# Developing an Intelligent Cellular Structure Design for a UAV Wireless Communication Topology
## 4 Discussion and Proof-of-Concept  
This section validates the proposed AI framework that is used to optimize the cellular structure design of a UAV topology in the case of the Kingdom of Saudi Arabia, which is a massive country of densely and sparsely populated zones with some difficult terrain. Therefore, primary data were collected in KSA's capital city (Riyadh) to apply the proposed framework and evaluate the performance. These data are drawn from the real scenario of current population density, tele-traffic statistics, and geographic nature [51; 52]. At 20 km altitude of a UAV above the ground with a frequency ban of 28 GHz, the predictions of the proposed framework executed using MATLAB simulation are based on the algorithms that are sourced from (1) to (19). Figure 8 shows the GUI plots of the UAV's link budget for KSA at an altitude of 20 km using a MATLAB toolbox. PL shows an increase with distance, which also affects results of RSS and T. The average PL floats just below the maximum allowable PL, which is 146.3 dB, while RSS reaches an acceptable range. An observed point is that the RSS is enhanced when increasing transmitter altitude and/or transmission power. On the other hand, T declines with increasing UAV altitude, high PL, and shadowing effects, yet T still ranges between 150 Mb/s to 105 Mb/s. The predicted results of the link budget parameters are obtained as shown in Figure 8, where these predictions were considered as input data to the intelligent AI framework to optimize and tune the cellular layout parameters. Figure 6: SOM layout using the GUI toolbox in MATLAB. Figure 7: NN layout using the GUI toolbox in MATLAB. The obtained non-optimized cellular structure design is shown in Figure 9, where the coverage of the designated area (Riyadh city in our scenario) is divided into multiple cells with almost equal size and shape within a 100 km radius. This is not efficient in terms of connectivity as well as power consumption. Thus, the expected optimized cellular structure design as shown in Figure 10 is the best scenario. There are small cells at the center and large cells at the city's edges. This meets the demand and the nature of the city, which has a high population density in the center, and the density decreases as it moves towards the suburbs. This can lead to better connection as well as less power consumption thanks to the seven parameters that were optimized using the SOM. The axes in Figures 9 and 10 are approximated to the scale of the coverage footprint radius, which is divided into a certain cellular structure layout according to input parameters. Another worthwhile point is that the cellular structure design is tuned not only based on population and zone size and type (urban, rural), but also based on the Quality of Service (QoS) requirements and application types such as short-term events or temporary events (e.g., sport events, natural disaster events). The onboard intelligent AI framework unit can act swiftly to meet reequipment needs, since UAVs have the advantages of high flexibility and rapid deployment. The performance of the proposed AI framework is shown in Figures 11-14 using the GUI toolbox in MATLAB, where the trained SOM is used to predict optimum output by relaying on a pattern recognition process that maps input to output layers. Clearly, the multi-dimensional lattice of neurons is deemed a cluster. Thus, SOM updates its winner neurons and their weights; thus, nearby neurons tend to have similar weight vectors to be responsive to similar input vectors. Figure 8: UAV link budget parameters using the GUI toolbox in MATLAB. Figure 10: Optimized cellular structure layout for Riyadh. Figure 9: Non-optimized cellular structure layout for Riyadh. Figure 11: SOM sample hit plot using the GUI toolbox in MATLAB. Figure 12: SOM neighbor distance plot using the GUI toolbox in MATLAB. By using the GUI in MATLAB, Figure 11 shows the SOM sample hit plot to analyze the training results, where it presents how many training samples fell into a cluster.