In recent years, the popularity of LEO satellite internet has made satellite security the focus of industry and academia, such as jamming and spoofing attacks aimed at physical satellite signals. Therefore, developing sophisticated anti-jamming and anti-spoofing technologies is essential, and individual identification of satellites is a prerequisite for implementing this technology. However, off-the-shelf LEO system signals exhibit significant differences in fading characteristics, communication protocols, modulation schemes, and Doppler shifts compared to terrestrial signals, which challenges current individual identification. Besides, individual identification research on off-the-shelf LEO systems such as Starlink remains relatively scarce. This paper proposes SatTransformer, a novel satellite signal individual identification method for LEO system signals, integrating nonlinear mapping and Vision Transformer techniques. We enhanced signal spectrum and doppler shift features by nonlinear transform in our identification method, while maintaining a balance between local and global feature extraction. To evaluate the model's performance, we conducted an extensive data collection campaign, acquiring signals from Starlink satellites over a 25-day period, resulting in a dataset comprising over 30,000 data samples. Experimental results demonstrate that our proposed method achieves superior accuracy (91.3%) compared to existing approaches in the field of satellite signal identification.

The use of large-scale LEO constellations to provide network and communication services is one of the emerging technologies to bridge the digital divide between urban and remote areas. Many companies have been implementing commercial deployments of large-scale LEO constellations, some of which are gradually putting into use. Unfortunately, due to the dramatic increase in constellation size, conventional satellite system performance simulation methods are no longer applicable. How to accurately analyze and quickly simulate the network performance of large-scale LEO constellations has become a hot research topic. In this paper, a method for analyzing the performance of large-scale LEO constellations based on stochastic geometry is presented. We solve the problem of performance analysis for constellations of satellites at different altitudes by modeling the positions of satellites as a Poisson point process in a 3D spherical cap space and deriving an expression for the coverage probability. To reduce the gap between the theoretical model and the actual simulation, we propose a large-scale LEO constellation simulation framework based on stochastic geometry. In addition, we have compared our simulation methods with state-of-the-art simulation software, and the statistical results prove that our simulation time is more efficient with the same accuracy rate.

In this paper, we propose an interference mitigation strategy for low Earth orbit (LEO) satellite systems based on adaptive beamforming that incorporates both sidelobe level (SLL) control and dynamic adaptation to reduce co-frequency interference by analyzing the real-time positions of interfering satellites and serving satellite relative to the user terminal. In this study, we consider a uniform rectangular array (URA) as the user terminal antenna configuration in the LEO satellite system. The adaptive beamforming technique based on the Taylor weighting algorithm is applied for sidelobe suppression by generating a beam pattern with the desired SLL. The real-time positions of interfering satellites and serving satellite relative to the user terminal are computed by solving the orbital parameters. Based on real-time position information, the adaptive beamforming technique is utilized to dynamically adjust the beam pattern and generate an appropriate SLL, thereby minimizing the impact of co-frequency interference to its maximum extent. The simulation results demonstrate that the proposed strategy achieves a user terminal received carrier-to-interference ratio (C/I) exceeding 27dB for 95% of the simulation time, representing a significant improvement of 47.5% compared to conventional methods. Moreover, the upper limit of C/I has also significantly escalated from 40dB to 80dB. These findings strongly validate the effectiveness of the proposed strategy in mitigating interference and enhancing overall system performance.

It has been an emerging trend to provide global Internet access using massive Non-Stationary Orbit (NGSO) networks, which are part of the Low Earth Orbit (LEO) networks. How to evaluate the performance of this new network is now a hot topic of research. Traditional terrestrial wireless networks consider interference as an important metric of network performance. However, interference scenario of massive NGSO networks changes dynamically in time and space, causing difficulty in interference analysis. In this paper, we propose a Monte Carlo algorithm based on stochastic geometry for simulating multi-tier NGSO networks’ interference. We utilize stochastic geometry to model the locations of satellites as a randomly distributed points process in a 3-D space. Then, Monte Carlo is used to randomly sample the interference scenario. The advantage of our analysis algorithm is to use the random point process to approximate the random characteristics of the spatiotemporal dynamic trajectory of high-density networks. Simulation results prove that with comparable accuracy, our algorithm has lower time cost versus to the conventional interference analysis method for massive multi-tier NGSO networks.