Low Earth Orbit (LEO) satellite communication constellations provide crucial support for the future of ubiquitous connectivity. However, the high dynamic nature of LEO satellites’ relative positions affects the stability and reliability of inter-satellite communication links while imposing stricter latency requirements. Fast adaptive beamforming technology offers a viable solution to address these challenges. To achieve fast adaptive beamforming, this paper proposes a two-step beamforming solution based on a Transformer neural network model and explores the feasibility of applying meta-heuristic algorithms for hyperparameter optimization in neural network (NN) models. Experimental results demonstrate that the angle-of-arrival predictor optimized using the Polar Lights Optimizer (PLO), a meta-heuristic algorithm inspired by the aurora phenomenon, significantly outperforms the unoptimized model, with its error consistently remaining within the half-power beamwidth of the proposed planar antenna array. Meanwhile, the beamforming accuracy improves by 15.2% compared to other NN-based models, and in all test scenarios, our algorithm reduced the average response time by approximately 82.1% compared to the null-steering beamforming algorithms. This study provides a novel solution for achieving low-latency, high-reliability 3D beamforming in LEO inter-satellite communication, integrating both speed and accuracy, thereby contributing to the advancement of 6G and future ubiquitous connectivity.

As a key component of the space-air-ground integrated network (SAGIN), low Earth orbit (LEO) satellites aim to provide global coverage and reliable services under high-speed mobile conditions, which are critically challenged by severe Doppler effect and inherent broadcast security threats. To address these issues, this paper investigates a multi-reconfigurable intelligent surface (RIS)-assisted orthogonal time frequency space (OTFS) downlink transmission system, where a LEO satellite serves multiple information receivers and potential eavesdroppers acting as energy receivers via simultaneous wireless information and power transfer (SWIPT). By jointly optimizing multi-dimensional resource variables, such as transmit beamforming and RIS reflection coefficients of the spatial domain, and the symbol scheduling matrix of the time-frequency domain, this paper aims to maximize the sum secrecy rate while satisfying constraints on satellite transmit power, the legitimate users’ quality of service, and energy-harvesting requirements. Given the high-dimensional, non-convex, and NP-hard nature of this problem, we develop an enhanced actor-critic deep reinforcement learning (DRL) framework. The core innovation lies in designing an episodic return-prioritized experience selection mechanism with online mixing, which significantly improves the sampling efficiency and policy stability by intelligently selecting training data. Simulation results demonstrate that the proposed approach outperforms existing schemes in achieving a higher sum secrecy rate, providing a practical and highly efficient resource scheduling solution for building secure and reliable next-generation SAGIN.

The frequency-offset-governed range-angle-dependent dynamic beam pattern of frequency diverse array (FDA) constitutes a critical mechanism for enhancing spatial-domain covertness in wireless communication systems. This paper investigates FDA-assisted satellite covert communication. By considering covertness constraints based on average Kullback-Leibler (KL) divergence, frequency offset limitations, and power budget, we jointly optimize the satellite transmit power and FDA element frequency offsets to simultaneously maximize the time-varying covert rate and minimize the time-varying covert angle. To address practical channel dynamics, we develop a stochastic channel model incorporating Doppler compensation and channel aging effect mitigation for the FDA scheme. Closed-form and approximate expressions for the beam peak angle and half-power beamwidth (HPBW) with respect to frequency offsets are derived and analytically verified. The principle of linear uniform FDA frequency offset interval is established, with covertness quantified through KL divergence, enabling an optimized FDA frequency offset design from an angular perspective. To solve it, an improved deep reinforcement learning (DRL) with soft actor-critic (SAC) algorithm is proposed. Simulation results demonstrate that the interception probability is improved, and the proposed dynamic FDA algorithm exhibits better covert performance than other DRL-based methods.

This paper investigates distributed link selection (LS) for a multi-hop buffer-aided relay network consisting of one source, one destination, and multiple relays, where each node has access only to local instantaneous channel state information (CSI) and the buffer status of its adjacent nodes. In particular, by improving the alternate-transmission strategy, we propose a novel flexible distributed LS scheme that flexibly selects odd- and even-numbered links in each time slot. Notably, acquisition of local buffer-state information is integrated into the distributed LS agreement process, so it incurs no extra signaling overhead. We also derive the average throughput and packet delay of the proposed scheme by constructing a two-layer Markov model and enumerating all feasible buffer-state transitions. In addition, a simplified expression and an asymptotic analysis are provided to give further insight into the average throughput. Theoretical analysis and simulations show that the proposed flexible scheme substantially outperforms a baseline alternate scheme and closely approaches the performance of a related centralized LS scheme.

