Caching Efficiency Maximization for Device-to-Device Communication Networks: A Recommend to Cache Approach

被引:54
作者
Fu, Yaru [1 ]
Salaun, Lou [2 ]
Yang, Xiaolong [3 ,4 ]
Wen, Wanli [5 ]
Quek, Tony Q. S. [6 ]
机构
[1] Open Univ Hong Kong, Sch Sci & Technol, Hong Kong, Peoples R China
[2] Nokia Bell Labs, F-91620 Paris, Nozay, France
[3] Beijing Informat Sci & Technol Univ, Key Lab Modern Measurement & Control Technol, Minist Educ, Beijing 100101, Peoples R China
[4] Beijing Informat Sci & Technol Univ, Sch Informat & Commun Engn, Beijing 100101, Peoples R China
[5] Chongqing Univ, Coll Commun Engn, Chongqing 400044, Peoples R China
[6] Singapore Univ Technol & Design, Dept Informat Syst Technol & Design, Singapore 487372, Singapore
基金
新加坡国家研究基金会; 中国国家自然科学基金;
关键词
Device-to-device communication; Wireless communication; Optimization; Servers; Decision making; Computer architecture; Simulation; Content caching; device-to-device (D2D) communications; caching hit ratio; recommendation mechanism; NP-hardness;
D O I
10.1109/TWC.2021.3075278
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Edge side caching assisted device-to-device (D2D) communication has been acknowledged as a promising technique to alleviate the heavy burden of backhaul transmission link and to reduce the network latency. However, the effectiveness of caching strategies at the network edge is highly dependent on the distribution of individual user's content preference. To fully attain the benefits of edge caching, some proactive mechanisms shall be considered. Among which, recommendation performs noticeably well due to its capability of reshaping the content request probabilities of different users, which in turn affects the cache decision significantly. In this work, we quantitatively investigate how recommendation can be applied to enhance the caching efficiency of D2D enabled wireless content caching networks. And for that, the cache hit ratio maximization problem for a generic network model is formulated taking into account the requirements of each user's personalized recommendation quality, recommendation quantity and cache capacity. Then, we show that the optimal recommendation and caching policies which jointly maximize the cache efficiency is NP-hard to compute. Further, a time-efficient sub-optimal algorithm is designed, which works in an iterative manner and has provable convergence guarantee as well as polynomial time complexity. Monte-Carlo simulation results demonstrate the convergence performance of our proposed joint decision algorithm and its cache efficiency improvements compared to extensive benchmarks.
引用
收藏
页码:6580 / 6594
页数:15
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