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Experimental performance evaluation of wireless local area networks using machine learning.

机译：使用机器学习的无线局域网的实验性能评估。

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摘要

The performance of a wireless network is often much lower than what one might expect. Although the data rate is defined in the IEEE 802.11b standard as 11 Mbps, it can be only 2.5--4 Mbps in practice. A major reason for this poor performance is that most devices, particularly wireless access cards, use default settings that, while they might "work" in most environments, are not guaranteed to work well. Environmental conditions, such as access point traffic or interference, greatly affect which wireless access card settings are optimal. Thus, an access card might be able to improve its performance if it could adjust its parameters according to what environmental conditions it detects. For example, if the access card detects a low signal to noise ratio, it could increase its transmitting power to avoid packet loss. On the other hand, if the access card determines that the signal to noise ratio is high, it could decrease its transmitting power to increase battery life.; In this project, we examine the application of machine learning to improve the performance of wireless network. First, we define a case study and select variables to randomly generate a number of scenarios. Next, we collect information for these scenarios using a wireless network simulator, Opnet(TM). Finally, we apply machine learning to learn models for predicting the throughput of a wireless card. Our hypothesis is (1) that machine learning can be used to learn models for predicting throughput given environmental conditions and access card settings and (2) that the learned models can be used to select access card settings that will improve performance in a variety of environmental conditions.
机译：无线网络的性能通常远低于预期。尽管IEEE 802.11b标准中将数据速率定义为11 Mbps，但实际上它只能是2.5--4 Mbps。造成这种性能下降的主要原因是，大多数设备（尤其是无线访问卡）使用默认设置，尽管这些设置在大多数环境中可能“起作用”，但不能保证它们能正常工作。诸如接入点流量或干扰之类的环境条件极大地影响了哪种无线接入卡设置是最佳的。因此，如果门禁卡可以根据其检测到的环境条件调整其参数，则可能能够提高其性能。例如，如果访问卡检测到较低的信噪比，则可以增加其发射功率以避免丢包。另一方面，如果访问卡确定信噪比较高，则可以降低其发射功率以延长电池寿命。在这个项目中，我们研究了机器学习在提高无线网络性能方面的应用。首先，我们定义一个案例研究并选择变量以随机生成许多方案。接下来，我们使用无线网络模拟器Opnet（TM）收集这些方案的信息。最后，我们应用机器学习来学习用于预测无线卡吞吐量的模型。我们的假设是（1）机器学习可用于在给定环境条件和门禁卡设置的情况下学习用于预测吞吐量的模型，以及（2）所学习的模型可用于选择将在各种环境下提高性能的门禁设置条件。

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  • 作者

    Liu, Chun-Yin.;

  • 作者单位

    University of Massachusetts Lowell.;

  • 授予单位 University of Massachusetts Lowell.;
  • 学科 Artificial Intelligence.; Computer Science.
  • 学位 M.S.
  • 年度 2006
  • 页码 26 p.
  • 总页数 26
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 人工智能理论;自动化技术、计算机技术;
  • 关键词

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