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Spatial diversity based coverage map building in complex tunnel environments

机译:复杂隧道环境中基于空间分集的覆盖图构建

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Knowing certain characteristics about signal behavior in specific environments is crucial in order to perform effective utilization of a wireless network, and one manner to achieve this is through the employment of coverage maps. We designed a tool for coverage map building, based on various modules, and implemented them over a real all-terrain vehicle and a Pioneer P3AT robot. We test both tools in the Somport railway tunnel, for which a specific propagation model is unavailable due to characteristics such as lateral vaults and galleries, horseshoe shape, and a change in slope, among others. Results were compared to a Straight Tunnel Ray Tracing model. We found similarities and differences regarding fadings. Also, we were able to dismiss significant influence of vaults and lateral galleries over propagation. We found that due to transversal variations, good quality signal can be achieved in almost the entire studied area by applying a large scale diversity for antennas, and coverage maps were constructed on this basis. Results derived from this work can be used from optimal deployment of a wireless network, up to the development of multi-robot navigation strategies under communication constrains in this type of scenarios.
机译:为了执行无线网络的有效利用,了解有关特定环境中信号行为的某些特征至关重要,而实现这一目标的一种方法是通过使用覆盖图。我们基于各种模块设计了一种用于构建覆盖地图的工具,并将其在真正的全地形车和Pioneer P3AT机器人上实现。我们在Somport铁路隧道中测试了这两种工具,由于诸如侧向拱顶和画廊,马蹄形以及坡度变化等特性,因此无法使用特定的传播模型。将结果与“直线隧道射线追踪”模型进行比较。我们发现了在衰落方面的异同。而且,我们能够消除拱顶和侧廊对传播的重大影响。我们发现由于横向变化,通过对天线应用大规模分集,几乎可以在整个研究区域中获得高质量的信号,并在此基础上构建了覆盖图。从这种工作中获得的结果可用于无线网络的最佳部署,以及在这种情况下在通信约束下开发多机器人导航策略的结果。

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