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arXiv 1510.08027cs.NI
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iCellular:在商用智能手机上定义您自己的蜂窝网络接入

iCellular: Define Your Own Cellular Network Access on Commodity Smartphones

Yuanjie Li, Haotian Deng, Chunyi Peng, Guan-Hua Tu, Jiayao Li, Zengwen Yuan, Songwu Lu

摘要:

利用多载波接入提供了一种有前途的方法来提高移动网络中的接入质量。然而,我们的实验表明,由于网络控制设计的基本限制,潜在的好处很难实现。为了克服这些限制,我们提出了iCellular,它允许用户定义和智能地选择自己的蜂窝网络接入从多个运营商。iCellular重复使用现有的设备端机制和标准蜂窝网络程序,但利用终端设备的智能在多载波选择中具有主动性和自适应性。它执行自适应监控以确保响应性选择和最小的服务中断,并通过在线学习和运行时决策故障预防来增强运营商选择。它可以部署在商品手机上,而无需任何基础设施/硬件更改。我们在商品Nexus 6手机上实现了iCellular,并利用Google Project-Fi的努力来测试美国两家顶级运营商(T-Mobile和Sprint)之间的多运营商接入。我们的实验证实,iCellular帮助用户在最先进的选择上提高高达3.74倍的吞吐量(7倍的暂停和1.9倍的延迟减少)。此外,iCellular在大多数情况下都能找到质量最好的运营商,CPU、内存和能耗的开销可以忽略不计。

英文摘要:

Leveraging multi-carrier access offers a promising approach to boosting access quality in mobile networks. However, our experiments show that the potential benefits are hard to fulfill due to fundamental limitations in the network-controlled design. To overcome these limitations, we propose iCellular, which allows users to define and intelligently select their own cellular network access from multiple carriers. iCellular reuses the existing device-side mechanisms and the standard cellular network procedure, but leverages the end device's intelligence to be proactive and adaptive in multi-carrier selection. It performs adaptive monitoring to ensure responsive selection and minimal service disruption, and enhances carrier selection with online learning and runtime decision fault prevention. It is deployable on commodity phones without any infrastructure/hardware change. We implement iCellular on commodity Nexus 6 phones and leverage Google Project-Fi's efforts to test multi-carrier access among two top US carriers: T-Mobile and Sprint. Our experiments confirm that iCellular helps users with up to 3.74x throughput improvement (7x suspension and 1.9x latency reduction) over the state-of-art selection. Moreover, iCellular locates the best-quality carrier in most cases, with negligible overhead on CPU, memory and energy consumption.

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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