In this paper, we investigate the outage probability of a multi-tier dual-hop terrestrial-low earth orbit (LEO) satellite-geostationary earth orbit (GEO) satellite hybrid wireless communication system. The system comprises multi-tier LEOs and one GEO act as relays in the uplink, which help the terrestrial ground station (S) transmit information to the terrestrial destination (D). In uplink transmission, we introduce a one-dimensional hardcore point process to model different altitudes of LEOs’ different tiers. We also use a generalized selection combining technique to achieve a trade-off between maximum ratio combining and selection combining. In downlink, GEO offers the maximum coverage to randomly distributed D. Moreover, the independent identically distributed Nakagami-m fading and shadowed Rician distribution are brought to model the different channels. Finally, Monte-Carlo simulations are presented to affirm the precision and accuracy of the derived analytical models and the proposed analysis. This framework offers crucial insights for system designers and network operators, enabling the optimization of resource allocation, relay strategies, and overall reliability in terrestrial-satellite hybrid networks.

This paper investigates the internal secrecy and external covertness of a mixed-trust autonomous aerial vehicle (AAV) communication system assisted by rate-splitting multiple access (RSMA). In this setting, a semi-trusted user with partial decoding capability poses an internal eavesdropping threat, while multiple distributed wardens attempt to detect the transmission from the AAV to the semi-trusted user, creating an external covertness challenge. To characterize these security aspects, a unified analytical framework is developed. First, the internal eavesdropping capability of the semi-trusted user is quantified by deriving a closed-form expression for its eavesdropping success probability. Based on the outcome of the eavesdropping attempt, tractable expressions for the secrecy outage probability of the legitimate user are obtained. Furthermore, the external covertness performance is analyzed by deriving closed-form false alarm probability, missed detection probability, and detection error probability (DEP) for an individual warden, together with the optimal detection threshold and the corresponding minimum DEP. The cooperative global detection performance with multiple wardens is further characterized under conservative fusion rules. Extensive Monte Carlo simulations validate the analytical results and, through a joint evaluation of secrecy, reliability, and covertness metrics, illustrate the feasible operating regions enabled by RSMA power allocation in comparison with a NOMA baseline. The results provide a comprehensive theoretical basis for the design of secure and covert AAV communication strategies in mixed-trust environments.

The rapid expansion of Low Earth Orbit (LEO) satellite constellations has brought significant spectrum management challenges, including spectrum scarcity and complex interference issues. Traditional algorithms and prior Artificial Intelligence (AI) methods fail to meet LEO’s demands for managing extreme dynamics, massive scale, and multi-objective optimization. This paper introduces an innovative Large Language Model (LLM) framework for intelligent spectrum sharing and dynamic resource allocation in satellite-terrestrial down-link systems. First, we established a geometric model for satellite-terrestrial down-link communication, and accurately derived the statistical distribution function of satellites within the space enclosed by a specific orbital line by combining the stochastic geometry theory. Under this geometric model, a communication scenario was introduced, and an adaptive modulation transmission mechanism based on orthogonal frequency division multiplexing signals was designed. Then, the system combines the real-time spectrum sensing results with the natural language description of the quality of service of multi-service data using prompt engineering techniques, and delivers the comprehensive information to the LLM for resource allocation and generation of a transmission scheme. Finally, the resource allocation and transmission scheme determined by the LLM is applied to the established communication model, and the system performance is comprehensively evaluated by analyzing indicators such as outage probability, system throughput, and transmission and waiting delays. Primary contributions include novel dynamic service-to-strategy generation, an LLM-centric prompt-driven architecture, and a new paradigm that positions the LLM as an intelligent “spectrum orchestration brain” for complex global LEO resource management. Collectively, these advancements enhance spectrum utilization intelligence, adaptability, and efficiency, offering a transformative approach to overcome the limitations of prior methods in demanding LEO environments.

