4.6 Article

Cache Partitioning and Caching Strategies for Device-to-Device Caching Systems

期刊

IEEE ACCESS
卷 9, 期 -, 页码 8192-8211

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3049442

关键词

Device-to-device communication; Base stations; Performance evaluation; Wireless networks; High frequency; Explosions; Broadband communication; D2D caching; wireless caching; mobile caching; content preference; cache partitioning; data offloading

资金

  1. National Research Foundation of Korea, Ministry of Science and ICT (MSIT), Korea Government [2019R1F1A1056963]
  2. Samsung Research Funding and Incubation Center for Future Technology [SRFC-IT1702-13]
  3. National Research Foundation of Korea [2019R1F1A1056963] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

向作者/读者索取更多资源

The increasing amount of traffic in wireless networks can be mitigated using device-to-device caching technology, but in the early stages, limited number of devices and small storage might pose a challenge. Common content popularity makes it difficult to achieve satisfactory performance with small caches, while individual users may have diverse content preferences.
The amount of traffic in wireless networks is increasing exponentially, and this problem can be mitigated using device-to-device (D2D) caching technology, which installs a cache on a mobile end device. Devices can reduce the cell load through self-offloading via content in their own cache and D2D offloading using content in others' caches. However, especially in the early stage of D2D caching systems, a limited number of devices with a small storage might be used, and it is required to develop a caching scheme with excellent performance despite the small cache size. Regarding content popularity, which is common to most users, the preference probability values are not concentrated on some pieces of content, making it difficult to achieve satisfactory performance using a small cache. On the other hand, when considering individual users, content preferences may contain large values for specific content based on individual characteristics. In addition, the performance can be improved by considering short-term content preferences that reflect changes in content preferences over time or newly created content during peak hours. In this article, the hit ratio is divided into six parts considering self- and D2D offloading, common and individual user preferences, and little and large temporal changes in content preferences during peak hours. We also conceptually divide the cache of a helper into six areas in relation to the six parts of the hit ratio, and discuss cache partitioning and proactive caching strategies according to the environment.

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