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用户请求感知的边端缓存与用户推荐联合优化策略

王汝言 蒋昊 唐桐 吴大鹏 钟艾玲

王汝言, 蒋昊, 唐桐, 吴大鹏, 钟艾玲. 用户请求感知的边端缓存与用户推荐联合优化策略[J]. 电子与信息学报. doi: 10.11999/JEIT230898
引用本文: 王汝言, 蒋昊, 唐桐, 吴大鹏, 钟艾玲. 用户请求感知的边端缓存与用户推荐联合优化策略[J]. 电子与信息学报. doi: 10.11999/JEIT230898
WANG Ruyan, JIANG Hao, TANG Tong, WU Dapeng, ZHONG Ailing. A Joint Optimization Strategy for User Request Perceived Edge Caching and User Recommendation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230898
Citation: WANG Ruyan, JIANG Hao, TANG Tong, WU Dapeng, ZHONG Ailing. A Joint Optimization Strategy for User Request Perceived Edge Caching and User Recommendation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230898

用户请求感知的边端缓存与用户推荐联合优化策略

doi: 10.11999/JEIT230898
基金项目: 国家自然科学基金(62271096, U20A20157),重庆市教委科学技术研究项目(KJQN202000626),重庆市自然科学基金(cstc2020jcyj-zdxmX0024),重庆市高校创新研究群体(CXQT20017),重邮信通青创团队支持计划(SCIE-QN- 2022-04),重庆市博士后研究项目特别资助(2021XM3058),重庆市研究生科研创新项目(CYB22250)
详细信息
    作者简介:

    王汝言:男,教授,研究方向为泛在网络,多媒体信息处理等

    蒋昊:男,硕士生,研究方向为边缘缓存

    唐桐:男,讲师,研究方向为视频编码传输等

    吴大鹏:男,教授,研究方向为泛在无线网络、社会计算等

    钟艾玲:女,博士生,研究方向为无线网络优化等

    通讯作者:

    吴大鹏 wudp@cqupt.edu.cn

  • 中图分类号: TN929.5

A Joint Optimization Strategy for User Request Perceived Edge Caching and User Recommendation

Funds: The National Natural Science Foundation of China (62271096, U20A20157), The Science and Technology Research Prgram of Chongqing Municipal Education Commission (KJQN202000626), The Science and Natural Science Foundation of Chongqing (cstc2020jcyj-zdxmX0024), The University Innovation Research Group of Chongqing (CXQT20017), The Youth Innovation Group Support Program of ICE Discipline of CQUPT (SCIE-QN-2022-04), Chongqing Postdoctoral Science Special Foundation (2021XM3058), Chongqing Postgraduate Research and Innovation Project (CYB22250)
  • 摘要: 针对当前边缘缓存场景中普遍存在的用户偏好未知与高度异质问题,该文提出一种用户请求感知的边端缓存与用户推荐联合优化策略。首先,建立点击率(CTR)预测基本模型,引入对比学习方法生成高质量的特征表示,辅助因子分解机(FM)预测用户偏好;然后,基于用户偏好设计一种动态推荐机制,重塑不同用户的内容请求概率,从而影响缓存决策;最后,以用户平均内容获取时延最小化为目标建立边端缓存与用户推荐联合优化问题,将其解耦为边端缓存子问题和用户推荐子问题,分别基于区域贪婪算法和一对一交换匹配算法求解,并通过迭代更新获得收敛优化结果。仿真结果表明,相较于基准模型,引入对比学习方法的预测模型在曲线下面积(AUC)和准确率(ACC)上分别提升1.65%和1.30%,且联合优化算法能够有效降低用户平均内容获取时延,提升系统缓存性能。
  • 图  1  支持缓存和推荐的无线网络架构图

    图  2  FM_CL模型框架

    图  3  FM_CL模型训练表现

    图  4  不同模型性能对比

    图  5  用户缓存容量 vs 平均时延($ {R_u} $=6个, $ {t_u} $=0.10)

    图  6  用户推荐内容数 vs 平均时延($ {C_{{\text{UE}}}} $=40 Mbit, $ {t_u} $=0.10)

    图  7  UPD容忍度 vs 平均时延($ {C_{{\text{UE}}}} $=40 Mbit, $ {R_u} $=6个)

    图  8  用户缓存容量 vs 系统缓存命中率($ {R_u} $=6个, $ {t_u} $=0.10)

    表  1  不同模型引入对比学习方法后性能对比

    模型AUCACCLoss
    FM0.80640.73750.5264
    FM_CL0.81970.74710.5088
    DeepFM0.81170.74010.5203
    DeepFM_CL0.81260.74110.5196
    xDeepFM0.81230.74020.5200
    xDeepFM_CL0.81340.74140.5190
    DCN_V20.81540.74320.5155
    DCN_V2_CL0.81710.74480.5135
    下载: 导出CSV

    表  2  仿真参数表

    参数名参数值参数名参数值
    MBS覆盖范围300 mD2D带宽20 MHz
    SBS覆盖范围150 m无线回程带宽20 MHz
    UE间通信阈值60 mMBS发送功率46 dBm
    文件库大小100SBS发送功率30 dBm
    内容大小10 MbitUE发送功率23 dBm
    SBS缓存容量400 Mbit噪声功率谱密度174 dBm/Hz
    MBS带宽10 MHz路径损耗因子4
    SBS带宽20 MHz系统参数0.01
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-08-15
  • 修回日期:  2024-01-18
  • 网络出版日期:  2024-01-23

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