Satellite communication systems operate across diverse orbital configurations and terrestrial environments, where channel characteristics vary significantly due to differences in Doppler shifts, shadow fading, and propagation conditions. This heterogeneity causes severe domain shift, degrading the performance of interference recognition models trained on one channel environment when deployed in another. To address this challenge, this paper proposes a Cross-Domain Adversarial Disentanglement Network (DADN) that explicitly separates interference-invariant features from channel-dependent features through a dual-stream architecture. Unlike conventional domain adaptation methods that perform global feature alignment, DADN employs a parallel dual-stream architecture where the interference feature extractor and channel feature extractor operate independently with separate parameters, enabling explicit separation of transferable semantic information from environment-specific propagation effects.The interference feature extractor learns intrinsic signal characteristics shared across environments, while the channel feature extractor captures environment-specific propagation attributes. A gradient reversal mechanism adversarially removes residual channel information from interference features, and KL-divergence regularization prevents channel features from encoding interference category information, jointly ensuring feature orthogonality. Experiments across four representative satellite scenarios (LEO-Urban, LEO-Open, MEO-Suburban, GEO-Rain) demonstrate that DADN achieves 98.49% average recognition accuracy, outperforming CNN baseline by 2.08%, CLDNN by 1.24%, and DANN by 1.57%. Furthermore, DADN maintains over 97% accuracy with only 5% labeled target-domain data, confirming its strong cross-domain adaptability and data efficiency for practical satellite interference recognition applications.

Satellite communications (SatCom) face fundamental security challenges due to their inherent broadcast nature. To address this, we exploit beyond-diagonal reconfigurable intelligent surface (BD-RIS) to unleash its full-scattering matrix control for enhanced secure beamforming flexibility in SatCom with rate-splitting multiple access (RSMA), where the satellite attempts to convey private signals to legitimate users with blocked direct downlinks and multiple eavesdroppers. To maximize the worst-case secrecy rate among legitimate users, a max-min fairness (MMF) problem is formulated with imperfect wiretap channel state information (CSI) via joint precoding, RIS configuration, and rate splitting optimization. By using the block coordinate descent (BCD) method, these optimization variables are decoupled with different subproblems and solved by the penalty dual decomposition (PDD) method iteratively. Furthermore, we develop a computationally efficient suboptimal solution that employs diagonal RIS (D-RIS) with reduced hardware and computational complexity, where alternating optimization (AO) and successive convex approximation (SCA) methods are employed to solve the non-convex problem. Simulation results demonstrate that our proposed BD-RIS-RSMA scheme achieves significant performance improvements compared to baseline schemes, while the suboptimal diagonal RIS scheme offers a favorable performance-complexity tradeoff.

Shore-based communication is an essential component of the Space-air-ground integrated network(SAGIN). However, due to complex marine environmental factors such as wave motion and tidal effects, it’s challenging to accurately model its channel characteristics. Therefore, this paper conducted a wideband shore-to-ship channel measurement experiment with 4 GHz frequency and 100 MHz bandwidth. To deeply analyze the impact of waves and tides on the channel, a ship attitude recorder was introduced for the first time to capture the real-time attitude information of the ship. Based on the measured data and combined with the Kirchhoff approximation theory, a path loss model in the nearshore area was established and proved better than the fitting effect of the two-ray model. Moreover, the delay spread of the channel was analyzed, showing that its 90% value is within 185.64 ns. The paper contributes to the development of a more realistic and detailed model of dynamic maritime channels.

This paper presents a general framework for probabilistic relay selection (RS) in asymmetric buffer-aided cooperative relaying systems, which caters to scenarios with both perfect and imperfect channel state information (CSI) during the RS process. The framework extends and generalizes many existing buffer-aided RS schemes. In particular, we introduce an auxiliary stochastic process which assigns varying selection probabilities to different links, considering the dynamic wireless channel and buffer states. Subsequently, we leverage the obtained outage probability and average packet delay (APD) to formulate outage optimization problems while adhering to APD. To address the intricate high-dimensional optimization problems, we employ a deep learning (DL) approach, which involves designing probability mass functions for the auxiliary stochastic process and developing an effective loss function to update the neural network. Simulation results unequivocally demonstrate the superior performance of the proposed DL-based probabilistic RS scheme compared to benchmark schemes, particularly in scenarios involving imperfect CSI.

The rapid expansion of mega Non-Geostationary Orbit (NGSO) constellation systems has aggravated downlink co-frequency interference (CFI) problem. This work proposes an interference analysis method based on the "distance to center" (DTC) of manifold to analyze CFI in NGSO constellation systems. We establish the manifold geometric structure and parametric characterization model for the probability distribution of the downlink CFI between NGSO systems. The performance of the proposed method under two metrics, Affine Invariant Riemannian Metric (AIRM) and Symmetric Kullback-Leibler Divergence (SKLD), in scenarios involving potential interference from two NGSO satellites is analyzed through simulation, and compared with traditional energy-based method. The results show that the proposed method can effectively mitigate the complexity of interference analysis caused by the time-varying characteristics of NGSO constellations. Additionally, by mining high-dimensional information from signals, interference can be rapidly and accurately analyzed, which provides a novel and efficient method for the analysis of the CFI between NGSO constellation systems and system link design. Finally, the interference signals received by NGSO Earth station are visualized in 3D space by affine embedding method, which can provide an intuitive reference for interference analysis between NGSO systems.

