# Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model
## 3 Proposed Drone-Enabled AI Framework
### The Proposed AI Framework  
To train the neural network to obtain an accurate channel propagation model, the five selected models and predicted path loss for an optimized wireless channel, we need to ensure the best coverage, considering maximum RSS and throughput, minimum PL, and fixed transmission power at various transmitter/receiver heights. Therefore, an optimization technique should be considered to design an intelligent switch-unit that could estimate the best model in the performance among several models which depends on the maximizing of some parameters and minimizing others across various drone altitudes. Neural Networks (NN) are considered as one of the most common optimization approaches because of their simplicity, rapid implementation, and high performance. The NN-RBFN technique has been widely applied to many optimization issues, where it consists of more than one neuron that was trained to predict an optimum output based on algorithms and some input elements. The three basic elements of a neuron are the synaptic weights (or weights), the summing junction, and the activation function. One of the NN types is called Radial base function network (RBFN) approach [32; 33; 34; 35]. The RBFN has three different layers: an input layer, a hidden layer, and an output layer. The input layer gets its input vector from the PL, RSS, SINR, Th, and R parameters of the five selected propagation models. Figure 3 shows a structure of the proposed NN-RBFN technique. The hidden layer includes several Gaussian functions that create arbitrary basis functions, so-called radial basis functions (RBF)), which are utilized to enlarge the input pattern onto the hidden layer nonlinearly. The learning approach that was chosen in this work is a semi-supervised learning approach, where the radial basis functions are selected randomly, while the determination of weights are selected in advance. The third layer produces the network output, which solves pattern classification problems. The learning rate of RBFN is accelerated, and the usual local minimum issue is avoided. The NN-RBFN technique can be expressed mathematically as  
\[Z_{j}=exp\ \left(-\left(\left(x_{i}-\mu_{ij}(n)\right)\right)^{2}/2_{j}^{2}\ \delta\ (n)\right. \tag{30}\]  
\[\mu_{ij}(n+1)\ =\mu_{ij}\ (n)+\tau_{\mu}\ \frac{Z_{j}}{\delta_{j}}\big{(}x_{i}- \mu_{ij}(n)\big{)}\sum e_{k}\ w_{kj} \tag{31}\]  
\[\delta_{j}(n+1)\ =\delta_{j}\ (n)-\frac{2\ \tau_{\delta}\ \!Z_{j}}{\delta_{j}\ \!(n)}\ ln\ z_{j}\sum e_{k}\ w_{kj}\ (n) \tag{32}\]  
where \(Y_{k}\) refers to the output of neuron, \(Z_{j}\) refers to output of hidden layer, \(\mu_{ij}\) refers to center value, \(\delta_{j}\ (0)\) refers to span value, \(W_{k}\ (0)\) refers to weight vector, \(e_{k}\) refers to error, \(d_{k}\) refers to desired pattern, \(W_{kj}\ (n+1)\) refers to update the weight, \(\tau_{\delta}\) refers to learning rate of span, \(\tau_{\mu}\) and \(\tau_{w}\) refer to the learning rate of weight and center, respectively, \(\mu_{ij}\ (n+1)\), and \(\delta_{j}\ (n+1)\) refer to update the center and span momentum, respectively. The proposed technique solves a multi-objective optimization, which is heavily based on the \"Weighted sum approach\". This approach is based on assigning a weight to each normalized objective function, thus converting the problem to a single objective problem with a scalar objective function, then, randomly generating an initial set of solutions and evolving these solutions over time and converting it into a single solution to eventually generate an optimal solution. This requires a minimax technique with as-signed weights for different parameters at various drone altitudes and elevation angles in an urban environment. In our scenario, this represents obtaining an optimum propagation model for journalism. The metrics have a wide range of parameters (e.g., PL and RSS) at various drone altitudes, and elevation angles are inserted as inputs to the NN-RBFN optimization technique to get optimized output.