Socially Motivated Data Caching in Ultra-Dense Small Cell Networks

Zhu, Konglin; Zhi, Wenting; Xu, Chen; Zhang, Lin

doi:10.1109/mnet.2017.1600320

Cited by 30 publications

(11 citation statements)

References 11 publications

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“…We compare the caching performance of the proposed auction based caching (ABC) with the social-aware caching game (SAGG) [28] and random caching (RC). In RC, each UT randomly selects a content for caching.…”

Section: B Performance Evaluationmentioning

confidence: 99%

Content-Centric Mobile Edge Caching

et al. 2020

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With the explosion of data volume, it becomes challenging to deliver high quality service to mobile users. Therefore, edge caching has received significant attentions since it can bring contents near mobile users, to boost spectral efficiency, and reduce backhaul load of mobile networks. Due to limited storage resources within mobile networks, it is important to improve efficiency of content management in edge caching. In this article, we propose an integrated content-centric mobile network framework for edge caching in 5G networks, which can leverage content-centric networking (CCN), achieve content-oriented information management, and increase content delivery efficiency. We elaborately design the cache-enabled mobile network architecture, CCN based function entities, CCN embedded protocol stack, and content retrieval process, and develop several effective approaches for tackling practical implementation constraints of CCN based edge caching. We demonstrate that our content caching strategies can significantly enhance edge caching performance. To further improve the performance of content-centric mobile edge caching, we identify promising open research directions. INDEX TERMS Cache, content-centric networking, edge caching, mobile networks.

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Section: B Performance Evaluationmentioning

confidence: 99%

Content-Centric Mobile Edge Caching

et al. 2020

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“…(4) The cache server requests content prefetching by searching for a mobile helper that is expected to approach the nomadic/fixed helper after passing through an oversupply region, which may be a cell with broadband radio resources and a high capacity backhaul. (5)(6) The mobile helper downloads the requested contents using cellular communication. (7) The mobile helper moves to deliver the contents to the nomadic/fixed helper through single-hop or multi-hop D2D communication [10][11][12][13][14][15][16].…”

Section: System Modelmentioning

confidence: 99%

“…Nevertheless, its coverage will be seriously reduced due to the inherent radio characteristics, and if a sufficient number of base stations have not been installed due to their high cost, there can be considerable shaded areas of the high frequency carrier. In addition, because the wireless bandwidth can exceed the existing wired backhaul capacity, the wired backhaul can become a bottleneck if the ultra-high-speed wired backhaul is not reinstalled [4][5][6][7][8]. Unless a large number of base stations are installed and high capacity backhauls are supplied to all base stations, the radio resources in many areas may not be fully utilized.…”

Section: Introductionmentioning

confidence: 99%

Content Prefetching of Mobile Caching Devices in Cooperative D2D Communication Systems

Kang

2020

IEEE Access

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The increase of mobile traffic volume in a wireless network using high carrier frequency can lead to coverage and backhaul bottleneck issues, and these issues can be solved by the use of device-todevice (D2D) caching technology that installs a content cache on a device and uses D2D communication to supply contents to other devices. In this paper, a special device that can supply contents using D2D communication to other devices is called a helper, while a device that can receive contents from a helper is called user equipment (UE). Even though a helper may have a large storage capacity compared to a UE, the cache capacity of a helper is still limited, making it difficult to obtain a sufficient hit ratio using a caching scheme based on the long-term content popularity of a large number of UEs while considering the distant future. In this paper, to solve the problem of the conventional D2D caching system, we suggest instead updating the cache contents based on the short-term content preference of individual user for the near future. In the proposed caching scheme, a UE in an area with insufficient data supply predicts the content that will be consumed in the near future. This information is then passed to a mobile helper moving along a predictable travel path, such as a bus and a self-driving car that is passing through or will pass though an area with sufficient data supply. Then, the mobile helper prefetches the contents and moves to update the cache of the nomadic or fixed helper near the UE. The simulation results show that content prefetching considering each UE and the near future can achieve better performance than content caching considering a large number of UEs and the distant future.

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“…Zhao et al [52] considered the spatiotemporal variation in network states to incent asynchronous arriving users as SUs. Relevant theories in game theory have been used, such as in Wu et al's [53] pricing-based method, Chen et al's [54] contract-based method, and Zhu et al's [55] payment-based method. With spatiotemporal variation in the physical and SA information, Semiari et al [56] proposed a self-organizing algorithm to match SUs and spectral resources.…”

Section: Related Workmentioning

confidence: 99%

Socially Aware Device-to-multi-device User Grouping for Popular Content Distribution

2020

KSII TIIS

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Socially Motivated Data Caching in Ultra-Dense Small Cell Networks

Cited by 30 publications

References 11 publications

Content-Centric Mobile Edge Caching

Content-Centric Mobile Edge Caching

Content Prefetching of Mobile Caching Devices in Cooperative D2D Communication Systems

Socially Aware Device-to-multi-device User Grouping for Popular Content Distribution

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