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.

Achieving accurate and low-latency spectrum sensing on resource-constrained devices is essential but very difficult. Traditional In-phase and Quadrature (I/Q)-based and the ShortTime Fourier Transform (STFT)-based methods fail to balance the computational overhead and classification accuracy. In this paper, we propose a novel framework –Spectrum Painting (SP)– which enables on-device signal classification with low latency and high accuracy. We design new signal processing methods to compress spectrograms while keeping global signal features and augmenting the salient features of small objects. SP achieves high-accuracy signal classification, assisted further by our proposed Dual-channel Convolutional Neural Network (DualCNN). We collect diverse datasets to evaluate the proposed SP, including synthesized data, and testbed data (from up to 18 commodity devices) obtained from real-world environments in the wild and office settings. Experimental results of SP running on Raspberry Pi 4B show a great reduction in latency up to $20 \times$ while maintaining a 95% accuracy. Furthermore, SP demonstrates superior performance within both the centralized learning architecture and the Federated Learning (FL) architecture. For example, the challenging cross-environment evaluation of the SP in the iid-FL scenario yields a substantial accuracy improvement, on average from 24.6% to 83.8%.

With the explosive growth in the number of NSGO satellites, there is an increased demand for satellite tracking technology, making the accurate prediction of satellite trajectory and optimization of target tracking efficiency crucial. This paper proposes a strategy for tracking NGSO satellites, which employs a two-stage stochastic model with a greedy approach to optimize the real-time prediction of multiple NGSO satellite passes and improve the efficiency of noncooperative ground equipment. The first stage of the model employs SGP4/SDP4 functions for initial orbit predictions, isolating satellite data from ground equipment performance. The second stage iteratively refines these predictions, addressing the complexities associated with noncooperative ground equipment, constrained resources, the high frequency of NGSO constellation satellites pass, and effective target management. The resulting two-stage model achieves precise and efficient operations, effectively overcoming the traditional challenges of stochastic optimization in dynamic conditions involving multiple satellites. It successfully copes with the exponential surge in computational requirements as the number of satellites increases. A case study with an antenna system validates the practicality and effectiveness of the model. The optimized sequence significantly enhances overall performance concerning efficiency, utilization, idle time, angular displacement, and trajectory symmetry, achieving a 27.48% improvement over the traditional solution and a notable 58.33% increase in the number of effectively tracked satellites, all within a rapid 15-second execution window.

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.

This paper proposes a novel joint prediction model of atmospheric attenuation for the accurate interference prediction of low earth orbit (LEO) constellation systems. Firstly, a total atmospheric attenuation joint prediction model based on the actual satellite link elevation angles is defined. Secondly, we apply the elevation-based total atmospheric attenuation joint prediction model to the interference analysis of LEO constellation systems in the downlink direction, and the modified analytical expression of interference evaluation indicator ΔT/T based on the proposed model is also derived. Finally, we select OneWeb and GW satellite systems for simulation to verify the accuracy and efficiency of the proposed model in this paper. The results show that the elevation-based total atmospheric attenuation joint prediction model can provide more accurate interference prediction results than the existing attenuation prediction models, while the simulation time overhead reduce by 93.78%. Furthermore, the results indicate that in order to accurately predict the interference between LEO constellation systems, the total atmospheric attenuation cannot be ignored.

Automatic modulation classification (AMC), which aims to identify the modulation types of unknown signals without any prior knowledge, plays a key role in intelligent wireless communication. In recent years, the outstanding achievements of deep learning in the fields of computer vision and nature language processing have promoted the continuous researches of deep learning in AMC. In view of the fact that the existing deep learning-based AMC ignores the frequency attribute of the modulated signals, this paper proposed an AMC approach based on deep learning which comprehensively considers the contribution of frequency and spatiotemporal characteristics of the modulated signals. Specifically, we utilize the sliding window to divide the raw signals into short-time signal slices. In order to obtain the representation of frequency domain of the signals, each short-time signal slice is decomposed into several modes by variational mode decomposition (VMD), which is an adaptive signal decomposition technique. Finally, all the modes are used as input of the convolutional neural network (CNN) as a tensor to learn high-level features from the frequency and spatiotemporal domain to identify the modulation type. In this paper, we verify the effectiveness of the proposed approach with 10 kinds of modulation types generated by SMW200A Vector Signal Generator. More importantly, the proposed approach achieves 99.2% of the overall classification accuracy at 8dB signal to noise ratio (SNR) and shows better robustness to noise than the existing AMC approaches.

According to ITU rules and recommendations, all NGSO systems must protect the GSO system from co-frequency harmful interference. However, the existing methods normally calculate the relative positions of satellites and earth stations through real-time data acquisition, and choose strategies such as satellite switching or isolation zone. As for the mega-constellations under construction, these methods still have great challenges to completely avoid harmful interference. In this circumstance, we establish a novel GSO protected area calculation model based on controllable NGSO system parameters. The proposed model not only has the advantages of low complexity and small calculation load, but also suitable for any NGSO constellation configuration as well as GSO earth station which located anywhere. The theoretical and simulation results both show that the harmful interference can be completely eliminated by setting the proposed GSO protected area. Furthermore, beams off and power control technologies are considered to lessen the protected area in a quantifiable degree, which is more achievable and more likely to be adopted by the existing NGSO systems.

Automatic wireless spectrum anomaly detection is vital to intelligent management of electromagnetic spectrum, which aims to detect various jamming and anomalous working states, especially intentional jamming. The intentional jamming has evolved in a variety of ways, but the existing spectrum anomaly detection efforts give little consideration to the diverse intentional jamming. Here, we firstly generate a rich dataset consisting of five types of normal signals and four types of intentional jamming. In order to effectively detect anomalies, we propose AWGAN, a novel anomaly detection method based on Wasserstein generative adversarial network. AWGAN can not only learn the distribution of normal time-frequency waterfall images in a latent space, but also remember the detailed features of normal images, and generate same images as the normal images by adversarial training. To detect anomalies, we propose a random reverse mapping (RRM) method based on backpropagation, to map a new time-frequency waterfall image into the latent space, so as to find the vector closest to the distribution of the new image in the latent space. We also define a scoring criterion to score images indicating their fit into the learned distribution. The experimental results show that the comprehensive detection ability of our method is superior to other methods for detecting the four types of anomalies.