D2D cooperative caching strategy based on graph collaborative filtering model

被引：0
|
作者：
Chen N. [1 ,2 ,3 ]
Lian L. [1 ]
Ou P. [1 ]
Yuan X. [1 ]
机构：
[1] School of Computer and Electronic Information, Guangxi University, Nanning
[2] Key Laboratory of Parallel, Distributed and Intelligent Computing, Guangxi University, Education Department of Guangxi Zhuang Autonomous Region, Nanning
[3] Guangxi Intelligent Digital Services Research Center of Engineering Technology, Nanning
来源：
Tongxin Xuebao/Journal on Communications | 2023年 / 44卷 / 07期
基金：
中国国家自然科学基金;
关键词：
cooperative caching; D2D; deep reinforcement learning; graph collaborative filtering;
D O I：
10.11959/j.issn.1000-436x.2023131
中图分类号：
学科分类号：
摘要：
A D2D cooperative caching strategy based on graph collaborative filtering model was proposed for the problem of difficulty in obtaining sufficient data to predict user preferences in device-to-device (D2D) caching due to the limited signal coverage of base stations. Firstly, a graph collaborative filtering model was constructed, which captured the higher-order connectivity information in the user-content interaction graph through a multilayer graph convolutional neural network, and a multilayer perceptron was used to learn the nonlinear relationship between users and content to predict user preferences. Secondly, in order to minimize the average access delay, considering user preference and cache delay benefit, the cache content placement problem was modeled as a Markov decision process model, and a cooperative cache algorithm based on deep reinforcement learning was designed to solve it. Simulation experiments show that the proposed caching strategy achieves optimal performance compared with existing caching strategies for different content types, user densities, and D2D communication distance parameters. © 2023 Editorial Board of Journal on Communications. All rights reserved.
引用
收藏
页码：136 / 148
页数：12
相关论文
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