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arXiv 1503.05448cs.ITcs.NImath.IT
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支持缓存的无线网络的迁移学习方法

A Transfer Learning Approach for Cache-Enabled Wireless Networks

Ejder Baştuğ, Mehdi Bennis, Mérouane Debbah

摘要:

在网络边缘本地缓存内容构成了$5$ G无线网络中最具破坏性的方法之一。边缘缓存的好处取决于解决无数的挑战,例如如何,什么以及何时策略性地缓存受存储约束,流量负载,未知时空流量需求和数据稀疏性影响的内容。受此启发,我们提出了一种新颖的基于迁移学习的缓存过程,在每个小小区基站处执行。这是通过利用丰富的上下文信息(即,用户的内容观看历史、社会关系等)从设备到设备(D2D)交互中提取,称为源域。该先验信息被合并在所谓的目标域中,其中目标是根据存储、估计的内容流行度、业务负载和回程容量在小小区处最优地高速缓存战略内容。结果表明,所提出的方法克服了臭名昭著的数据稀疏性和冷启动问题,在用户的体验质量(QoE)和回程卸载方面产生显着的增益,在由四个小小区基站组成的设置中,增益高达$22\%$。

英文摘要:

Locally caching contents at the network edge constitutes one of the most disruptive approaches in $5$G wireless networks. Reaping the benefits of edge caching hinges on solving a myriad of challenges such as how, what and when to strategically cache contents subject to storage constraints, traffic load, unknown spatio-temporal traffic demands and data sparsity. Motivated by this, we propose a novel transfer learning-based caching procedure carried out at each small cell base station. This is done by exploiting the rich contextual information (i.e., users' content viewing history, social ties, etc.) extracted from device-to-device (D2D) interactions, referred to as source domain. This prior information is incorporated in the so-called target domain where the goal is to optimally cache strategic contents at the small cells as a function of storage, estimated content popularity, traffic load and backhaul capacity. It is shown that the proposed approach overcomes the notorious data sparsity and cold-start problems, yielding significant gains in terms of users' quality-of-experience (QoE) and backhaul offloading, with gains reaching up to $22\%$ in a setting consisting of four small cell base stations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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