The integration of Low-Earth Orbit (LEO) satellites with hypersonic aircraft presents a critical frontier for next-generation non-terrestrial networks (NTN). However, the ultra-high dynamics inherent in these scenarios introduce severe Doppler frequency shifts and time-varying delays, significantly degrading link reliability. To address these challenges, this article proposes a robust Orthogonal Time Frequency Space (OTFS) communication framework tailored for hypersonic-LEO links. First, we establish a three-dimensional (3D) kinematic model based on the Earth-Centered Inertial (ECI) coordinate system. This model accurately characterizes the dynamic delay-Doppler (DD) channel features across the aircraft’s ascent, cruise, and descent phases. Guided by these channel insights, we develop a phase-adaptive dynamic pilot design scheme that jointly optimizes pilot position, pilot power, pilot quantity, and guard intervals to maximize spectral efficiency and estimation accuracy. Furthermore, leveraging the temporal correlation of the OTFS channel in the DD domain, we propose a Multi-Frame Joint Cross-Correlation Matching (MF-JCM) channel estimation algorithm. This algorithm aggregates multi-frame channel responses to suppress noise and enhance detection probability. Simulation results demonstrate that the proposed scheme achieves a Bit Error Rate (BER) of 10−4at an signal-to-noise ratio (SNR) of 7.5 dB in the ascent phase and 11 dB in the descent phase. Hardware experiments based on Software-Defined Radio (SDR) further validate the feasibility and superiority of the proposed approach in practical ultra-high dynamic scenarios.

Future sixth-generation (6G) low Earth orbit (LEO) satellite systems will utilize large-scale multi-beam phased arrays operating at millimeter-wave (mmWave) frequencies, with embedded power amplifiers (PAs) at each antenna element. However, the severe path loss drives PAs near saturation, and dynamic beamforming leads to nonuniform, time-varying PA conditions, jointly resulting in complex memory nonlinearities that degrade system performance. Such in-band distortions require mitigation by PA linearization, which remains highly constrained in LEO satellite systems. Therefore, accurate modeling and quantification of these nonlinearities are critical for realistic performance evaluation and to guide the development of effective linearization strategies. Current analytical methods focus on single-PA or memoryless-array models, without addressing the frequency-dependent statistical characteristics and spatially varying radiation patterns of nonlinear distortions in multi-beam phased arrays. In this paper, we develop statistical models for the PA output and the received signals in multi-beam phased arrays, explicitly decomposing the distortion structure and deriving closed-form expressions for each distortion component’s statistical properties. Furthermore, we analyze the spatial beam-pattern distortions and derive a closed-form expression for the signal-to-distortion-plus-interference-and-noise ratio (SDINR) under nonlinear distortion. Extensive simulations, conducted measurements, and over-the-air (OTA) experiments validate that the proposed model reduces optimal input back-off estimation error by 2 dB and improves SDINR prediction accuracy by approximately 3 dB. This research establishes a robust analytical foundation for future LEO satellite system design and linearization technology development.

Data-assisted channel estimation (DA-CE) faces unique challenges in Low Earth Orbit (LEO) scenarios with large Doppler, where phase distortion and pilot sparsity degrade the utility of data symbols for refinement. Moreover, myopic, context-agnostic reliability metrics often fail to identify data symbols that are truly useful for improving estimation accuracy. To resolve these challenges, we innovatively reformulate the data selection as a pixel-level utility masking problem, similar to semantic segmentation tasks, where each resource element (RE) is evaluated for its utility in channel refinement. We introduce an integrated framework rooted in physics-informed reinforcement learning (PIRL), addressed by two symbiotic components. First, a Physics-informed Subspace Basis Expansion Models-LMMSE (PiSBEM-LMMSE) algorithm acts as the perception layer of our framework, which yields a high-fidelity, physics-consistent state representation by expressing the dominant Doppler-induced dynamics via a low-rank complex-exponential basis and decoupling them from residual stochastic fading. Second, a lightweight U-Net-based deep reinforcement learning (DRL) agent, acting as the cognitive decision core, learns an optimal, context-aware masking policy upon this structured representation. The U-Net architecture, with its encoder-decoder structure and skip connections, is specifically chosen to process the multi-channel, image-like state representation, capturing both global channel dynamics and local perturbations. Extensive simulations demonstrate that our framework achieves significant performance improvements over state-of-the-art methods, exhibiting remarkable robustness in LEO scenarios where conventional approaches fail.

