The Non-Terrestrial Network (NTN) is a promising technology to achieve the ubiquitous, low latency, and high data rate next-generation communication system, however, since the broad application of the NTN, the security problem in the NTN is of paramount importance. In this regard, this paper investigates a novel secure communication strategy within the NTN, where a legitimate user of the LEO satellite-integrated unmanned aerial vehicles network (ISUAVN) is equipped with a high-gain antenna and attempts to cross-layer eavesdrop on the GEO satellite network.In the considered NTN system, the cell-free massive multi-input multi-output (CFmMIMO) network is utilized at the ISUAVN to enhance the spectral efficiency of the terrestrial users. Furthermore, the ISUAVN shares the millimeter wave spectrum with the GEO satellite multicast downlink communication link. We first formulated a multi-objective optimization (MOO) based joint beamforming design problem for the secure communication of the considered CFmMIMO NTN system to achieve a Pareto optimal trade-off between two conflicting and essential objectives: minimizing total transmitted power and maximizing the secret data rate of the satellite network. Thereafter, to address this mathematically complicated optimization problem, we utilized successive convex approximation methods to transform this intractable problem into an equivalent convex optimization problem. Then, the commercial optimization package is employed to achieve the optimal solution of the MOO problem, commencing from a feasible point that is identified by a semi-definite programming initialization algorithm. Ultimately, the numerical simulation results demonstrated that the Pareto optimal trade-offs for the formulated MOO problem were achieved using the proposed algorithm, and the effectiveness of our proposed algorithm was shown through comparisons with existing related methods.

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.

Low Earth Orbit (LEO) satellite communications provide uninterrupted coverage and seamless services, which are becoming a crucial element in the Sixth-Generation of wireless communications. However, LEO communications are subject to high path loss and Doppler frequency shift, which presents significant challenges for such large-distance and time-changeable satellite-terrestrial transmission links. Direct Sequence Spread Spectrum (DSSS) has been widely adopted as a robust modulation technique in LEO satellite systems. Traditional DSSS signal detection methods, which rely on 2D searching, fail to effectively acquire the weak uplink signals due to the extremely low signal-to-noise ratio and pronounced channel dynamics. In light of these considerations, this study proposes an algorithm designated as time synchronization-aided signal detection, with the objective of enhancing the signal detection probability in low signal-to-noise ratio and high-mobility communication scenarios. Furthermore, we develop a novel transmission system based on time synchronization, with the objective of reducing the signal detection threshold by minimizing the impact of the Doppler frequency shift and time delay searching ranges. We theoretically analyze performance at varying time synchronization precision, numerical simulations and hardware experiments under diverse conditions demonstrate that, in comparison to the conventional algorithm, time synchronization can enhance the probability of signal detection.

In recent years, low Earth orbit (LEO) satellite communication has emerged as a focal area of extensive research due to its potential for global broadband connectivity. Multi-carrier direct sequence spread spectrum (MC-DSSS) technology, leveraging the inherent anti-jamming advantages of spread spectrum signals, has shown great promise in enhancing communication reliability. However, the acquisition of MC-DSSS signals in LEO transmission link presents significant challenges, primarily due to the coexistence of extremely low signal-to-noise ratio (SNR) and substantial Doppler effect.To address these issues, this paper proposes an optimal two-dimensional (2D) acquisition framework for MC-DSSS signals. Based on the maximum likelihood (ML) criterion, the proposed framework enables coherent combination of subcarriers with high time resolution, thereby improving the acquisition performance in harsh LEO environments. Furthermore, a two-step low-complexity coherent acquisition algorithm is developed. This algorithm significantly reduces the computational burden while maintaining the performance of fully coherent subcarrier combination, making it more suitable for real-time implementation in resource-constrained LEO communication terminals. Moreover, this paper derives closed-form solutions for the false alarm probability, detection probability, and mean squared error (MSE) in additive white Gaussian noise (AWGN) channel, which are validated through simulations. The results demonstrate that the proposed algorithm achieves 2 dB performance improvement in SNR compared to noncoherent combining method, with a 1024-fold reduction in MSE when the number of subcarriers is 16.

