Low-earth orbit (LEO) satellite systems provide global coverage and low latency, yet their rapid motion induces severe channel variations, rendering full-service-period physical layer security design prohibitively costly and inefficient. To address this issue, we investigate a high-altitude platform (HAP)-assisted secure transmission scheme, where the HAP transmits artificial noise (AN) to enhance satellite communication security and improve user fairness. Specifically, we formulate a minimum secrecy rate maximization problem with joint power allocation and the HAP beamforming vector as optimization variables. Due to the non-convexity nature of the problem, solving it for a single time slot is computationally intensive, and directly extending the optimization across all time slots incurs prohibitive overhead. Therefore, we propose a learning-based framework that first optimizes key time slots and then trains a kernel ridge regression (KRR) model. In particular, the number of training samples is adaptively adjusted based on the minimum distance between eavesdroppers and legitimate users, while a warm-start strategy is used to accelerate optimization over the sampled slots. The trained KRR model learns the mapping from satellite angular positions to secure transmission parameters and predicts suitable solutions for the remaining time slots. Simulation results show that the proposed method achieves secrecy and user fairness performance comparable to per-slot optimization with much lower computational complexity, offering a scalable and efficient solution for secure LEO satellite communications.
Low-earth orbit (LEO) satellite systems provide global coverage and low latency, yet their rapid motion induces severe channel variations, rendering full-service-period physical layer security design prohibitively costly and inefficient. To address this issue, we investigate a high-altitude platform (HAP)-assisted secure transmission scheme, where the HAP transmits artificial noise (AN) to enhance satellite communication security and improve user fairness. Specifically, we formulate a minimum secrecy rate maximization problem with joint power allocation and the HAP beamforming vector as optimization variables. Due to the non-convexity nature of the problem, solving it for a single time slot is computationally intensive, and directly extending the optimization across all time slots incurs prohibitive overhead. Therefore, we propose a learning-based framework that first optimizes key time slots and then trains a kernel ridge regression (KRR) model. In particular, the number of training samples is adaptively adjusted based on the minimum distance between eavesdroppers and legitimate users, while a warm-start strategy is used to accelerate optimization over the sampled slots. The trained KRR model learns the mapping from satellite angular positions to secure transmission parameters and predicts suitable solutions for the remaining time slots. Simulation results show that the proposed method achieves secrecy and user fairness performance comparable to per-slot optimization with much lower computational complexity, offering a scalable and efficient solution for secure LEO satellite communications.
Low Earth Orbit (LEO) satellite communications are vulnerable to eavesdropping due to open wireless links. To address this issue, a simplified artificial noise (AN)-aided physical layer security scheme is proposed. A practical system model including a multi-antenna LEO satellite, a single-antenna ground user, and multiple eavesdroppers is established, considering shadowed Rician fading and imperfect channel state information (CSI). The AN is projected into the null space of the legitimate user’s channel to avoid interference, while suppressing eavesdroppers. A fixed power allocation strategy is adopted for easy implementation. Simulation results show that the proposed scheme outperforms traditional methods in secrecy rate and secrecy outage probability (SOP) with low computational complexity, making it suitable for resource-constrained LEO satellite systems.
In this paper, we propose a multi-agent deep reinforcement learning (MADRL) strategy for adaptive beamforming and artificial noise (AN) transmission to enhance physical layer security in low Earth orbit (LEO) satellite networks. Multiple satellites are jointly scheduled to cooperatively transmit data and AN against potential eavesdroppers such as hostile unmanned aerial vehicles. In the proposed scheme, each satellite independently selects its transmission mode (idle, data, or AN) and the corresponding beamforming vector to maximize the secrecy rate within a centralized training decentralized execution (CTDE) framework using the soft actor-critic (SAC) algorithm. The MADRL agents are trained using only statistical channel information of the adversary instead of full instantaneous channel state information. Simulation results demonstrate that the proposed scheme achieves a higher secrecy rate than conventional baseline schemes.
