# Data Traffic Modeling in RPAS/UAV Networks with Different Architectures
## 2 Related Works  
The articles below consider technologies and applications of UAVs in cellular networks, prospective RAN with UAVs, reliable communication for aerial vehicles over cellular networks, and other important issues. The article [11] analyzes UAV cellular communication and values the performance of the downlink command and control channel when supported by either: A traditional cellular network serving one user per transmission time interval, or a multi-user massive Multiple Input Multiple Output (MIMO) network exploiting spatial multiplexing. A RAN with a UAV acting as an aerial platform for communication with ground users in the modes of data collection, data transmission, or relaying is considered in [12]. Iterative design algorithms of the UAV flight trajectory are proposed there. A single cell cellular network, several cellular users and several UAVs that upload their collected data to a base station are investigated in [13]. The UAV with a high Signal-to-Noise Ratio (SNR) for the User-Infrastructure (U2I) channel downloads the collected data directly to the base station, and the UAV with a low SNR for the U2I channel transmits the data to the nearest UAV through the U2U backbone to ensure required Quality of Service (QoS). The distribution of subchannels and the problem of optimizing the speed of the UAV to maximize the total bandwidth of the uplink are formulated. The network architecture of Distributed and Multi-layer UAVs (DAMUs) has been presented for 5G/B wireless communication [14]. DAMU architecture due to hierarchical flexibility can be reconfigured for a specific deployment and application scenario, taking into account key technical factors. A numerical analysis of the attenuation introduced by typical meteorological conditions is carried out in this paper. Drone Assisted Vehicular Networks (DAVNs) for the efficient integration of communication and networking technologies for drones and connected vehicles are presented in [15]. A comprehensive DAVN architecture is proposed and it is demonstrated that the performance of automotive networks can be significantly improved with the proposed architecture. An overview of the Long-Term Evolution (LTE) connectivity for UAVs at low altitude is presented in [16]. Typical requirements and characteristics of airborne communication are determined, the different propagation conditions for UAVs and mobiles on the ground are emphasized, and the results of LTE connection modeling for UAVs are presented. To ensure collaboration between UAVs and terrestrial users, two solutions have been proposed to mitigate interference in existing LTE networks: Interference suppression and antenna beam selection [17]. It is shown that each of solutions can provide up to 30% increase in throughput and more than 99% of radio communication. A path planning scheme taking into account interference for a network of UAVs connected to cellular communication is proposed in [18]. A deep reinforcement learning algorithm, based on Echo State Network (ESN) cells, is proposed to achieve a trade-off between maximizing energy efficiency, reducing wireless latency and interference in the terrestrial network. Each UAV uses the ESN to determine its optimal path, transmit power, and cell association vector at different locations along its path. Various communication architectures of the Future Flying Ad-Hoc Network (FANET), including existing wireless technologies, are discussed in [19]. A hybrid wireless communication scheme is proposed to take advantage of the 802.11 high data rate and 802.15.1 low power features. The proposed scheme significantly reduces communication costs and improves network performance in terms of bandwidth and latency. The results of modeling using the Optimized Network Engineering Tool (OPNET) are presented, which additionally confirm the effectiveness of our proposed scheme. The Internet of Things (IoT) requires high-performance two-way communication between UAVs and their ground pilots/users using a cellular network [20].