A caching strategy based on many-to-many matching game in D2D networks

Yu, Kaihang; Ma, Zhuang; Ni, Runyu; Zhang, Tao

doi:10.26599/tst.2020.9010044

Cited by 10 publications

(1 citation statement)

References 29 publications

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“…In 2021, ref. [30] proposed a new caching strategy based on the existing caching placement strategies, which expresses the preference list of DUEs as the ratio of content popularity to cached probability. This caching strategy reduces latency while increasing the cache hit rate.…”

Section: Related Workmentioning

confidence: 99%

A user cache management and cooperative transmission mechanism based on edge community computing in opportunistic social networks

et al. 2022

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With the advent of the 5G era, people's demand for information and bandwidth is increasing. The signal source will mainly be high-frequency radio waves with high bandwidth. However, the coverage of high-frequency radio waves is very limited, which leads to the emergence of micro base stations. Opportunistic social networks are just suitable for this new transmission method, it is easy for users to simultaneously request content with high popularity in a short time, which will cause a heavy load on the micro base station and a high transmission delay. This paper establishes the community mechanism and proposes a user cache management and cooperative transmission mechanism (CMCT) based on edge community computing by using the predictability of users' requests for popular content. The free user equipment space is used to manage cache distribution through the edge community computing to perform cooperative transmission, thereby reducing the content delivery delay and the load of the micro base station. Simulation experiments show that the CMCT algorithm can reduce end-to-end delay and overhead, and improve data transmission efficiency.

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Section: Related Workmentioning

confidence: 99%

A user cache management and cooperative transmission mechanism based on edge community computing in opportunistic social networks

et al. 2022

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Online data caching in edge-cloud collaborative system with the data center

et al. 2022

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Enhanced graph recommendation with heterogeneous auxiliary information

Yin

Wang

et al. 2022

Complex Intell. Syst.

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The boom in the field of movies and TV programs, which is a kind of information overload, may lead to poor user experience and are detrimental to the healthy development of the industry, hence personalized program recommendation is crucial. Since program names, labels, and synopsis are highly condensed languages, to enable better semantic representations for personalized recommendations and enrich the completeness requirements of data resources, we propose an enhanced graph recommendation with heterogeneous auxiliary information (EGR-HA), focusing on auxiliary information knowledge representations, and graph neural network-based node updates. Firstly, multi-source heterogeneous auxiliary information knowledge is fused to supplement semantics of program and user to obtain initial representations that contain rich semantics, then user and program node embedding representations are aggregated in multiple layers through graph neural networks to model higher-order interaction history information and realize user and program representation update; finally, user viewing prediction is performed based on deep networks to realize personalized program recommendation. The final experiment results in indicators, such as normalized discounted cumulative gain (NDCG), hit rate (HR) and root mean square error (RMSE), verified the effectiveness of this method by comparing with various methods.

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A caching strategy based on many-to-many matching game in D2D networks

Cited by 10 publications

References 29 publications

A user cache management and cooperative transmission mechanism based on edge community computing in opportunistic social networks

A user cache management and cooperative transmission mechanism based on edge community computing in opportunistic social networks

Online data caching in edge-cloud collaborative system with the data center

Enhanced graph recommendation with heterogeneous auxiliary information

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