Non-terrestrial networks (NTNs), connecting components such as low-earth orbit (LEO) satellites, exhibit highly dynamic behaviors in both their topological configurations and radio channels. Traditional pilot-aided channel estimation (PACE) methods are susceptible to significant Doppler shifts and rapid channel variations, limiting further enhancements in data transmission rates or incurring unaffordable pilot overheads. Data-aided channel estimation (DACE), as a potential improvement path, leverages data vectors to supplement pilot signals. Nevertheless, conventional DACE faces difficulties in effectively selecting data vectors under frequency offsets and rapid channel variations. To address this, this paper introduces a dynamic threshold-based scheme for selecting reliable data vectors, which leverages an adaptive threshold to precisely identify reliable data vectors within a frame. Furthermore, we propose a data vector deviation estimation method as a preprocessing step to correct the overall offset of data vectors, thereby ensuring the efficacy of data selection even amidst significant channel variations. Our approach significantly elevates the performance of LEO links within current protocols and is compatible with higher modulation orders and frequency bands. Simulation results validate the effectiveness of the proposed scheme.

The 3rd Generation Partnership Project (3GPP) 5G Non-Terrestrial Networks (NTN) adopt Orthogonal Frequency Division Multiplexing (OFDM) to enable integrated space-ground networks via Low Earth Orbit (LEO) satellite global connectivity. However, the rapid movement of LEO satellites induces a significant non-uniform Doppler shift across subcarriers, resulting in the signal bandwidth changes that leads to sampling point offsets and severely impacting demodulation performance. Traditional Doppler compensation algorithms focus mainly on addressing the uniform Carrier Frequency Offset (CFO) caused by crystal oscillation. In LEO satellite broadband communication systems, the Sampling Frequency Offset (SFO), caused by the non-uniform Doppler frequency shift, can be tens of times greater than the CFO, leading to non-negligible phase rotation, inter-carrier interference (ICI), and inter-symbol interference (ISI). Consequently, a low-complexity algorithm is required to address the fast time-varying SFO — one of the core challenges in these systems. In this paper, we derive a closed-form expression for the signal distortion and propose a model-driven compensation method that leverages the predictability of satellite trajectories. The proposed method effectively removes phase rotation and mitigates ICI and ISI through fast Fourier transform (FFT) window adjustment and phase rotation compensation. The accuracy of the model is validated through extensive simulations and a real satellite communication trial. Results demonstrate that the proposed method supports higher-order modulations, leading to an average spectral efficiency improvement of approximately 50%. This pioneering research promises to ensure robust performance in dynamic LEO satellite environments.

Due to the constraints of satellite payloads, traditional multi beam approaches often lead to the inefficient allocation of limited resources when addressing uneven traffic demands. One major trend noticed in satellite networks is the implementation of beam-hopping and full frequency multiplexing. It gives us flexibility in adjusting the time and power domains, enabling us to fulfil the uneven traffic demands and user latency performance requirements through appropriate beam-hopping pattern (BHP) scheduling and power allocation. This paper proposes a joint beam-hopping pattern scheduling and power allocation scheme for low earth orbit (LEO) satellite networks to address the uneven traffic demands while ensuring the latency performance requirements of users. The problem is formulated as maximizing the minimum traffic satisfaction rate by optimizing the BHPs and power allocation across time slots. To solve this mixed-integer non-convex problem, a two-step approach is adopted. For areas with high levels of traffic demand, the proposed joint beam-hopping and power allocation strategy delivers over 90% traffic satisfaction. When compared to optimization without beam-hopping, it increases total system throughput by 15%.

