Satellite communication faces a great risk of eavesdropping due to the its broadcasting nature and vast coverage. Conventional beamforming-based physical-layer security technology encounters difficulty in preventing eavesdropping within the main-lobe of the satellite, which causes a large insecure area. In this paper, we propose a cooperative multi-beam transmission framework, which elaborately superimposes multiple beams to shrink the intersected coverage area, and exploits the additional design degrees of freedom to enhance the security. The designs of symbol mapping and beamforming are jointly studied at multiple beams to achieve transparent communication in the intersected area while randomizing the signals otherwise. Information-theoretic performance metric is identified to characterize the information leakage. A tight upper bound of the intractable metric is proved using variational methods, and a deep learning-based approximation and optimization scheme is then proposed to efficiently design the multi-beam transmit signals. To further distort the leaked signal, a convergence-guaranteed cross-entropy algorithm is developed for joint antenna subset selection at multiple beams. Simulations show that the proposed scheme can shrink the insecure area by an order of magnitude compared with the conventional methods.

This paper studies the fundamental limits of covert communications covered by randomly activated overt users in both single-frame and multi-frame transmission scenarios. While traditional covert communications mainly consider concealing signal power characteristics, the existence of overt users provides opportunities such that covert communications can be achieved through the confusion between the users. This benefit is first revealed in single-frame transmission scenario. The major obstacle in analyzing performance limits is that the conventional Kullback-Leibler divergence based covertness measurement becomes infinite. To overcome the intractability, a tighter upper bound of the total variation distance (TVD) is then developed using a novel recursive-iterative approximation. On this basis, the collapse effect of the TVD is derived, which shows that the TVD is strictly less than 1 if the covert user sets the transmit power to be an integer multiple of that of the overt users. Then, we find that$\mathcal {O}(N)$-bit information can be transmitted over N channel uses under the above setting, which breaks the well-known square root law. If the above setting is violated, the TVD instantly approaches 1 as$N\rightarrow \infty $, and only$\mathcal {O}(\sqrt {N})$-bit information can be covertly transmitted. To prove this, the detection method of the warden is modified to cope with the random activation of overt users. These conclusions also hold for the transmission with uncertain powers or in fading channels, which resembles realistic wireless transmissions. In multi-frame transmission scenario, however, the access characteristics of overt users can be exposed from a statistical perspective, such that the rate gain disappears and the covert transmission rate drops to$\mathcal {O}(\sqrt {N})$bits per frame. To obtain a positive covert transmission rate, we propose a rate-splitting based covert transmission scheme that introduces an opportunistic access branch to bring randomness, through which the covert user can transmit up to$\mathcal {O}(NL)$-bit information over L frames.

Satellite-aided airborne systems can enable data acquisition in remote areas for the intelligent transportation systems (ITS), leveraging the satellite coverage alongside the mobility and multifunctional capabilities of autonomous aerial vehicles (AAVs). However, due to the harsh environment, ensuring secure and timely task execution is complicated by uncertainties related to both channel conditions and eavesdropping threats. This paper proposes a two-stage optimization method to fully exploit AAVs’ flying, sensing, compressing, and transmitting capabilities for secure data acquisition under dual uncertainties. In the first stage, a deep reinforcement learning strategy is employed to optimize sensing and trajectory planning to explore the eavesdropping environment and balance computational and transmission demands. Building on the sensed information about the eavesdropping environment, the second stage focuses on minimizing task completion time through optimal resource allocation and hierarchical A* path planning, with the channel uncertainty addressed by incorporating an outage probability constraint. Simulations demonstrate that the proposed method can reduce data completion time by 35.3%, validating its effectiveness in uncertain environments.

Covert communications studied in prior works typically consider only the average power constraint on the transmit signal. In this paper, we explore the optimal signaling for covert communication under the peak power constraint, in view of the realistic limitation at the transmitter. Our main result is that the rate-optimal transmit signal distribution under the covertness constraint forms finite hyperspheres, on each of which the points distribute uniformly. To prove this, a lemma is first deduced to explore the equivalence between maximizing the achievable rate and minimizing the covertness measured by the Kullback-Leibler divergence. The equivalence property is then exploited to characterize the covert communication as a specific two-user broadcast channel, which simplifies the intractable covertness constraint. After that, by exploiting the spherical symmetry property of Gaussian noise and the identity theorem of holomorphic functions, the main result is derived. Furthermore, the analytical representation of the optimal signaling in low signal-to-noise ratio (SNR) region is studied following Shamai’s approach. For high SNR region, a nonlinear dynamic programming algorithm is developed to generate the optimal signal distribution. For ease of practical implementation, the discrete constellations are optimized based on the sequential quadratic programming algorithm. Simulation results verify the optimality of the proposed hyper-sphere signaling and demonstrate the performance gain of the optimized constellations over typical modulation constellations.

The reliability of low Earth orbit (LEO) mega constellation networks (MCNs) under large-scale geographical failure of satellites remains unrevealed. In this paper, we propose an algorithm to assess the connectivity, average latency and hop count by considering topology changes resulting from geographical failure, under different topology management. Numerical simulations are conducted based on the traffics source from end users distributed among the 100 most populous cities. The results show that the MCNs are generally reliable against geographical failure, as a geographical failure with a radius of 3000 km can at most disconnect 8% of the end users, while increasing the average hop count and latency of the remaining users by less than 10%. Also, topology reconfiguration after failure have a greater impact on the hop count than latency.