Work

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

Year: 2024

Type: article

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, arealism-orientedintelligent 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.

Source: IEEE Transactions on Sustainable Computing

DOI: 10.1109/tsusc.2024.3441606

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

Institutions: Shenyang Aerospace University, University of Electro-Communications, University of Electronic Science and Technology of China, Edinburgh Napier University

Language: English

Topic: Caching and Content Delivery

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Natural Science Foundation of Liaoning Province, Liaoning Revitalization Talents Program

Awards: G2668983657, G330008365, G3790399814, G5046278416