International Journal of Satellite Communications and Networking最新文献

Low earth orbit (LEO) satellites have the characteristics of low communication delay, low deployment cost, and wide coverage, which have become an important component of the 6G air-space-ground integrated information network. However, satellite-ground communication has a large propagation distance, complex fading, and fast terminal movement speed, causing the channel characteristics different from terrestrial communication networks. Therefore, channel modeling is necessary when deploying a satellite-ground communication network. In this paper, a 3D geometry-based stochastic model (GBSM) is proposed for satellite-ground communication links. The proposed channel model includes several environments such as urban, suburban, and rural. Based on this model, the channel impulse response (CIR) can be obtained, and the closed-form expression of spatial-temporal correlation function and Doppler power spectrum density are derived. Through simulation, the characteristics of large-scale fading and small-scale fading are analyzed, which depict the significant differences from the terrestrial networks. The relevant results can provide contributions to the design of future satellite-ground communication systems.

The paper analyzes the error performance of a basic satellite-terrestrial communication system, which uses a satellite as a source and receiver at the earth station as a destination. The system model accounts for an independent channel of fading and applies the theory of non-orthogonal multiple access (NOMA) to provide fair resource sharing and better connectivity among multiple users. The paper investigates the transmission characteristics and derives the expressions for the total symbol error rate (SER) of the proposed system model. Furthermore, it examines the transmission efficiency with the help of the elevation angle between the source and the destination. The paper also explores the impact of different fading environments on SER.

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.