International Journal of Satellite Communications and Networking最新文献

Doppler frequency offset, Doppler spreading, and multipath effects are induced by the rapid movement of Low Earth Orbit (LEO) satellites, which collectively result in two-dimensional (2D) time-frequency fading within nonterrestrial network (NTN) systems. Consequently, this leads to a degradation in the signal-to-noise ratio (SNR) uniformity and deep fading across the time-frequency grid in orthogonal frequency division multiplexing (OFDM) systems, which significantly impairs the bit error rate (BER) performance. The orthogonal time-frequency space (OTFS) scheme is capable of addressing 2D time-frequency fading but at the cost of increasing the complexity of the receiver. In this work, an orthogonal transform-assisted OFDM (OTA-OFDM) scheme is proposed, which is based on orthogonal transforms to map data symbols into the time-frequency grid, effectively spreading the data symbol energy throughout the time-frequency domain. Simulations within a 400 MHz NTN system indicate that at a BER of 10⁻³, OTA-OFDM outperforms OFDM with SNR gains of 3.73 and 1.92 dB in NTN-TDL-B and NTN-TDL-D channels under QPSK modulation. It also obtains 3.24 and 1.54 dB SNR gain respectively under 16-QAM modulation. Furthermore, OTA-OFDM achieves performance comparable to OTFS while reducing the complexity of the channel estimation and equalizer modules in the receiver by 93.33%.

In the rapidly advancing field of telecommunications, multibeam satellite systems are essential for delivering fast and extensive connections. These systems require adaptable beamforming algorithms to improve performance measures like peak throughput, beam width control, side lobe level regulation, and effective isotropic radiated power (EIRP). This research shows a novel supervised learning approach for the efficient categorization and creation of beamforming matrices in multi beam satellite systems. The proposed method gives machine learning techniques to increase system agility, quick enabling, and informed responses to evolving communication needs. This method gives a test accuracy of 0.988 using a neural network model developed to classify flattened beamforming matrices into clusters, demonstrating remarkable model effectiveness. Key components for facilitating this achievement include principal component analysis (PCA) for dimensionality reduction, spectral clustering for accurate cluster identification, and a neural network architecture with hidden layers and ReLU activation functions to clarify intricate data patterns. The model's ability to generalize effectively with new data highlights its robustness and reliability. This approach substantially enhances intelligent, high-performance multibeam satellite systems by guaranteeing optimal working and reliability in variable settings.

Spectrum scarcity can be effectively mitigated through spectrum sharing between LEO and GEO satellites. However, severe interbeam interference may be caused by the dense distribution and wide coverage of multilayer satellite systems. Furthermore, the uneven distribution of traffic demand generated by users may lead to load imbalance among satellites, by which service fairness may be degraded. In this paper, beam management and precoding design are investigated in GEO-LEO coexistence networks to mitigate interbeam interference and improve service fairness in multi-layer satellite systems. To solve the load imbalance problem, a serving satellite allocation algorithm based on game matching theory is proposed. Moreover, a heuristic-based joint beam management and precoding algorithm is proposed to mitigate interference and enhance service fairness. Simulation results show the effectiveness of the proposed algorithms.

Space-to-ground laser communication (SGLC) utilizes laser beams to establish high-capacity bidirectional links between satellites and ground stations (GSs). However, its performance is significantly impaired by cloud cover and atmospheric turbulence. In practical SGLC downlink scheduling, uncertainties derived from such atmospheric conditions are inevitable. To the best of our knowledge, this work is the first to tackle downlink scheduling for SGLC under such uncertainties, with the objective of maximizing the total amount of data downloaded from satellites. We present a robust formulation of the scheduling problem that incorporates multisource uncertainties through budgeted uncertainty sets, consequently transforming the original problem into a bi-level optimization one with conflicting objectives. To address such problems, we first utilize McCormick envelopes to linearize bilinear terms in the inner optimization problem. We subsequently propose a KKT condition-based method to convert the bi-level structure into a single-level reformulation, which is further transformed into a tractable mixed-integer linear programming (MILP) model. Compared with the existing method, which does not consider such uncertainties, the proposed approach achieves robust scheduling strategies with respect to data throughput.

The low earth orbit (LEO) mega-constellation network, with its extensive coverage and low-latency characteristics, offers new opportunities to meet the demands of computation-intensive and latency-sensitive applications in remote areas. However, with the increasing complexity of task offloading demands and the limited availability of satellite resources, resource management and scheduling face significant challenges. To tackle these challenges, we propose a satellite-terrestrial integrated LEO mega-constellation edge computing network (LMCECN) management architecture, which enables satellite-terrestrial resource allocation and task offloading through the cooperative scheduling of primary and secondary satellites. Based on this architecture, we design a deep reinforcement learning-based task-oriented mega-constellation edge offloading (TOMEO) scheme, which significantly improves task offloading efficiency by incorporating task sorting and resource clustering preprocessing mechanisms. Furthermore, a multiobjective double dueling noisy deep Q-network (DDNDQN) algorithm is introduced, which comprehensively considers multiple optimization objectives, including task completion rate, load balancing degree, task delay, and energy consumption, further enhancing task offloading efficiency. The experimental results demonstrate that the proposed offloading scheme outperforms the baseline schemes across all optimization objectives and improves the task offloading performance.

Paired carrier multiple access (PCMA) is a critical technique in satellite communication systems for frequency reuse and optimizing spectrum utilization. While separation of PCMA signals in Gaussian noise environments is well-established, designing algorithms for impulsive noise remains a challenge. This paper proposes a novel correntropy-based adaptive filter with an equalizer to separate PCMA signals in the presence of impulsive noise. It is important to note that our approach is currently limited to single-beam applications and specifically for constant QPSK modulations. We compare the performance of our method with the well-known least mean square (LMS) adaptive filter using bit error rate (BER) and mean square deviation (MSD) simulations, demonstrating the superiority of our approach.

Dense low earth orbit (LEO) satellite networks with full frequency reuse can offer seamless global coverage and high spectrum efficiency. However, multiple satellites have overlapping coverage areas, leading to co-channel interference that degrades communication system performance. Moreover, the high dynamic nature of LEO satellites makes the interference varies over time. In this paper, we analyze the receive beamforming to mitigate the complex and time-varying interference in dense LEO satellite networks, and the interference mitigation is formulated as a long-term data rate maximizing problem. To address this problem, a joint intelligent interference prediction and receive beamforming design algorithm is proposed. First, an interference prediction algorithm based on long short-term memory (LSTM) is employed to predict the direction of arrival (DOA) information. Then, a hybrid beamforming algorithm based on deep reinforcement learning (DRL) is proposed to mitigate interference. Simulation results show that the proposed algorithm effectively improves long-term data rate for users and outperforms other benchmark algorithms.