With the rise of the complexity of the IEEE 802.11 standard, rate adaptation algorithms have to deal with a large set of values for all the different parameters which impact the network throughput. Simple trial-and-error algorithms can no longer explore solution space in reasonable time and smart solutions are required. Most of the WiFi controllers rely on proprietary code and the used rate adaptation algorithms in these controllers are unknown. Very few WiFi controllers provide their rate adaptation algorithms when they do not rely on the Minstrel-HT algorithm, which is implemented in the Linux kernel. Intel WiFi controllers come with their own rate adaptation algorithms that are implemented in the Intel IwlWifi Linux Driver which is open-source. In this paper, we have reverse-engineered the Intel rate adaptation mechanism from the source code of the IwlWifi Linux driver, and we give, in a comprehensive form, the underlying rate adaptation algorithm named Iwl-Mvm-Rs. We describe the different mechanisms used to seek the best throughput adapted to the network conditions. We have also implemented the Iwl-Mvm-Rs algorithm in the ns-3 simulator. Thanks to this implementation, we can evaluate the performance of Iwl-Mvm-Rs in different scenarios (static and with mobility, with and without fast fading). We also compare the performances of Iwl-Mvm-Rs with the ones of Minstrel-HT and IdealWifi, also implemented in the ns-3 simulator.

In this paper, we investigate the problem of optimizing the network performance of a fleet of unmanned aerial vehicles (UAVs) in static positions. More precisely, we allow each UAV to change its orientation in order to improve the quality of communication with its neighbours. This form of controlled mobility takes advantage of the effective radiation pattern of each UAV. We build a decentralized scheme based on the hill climbing optimization approach without a priori knowledge of the antennas radiation patterns. Then, we propose a simulation framework, based on ns-3, allowing to evaluate the gain in network performance. We provide results in several deployment scenarios involving different rate adaptation algorithms and network sizes. 1

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In this article, we describe how a fleet of unmanned aerial vehicles (UAVs) can optimize communication performances by having its members independently change their orientations. This distributed solution, based on a hill-climbing approach, relies on information available locally at each node, namely the reception power of the received frames. The solution is evaluated using the ns-3 network simulator, whose source code is modified to be able to deal with nonisotropic antennas in the context of Wi-Fi networks, as well as simulate angular movements. As isotropic antennas are only theoretical objects, this step is mandatory in order to increase the realism of network simulations. The results, obtained using realistic antenna models, highlight that controlled mobility, in particular controlled orientation needs to be considered in order for UAV networks to provide better performances.