基于用户偏好的HetNets中具有特征差异的主动内容缓存

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Na Lin;Yamei Wang;Enchao Zhang;Shaohua Wan;Ahmed Al-Dubai;Liang Zhao
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引用次数: 0

摘要

随着移动应用程序的激增,移动数据流量的爆炸式增长给异构蜂窝网络(HetNets)中容量有限的回程链路带来了巨大的负担。为了缓解这一挑战,基于小型基站(sbs)流行度的内容缓存已经成为一种很有前途的解决方案。然而,由于内容特征和用户偏好的变化,准确预测SBSs的文件流行概况仍然是一个关键挑战。此外,内容大小和时隙长度(即SBSs更新周期的持续时间)等因素严重影响存储容量有限的缓存方案的性能。在本文中,提出了一种面向现实的智能缓存(RETINA)来解决未知文件流行概况的内容缓存问题,考虑到不同的内容大小和时隙长度。我们的模拟结果表明,与现有的内容缓存方案相比,RETINA可以显着提高缓存命中率4%-12%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
User Preferences-Based Proactive Content Caching With Characteristics Differentiation in HetNets
With the proliferation of mobile applications, the explosion of mobile data traffic imposes a significant burden on backhaul links with limited capacity in heterogeneous cellular networks (HetNets). To alleviate this challenge, content caching based on popularity at Small Base Stations (SBSs) has emerged as a promising solution. However, accurately predicting the file popularity profile for SBSs remains a key challenge due to variations in content characteristics and user preferences. Moreover, factors such as content size and the length of time slots (that is, the time duration of the update cycle for SBSs) critically impact the performance of caching schemes with limited storage capacity. In this paper, a realism-oriented intelligent caching (RETINA) is proposed to address the problem of content caching with unknown file popularity profiles, considering varying content sizes and time slots lengths. Our simulation results demonstrate that RETINA can significantly enhance the cache hit rate by 4%–12% compared to existing content caching schemes.
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来源期刊
IEEE Transactions on Sustainable Computing
IEEE Transactions on Sustainable Computing Mathematics-Control and Optimization
CiteScore
7.70
自引率
2.60%
发文量
54
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