# Flying Ad Hoc Networks: A New Domain for Network Communications
## 1 Introduction
As a consequence of the higher mobility degree of the nodes in FANETs, network topology changes are also more frequent in FANETs than in MANETs and VANETs. Other factors that also influence topology changes in FANETs are the speed of the nodes, changes in distances among nodes, fast changes in the quality of the communication links, and the (likely) reduced life time of the nodes due to energy limitations and/or failures. In traditional MANETs and VANETs, these circumstances are not always present. On the other hand, the model that predicts propagation losses for electromagnetic waves in FANETs is different from the model used in MANETs and VANETs. In MANETs and VANETs, loss models usually assume that there is not line of sight (nLoS) between the transmitter and the receiver and that there are a large number of reflections from environmental obstacles and diffraction of buildings. On the contrary, FANET nodes are flying at a considerable height, away from possible reflections that may originate in the ground, in buildings, or obstacles. Moreover, there is usually line of sight (LoS) between the interconnected FANET nodes. Due to the position of the nodes in FANETs, two types of links with different characteristics can be distinguished (see Figure 1): a link that connects two flying nodes (UAV-to-UAV or U2U) and a link that connects a flying UAV and a terrestrial node (UAV-to-Ground, UAV-to-BS, or U2G). LoS is predominant in U2U links. In U2G links, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Type of UAV**} & **Speed** & **Energy Autonomy** & **Mobility Degree** & **Static Hover** & **Altitude** \\ \hline \multirow{2}{*}{Large-UAV} & FW & High & High & Low & No & High \\  & RW & Low-Med & Low-Med & Med-High & Yes & Med-High \\ \hline \multirow{2}{*}{Small-UAV} & FW & Med-High & Med-High & Low-Med & No & Low-Med \\  & RW & Low & Low & High & Yes & Low \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of UAVs by size. if there is LoS then the direct and reflected components in the ground will predominate. Otherwise, if there is nLoS then there will be a large number of components due to reflections and diffraction. Therefore, the model of propagation losses should take into account for these two types of links, resulting in a different approach compared to propagation models used in MANETs and VANETs. Table 2 summarizes the main differences among MANETs, VANETs, and FANETs. Particularly for the telecommunications field, UAVs could be used to provide WiFi coverage through an aerial network (e.g., in emergency situations) or to substitute wireless base stations when these are not operational, among other examples. There are several works in the related literature that tackle communication challenges in FANET. For instance, some studies address the communication characteristics of UAVs and others evaluate the received signal level in order to obtain an approximate channel model that could predict propagation losses [11; 12; 13; 14; 15; 16]. However, most of these studies do not usually take into account the particular radiation pattern (RP) of the communication device embedded in the UAV, do not include a study of the RP in a controlled environment (e.g., an electromagnetic simulator software, such as 4 NEC2 (Numerical Electromagnetic Code) or CST (Computer Simulation Technology), or an anechoic chamber), do not take into account the orientation of the communication module antennas, or even simplify the RP to an isotropic one. The study of the RP in a controlled environment would allow a more effective deployment of UAVs in a real environment. The reason lies on the fact that with that knowledge, we could optimize the power radiated by the antennas and thus better results in terms of throughput, signal level, and battery lifetime could be achieved. In this work, we present a comprehensive survey on FANETs, focusing on three related topics: mobility models, positioning algorithms, and propagation models.