This paper investigates a dual-robust beamforming design for covert satellite communication, where covert transmission is embedded within an overt channel. While prior studies on covert communication over overt channels typically address robustness for covert user under wiretap channel (WC) uncertainty, achieving simultaneous robustness for both overt and covert users remains challenging in satellite-to-ground links with uncertain channel state information (CSI). We propose a dual-robust beamforming framework that jointly designs overt and covert beamformers to maximize the minimum covert rate, subject to the overt user’s rate outage constraints (ROCs) and covertness constraints. The resulting optimization problem is intrinsically nonconvex due to CSI and WC uncertainties, variable coupling, and the absence of closed-form expressions for the ROCs. To solve the problem, we reformulate the problem into a sequence of convex subproblems by utilizing semidefinite relaxation, the S-procedure, large deviation inequality, and fractional programming. We then develop a dual-robust beamforming algorithm (DRBA) that yields near-optimal beamforming vectors. Numerical results demonstrate that the proposed approach achieves superior covert communication performance while ensuring robust service for both overt and covert users, outperforming benchmark schemes.

In this article, we investigate a novel covert transmission scheme for satellite communication systems. Specifically, the satellite employs a rate-splitting multiple access technique to covertly transmit messages to multiple users and simultaneously transmit jamming signals, thereby enhancing the covert rate and robustness while avoiding detection by a warden. Due to the long propagation delay and lack of the warden’s precise location information, acquiring perfect channel state information (CSI) between the satellite, covert users, and the warden is difficult. To this end, we establish a dual-CSI uncertainty model, which incorporates phase and norm-bounded uncertainty for the covert and wiretap channels to characterize the actual CSI. In addition, we formulate a stochastic optimization problem with the objective of maximizing the minimum covert rate while adhering to covert communication constraints. The optimization problem is nonconvex and difficult to solve directly due to the dual-CSI uncertainty and the coupled nature of the variables. To solve the problem, we reformulate the original problem into a series of convex optimization problems by utilizing semidefinite relaxation, fractional programming, and the S-procedure methods. Then, we propose a robust common rate allocation and beamforming (CRAB) algorithm to obtain near-optimal solutions for the beamforming vectors and common rate allocation. Extensive simulation results demonstrate that the proposed algorithm significantly outperforms baseline schemes in terms of both covert rate and robustness.

The Non-geostationary orbit (NGSO) satellite constellation has gained widespread recognition owing to its exceptional low latency and seamless global coverage. However, with the increasing number of countries launching satellites, there has been a surge in the amount of satellites orbiting in low earth orbit, resulting in a growing strain on both frequency and orbital resources. Therefore, the issue of satellite coexistence has become increasingly critical. Moreover, the uneven distribution of terrestrial users imposes higher demands on beam resource management. This paper proposes a beam allocation strategy based on matching theory, which effectively mitigates harmful interference from the main constellation while optimizing the throughput of the minor constellation. The proposed strategy enables on-demand allocation, which maximizes both service quality and resource utilization, ultimately achieving the objective of constellation coexistence. Simulation results demonstrate that the performance of the proposed strategy is significantly superior compared to other link establishment schemes.

In this paper, we investigate a dynamic resource allocation problem for remote Internet of things (IoT) data collection in space-air-ground integrated networks (SAGIN), in which the aerial platforms are deployed to bridge the communications between IoT nodes and satellites. To obtain an efficient resource allocation strategy that accommodates the stochastic data arrivals of IoT nodes and the dynamic network topology due to the high mobility of non-geostationary orbit (NGSO) satellites, we first formulate a resource allocation problem with queue stability constraints. Our objective is to maximize the long-term network utility, ensuring a balance between throughput and fairness among the IoT nodes. The formulated long-term problem is challenging to solve due to the unknown future network states and the coupling between continuous and integer variables. Therefore, we adopt the Lyapunov optimization theory to transform the problem into a deterministic problem in each time slot. Moreover, an online resource allocation algorithm is proposed to dynamically determine data admission, subchannel assignment, and power control in each time slot based on the current network status and data backlog. In addition, theoretical analysis indicates that there is an [O(1/V ), O(V)] trade-off between network utility and data backlog with control parameter V. Numerical results demonstrate that the proposed algorithm can greatly enhance the system throughput and reduce data queue backlog as well as preserve queue stability as compared with the benchmarks.