# Performance Modeling Framework for IoT-over-Satellite Using Shared Radio Spectrum
###### Abstract  
Delivering Internet-of-Things (IoT) connectivity over satellite is a promising solution for applications in remote and sparsely populated areas. These applications range from smart agriculture, logistics, asset tracking to emergency services. Using a shared radio spectrum with terrestrial services will facilitate a cost-effective and rapid deployment of IoT-over-Satellite since it reduces the administrative and financial hurdles of leasing a dedicated segment of the spectrum. Although IoT-over-Satellite communication provides larger service coverage, the vast number of IoT devices also increase the interference in the satellite uplink channel, and it becomes a significant challenge for the reliable performance of the IoT-over-satellite. In this paper, we propose a framework for modeling the performance of IoT-over-Satellite access systems when sharing the radio spectrum with terrestrial networks. We take into consideration several important aspects, namely; satellite orbit, terrestrial IoT devices uplink interference, atmosphere and gas absorption, and the probability of line-of-sight. The performance of the overall system is presented in terms of the uplink signal-to-interference-plus-noise ratio (SINR), and thus the time-availability of the satellite link during a typical pass. We focus on low earth orbit satellites due to their potential use in IoT applications, where we evaluate the framework using actual parameters of satellites located in 300-800 km orbits. Furthermore, the paper presents a numerical model to obtain the most suitable antenna beamwidth that maximizes the link-availability of the satellite link by the simultaneous reduction in the terrestrial interference and the boosting of the underlying IoT signal of interest. ISM band; IoT access networks; IoT over satellite; satellite-terrestrial spectrum sharing 2020  
To cater for these scenarios, IoT-over-Satellite access is proposed as a viable solution for a cost-effective and easy-to-deploy network in remote locations [1; 2; 3] having several commercial solutions that are currently undergoing large-scale deployment [3; 4]. With the ongoing trend to unify communication protocols and systems, hybrid terrestrial-satellite systems are increasingly proposed. This trend can be clearly observed in the 3 rd Generation Partnership Project (3 GPP) efforts to expand 5G into satellite communication services [5; 6]. In this paper, we explore the possibility of using the shared Industrial Scientific Medical (ISM)-band for IoT access by sharing this band with existing terrestrial wireless services. Shared radio bands can significantly reduce spectrum acquisition hurdles along with the associated spectrum leasing costs because of the license-free nature of this band. However, such a sharing scheme might come at the cost of the reduced performance due to the mass-interference generated by millions of incumbent terrestrial sources. Therefore, to develop a model of predicting the performance of IoT-over-Satellite communication systems using a shared spectrum is of utmost importance for the IoT-over-satellite industry. With a reliable and robust analytical model, researchers and designers can evaluate the performance of the system in terms of coverage availability and signal quality. Although different researchers propose a simulation framework or simulation system model of satellite communications, such a performance model, or framework is still lacking from today's literature as most of the current satellite systems are using licensed frequency bands. To bridge this gap, we develop in this paper a performance evaluation framework for the IoT-over-Satellite access in terms of the uplink signal-to-interference-and-noise-ratio (SINR). We derive the SINR at the satellite by considering several factors affecting the performance. These factors include satellite orbit, terrestrial IoT interference, atmosphere and gas absorption, probability of line of sight, and antenna beamwidth. We obtain the satellite location across time by using a common orbital predictor.