Achieving secure communication with a low probability of detection (covertness) is critical yet challenging, particularly when employing practical digital modulations that can compromise the statistical indistinguishability assumed in theoretical models. This paper introduces a novel end-to-end framework leveraging digitally modulated covert signal modeling, obfuscation, and deep learning to attain simultaneous covertness and reliability. Firstly, we propose a novel approach to covert performance evaluation for modulated covert signals against detection. To address the deteriorated covertness considering modulation schemes, we further propose generating Gaussianized camouflage signals via a multi-stage transmitter pipeline encompassing spreading, jitter, filtering, and non-linear transformations, designed to mimic noise statistics effectively. At the receiver, a specialized deep learning architecture, CovertSyncNet, performs robust joint dynamic synchronization and symbol recovery. This receiver incorporates dedicated components to precisely estimate time-varying chip offsets and invert the complex, nonlinear distortions inherent in the camouflaged signal, enabling accurate demodulation. Extensive simulations rigorously validate our approach, demonstrating that high reliability is maintained despite the heavy camouflage. Concurrently, enhanced covertness is confirmed through metrics indicating low statistical distinguishability from Gaussian noise. This work highlights the significant potential of deep learning to bridge the gap between theory and practice, realizing communication systems that are simultaneously reliable, secure, and highly covert, even under realistic operational conditions.

Next-generation satellite systems require efficient collaboration in terms of wide area coverage and signal augmentation, enabling intelligent allocation of available wireless resources to ensure the security of information. Meanwhile, machine learning (ML) is widely considered well-suited to massive, real-time data scenarios in satellite communication networks, and graph neural network (GNN) is a specific branch for processing the irregular data within such networks. In this paper, we propose physical layer security for a multi-satellite collaborative (MSC) system involving LEO satellites, users, and eavesdroppers. Specifically, the GNN-based security communication of the MSC (G-MSC-SC) architecture is designed to maximize the secrecy rate. Since heterogeneous and isomorphic methods can effectively solve multi-type node mapping and complex communication problems, the G-MSC-SC architecture is divided into two steps: A heterogeneous graph pruning attention coefficient network (HGPAN) and an isomorphic graph eavesdropper as an auxiliary node network (IGEAN). In the HGPAN architecture, different types of device nodes are embedded in the same dimensional space, addressing the challenge of matching LEO satellites to users. The IGEAN architecture maps user channel state information (CSI) to beamforming (BF) vectors through attention aggregation and an improved loss function. Moreover, the corresponding conventional optimization algorithms are designed as test and comparison baselines. Simulation results show that 1) the G-MSC-SC architecture outperforms neural networks and heuristic algorithms in terms of accuracy and efficiency; 2) as the numbers of users and virtual eavesdroppers increase, the directional alignment between the BF vectors and the LEO satellite-user channels shows an improvement; and 3) with imperfect CSI, the G-MSC-SC architecture still achieves an excellent balance between user secrecy rate and communication rate.

Satellite-to-ground communication systems typically operate in environments with high interference levels, complex topologies, and stringent platform constraints. Therefore, intelligent, anti-interference, and low-power systems are required to achieve the desired transmission performance. This paper proposes a system for optimizing high-frequency satellite-to-ground communications using computer vision (CV) technology, like millimeter-wave (mmWave) satellite communication systems. The system uniquely combines CV-based target localization with adaptive beamforming and power control to optimize communication links with ground targets such as base stations, ships, and aircraft. This approach significantly outperforms traditional radio frequency-based methods in accuracy and efficiency, particularly in dynamic mmWave scenarios. Simulation results confirm the superiority of our system in terms of sum rate and energy efficiency, demonstrating its potential to revolutionize high-frequency satellite communications by providing reliable, high-quality service to terrestrial targets. Finally, simulation results are presented to demonstrate the efficiency of the proposed schemes.

Unmanned aerial vehicle (UAV) communication systems usually operate in harsh scenarios, which require accurate information about the topology and wireless channel to achieve the desired transmission performance. Therefore, when millimeter-wave (mmWave) communication with its intrinsic Line-of-Sight (LoS) condition is adopted, accurate target localization is essential to determine the spatial relationship between the UAV and the grounded receivers (Rxs). In this article, a computer-vision (CV)-aided jointly optimization scheme of flight trajectory and power allocation is designed for mmWave UAV communication systems by utilizing the visual information captured via cameras equipped at the UAV. Compared with traditional schemes, the implementation cost and overhead can be greatly saved as no radio frequency transmissions are required in the proposed localization scheme. In addition, the transmit power at the UAV is jointly optimized with its flight trajectory in two different cases. Finally, simulation results are presented to demonstrate the efficiency of the proposed schemes.