User Preferences-Based Proactive Content Caching with Characteristics Differentiation in HetNets

Lin, Na; Wang, Yamei; Zhang, Enchao; Wan, Shaohua; Al-Dubai, Ahmed; Zhao, Liang

Abstract

With the proliferation of mobile applications, the explosion of mobile data traffic imposes a significant burden on backhaul links with limited capacity in heterogeneous cellular networks (HetNets). To alleviate this challenge, content caching based on popularity at Small Base Stations (SBSs) has emerged as a promising solution. However, accurately predicting the file popularity profile for SBSs remains a key challenge due to variations in content characteristics and user preferences. Moreover, factors such as content size and the length of time slots (that is, the time duration of the update cycle for SBSs) critically impact the performance of caching schemes with limited storage capacity. In this paper, a realism-oriented intelligent caching (RETINA) is proposed to address the problem of content caching with unknown file popularity profiles, considering varying content sizes and time slots lengths. Our simulation results demonstrate that RETINA can significantly enhance the cache hit rate by 4%–12% compared to existing content caching schemes.

Citation

Lin, N., Wang, Y., Zhang, E., Wan, S., Al-Dubai, A., & Zhao, L. (online). User Preferences-Based Proactive Content Caching with Characteristics Differentiation in HetNets. IEEE Transactions on Sustainable Computing, https://doi.org/10.1109/TSUSC.2024.3441606

• Journal Article Type: Article
• Acceptance Date: Aug 6, 2024
• Online Publication Date: Aug 12, 2024
• Deposit Date: Aug 7, 2024
• Publicly Available Date: Aug 12, 2024
• Electronic ISSN: 2377-3782
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1109/TSUSC.2024.3441606
• Keywords: Heterogeneous Cellular Networks, Reinforcement Learning, Content Caching, unknown file popularity profiles, content characteristics, user preferences