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.
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.
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.
This paper investigates the secure transmission in satellite communication systems, where a geostationary orbit (GEO) satellite serves an earth station while multiple eavesdroppers attempt to intercept the confidential message. Assuming that only the imperfect channel state information (CSI) of the wiretap channels are available, we propose a secure beamforming scheme to maximize the secrecy energy efficiency (SEE) of the earth station while satisfying the signal-noise-ratio (SNR) requirement at earth station, the secrecy constraints at eavesdroppers, and per-antenna power constraints at satellite antenna feeds. Since the formulated optimization problem is mathematically intractable, we propose a two-stage beamforming scheme to convert the original nonconvex problem into a solvable one and obtain the beamforming weight vectors. Numerical results are finally provided to verify the effectiveness of our proposed scheme.
This letter investigates the secure transmission optimization in a multibeam satellite downlink network with multiple eavesdroppers, where each legitimate user is wiretapped by a corresponding eavesdropper. Our design objective is to maximize the sum secrecy rate of the multibeam satellite network in the presence of imperfect channel state information (CSI) of eavesdroppers, while guaranteeing the total transmit power constraint on-board the satellite. Due to the nonconvexity and intractability resulting from the eavesdroppers' CSI uncertainty, a robust beamforming scheme is proposed to transform the initial optimization problem into a convex framework by jointly applying the Taylor expansion, S-Procedure, and Cauchy-Schwarz inequality. Meanwhile, an iterative algorithm is introduced to obtain the optimal solution. Finally, the validity and superiority of the proposed scheme are confirmed through comparisons with the existing nonrobust and perfect CSI approaches.
Satellite communications have become essential in several domains such as broadcasting, navigation, and military applications. However, wireless communications including satellite communications are vulnerable to eavesdropping by malicious users. In this paper, we propose artificial noise assisted low earth orbit (LEO) satellite scheduling to enhance the secrecy of satellite communications. Further, we evaluate our proposed scheme through extensive simulations in terms of the secrecy rate and finally provide intuition to design future satellite communication systems from the aspect of security.
Satellite communication systems serve as an indispensable component of wireless heterogeneous networks in 5G era for providing various critical civil and military applications. However, due to the broadcast nature and full accessibility of wireless medium, serious security threats exist from such systems. As an effort to address this issue, this paper, for the first time, investigates the secure communication in a non-geostationary orbit (NGSO) satellite system from a physical layer security perspective. Specifically, we focus on the downlink of an NGSO satellite which provides services to a fixed earth station and is wiretapped by a fixed eavesdropper. We first apply three types of orbiting models to characterize the movement state of the satellite. Based on the orbiting models, we then provide theoretical analysis for the secure communication performance of such a system. The expressions of two fundamental performance metrics, secrecy capacity and secrecy outage probability, are derived in a closed form for any system time. Finally, we conduct extensive simulations to validate our theoretical performance analysis and illustrate the security performance in a practical NGSO satellite communication system.
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.