# Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model
## 3 Proposed Drone-Enabled AI Framework
### The Proposed AI Framework
Thus, to meet the optimal solution, the NN-RBFN should observe the minimax of the following parameters along with their assigned weights. The advantage of smart decision-making by the optimization would help to provide proper wireless services to a given environment. Figure 4 describes several desirable features of optimization, where parameters of Th and R should be as high as possible to obtain a high data rate. Additionally, an increase in coverage area to be as high as possible is considered to provide a wider footprint, thus better media coverage and smart reporting. Parameters RSS and SINR should be maintained between upper and lower thresholds. Parameters PL and A should be minimized to obtain better results, where parameter PL is widely considered as one of the major components in the analysis and design of the link budget of a telecommunication system. Figure 5 shows a flowchart of applying a decision tree algorithm that helps the NN-RBFN optimization to reach a local optimal solution based on minimax technique of vital parameters. A decision tree as a classification tool is being used successfully in many areas. The purpose of using the decision tree is to generate a training model that can be used to predict the class of the target variable by learning simple decision rules. The key benefit of a decision tree classifier is its expertise to break down a complicated construct  
Figure 4: Desirable optimization features. into a collection of simpler constructs; hence offering a solution that is straightforward to interpret. Clearly, the proposed algorithm consists of two phases. First, we construct a decision tree and choose the relevant variables. Second, we find the optimum thresholds simultaneously. Therefore, the process starts with defining the input parameters (A, Pt, Gt, Gt, L, and \(\theta\)), whereas the rest are considered as output parameters. Then, we assigned the weights of the NN-RBFN that linked to effective parameters (PL = 10%, RSS = 20%, SINR = 10%, Th = 20%, R = 30%, and A = 10%), which in turn achieve this work's objectives. RSS and SINR should be minimax between 40 dBm to 85 dBm, and 4 dB to 17 dB, respectively. A is ranging between 10-70 m altitude above the ground. At every altitude, \(\theta\) is kept fixed at 10\({}^{\circ}\), while PL, RSS, SINR, Th, and R values will be different for every model. To note, M1, M2, M3, M4, and M5 are acronyms that refer to ITU-R P.529-3 model, Hata-Davidson model, Okumura model, ATG model, and Two-Ray model, respectively. Our entry point to the simulation is the primary dataset of the five propagation models including 470 samples which were divided into three subsets: a training set, testing set, and validating set. Where 328 of them were dedicated to the training phase, 71 were dedicated to the testing phase, and 71 were dedicated to the validating phase. The dataset distribution of the total dataset was 70%, 15%, and 15% for training, testing, and validating, respectively. The dataset is gained for the five selected propagation models, where at every altitude step of 5 m, a new dataset gets generated to include PL, RSS, SINR, Th, and R. To note, the altitude is ranging between 10-70 m altitude above the ground. Table 1 shows a representative sample of the dataset. Based on the optimization algorithm and the assigned weights, after each step of changing the altitude, the algorithm evaluates the objective function, and checks the results; if it is better than the previous results are changed, if not, the algorithm remains and moves on. This process continues until reaching the maximum altitude or until the mean square error (MSE) convergence is less than a small number, whichever one is first. This process of the proposed NN-RBFN optimization technique will give advantages to the drone reaching a wider area with a strong signal and higher data rate, which collectively enhances the experience of using a drone as an aerial reporter.