Doppler shift has significant implications in Low Earth Orbit (LEO) constellations due to the rapid relative movement of satellites. This paper presents a novel approach based on stochastic geometry to capture the statistical distribution of Doppler shift in modern mega satellite constellations. We derive a simplified expression for both the Doppler shift s-curve and its maximum value, highlighting the dependency of Doppler shifts on the zenith angle of the closest approach during a satellite pass. Additionally, we present a stochastic model for the distribution of these zenith angles within the random constellation model. Furthermore, the statistical distribution of the Doppler bound is analytically formulated and validated through extensive Monte Carlo simulations of two major satellite constellations currently in orbit.

Earth observation has a crucial role in understanding and monitoring our planet’s health and changes. Spaceborne Synthetic Aperture Radar (SAR) has become a valuable technology for Earth monitoring, leading to a massive expansion of satellite launches. However, within the limited radio frequency (RF) band, Radio Frequency Interference (RFI) poses a significant challenge for SAR technology. RFI can have a significant impact on the overall system performance and particularly on SAR image quality. To analyze and solve the interference problem, a simulator/emulator is required at the RF level to emulate and analyze the effects of different RFI sources on the final focused spaceborne SAR image. This paper presents an open-source RF-level SAR emulator for spaceborne applications called SEMUS. SEMUS is an integrated end-to-end framework for realistic spaceborne SAR scenarios that can generate raw RF data (Level-0) for an arbitrary scene and reconstruct the final SAR-focused image (Level-1). Moreover, the emulator is capable of injecting arbitrary RFI waveforms into the raw SAR data. The simulation results prove SEMUS’s ability to generate high-quality Level-0 SAR data above Melbourne, Australia. Affirming its capability, SEMUS is able to reconstruct Level-1 free of RFI or contaminated with interference.

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.

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.

While   global   navigation   satellite   system   (GNSS)technologies have always been the go-to solution for localizationproblems, they may not be the best choice for some Internet-of-Things (IoT) applications due to the incurred power consumptionand cost. In this paper, we present an alternative satellite-basedlocalization method exploiting the signature of Doppler shifts andangle  of  arrival  measurements  as  seen  by  a  receiving  satellite.We first derive the joint likelihood function of the measurements,which  is  represented  as  a  combination  of  three  Gaussian  distri-butions.  Then,  we  show  that  the  maximum  likelihood  problemreduces  to  a  more-efficient  mean  squared  error  minimizationin  the  Gaussian  case  as  inferred  from  real  measurements  wecollected  from  low-earth-orbit  satellite  using  a  tracking  groundstation.  Thus,  we  propose  utilizing  a  stochastic  optimizer  tosearch for the global minimum of the mean squared error whichrepresents the location of the ground IoT device as estimated bythe  satellite  platform.  The  emulated  results  show  that  the  IoTdevice localization, under such realistic model, can be performedwith  sufficient  accuracy  for  IoT  applications.

Many Internet-of-Things (IoT)-over-satellite applications rely on affordable location-aware but energy-constrained IoT sensors. In this paper, we propose a novel method to estimate the satellite visibility window in IoT devices based on simple Doppler measurements. We present two scenarios where the orbital information of the serving satellite’s is initially unknown to the IoT device: (i) we assume that the geographic coordinates are known to the IoT device, and (ii) we assume that the coordinates are completely unknown. Accordingly, we  derive the Doppler measurement likelihood function, and simplify it to a root mean square error (RMSE) minimization problem. From a sequence of Doppler measurements, we estimate the orbital parameters of the serving satellite using a stochastic optimizer to minimize the RMSE. From the orbital estimation, we then predict the satellite visibility window (satellite pass). To gauge the accuracy of the window estimation, we apply the intersection-over-union metric to compute the overlapping visibility window between the ground truth and the estimation, and consequently present the results based on extensive Monte Carlo simulations.