To keep up with the continuously growing coverage demands and attain true global coverage, the deployment of multi-layered low Earth orbit satellite constellations is necessary. Next-generation mega satellite constellations are expected to rely on inter-satellite links to relay information, which will enable fast and reliable communications between the different satellite nodes in free-space and facilitate the utilization of coverage diversity modes that can further enhance the quality-of-service provided in the network. However, materializing these high performing systems is challenging due to the complexity of the network architecture which may require long and complex simulation processes during design. In this article, we develop theoretical modeling for the probability of coverage for various diversity modes in mega satellite constellations by leveraging tools from stochastic geometry. We first develop analytical models for conventional single-shell networks and then extend these models to incorporate multi-shell networks. The analysis is validated using Monte-Carlo simulations which show a close fit to the analytical models derived. Moreover, the analytical models provide a performance baseline that is comparable to practical networks that rely on regular network architectures such as SpaceX’s Starlink. This allows network operators to devise expansion strategies to cater for expanding demands and gain insights into the performance of the network as more shells are introduced into the network.

Mega satellite networks recently emerged to com-plement the current terrestrial infrastructure to attain global coverage and faster communication links. However, with in-creased congestion in the radio spectrum, migrating satellite communication to high frequencies can provide higher data rates. Nevertheless, signal fading due to weather conditions and atmospheric losses becomes significant at such high frequencies. In this paper, we leverage tools from stochastic geometry to provide an analytical framework that captures the effect of high frequency fading for satellite constellations in the downlink. The analytical framework presented exploits the spatial diversity provided by the satellite network and examines the performance of the dominant satellite (satellite providing the best quality of service). Results show a close fit to the performance of practical networks and can thus provide network designers with an analytical benchmark to assist with network tuning.

Utilizing Low Earth Orbit (LEO) satellite networks equipped with Inter-Satellite Links (ISL) is envisioned to provide lower delay compared to traditional optical networks. However, LEO satellites have constrained energy resources as they rely on solar energy in their operations. Thus requiring special consideration when designing network topologies that do not only have low-delay link paths but also low-power consumption. In this paper, we study different satellite constellation types and network typologies and propose a novel power-efficient topology. As such, we compare three common satellite architectures, namely; (i) the theoretical random constellation, the widely deployed (ii) Walker-Delta, and (iii) Walker-Star constellations. The comparison is performed based on both the power efficiency and end - to-end delay. The results show that the proposed algorithm outperforms long-haul ISL paths in terms of energy efficiency with only a slight hit to delay performance relative to the conventional ISL topology.

Recent satellite constellations are being deployed to serve massive numbers of wireless devices, especially targeting those located in rural and offshore settings. Accordingly, business models relying on data reported via wireless Internet-of-Things (IoT) networks can now easily utilize satellite constellations to expand their offerings. In this paper, we present an analytic framework for modeling the uplink performance of massive IoT-over-Satellite networks. The framework utilizes tools from stochastic geometry to model the satellites and the users as two random point processes enabling the development of a tractable analytic model for the uplink outage probability. Furthermore, the paper derives the expected normalized throughput and compares the results to Monte-Carlo simulations for intractable constellations such as the Walker models adopted by current satellite deployments. Comparisons show that random constellations provide a tractable lower bound to the performance and average throughput compared to Walker constellations. The analytic model can provide the fast estimation of the uplink performance aiding in designing the IoT-over-Satellite system.