# Developing an Intelligent Cellular Structure Design for a UAV Wireless Communication Topology
## 2 Related Studies
Three predefined scenarios have been put in place to achieve the proposed model. Authors in [24] present a drone that utilizes an omnidirectional antenna that can tune some parameters for better connectivity. Such parameters should be optimized to improve coverage probability and throughput. Authors highlighted the importance of improving the integration of UAVs and cellular technologies. Designing a macro cell network using a swarm of UAVs is presented in [25], where a multiple-antenna is used. This would achieve higher capacity in addition to degree-of-freedom when designing cellular networks. A multi beam UAV communication for better cellular structure design is introduced in [26], which aims to enhance data streams with less interference. The proposed work assumes that the network topology uses a fixed beam pattern at a fixed location. A machine learning (ML) approach is considered in [27] to enhance coverage and power consumption for a LAP standalone topology. The proposed ML model has assumed \begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **UAV Platform Type** & **Network Topology** & **AI Framework** & **Cellular Design** \\ \hline
[21] & HAP & Standalone & - & Adaptive beamforming with fixed cellular design \\ \hline
[22] & LAP & Standalone & NN & Adaptive beamforming with fixed cellular design \\ \hline
[23] & Drone & Standalone & - & Predefined cellular scenarios \\ \hline
[24] & Drone & Standalone & - & Optimized omnidirectional antenna for micro cells \\ \hline
[25] & Swarm of UAVs & Multilayer & - & Beamforming designs using multiple-antenna \\ \hline
[26] & Drone & Standalone & - & Multi beam at a fixed location \\ \hline
[27] & LAP & Standalone & ML & Static circular cellular structure design \\ \hline
[28] & UAV & Integrated UAVs & - & Predefined trajectory of cellular structure design \\ \hline
[29] & UAV & Standalone & - & Beam switching technique used to structure the cellular network \\ \hline
[30] & UAV & Standalone & - & Modified parallel projection algorithm to adjust the distance of cellular \\ \hline
[31] & Drone & Fleet of drones & - & Cooperative cellular design \\ \hline
[32] & Tethered platform & Standalone & RBF & Semi-adaptive cellular structure design \\ \hline
[33] & Drone & Standalone & - & Massive MIMO for small cellular structure \\ \hline
[34] & Drone & Standalone & RL & Enhance cellular system by focusing on jamming channels \\ \hline
[35] & Drone & Integrated UAVs & DRL & Focusing on the central of cellular structure \\ \hline Proposed Model & UAVs & Heterogenous UAVs Topology & SOM and NN & Adaptive and intelligent cellular structure design via MIMO \\ \hline  & UAV: Unmanned Aerial Vehicle; HAP: High Altitude Platform; LAP: Low Altitude Platform; AI: Artificial Intelligence; NN: Neural Network; ML: Machine Learning; RBF: Radial Basis Function; MIMO: Multiple Input Multiple Output; RL: Reinforcement Learning; DRL: Deep Reinforcement Learning; SOM: Self Organizing Map. \\ \end{tabular}
\end{table} 
Table 1: Comparison between related studies against the proposed model. a circular cellular structure design for simplicity. Researchers in [28] present a drone trajectory design under cellular connectivity. The proposed work relies on a combination of a 3D map of the environment and propagation models. It is shown that the targeted footprint zone did not change when increasing the altitude of the UAV since the trajectory is predefined. Authors in [29] utilize a directional antenna to extend coverage for cellular-connected UAVs. A beam switching technique is used as the cellular structure design over a targeted area to improve range and link quality. A modified parallel projection algorithm is used in [30] for fair data collection and trajectory design using a UAV wireless cellular system. Such a homogeneous trajectory could adjust the distance of cellular cells. A cooperative cellular design via a fleet of drones is presented [31] to improve trajectory design and radio resource management. Results were promising; using a MIMO antenna is recommended for better performance.