# Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model
## 4 Results and Discussion
Figure 10 illustrates the SINR predicted results of the five models. Predicted results show that all SINR values across the five models have been attained within the average of 5 dB and 19 dB across all altitudes from 100 m and upward. SINR values below 5 dB are considered undesirable, whereas above 19 dB are viewed as wasted transmitter power. Such a parameter is commonly seen in wireless communications as a wireless link quality model. Figure 11: Predicted results of Th across different altitudes and models. Figure 10: Predicted results of SINR across different altitudes and models. indicator. Clearly, SINR predicted results which show positive correlation with distance R. Another observation is that SINR increases as drone altitude and/or distance increase. Thus, it must be maintained. Figure 11 illustrates the Th predicted results of the five models. Since this work's focus is on media and journalism, the throughput is a vital parameter for livestreaming and/or HD multimedia transmission. Predicted results show that all Th values across the five models have been achieved with a high level that ranges from 28 Mb/s to 5 Mb/s. Overall, Two-Ray model (M5) yields the best throughput predicted results, since the model heavily depends on both LoS and NLoS conditions when calculating their link budgets. Therefore, it experiences less effect of shadowing and reflection, as well as delivering better wireless connectivity services. The Th predicted results across all models and environment show that negative correlation with altitudes and/or distance increase. Another observation is that using 5G MIMO antenna with its diversity capacities has helped in enhancing the throughput predictions. To provide large coverage with better connectivity and less path loss, it is vital to consider the elevation angle when calculating coverage radius for drone communications. This aspect distinguishes the UAVs' systems in comparison to terrestrial systems, which yield better LoS connectivity, reduced path loss, and extended coverage. As can be seen in Figures 9-11, that distance is increasing in corresponding to other link budget parameters. Another observed point is that coverage can be affected by transmitter antenna specifications (e.g., altitude, gains, and frequency), as well as geomorphology due to multipath, shading, reflections, and scattering, which is quite acceptable for any space-based systems like UAV. Overall, the simulated propagation models and their generated predicted results of the full range of link budget parameters has been carried out to monitor system performance, network planning, and coverage to achieve perfect reception of the five selected propagation models. Several factors are observed in the overall performance of these propagation models when RF signal is propagating to a maximum distance. These factors are terrain, frequency, and transmitter and receiver antennae heights' system performance and coverage. The rest of this section presents the results of the proposed NN-RBFN framework as can be seen from Figures 12-14. The framework aims to optimize a propagation channel model for UAV using the proposed framework to enhance aerial media missions. The NN-RBFN network get inputs from the previous step of predicted results as can be seen from Figures 9-11 above, where these data have been divided into training, test, and validation datasets. Figure 12 displays the training process that lasts for 42 iterations, during which error rates are not reduced. However, at the 43 iterations, training stops as the error rate increases. The obtained results of the proposed NN-RBFN framework seem rational since there is no considerable overfitting which has occurred by iteration 43, the final Mean Square Error (MSE) is small, and the test set error and the validation set error have comparable features. The performance plot shows the MSE dynamics for all datasets in logarithmic scale.