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 uplink expansion strategies to cater for expanding user demands and gain insights into the performance of the network as more shells are introduced into the network.

Mega satellite networks recently emerged to complement the current terrestrial infrastructure to attain global coverage and faster communication links. However, with increased 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.

With the exponential growth of the Internet of Things (IoT) landscape and the resulting spectrum congestion, innovative techniques for spectrum monitoring are crucial. This paper presents a groundbreaking approach to spectrum monitoring harnessing the power of spiking neural networks (SNNs) with a focus on image segmentation using the UNet architecture. Traditional methods, including energy detection, have been widely used but are not without challenges, especially in environments with varying signal-to-noise ratios. In contrast, the presented SNN approach in this paper leverages the leaky integrate-and-fire neuron model and provides superior energy efficiency, real-time inference capability, and higher detection performance. Through extensive simulations, the proposed SNN framework exhibited performance metrics that significantly surpass energy detection methods and closely align with conventional convolutional neural network techniques. Future explorations will delve into enhancing the framework using machine learning techniques for advanced feature extraction and multi-class segmentation.

Cell-free (CF) massive multiple-input multiple-output (MIMO), as a promising network architecture for beyond the fifth generation (5G), has a great potential to support grant-free (GF) transmission for machine-type communication (MTC). To shed light on this subject, this work aims to model and evaluate the performance of GF transmission in CF massive MIMO under a realistic network deployment scenario, where the spatial locations of both access points (APs) and devices are assumed to be random in nature. In particular, by capitalizing on the distinctive CF network architecture and features, we design a new two-disk based geometric model for GF transmission, which facilitates analysis and understanding in CF massive MIMO. Based on the proposed two-disk model, we derive an approximated closed-form expression for the access success probability by leveraging on techniques from stochastic geometry, and investigate the impact of different key system parameters on the network performance. To highlight the performance superiority of CF massive MIMO, we further provide a comparative analysis by using an analogous single-disk model in an equivalent co-located massive MIMO network. Simulation results verify our analysis and demonstrate that CF massive MIMO is able to significantly outperform its co-located counterpart in terms of access success probability and provide robust performance against increased access density, which well suits to crowd scenarios.

This is a preprint of the paper, “Artificial Intelligence Techniques for Next-Generation Massive Satellite Networks”.

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.

The obtained results demonstrate a significantly enhanced detection performance under heavy interference conditions when employing spiking-based and deep learning detection techniques, as opposed to traditional baseline matched-filter methods. Although spiking-based detection approaches exhibit error rates comparable to those of deep learning techniques, they consume considerably less power -- several orders of magnitude lower, in fact. Owing to their power efficiency, spiking-based detection networks emerge as the optimal choice for signal detection in resource-limited systems, such as low-Earth orbit satellites.

This paper presents a practical approach for Q/V-band modeling for low Earth orbit satellite channels based on tools from machine learning and statistical modeling. The developed Q/V-band LEO satellite channel model is presented in two folds; (i) a real-time forecasting method using model-based deep learning, intended for real-time operation of satellite terminals, and (ii) a statistical channel simulator that generates the path loss as a time-series random process, intended for system design and research. The provided approach capitalizes on real satellite measurements that are obtained from AlphaSat’s Q/V-band transmitter at different geographic latitudes, to model the radio channel. The results show that model-based deep learning forecasting can outperform conventional statistically derived prediction methods for varying rain and elevation angle profiles. Moreover, it can also provide more accuracy in long-term prediction in comparison to current state-of-the-art machine learning approaches for radio channel prediction. Results for the statistical channel simulator is shown to produce synthetic radio excess path loss values for varying satellite passes by capitalizing on empirical statistical models obtained from real measurements.

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