# Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model
## 2 Related Works
For instance, Fifth Generation (5G), Worldwide Interoperability for Microwave Access (WiMAX), and Wireless Fidelity (WiFi) [26; 27; 28]. Due to the aerial vehicle's nature and capabilities, there are a list of advantages which can be gained from deploying such a system as follows:  
* Large coverage footprint area depending on the elevation angle and altitude,
* High capacity since utilizing high frequency bands (e.g., 28 GHz, 47 GHz),
* Incremental deployment and scalability,
* Mobility when accessing difficult terrain regions (e.g., rural, or disastrous zones),
* Fixable reconfiguration to cover short-term, large-scale events (e.g., sport or social events),
* Low propagation latency due to their low altitude in comparison to satellites; hence, lower power consumption,
* Low cost in manufacturing, operation, and maintenance. As mentioned above, propagation models play a vital role in wireless communications since they measure the power density of an electromagnetic wave as it propagates over space. Numerous valuable information can be attained from these propagation models such as pathloss (PL) and coverage, which help in monitoring system performance and network planning. Researchers are striving to investigate propagation path loss model types and characteristics that are used for UAVs, since Radio Frequency (RF) signals propagate through free space to reach a complex ground ubiquitous environment with scattering and shadowing effects. Thus, references such as [29] consider free space-based models such as Air-To-Ground (ATG) and Two-Ray, while references such as [30] consider empirical propagation models such as Hata and Okumura. Generally speaking, these propagation models have various factors that affect their performance such as frequency, terrain, and transmitter and receiver's antennae heights. Although these different propagation models have collectively offered many advantages, they also exhibit shortcomings across different environments. Therefore, it is clear to us that there is no generic propagation model that suits all environments and shows optimal performance. Further, deploying of UAV for aerial journalism is scarcely reported in the literature from a technological perspective (e.g., simulation or experiment), let alone taking the view of coupling such a technology with AI techniques. Therefore, this paper aims to integrate a drone with an AI technique called Neural Network-Radial base function network (NN-RBFN) to train the neural network to obtain an accurate channel propagation model for the sake of enhancing aerial media missions, which in turn could offer numerous media and journalism services in timely and cost-effective manners. In our study, five propagation models have been nominated that are representatives of their types and exhibit better performance across different terrains in relation to other models. Where these models are programmed in a smart decision unit, they are able to achieve a wider coverage footprint, efficient Received Signal Strength (RSS) [31], higher throughput, better line-of-sight (LoS), and non-line-of-sight (NLoS), which collectively help in:  
* Taking footage and pictures of breaking news with better angles and altitude,
* Livestreaming with high capacity since the channel model is optimized,
* Communicating with ground which corresponds with an efficient RSS threshold.