Efficient and reliable neighbor discovery is critical for UAV networks equipped with directional antennas, particularly in dynamic and energy-constrained environments. We present a novel optimization framework that jointly minimizes delay and power consumption using the power-delay product as the optimization metric. The framework is formulated for both synchronous and asynchronous schemes, leveraging upper-bound metrics and a convex-concave procedure to achieve tractable convex formulations. Simulation results validate the theoretical models and show significant improvements over baseline methods. We also conducted real-world experiments, where our method reduced power-delay product by 11% compared to the baseline, though the dual-sector hardware configuration limited the achievable gains. This work provides a comprehensive solution for UAV neighbor discovery, with high potential for scalability in more complex and dynamic environments.

Millimeter-wave (mmWave) communications, coupled with directional antenna-based Flying Ad-Hoc Networks (FANETs), have received considerable attention for their potential to provide high-speed, low-latency communications for a variety of applications. However, the high mobility of Unmanned Aerial Vehicles (UAVs) in FANETs leads to dynamic changes in relative positions, resulting in frequent link failures. Effective link maintenance in such networks has become a critical challenge. This paper addresses this issue by developing mathematical models of link disconnections in directional antenna-based FANETs. Specifically, we derive the probability density functions for link disconnections due to distance and angular misalignment in closed-form expressions. Based on these prediction models, we propose the Adaptive Link Breakage Prediction with Directionality (ALBP-D) method, which exploits the high directional gain of directional antennas to extend link lifetime and improve network performance. We compare ALBP-D with two baseline methods, the Periodic Link Maintenance (PLM) method and the Residual Path Lifetime (RPL) method, through extensive simulations. The results show that ALBP-D achieves superior performance, with approximately a 10-fold improvement in both link lifetime and network connectivity duration compared to the baseline methods. In addition, ALBP-D exhibits significant improvements in maintenance overhead efficiency, especially at higher max range adjustment count, achieving a 5 to 7-fold improvement over baseline methods. These results highlight the effectiveness of ALBP-D in directional antenna-based FANETs. We also implemented a prototype system consisting of a directional antenna node and an omnidirectional antenna node using realistic UAV trajectory data. Experimental results show that the prediction models agree well with the real link disconnection data, confirming the practical feasibility and accuracy of the proposed method.

Low Earth Orbit (LEO) satellite systems with sensing capabilities are widely regarded as promoting reliable and efficient communication services globally. This paper proposes a Multi-Satellite Collaborative Security System with Integrated Sensing and Communication (ISAC-MSC). Considering the potential benefits of LEO satellites and ISAC, we exploit sensing performance in the MSC system by jointly optimizing LEO satellite assignments, communication Beamforming (BF) vectors, and sensing BF vectors. Specifically, we design improved Continuous Particle Swarm (CP) optimization and Discrete Particle Swarm (DP) optimization algorithms to maximize the target sensing Signal-to-Noise Ratio (SNR) for LEO satellite assignments. Additionally, with respect to the BF vector optimization, we develop Power Approximation (PA) optimization algorithm, Inner Approximation (IA) optimization algorithm, and Joint Sensing and Communication BF (JSC-BF) optimization algorithm. Multiple algorithms are tightly integrated and alternately iterated. Numerical results show that: 1) the JSC-BF algorithms outperform the PA and IA algorithms in terms of sensing performance and communication secrecy rate; 2) compared to the single satellite case, the ISAC-MSC system performance approximately linear growth, and has strong extensibility; 3) with imperfect MSC synchronization case, the communication secrecy rate appears inflection point and stabilization, but the JSC-BF algorithms still have excellent performance.

With the increasing demands of the Industrial Internet of Things (IIoT), highly dynamic multi-unmanned aerial vehicle (UAV) networks are becoming indispensable to IIoT due to their flexibility, cost-effectiveness, robust safety measures, and real-time data collection capabilities. Accurate time synchronization is crucial for coordinated missions of multi-UAV networks, yet the time-varying nature of clock parameters and the rapid movements of UAVs pose significant challenges to achieving precise synchronization. This article introduces new state and observation models for clock and velocity parameters and proposes a Doppler and timestamp-based distributed algorithm for tracking clock parameters using the Kalman filter. To evaluate the performance of the proposed algorithm, we derive the Bayesian Cramér–Rao lower bound and conduct numerical simulations. The results of the simulations demonstrate that our algorithm surpasses existing methods in terms of accuracy in tracking clock parameters.

