Reliability estimation for drone communications by using an MLP-based model

Abstract

Unmanned aerial vehicles (UAVs) or drones have been widely employed in both military and civilian tasks due to their reliability and low cost. UAVs ad hoc networks also acknowledged as flying ad-hoc networks (FANETs), are multi-UAV systems arranged in an ad hoc manner. In order to maintain consistent and effective communication, reliability is a prime concern in FANETs. This paper presents an analytical framework to estimate the reliability of drones’ communication in FANETs. The proposed system takes into account the reliability of communications in FANETs, including channel fading. The suggested analytical investigation is used to generate a dataset, then an artificial neural network (ANN) based multi-layer perceptron (MLP) model is used to estimate the reliability of drones’ communication. Moreover, to define the best MLP model with hidden layers, the correlation coefficient (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are obtained. Moreover, numerical results are presented which verify analytical studies.