Predicting Joint Effects on CubeSats to Enhance Internet of Things in GCC Region Using Artificial Neural Network

Abstract

Satellite telecommunication systems promise to bridge digital gaps and deliver wireless communication services to any corner of the world. However, despite satellites’ global connectivity and wide footprint, still atmospheric and dust impairments are open challenges that face satellite systems, especially at high-frequency bands in arid and semiarid regions. Therefore, this paper aims to predict joint effects of atmospheric and dust attenuations in Gulf Cooperation Council (GCC) countries on CubeSat communications using Artificial Neural Network (ANN). The prediction model has been carried out using a massive Multiple-Input Multiple-Output (MIMO) antenna payload at K-frequency Bands. Consider these joint effects have positive relations in calculating satellites link margin, which leads to obtaining efficient communication system, delivering better quality of service (QoS), and enhancing Internet of Things (IoT) connectivity, or even Internet of Space Things (IoST). Predicated results infer that the ANN attenuation predictions, along with the 5G MIMO antenna on-board the CubeSat, offer much promise channel model for satellite communications, which in turn leads to not only supporting IoT connectivity but also reducing power consumption, thus enhancing lifetime of CubeSat. Also, this study can provide a reference for CubeSat engineers to guarantee large-capacity communication.