Compared with terrestrial communication, satellite communication has played a crucial role in 6G communication as a result of wide-area coverage characteristics. Among them, beamforming scheduling and information security are critical research issues to improve system transmission performance. However, in geostationary earth orbit (GEO)-based satellite communication systems, owing to the wide-area coverage, ground users are susceptible to inter-beam interference caused by multibeam antennas at the satellite, and the information received by terrestrial users may be eavesdropped by air or terrestrial eavesdroppers. To this end, in this article, we propose a new robust security transmission strategy by combining beamforming and artificial noise (AN) techniques. Specifically, based on the actual satellite communication network scenario, the uncertain channel models are formulated as bounded uncertainty sets by considering the impact of imperfect channel state information of both legitimate and wiretap channels. Then, we build a bounded-constrained beamforming optimization to maximize the sum rate of legitimate users, while guaranteeing the constraints of total transmit power on the satellite and the minimum security rate of each legitimate user. Since the original problem is non-convex and mathematically intractable, we jointly exploit the successive convex approximation, Taylor series expansion, S-Procedure, and the semi-definite relaxation approach, with which the original problem can be transformed into a tractable one with a series of linear matrix inequality constraints. Furthermore, we propose an iterative algorithm to obtain the optimal beamforming and AN vectors. Finally, simulation results show that, compared with the existing algorithms, the proposed algorithm has better security and robustness.
In recent years, satellite communications have become the focus of research on space-air-ground-sea integrated wireless communication networks because of their wide coverage. Due to the broadcasting nature of satellite communications, the security of satellite systems has become a critical issue. In this paper, we study the secure transmission of satellite communications, focusing on the geostationary earth orbit (GEO) and low earth orbit (LEO) satellite scenarios, respectively. Because of the satellite's high mobility and long propagation delays, the transmitter side cannot obtain accurate channel state information (CSI). To this end, we establish the channel phase uncertainty model of GEO satellite users, the channel angle uncertainty model of LEO satellite users, and the norm-bounded model of eavesdroppers, respectively. Then, a corresponding robust secure transmission design is proposed. Since the resulting optimization problems are non-convex and challenging to handle, we propose an algorithm that transforms the original problem into a series of convex optimization subproblems and obtains the beamforming weight vectors through iterative calculation. Simulation results indicate that the security and robustness performance of the proposed algorithms are significantly improved compared with the conventional baselines.
Low Earth Orbit (LEO) satellite communications are vulnerable to eavesdropping over open wireless links. This paper proposes a simplified artificial noise (AN)-aided physical layer security scheme. A system model with a multi-antenna LEO satellite, a single-antenna ground user, and multiple eavesdroppers is built under shadowed-Rician fading and imperfect channel state information. AN is projected into the null space of the legitimate channel to avoid interference, and fixed power allocation is adopted for low complexity. Simulations show the scheme achieves better secrecy rate and secrecy outage probability than traditional methods, suitable for resource-constrained LEO satellite systems.
As key enablers of 6G, low Earth orbit (LEO) satellite constellations are expected to offer global and low-latency connectivity. However, the broadcast nature and open-space propagation of satellite communications make it inherently vulnerable to eavesdropping. In dense LEO constellations where multiple satellites simultaneously cover overlapping regions, satellite scheduling can significantly contribute to secrecy performance. In this letter, we investigate a novel deep learning-based secure scheduling problem for physical layer security (PLS) in LEO satellite networks. During the scheduling process, we jointly consider multi-satellite transmission and cooperative artificial noise (AN) generation to defend against eavesdropping. The proposed deep learning-based scheduler, designed using attention-based permutation-invariant neural networks, learns security-oriented scheduling policies based solely on statistical channel information of eavesdroppers. Simulation results demonstrate that the proposed scheduling algorithm significantly enhances secrecy performance in terms of both secrecy rate and secrecy outage probability, outperforming conventional baseline schemes.