Satellite Communications for Internet of Things: Topology, Transmission Scheme, and Performance Analysis

Abstract

The proliferation of the Internet of Things (IoT) has revolutionised numerous aspects of daily life, ranging from personal home automation to the development of smart cities. However, the deployment of IoT devices is often limited to areas with available terrestrial infrastructure, leaving many locations without seamless connectivity. The emergence of mega-low Earth orbit (LEO) satellite constellations presents an opportunity to extend IoT deployments to previously uncovered areas, ensuring ubiquitous coverage. From a communication perspective, IoT networks have specific challenges, such as the availability of limited transmit power and computational resources. These challenges are further exacerbated in satellite scenarios due to higher path loss, constrained link budget and interference from large deployments. The aim of this thesis is to address these challenges by proposing network topology and transmission schemes specifically designed for LEO satellite-based IoT networks. A star-of-star topology is proposed with a physical (PHY) layer combining that capitalises on the diversity advantages of numerous visible LEO satellites. It results in an improved link budget and enhanced coverage probability. Initially, a simple system model is assumed for analysing the outage probability (OP) without considering orbital dynamics and interference from other users. The OP of two combining schemes, selection combining (SC) and maximal ratio combining (MRC), is derived in closed form. It is also compared with a single satellite scenario to demonstrate the benefits of using multiple satellites. The diversity order analysis proves that the topology achieves a diversity order equal to the number of satellites involved in combining. The MRC scheme achieves higher coding gain and, thus, better OP performance than the SC scheme. Later, the performance analysis is extended for a more practical system model incorporating nonidealities in terms of interference and imperfect channel state information (CSI). Tools from stochastic geometry are also employed to model the satellite locations for computing the statistics of slant range and number of visible satellites in closed form. Considering interference, the OP is derived for two decoding schemes at the ground station (GS): The capture model (CM) and the successive-interferencecancellation (SIC). Simplified expressions for the OP under a high signal-to-noise ratio (SNR) assumption are also derived, which are further utilized to optimise the system parameters for achieving a target OP. The derived analytical results have been rigorously validated using Monte Carlo simulations. The results demonstrate that for the practical values of the system parameters like transmit power and number of visible satellites, the proposed topology is feasible and attractive for low-powered IoT networks. Finally, on the medium access control (MAC) layer, a transmission scheme based on change detection is proposed to accommodate more users within the network and improve energy efficiency. Machine learning (ML) algorithms are also proposed to reduce the payload size by leveraging the correlation among the sensed parameters. Real-world data from an IoT testbed deployed for a smart city application is utilised for the performance analysis of the proposed scheme. The findings reveal that the traffic pattern, post-implementation of the proposed scheme, differs from the commonly assumed Poisson traffic, thus proving the effectiveness of having IoT data from actual deployment. It is demonstrated how the transmission scheme facilitates accommodating more devices while targeting a specific collision probability. Considering the limited visibility of LEO satellites, the effective data received at the server is evaluated for the satellite’s link budget and visibility duration. The average battery lifetimes are also demonstrated to increase by many folds using the proposed transmission schemes and ML algorithms.