高级搜索

带有特征感知的D2D内容缓存策略

杨静 李金科

引用本文: 杨静, 李金科. 带有特征感知的D2D内容缓存策略[J]. 电子与信息学报, doi: 10.11999/JEIT190691 shu
Citation:  Jing YANG, Jinke LI. Feature -Aware D2D Content Caching Strategy[J]. Journal of Electronics and Information Technology, doi: 10.11999/JEIT190691 shu

带有特征感知的D2D内容缓存策略

    作者简介: 杨静: 女,1972年生,高级工程师,研究方向为泛在无线通信网络、物联网技术等;
    李金科: 男,1995年生,硕士生,研究方向为D2D通信
    通讯作者: 李金科,s170131104@stu.cqupt.edu.cn
  • 基金项目: 国家自然科学基金(61871062, 61771082),重庆市高校创新团队建设计划项目(CXTDX201601020)

摘要: 设备到设备通信(D2D)可以有效地卸载基站流量,在D2D网络中不仅需要共享大众化内容还需要个性化内容缓存。该文对缓存内容选择问题进行了深入研究,提出一种结合特征感知的内容社交价值预测(CSVP)方法。价值预测不仅可以降低时延也可以减少缓存替换次数降低缓存成本。首先结合用户特征和内容特征计算内容当前价值,然后通过用户社交关系计算未来价值。微基站根据内容的价值为用户提供个性化内容缓存服务,宏基站则在每个微基站的缓存内容中选择价值较大部分的内容。仿真结果表明,该文提出的缓存策略可以有效缓解基站流量,与其他方法相比降低时延约20%-40%。

English

    1. [1]

      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.

    2. [2]

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

    3. [3]

      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

    4. [4]

      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

    5. [5]

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

    6. [6]

      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

    7. [7]

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

    8. [8]

      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

    9. [9]

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

    10. [10]

      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

    11. [11]

      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

    12. [12]

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

    13. [13]

      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

    14. [14]

      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

    15. [15]

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

    16. [16]

      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

    17. [17]

      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

  • 图 1  网络架构

    图 2  用户间社交对价值的影响

    图 3  不同策略下的命中率

    图 4  不同策略下的请求时延

    图 5  不同齐夫参数下的命中率

    图 6  不同学习率下的命中率

    表 1  仿真参数设置

    参数参数值
    内容库数量5000
    内容包大小20 MB
    SBS-UE延时20 ms
    BS-UE延时50 ms
    CDN-UE延时100 ms
    D2D延时10 ms
    下载: 导出CSV
  • 加载中
图(6)表(1)
计量
  • PDF下载量:  2
  • 文章访问数:  51
  • HTML全文浏览量:  39
文章相关
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

/

返回文章