Diversity-Driven Proactive Caching for Mobile Networks

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IEEE Transactions on Mobile Computing


Content caching in mobile networks is a highly promising technology for reducing traffic load latency and energy consumption levels. Its fundamental goal is to satisfy the supply-and-demand relationships between content providers and content-requesting users. However, previous research primarily focused on the optimization goals of mobile network operators, and although these caching strategies yield improved latency and energy consumption levels, they fall short of satisfying the diverse content demands of users in real-world scenarios. Therefore, this paper proposes a diversity-driven proactive caching strategy that considers multiple stakeholders' requirements, in which the cache hit rate, cache gain for network operators, and content diversity are jointly optimized. Specifically, a novel improved Latent Dirichlet Allocation (LDA) is designed for radio access network caching, which enables diverse topic associations. For user device caching at the network edge, a Gradient-Guided Contrastive Learning (GGCL) is proposed to optimize the multiple objectives of cache systems with limited labeled data resources. Finally, extensive experiments conducted on the MovieLens dataset demonstrate that the proposed method significantly outperforms other methods in various aspects, including content diversity, the cache hit rate, and some network performance metrics, such as the traffic load and cache gain.



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Content Caching, Contrastive Learning, Costs, Data models, Deep learning, Diversity, Entropy, Mobile computing, Mobile Networks, Optimization, Task analysis

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