Social‐Aware Caching Strategy Based on Joint Action Deep Reinforcement Learning Article Swipe
Yang Jing
,
Zhuowei Song
,
Peng He
,
Yaping Cui
,
Dapeng Wu
,
Ruyan Wang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1155/2021/4293625
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1155/2021/4293625
Caching in device‐to‐device (D2D) networks is emerging a promising trend, which enables to reduce backhaul traffic. Moreover, social interaction among users influences the performance of overall system network. Therefore, it is crucial to consider social attributes in the D2D networks to develop a caching strategy to resolve the problem of unbalanced content distributed. In this paper, we consider two types of users according to their activeness, i.e., active users and inactive users. Inactive users assist active users cache contents during off‐peak periods and provide the contents to the active users during peak periods to relieve the pressure of base station (BS). In addition, caching system model is divided into physical domain model and social domain model. In physical domain, the quality of communication links is judged by the delay between D2D users. In social domain, based on a real‐world dataset, CiaoDVD, we calculate user similarity in three dimensions and obtain user trust by a trust topology to measure user relationships. Finally, in order to maximize the cache hit ratio, a joint action deep Q ‐networks (JADQN) framework is proposed to pair the active users with inactive users and distribute the contents to inactive users. Simulation results indicate that the proposed strategy improves the cache hit ratio by 42.9% and reduces the download delay by 48.8% compared with least frequency used (LFU) algorithm, which validates the effectiveness of our method.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2021/4293625
- https://downloads.hindawi.com/journals/wcmc/2021/4293625.pdf
- OA Status
- hybrid
- Cited By
- 1
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3201804568
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Raw OpenAlex JSON
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https://openalex.org/W3201804568Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2021/4293625Digital Object Identifier
- Title
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Social‐Aware Caching Strategy Based on Joint Action Deep Reinforcement LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-01-01Full publication date if available
- Authors
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Yang Jing, Zhuowei Song, Peng He, Yaping Cui, Dapeng Wu, Ruyan WangList of authors in order
- Landing page
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https://doi.org/10.1155/2021/4293625Publisher landing page
- PDF URL
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https://downloads.hindawi.com/journals/wcmc/2021/4293625.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/wcmc/2021/4293625.pdfDirect OA link when available
- Concepts
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Computer science, Reinforcement learning, Joint (building), Action (physics), Artificial intelligence, Human–computer interaction, Physics, Architectural engineering, Quantum mechanics, EngineeringTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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30Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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