D2D cooperative caching strategy based on graph collaborative filtering model
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 fil...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2023-07-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023131/ |
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| _version_ | 1841540074478501888 |
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| author | Ningjiang CHEN Linming LIAN Pingjie OU Xuemei YUAN |
| author_facet | Ningjiang CHEN Linming LIAN Pingjie OU Xuemei YUAN |
| author_sort | Ningjiang CHEN |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d1495712eb9048d9a7bd50e35977ef8e |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-07-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-d1495712eb9048d9a7bd50e35977ef8e2025-01-14T06:22:18ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-07-014413614859384040D2D cooperative caching strategy based on graph collaborative filtering modelNingjiang CHENLinming LIANPingjie OUXuemei YUANA 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.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023131/D2Dgraph collaborative filteringcooperative cachingdeep reinforcement learning |
| spellingShingle | Ningjiang CHEN Linming LIAN Pingjie OU Xuemei YUAN D2D cooperative caching strategy based on graph collaborative filtering model Tongxin xuebao D2D graph collaborative filtering cooperative caching deep reinforcement learning |
| title | D2D cooperative caching strategy based on graph collaborative filtering model |
| title_full | D2D cooperative caching strategy based on graph collaborative filtering model |
| title_fullStr | D2D cooperative caching strategy based on graph collaborative filtering model |
| title_full_unstemmed | D2D cooperative caching strategy based on graph collaborative filtering model |
| title_short | D2D cooperative caching strategy based on graph collaborative filtering model |
| title_sort | d2d cooperative caching strategy based on graph collaborative filtering model |
| topic | D2D graph collaborative filtering cooperative caching deep reinforcement learning |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023131/ |
| work_keys_str_mv | AT ningjiangchen d2dcooperativecachingstrategybasedongraphcollaborativefilteringmodel AT linminglian d2dcooperativecachingstrategybasedongraphcollaborativefilteringmodel AT pingjieou d2dcooperativecachingstrategybasedongraphcollaborativefilteringmodel AT xuemeiyuan d2dcooperativecachingstrategybasedongraphcollaborativefilteringmodel |