The deployment of low earth orbit (LEO) satellites megaconstellations presents a promising way for achieving global coverage and service, attributed to their comparatively low round-trip latency and launch costs. However, this surge in LEO satellite launches exacerbates the scarcity of the limited spectrum resources. Spectrum sharing between satellite constellations and terrestrial networks and beam hopping (BH) technology emerge as viable strategies to mitigate this spectrum shortage. To enhance spectrum efficiency and avoid serious inter-system interference, we investigate the beam hopping scheduling of satellites for interference avoidance. The beam hopping scheduling of the integrated satellite-terrestrial wireless networks system is formulated as throughput-driven beam hopping (TDBH) problem and satisfaction-rate-driven beam hopping (SDBH) problem, respectively. In particular, we decompose the TDBH problem into two sub-problems by relaxation, and a genetic algorithm (GA) is introduced to handle the SDBH problem. The impact of channel conditions and traffic load intensity on the satellite system throughput is analyzed in TDBH simulation. As for SDBH optimization problem, the simulation results show that the proposed GA algorithm improves the average traffic satisfaction rate by 16.96% at least, compared with other benchmarks and suits to scenarios with different traffic demands and fading channel conditions.

In the case of COVID-19, which requires stable and reliable tracking of personnel movement, aircraft identification by specific emitter identification (SEI) is a hot-button issue. It refers to the process of identifying individual aircraft by comparing features extracted from the Radio Frequency (RF) signal of a given aircraft. Deep learning (DL) has been widely used in SEI research due to its excellent feature extraction capability, but in the actual low-resource reception scenario, the aircraft signal data acquired for training are long-tailed in distribution, and the imbalance of the signal data increases the challenge of training the network. In this paper, we propose a novel long-tailed specific emitter identification (LT-SEI) method using decoupled representation (DR) learning. Specifically, we separate the learning process into two stages: representation learning and classification, which includes unbalanced training and balanced classifier learning. The proposed DR-based LT-SEI approach is assessed using aircraft Automatic Dependent Surveillance Broadcast (ADS-B) data collected in the real world and compared to state-of-the-art methods. Experiment results show that the method has better long-tail recognition performance than the existing methods. When the data imbalance factor is 0.01, the F1 score of the model for the recognition result can reach 71.2%, which is 9% higher than that of the baseline model.

The co-frequency interference (CFI) of the nongeostationary orbit (NGSO) constellation to the geostationary Earth orbit (GEO) satellite Internet of Things (IoT) system is an nonnegligible problem. In this article, based on the calculation method of the NGSO constellation satellite distribution state period, we propose a global characteristic method to analyze the CFI of the NGSO constellation to the GEO satellite IoT system by spatio-temporal slices. In order to analyze and evaluate the global characteristic and overall situation of interference, the temporal and spatial cut set of interference is established by fragmenting time and gridding space. The correctness of the proposed NGSO constellation satellite distribution state period is verified by taking the OneWeb constellation’s interference to the SINOSAT-5 satellite communication system as the simulation scenario. Furthermore, the global interference characteristic of the OneWeb constellation to the SINOSAT-5 satellite communication system is analyzed with the method mentioned above in the range of 80 °E–140 °E and 0 °N–50 °N. The results show that the analysis method can reflect the overall situation of the CFI of the NGSO constellation to the GEO satellite IoT system.

The collision of ranging signals in the process of massive concurrent access ranging seriously affects the networking speed of satellite communication networks. Random access schemes are usually used to reduce the collision probability, but their effects are limited due to the limitation of ranging access rules and the network size. To further improve the performance of access ranging, the idea of optimal access ranging parameters is introduced in this article. A multiobjective anti-collision algorithm (MOACA) is proposed to optimize the concurrent access ranging process of high-capacity nodes of Internet of Things (IoT) to multifrequency time-division multiple-access (MF-TDMA) satellite communication systems. The model of the ranging measurement process is put forward and the expressions of ranging measurement time, collision probability, and the number of ranging channels are derived. On this basis, a multiobjective optimization problem (MOP) of the access ranging process is established. In MOACA, an ideal point database is formed based on the solutions of the MOP problem with typical inputs, and the negotiation curves of objectives are introduced to obtain the most appropriate ideal point from the database under certain requirements and system states. Besides, MOACA also contains a single-objective problem (SOP) for achieving the ideal point in the situation where no matching pattern can be found in the database. The simulation result shows that MOACA can improve the performance of the concurrent access ranging process.

Power and spectrum are considered as the significantly resource in the satellite communication systems, recently multiple-input multiple-output (MIMO) precoding has been identified as an excellent choice for the multibeam satellite communication systems (MSCSs). In order to improve power and spectrum utilization while mitigate the intra-beam interference and inter-beam interference, the crucial challenge is joint precoding design for multiple terminals. This paper proposes a hybrid full frequency (HFF) joint precoding scheme and chooses transmit power minimization as the energy efficient optimization problem which can be formulated as an indefinite quadratic optimization program. An optimization algorithm based on semidefinite programming (SDP) is designed to obtain the optimal objective. Simulation results demonstrate the performance improvement of proposed HFF joint precoding outperforms conventional frequency reuse and existing algorithms with respect to per feed power on-board constrained.