基于图协同过滤模型的D2D协作缓存策略

doi:10.11959/j.issn.1000-436x.2023131

Abstract

Abstract:

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.

Key words: D2D, graph collaborative filtering, cooperative caching, deep reinforcement learning

CLC Number:

TN92

Ningjiang CHEN, Linming LIAN, Pingjie OU, Xuemei YUAN. D2D cooperative caching strategy based on graph collaborative filtering model[J]. Journal on Communications, 2023, 44(7): 136-148.

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名称	用户数量/个	电影数量/个	评分记录数量/个
MovieLens-1M	6 040	3 883	100 029
Douban	2 000	2 000	106 828

参数	值	参数	值
信道带宽B/ MHz	25	路径损失系数α	3
信噪比/ dB	30	最大通信距离r_c / m	10
文件大小O / B	1 024	文件数量	250
设备容量β/ KB	10	基站传输时延T_BS/ ms	100

D2D cooperative caching strategy based on graph collaborative filtering model

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