基于用户偏好预测的无人机部署和缓存策略

任佳智; 田辉; 范绍帅; 林远卓; 聂高峰; 李继龙

doi:10.11959/j.issn.1000-436x.2020104

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基于用户偏好预测的无人机部署和缓存策略

学术论文 | 更新时间：2024-06-05

- 基于用户偏好预测的无人机部署和缓存策略
- UAV deployment and caching scheme based on user preference prediction
- 通信学报 2020年41卷第6期页码：1-13
- 作者机构：
  
  1. 北京邮电大学网络与交换技术国家重点实验室，北京 100876
  2. 国家广播电视总局广播电视科学研究院，北京 100866
- 作者简介：
  
  [ "任佳智（1987- ），男，吉林省吉林市人，北京邮电大学博士生，主要研究方向为边缘缓存、网络切片、复杂网络" ]
  [ "田辉（1963- ），女，河南郑州人，博士，北京邮电大学教授、博士生导师，主要研究方向为自组织网络、无线资源管理" ]
  [ "范绍帅（1987- ），男，山东烟台人，博士，北京邮电大学讲师，主要研究方向为B5G组网及关键技术" ]
  [ "林远卓（1998- ），男，黑龙江哈尔滨人，北京邮电大学硕士生，主要研究方向为边缘缓存与区块链技术" ]
  [ "聂高峰（1988- ），男，河南周口人，博士，北京邮电大学讲师，主要研究方向为5G、6G系统关键技术及移动自组织网络" ]
  [ "李继龙（1976- ），男，河北邯郸人，博士，国家广播电视总局教授级高工，主要研究方向为5G广播、广播电视融合网、无线数字广播、信道编码和调制技术等" ]
- 基金信息：
  
  国家自然科学基金资助项目(61801044)
- DOI：10.11959/j.issn.1000-436x.2020104
  中图分类号： TN929.53
- 网络首发：2020-06，
  
  纸质出版：2020-06-25
- 稿件说明：
移动端阅览
任佳智, 田辉, 范绍帅, 等. 基于用户偏好预测的无人机部署和缓存策略[J]. 通信学报, 2020,41(6):1-13.

Jiazhi REN, Hui TIAN, Shaoshuai FAN, et al. UAV deployment and caching scheme based on user preference prediction[J]. Journal on Communications, 2020, 41(6): 1-13.
任佳智, 田辉, 范绍帅, 等. 基于用户偏好预测的无人机部署和缓存策略[J]. 通信学报, 2020,41(6):1-13. DOI： 10.11959/j.issn.1000-436x.2020104.

Jiazhi REN, Hui TIAN, Shaoshuai FAN, et al. UAV deployment and caching scheme based on user preference prediction[J]. Journal on Communications, 2020, 41(6): 1-13. DOI： 10.11959/j.issn.1000-436x.2020104.

摘要

针对蜂窝网络中的缓存问题，考虑用户内容请求的空间异构性及时间波动性，提出了一种基于单个用户内容偏好预测的蜂窝网中无人机位置部署及缓存内容部署方案。首先基于用户的历史上下文信息，利用文件相似性及用户相似性来预测每个用户的内容偏好特性，并使用一种基于线性回归的方法来预测用户未来发起内容请求时的位置和时间；然后根据预测的地理位置、请求时间和内容偏好，分别利用基于自组织映射神经网络（SOM）的聚类算法和基于凝聚嵌套（AGNES）的分簇算法设计无人机的部署位置，并根据相应的无人机位置设计内容部署方案。仿真结果表明，所提算法在缓存命中率和时延性能上均优于对比算法。对真实数据集的分析结果表明，不同的用户特征对内容偏好影响权重不等，因此需要对不同的用户特征赋予合理的权值。

Abstract

In order to design an efficient edge caching policy considering spatial heterogeneity and temporal fluctuations of users’ content requests

a proactive caching scheme was proposed with UAV’s deployment location design based on user preference prediction.Firstly

each user’s preference characteristics were predicted based on file similarity and user similarity

and the request time and user location were also predicted when a content request occurs.Thereafter

on the basis of the predicted geographical location

request time and user preference

each UAV’s deployment location and the corresponding content placement were determined by virtue of clustering method based on SOM and AGNES.Simulation results show that the proposed scheme outperforms other three comparison schemes in terms of hit ratio and transmission delay.Furthermore

the results also reveal that content preference is correlated with different user features by different weights.Accordingly

different impact weights should be matched with different user features.

关键词

Keywords

references

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