With the expanding demands for the space-air-ground integrated Internet of Things (IoT), the low-Earth orbit (LEO) satellite can be regarded as an important complement to IoT networks. Due to the transparency of satellite orbit information and the exposing nature of transmitting links, satellite-ground communication is extremely vulnerable to eavesdropping and jamming attacks. To establish a transmission link, the most crucial procedure was signal acquisition. Existing frequency hopping/direct sequence signal acquisition algorithms were either too time costing for the short visibility window of the LEO satellite or too resource costing for the LEO satellite devices. To alleviate this issue, we propose a novel differential coherent accumulation acquisition strategy for the LEO satellite-enabled IoT network to strike a balance between performance and complexity. It is also demonstrated that the proposed acquisition strategy is capable of achieving high-performance signal acquisition in the low-carrier-to-noise ratio and large dynamic regions. Moreover, we derive and simulate false alarm probability, detection probability, computational complexity, and mean square error of both the delay and Doppler factors in the additive white Gaussian noise channel. Numerical simulation results show that the proposed acquisition strategy improves the performance by 1.6 dB over the noncoherent accumulation strategy but at the expense of 0.43% complexity increase.

In many core problems of signal processing and wireless communications, Karush-Kuhn-Tucker (KKT) conditions based optimization plays a fundamental role. Hence we investigate the KKT conditions in the context of optimizing positive semidefinite matrix variables under nonconvex rank constraints. More explicitly, based on the properties of KKT conditions, we optimize a reconfigurable intelligent surface (RIS) aided multi-user multi-input multi-output (MU-MIMO) network. Specifically, we consider the capacity maximization and sum mean square error (MSE) minimization problems of both the RIS-aided MU-MIMO uplink (UL) and downlink (DL) under multiple weighted power constraints and rank constraints. As for the RIS-aided MU-MIMO UL, the optimal structures of the signal covariance matrices are derived based on the KKT conditions. Furthermore, an efficient procedure is designed for solving the capacity maximization and sum mean square error (MSE) minimization problems. Then the UL-DL dualities are exploited for solving the capacity maximization and MSE minimization problems of the RIS-aided MU-MIMO DL based on the results of the UL optimization. Hence in the proposed framework, the phase shifting matrix of the RIS is jointly optimized with the signal covariance matrices for both the UL and DL. Our simulation results demonstrate the performance advantages of the proposed framework.

Industrial Internet of Things (IIoT) brings opportunities for innovation and upgrading to industrial production, but meanwhile, it poses unprecedented challenges on the physical layer of wireless communications. In this article, we propose a novel frequency hopping transmission scheme for IIoT, which owns much high efficiency and strong anti-jamming ability. This new scheme is built based on the combination of coherent fast frequency hopping (CFFH) and cyclic code shift key (CCSK), named phase cyclic shift-keying-based CFFH (PCSK-CFFH). For the proposed PCSK-CFFH system, the shift of orthogonal base sequence is adopted to modulate the phases among interhop, instead of transmitting each hop with the same phase as conventional CFFH. This scheme is able to further exploit the extra dimensions of CFFH signals and enables the CFFH system to transmit additional information at no extra cost on either bandwidth or power. Moreover, the bit error rate performance of the proposed PCSK-CFFH system is analyzed theoretically. To improve its ability against severe narrow-band jamming, the selection combining is introduced in our scheme. Furthermore, the anti-jamming performance of the proposed PCSK-CFFH system is also demonstrated. Finally, the simulation results are shown to demonstrate that: by introducing the interhop dimension to CFFH, PCSK-CFFH improves the power efficiency and spectrum efficiency by multiple times and simultaneously inherits the excellent anti-jamming performance of the conventional CFFH system. It can be concluded that the proposed PCSK-CFFH scheme has a great potential to be applied to low-power and highly reliable IIoT communications.