International Journal of Satellite Communications and Networking最新文献

High-throughput satellites play an important role in emergency disaster relief, maritime, and other fields. A new generation of high-throughput satellites with large deployable antennas and broadband beamforming networks, namely, very high-throughput satellites (VHTS), is developing towards hundreds, even thousands of extremely narrow beams with Tbps capacity, which puts forward higher requirements for satellite pointing and system construction costs. In order to solve the problem that those traditional beam pointing measurement and calibration algorithms are difficult to apply or the performance is limited, this paper builds a service beam pointing measurement and calibration architecture. A user terminal-assisted beam pointing measurement algorithm based on the Gauss-Newton method is proposed for the general case, which can effectively reduce the construction cost of onboard and ground pointing measurement system, and improve the measurement accuracies of three axes of the satellite. Simulation results demonstrate the excellent performance under the ideal scenario. To achieve the future engineering application under the non-ideal scenario, the terminal positioning error can be first neglected, then the pattern processing error and the terminal signal measurement error must be reduced by decreasing the pattern sampling interval, increasing the number of participant terminals, and other means. By comparing with a traditional beam pointing measurement algorithm, the proposed algorithm can achieve much lower beam pointing error than the baseline.

In recent years, the demand for high-speed and reliable communication networks has grown exponentially. To meet this demand, researchers and engineers have been exploring innovative solutions that combine the benefits of both satellite and terrestrial networks. The complexity of accurately modeling and predicting dynamic network conditions to optimize resource distribution and maintain seamless connectivity. The objective of this work is to develop and implement a recurrent neuro-fuzzy model (RNFM)for optimizing traffic offloading and resource allocation in hybrid satellite-terrestrial networks within cognitive integrated systems. This work, begins with cognitive integrated hybrid satellite-terrestrial networks employing spectrum-sharing techniques. These techniques integrate with software-defined networks (SDN) to facilitate traffic offloading in hybrid satellite-terrestrial networks (H-STN). The process incorporates a second-price sealed-bid auction mechanism to efficiently allocate resources. Joint resource allocation is then optimized using two convex optimization methods. This allocation, in turn, informs the resource allocation strategy. The system further incorporates user behavior analysis and employs a recurrent neuro-fuzzy model with deep feed-forward neural networks to enhance the accuracy and efficiency of the entire process. MATLAB simulation that incorporates adaptive learning algorithms and fuzzy logic to dynamically manage network resources and improve system efficiency. The findings show that the proposed technique outperforms both one-step and multi-step prediction algorithms with an accuracy increase of 99.23%. The future scope for this research is to integrate advanced machine learning algorithms, such as reinforcement learning, with the recurrent neuro-fuzzy model to further enhance dynamic traffic offloading and resource allocation in increasingly complex and heterogeneous satellite-terrestrial network environments.

Free space optical communication has emerged as a promising technology for high-speed and secure data transmission between ground stations on Earth and orbiting satellites. However, this communication technology suffers from signal attenuation due to atmospheric turbulence and beam alignment precision. Low Earth orbit satellites play a pivotal role in optical communication due to their low altitude over the Earth surface, which mitigates the atmospheric precipitation effects. This paper introduces a novel data path control law for satellite optical communication exploiting artificial intelligence-based predictive weather forecasting and a node selection mechanism based on reinforcement learning. Extensive simulations on three case studies demonstrate that the proposed control technique achieves remarkable gains in terms of link availability with respect to other state-of-the-art solutions.

Signal-to-noise ratio (SNR) estimation is crucial for spectrum management and data transmission. However, the existing classical methods in satellite-to-ground (SG) communication links, particularly for broadband transmission and under ultra-low SNR conditions, often encounter substantial estimation errors. In this paper, a novel SNR estimation method based on time-domain channel impulse response (CIR) reconstruction is proposed. Least square (LS) algorithm in frequency domain and inverse fast Fourier transform (IFFT) with a rectangular window are employed to reconstructed CIR. The noise energy is calculated by computing the average energy outside the window. The signal power is obtained by subtracting the noise energy from the total energy inside the window. In addition, a numerical simulation with a signal bandwidth of 400 MHz is performed to evaluate the effectiveness of the proposed algorithm in real SG communication scenarios. The simulation results show that compared with existing classical methods, even under ultra-low SNR conditions, the proposed algorithm exhibits more accurate estimation ability and stronger resistance to frequency offset interference in nonterrestrial network (NTN) channels.

This paper presents low Earth orbit (LEO) satellite constellation configuration based on the performance of Doppler effect in laser intersatellite links (LISLs). It studies the impact of the LEO constellation's parameters on the performance of the Doppler effect in LISLs. The paper aims to develop LEO satellite constellation configurations that evolve LISLs with minimal Doppler shift. It evaluates the impact of the variation of the relative distance, the inclination angle of the LEO constellation orbital planes, the orbital planes number in the LEO constellation, and the altitude on the performance of Doppler wavelength shift (DWS) in LISLs, for different operating laser wavelengths (OLWs) with respect to two possible intersatellite links (ISL) connection modes within the constellation, straight ISL (n-to-n) and inclined ISL (n-to-n − 1). n is the order of the satellite in the orbital plane. Simulations are conducted to evaluate the performance of these configurations in terms of altitude, OLW, inclination angle, and the number of orbital planes. In addition, both OneWeb and Starlink constellations are studied to evaluate DWS performance. The study demonstrates that DWS decreases either with the diminution of the relative distance between linked LEO satellites, the inclination of LEO constellation, and the OLW, or with the augmentation of the orbital planes number and altitude. Moreover, the overall DWS between two LEO satellites in the proposed constellation is at least 50% lower than the constellation configuration in other literature. The paper proposes the LEO constellation's configurations that perform LISLs with less possible Doppler effect by optimizing the LEO constellation parameters that impact the Doppler effect. The result of this study helps in the early stage of LEO satellite constellation designing in terms of payload simplicity and cost.

Low Earth Orbit (LEO) satellite-based Internet of Things (IoT) has become a hot topic in IoT networks due to the ability of global coverage, especially in remote areas. How to design a commercial LEO satellite-based constellation to meet the IoT traffic requirement remains an open problem. In this paper, we propose a throughput optimization algorithm for LEO satellite-based IoT networks meanwhile reducing the number of LEO satellite. Based on stochastic geometry theory, a closed-form expression is derived for the throughput of a dynamic LEO satellite-based IoT networks when LEO satellite equips with capture effect (CE) receiver and successive interference cancelation (SIC) receiver, respectively. Furthermore, a joint altitude and beamwidth optimization problem is formulated under the constraint of Walker constellation to optimize the throughput and the number of LEO satellites. To solve this multi-objective optimization problem, we design an iterative non-dominated sorting genetic algorithm II (NSGA-II) for the rapid development of IoT traffic. Simulation results show that our proposed algorithm can effectively improve the throughput performance of random access (RA) protocol in LEO satellite-based IoT networks compared to benchmark problems.