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Measurement-Driven Modeling of Transmission Coordination for 802.11 Online Throughput Prediction

机译:802.11在线吞吐量预测的传输协调的测量驱动建模

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In 802.11 managed wireless networks, the manager can address underserved links by rate-limiting the conflicting nodes. In order to determine to what extent each conflicting node is responsible for the poor performance, the manager needs to understand the coordination among conflicting nodes' transmissions. In this paper, we present a management framework called Management, Inference, and Diagnostics using Activity Share (MIDAS). We introduce the concept of Activity Share, which characterizes the coordination among any set of network nodes in terms of the time they spend transmitting simultaneously. Unfortunately, the Activity Share cannot be locally measured by the nodes. Thus, MIDAS comprises an inference tool that, based on a combined physical, protocol, and statistical approach, infers the Activity Share by using a small set of passively collected, time-aggregate local channel measurements reported by the nodes. MIDAS uses the estimated Activity Share as the input of a simple model that predicts how limiting the transmission rate of any conflicting node would benefit the throughput of the underserved link. The model is based on the current network conditions, thus representing the first throughput model using online measurements. We implemented our tool on real hardware and deployed it on an indoor testbed. Our extensive validation combines testbed experiments and simulations. The results show that MIDAS infers the Activity Share with a mean relative error as low as 4% in testbed experiments.
机译:在802.11管理的无线网络中,管理器可以通过限制冲突节点的速率来处理服务不足的链路。为了确定每个冲突节点在多大程度上造成性能不佳,管理者需要了解冲突节点的传输之间的协调。在本文中,我们提出了一个使用活动共享(MIDAS)的管理框架,称为管理,推理和诊断。我们引入了活动共享的概念,该概念根据网络同时花费在传输上的时间来表征任何一组网络节点之间的协调。不幸的是,活动份额不能由节点本地测量。因此,MIDAS包括一个推理工具,该工具基于组合的物理,协议和统计方法,通过使用节点报告的一小组被动收集的,时间汇总的本地信道测量来推断活动份额。 MIDAS使用估计的活动份额作为简单模型的输入,该模型预测限制任何冲突节点的传输速率将如何使服务不足的链路的吞吐量受益。该模型基于当前的网络状况,因此代表使用在线测量的第一个吞吐量模型。我们在真正的硬件上实现了我们的工具,并将其部署在室内测试平台上。我们广泛的验证结合了试验台实验和模拟。结果表明,在试验台实验中,MIDAS推断出活动份额的平均相对误差低至4%。

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