Feature-Aware D2D Content Caching Strategy

Jing YANG; Jinke LI

doi:10.11999/JEIT190691

Volume 42 Issue 9

Sep. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(9): 2201-2207

Ke Jin-Song, Dong Guo-Zhong, Wang Fang-Hui. INVESTIGATION OF THE PHASE PERFORMANCE IN DUAL-TR TUBE[J]. Journal of Electronics & Information Technology, 1982, 4(2): 111-119.

Citation:

Jing YANG, Jinke LI. Feature-Aware D2D Content Caching Strategy[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2201-2207. doi: 10.11999/JEIT190691

Ke Jin-Song, Dong Guo-Zhong, Wang Fang-Hui. INVESTIGATION OF THE PHASE PERFORMANCE IN DUAL-TR TUBE[J]. Journal of Electronics & Information Technology, 1982, 4(2): 111-119.

Citation:

Jing YANG, Jinke LI. Feature-Aware D2D Content Caching Strategy[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2201-2207. doi: 10.11999/JEIT190691

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Feature-Aware D2D Content Caching Strategy

doi: 10.11999/JEIT190691

Jing YANG,
Jinke LI^,

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Optical Communication and Networks Key Laboratory of Chongqing, Chongqing 400065, China
3.
Ubiquitous Sensing and Networking Key Laboratory of Chongqing, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61871062, 61771082), The Program for Innovation Team Building at Institutions of Higher Education in Chongqing (CXTDX201601020)

Received Date: 2019-09-05
Rev Recd Date: 2020-05-03

Available Online: 2020-05-17

Publish Date: 2020-09-27

Abstract

Abstract

Device to Device (D2D) communications can effectively offload base station traffic. In D2D networks, the popular content is not only needs to be shared, but also the individual content is needs to be cached. In this paper, the problem of cache content selection is researched. A Content Social Value Prediction(CSVP) method based on feature perception is proposed. Value prediction can not only reduce latency, but also reduce the number of cache replacements and reduce cache costs. Firstly, the current value of content is calculated by combining user features and content features, and then future value is calculated through user social relationships. The small base station provides the user with a personalized content caching service according to the value of the content, and the base station selects a content with a larger value in the individual cache content of each small base station as popular content. Simulation results show that the caching strategy based on the proposed method can alleviate the base station traffic effectively, and reduce the delay by about 20%～40%.
- Edge network,
- D2D communication,
- Content caching,
- Exploration - exploitation,
- Value prediction

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References(17)

References

Cisco. Cisco visual networking index: Global mobile data traffic forecast update, 2017–2022 white paper[EB/OL]. https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-738429.html, 2019.

REBECCHI F, DE AMORIM M D, and CONAN V. Droid: Adapting to individual mobility pays off in mobile data offloading[C]. 2014 IFIP Networking Conference, Trondheim, Norway, 2014: 1–9. doi: 10.1109/IFIPNetworking.2014.6857087.

FANG Chao, YAO Haipeng, WANG Zhuwei, et al. A survey of mobile information-centric networking: research issues and challenges[J]. IEEE Communications Surveys & Tutorials, 2018, 20(3): 2353–2371. doi: 10.1109/COMST.2018.2809670

TATAR A, DE AMORIM M D, FDIDA S, et al. A survey on predicting the popularity of web content[J]. Journal of Internet Services and Applications, 2014, 5(1): 8. doi: 10.1186/s13174-014-0008-y

CHANDRASEKARAN G, WANG N, and TAFAZOLLI R. Caching on the move: Towards D2D-based information centric networking for mobile content distribution[C]. The 40th IEEE Conference on Local Computer Networks, Clearwater Beach, USA, 2015: 312–320, doi: 10.1109/LCN.2015.7366325.

KHAN F H and KHAN Z. Popularity-aware content caching for distributed wireless helper nodes[J]. Arabian Journal for Science and Engineering, 2017, 42(8): 3375–3389. doi: 10.1007/s13369-017-2505-3

TAGHIZADEH M and BISWAS S. Community based cooperative content caching in social wireless networks[C]. The 14th ACM International Symposium on Mobile Ad Hoc Networking and Computing, Bangalore, India, 2013: 257–262. doi: 10.1145/2491288.2491318.

柴蓉, 王令, 陈明龙, 等. 基于时延优化的蜂窝D2D通信联合用户关联及内容部署算法[J]. 电子与信息学报, 2019, 41(11): 2565–2570. doi: 10.11999/JEIT180408

CHAI Rong, WANG Ling, CHEN Minglong, et al. Joint clustering and content deployment algorithm for cellular d2d communication based on delay optimization[J]. Journal of Electronics &Information Technology, 2019, 41(11): 2565–2570. doi: 10.11999/JEIT180408

MEGIDDO N and MODHA D S. ARC: A self-tuning, low overhead replacement cache[C]. The 2nd USENIX Conference on File and Storage Technologies, San Francisco, USA, 2003: 115–130.

MEGIDDO N and MODHA D S. Outperforming LRU with an adaptive replacement cache algorithm[J]. Computer, 2004, 37(4): 58–65. doi: 10.1109/MC.2004.1297303

XIANG Lin, Ng D W K, GE Xiaohu, et al. Cache-aided non-orthogonal multiple access: The two-user case[J]. IEEE Journal of Selected Topics in Signal Processing, 2019, 13(3): 436–451. doi: 10.1109/JSTSP.2019.2907864

LI Lihong, CHU Wei, LANGFORD J, et al. A contextual-bandit approach to personalized news article recommendation[C]. The 19th International Conference on World Wide Web, Raleigh, USA, 2010: 661–670. doi: 10.1145/1772690.1772758.

ZIPF G K. Selected studies of the principle of relative frequency in language[J]. Language, 1933, 9(1): 89–92. doi: 10.4159/harvard.9780674434929

JIANG Wei, FENG Gang, QIN Shuang, et al. Multi-agent reinforcement learning for efficient content caching in mobile D2D networks[J]. IEEE Transactions on Wireless Communications, 2019, 18(3): 1610–1622. doi: 10.1109/TWC.2019.2894403

WALSH T J, SZITA I, DIUK C, et al. Exploring compact reinforcement-learning representations with linear regression[C]. The 25th Conference on Uncertainty in Artificial Intelligence, Montreal, Canada, 2009, 591–598.

MA Hao, ZHOU T C, LYU M R, et al. Improving recommender systems by incorporating social contextual information[J]. ACM Transactions on Information Systems, 2011, 29(2): 1–23. doi: 10.1145/1961209.1961212

YANG Bo, LEI Yu, LIU Jiming, et al. Social collaborative filtering by trust[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8): 1633–1647. doi: 10.1109/TPAMI.2016.2605085

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