Satellite Internet, as an important component of the integrated space-ground information network, is a hot research hotspot nowadays. Many scholars have undertaken research in the areas of constellation networking design, network protocol design, and communication performance assessment, and their main research tool is software simulation. Traditional stand-alone network simulation simulators based on OPNET or NS3 are constrained in the simulation efficiency of mega satellite networks because of the limitations of computer hardware conditions and software performance. Based on the above characteristics, we propose a parallel network simulation architecture based on low correlation between different areas of the global satellite network, and in order to improve the parallel network simulation performance, the network topology needs to be divided effectively. Therefore, firstly we consider CPU and memory resource consumption as a measure of topology partitioning performance indicators, propose a resource assessment algorithm and use the result of this assessment as the topology partitioning optimization objective; secondly, we propose a load balancing based intelligent topology partitioning algorithm (LBTP); thirdly, we propose a time slice algorithm (TSA) for parallel simulation in each time cycle. To demonstrate the algorithm proposed in this paper, we built a simulation platform based on the combination of STK (Satellite Tool Kit), OPNET and Proxmox VE, and experimentally verified that the proposed architecture and algorithm significantly improve the simulation efficiency.

As the demand for high data capacity steadily increases, high-throughput satellites based on frequency reuse are becoming the focus of future research. Under the circumstance, it is attractive that multiple multi-beam satellites with wide coverage serving the same area simultaneously can improve the system throughput by transmitting more data streams to covered terminals. However, the increased inter-beam interference become the major obstacle for increasing the overall system throughput. Considering that beamforming techniques will increase satellite complexity and linear precoding techniques will significantly amplifies noise, this paper focuses on the Tomlinson-Harashima precoding (THP) with a balanced performance and complexity in nonlinear precoding to mitigate inter-beam interference. Utilizing the feature that the performance of THP related to the ordering of data streams, this paper proposes a novel iterative optimization THP (IO-THP) based on iterative structure without modifying the existing THP structure. Based on the existing multi-branch THP (MB-THP), IO-THP configures the optimal transmission mode for each terminal through sorting and scheme selection strategies and iterative structure to obtain the optimal ordering of data streams. In addition, this paper develops IO-cTHP and IO-dTHP for two basic THP structures. The first one places the diagonal weighted filters at the terminals to reduce the amplification for noise and the second places the diagonal weighted filter at the base station to simplify terminals. Simulation results show IO-THP possess 4dB gain relative to MB-THP and more than 10 dB gain relative to conventional ZF, MMSE and THP.

As an important complement to sixth-generation (6G) systems, low earth orbit (LEO) satellite will increase system capacity and improve service coverage. A critical question in LEO satellite systems is how to increase spectral efficiency. The existing primary method is the adaptive modulation and coding (AMC) based on accurate channel state information (CSI). However, the long-time delay of LEO satellite will lead to outdated CSI. Existing works usually predicates the future CSI based on time series, which is usually influenced by the constriction on satellite payload, such as nonlinear distortion caused by a high-power amplifier (HPA). In this work, we investigate the effects of predicting CSI using an improved time series prediction model in order to resolve the above problems. The simulation results verify the accuracy of the improved model. Compared to the commonly-used model, the performance of the improved model has a significant increase in spectral efficiency.

As one of the next-generation mobile network visions, the Space-Air-Ground integrated network is an inevitable trend of the future network, and research on heterogeneous network switching algorithms under the Space-Air-Ground integrated network becomes more important. Existing heterogeneous network switching algorithms usually use fixed weights of attribute to make decisions, but when the single or multiple attributes of multiple networks are too different, users will be connected to the same network. In the Space-Air-Ground integrated network, large differences in networks between heterogeneous networks, such as delay, will result in excessive load on a single network. In this paper, we proposed the Monte Carlo-Markov decision process (MC-MDP) algorithm to balance the network load of multiple networks. It can dynamically adjust the access networks of users in the system while considering the user's service requirements and network differences. Monte Carlo method is used to improve the convergence speed of the Markov decision process (MDP) algorithm. Numerical results confirm the MCMDP can improve the bandwidth resource utilization efficiency of the heterogeneous network and the convergence speed of the MDP algorithm.