collaborative caching model that considers both user request content and content prediction, so as to improve the caching performance of the whole network. Firstly, the model uses the clustering algorithm based on Akike information criterion to cluster users. Then, combined with the clustering results, echo state network is used as the machine learning framework to predict the content. Finally, the cache contents are selected according to the prediction results and cached in the cache unit of the small base station. Simulation results show that compared with the existing cache algorithms, the proposed method has obvious improvement in cache hit ratio, accuracy, and recall rate.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%