# Communication Architecture in Mixed-Reality Simulations of Unmanned Systems
## 2 Related Work  
Wireless communication properties have been intensively studied, either on the level of signal propagation, throughput, and delays or on the level of network topology control and its effect on the asset connectivity [18]. [PERSON] et al. [19] conducted detailed experimental analysis, which showed that wireless connectivity among small unmanned aerial vehicles (UAVs) is challenged by the mobility and heterogeneity of the nodes, lightweight antenna design, body blockage, constrained embedded resources, and limited battery power. [PERSON] et al. [20] provided a model for the quality of the UAV-to-UAV link regarding packet delivery ratio as a function of distance, packet size, and orientation, which was further supported by an extensive measurement campaign. [PERSON] et al. [21] modeled the contributors to path loss and formed a combined propagation model that more accurately reflects the reality of tested scenarios. Various types of MAC protocols, whether they are contention-based, contention-free [22], or hybrid MAC protocols [23] have been suggested for vehicle ad-hoc networks (VANETs) and tested in simulations. Network simulators such as NS-2 [24] and OMNET++ [25] have been enhanced by supporting both wireless interactions and mobility for simulated nodes. Although these simple mobility protocols work well for simple mobile ad-hoc networks (MANETs), they fail to handle the mobility requirements of both flying ad-hoc networks (FANETs) [26] and VANETs. Use of a standard synthetic mobility model can result in incorrect conclusions, as the movement pattern can impact the networking performance of the system. FANETs require complex mobility models that involve autopilot behavior, effects of the wind, formation flights, and different mission scenarios [27]. Complex mobility models have been proposed based on an analysis of real movement traces [28]. For VANETs, there are traffic simulators such as SUMO [29] or VanetMobiSim [30] that are capable of simulating complex traffic scenarios and sophisticated driver behaviors. Specifically for the UAS reconnaissance, a pheromone-guided mobility model has been suggested in [31]. For different FANET application scenarios, various mobility models have been proposed in [32]. Despite these results, the existing approaches still fail to simulate complex operator interactions, effects of the environment, or multi-vehicle team coordination. Furthermore, the existing simulators can produce unrealistic scenarios and synthetic output [28]. Recent approaches to designing realistic simulation tools in the area of vehicular networks are presented in a survey by [PERSON] et al. [33]. Real testbeds can solve some of the simulation's drawbacks and can be used to test applications in native environments [34; 35]. However, the costs of experiments with real testbeds are high, and they suffer from multiple issues such as scalability, limitations on specific scenarios, long preparation and running times, a lack of large-scale evaluation, and the difficulty of repetition, profiling, and modification of an experiment. Therefore, a solution is needed to overcome these issues. The problems of real testbeds can be dealt with using an emulation concept where parts of the hardware components are emulated. The emulation data (e.g., the output of a simulator, real traces, and an event-based generator's output ) can be then processed by the network simulator. Even though representative approaches such as NS-2 and NS-3 [36] provide emulation extensions that enable the use of simulated nodes as if they were real network nodes, this emulation method can lead to unrealistic results due to the simulators' overhead caused by the required processing of all needed information [37]. This challenge is addressed by [PERSON] et al. [38], who introduced a flexible VANET testbed architecture for VANET applications that tries to minimize the emulator overhead.