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Quantifying Interference in WiLD Networks using Topography Data and Realistic Antenna Patterns
(2019)
Avoiding possible interference is a key aspect to maximize the performance in Wi-Fi based Long Distance networks. In this paper we quantify self-induced interference based on data derived from our testbed and match the findings against simulations. By enhancing current simulation models with two key elements we significantly reduce the deviation between testbed and simulation: the usage of detailed antenna patterns compared to the cone model and propagation modeling enhanced by license-free topography data. Based on the gathered data we discuss several possible optimization approaches such as physical separation of local radios, tuning the sensitivity of the transmitter and using centralized compared to distributed channel assignment algorithms. While our testbed is based on 5 GHz Wi-Fi, we briefly discuss the possible impact of our results to other frequency bands.
SDN and WMN evolved to be sophisticated technologies used in a variety of applications. However, a combined approach called wmSDN has not been widely addressed in the research community. Our idea in this field consists of WiFi-based point-to-point links managed by the OpenFlow protocol. We investigate two different issues regarding this idea. First, which WiFi operational mode is suitable in an OpenFlow managed broadcast domain? Second, does the performance decrease compared with other routing or switching principles? Therefore, we set up a real-world testbed and a suitable simulation environment. Unlike previous work, we show that it is possible to use WiFi links without conducting MAC address rewriting at each hop by utilizing the 4-address-mode.
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