Secure satellite-terrestrial communication (SSTC) systems are of critical importance across a range of sectors, including military, scientific, and commercial domains, especially in consumer electronics. As emergent threats become increasingly sophisticated, ensuring the security, reliability, and effectiveness of SSTC in consumer electronic scenarios has become paramount. However, traditional communication solutions are no longer adequate to address the evolving challenges of SSTC systems. To address these limitations, this article proposes a novel SSTC system based on generative foundation models (GFMs) to enhance security, reliability, and effectiveness. A fundamental aspect of this approach entails the proposal of a GFM structure to optimize signal design. Notably, the knowledge distillation (KD) technique has been incorporated to mitigate complexity. A range of critical performance parameters is taken into consideration for GFM, including the bit error rate (BER) and secrecy outage probability (OP). The findings illustrate that the proposed GFM enhances the security and reliability of information transmission in SSTC systems. Finally, the article engages with the challenges and potential future directions for the application of GFM to SSTC systems for future consumer electronic scenarios.

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.

To address the challenges of high-dimensional channel estimation and underutilized spatial correlations among users in holographic MIMO (HMIMO) systems, this paper proposes a joint graph-cut algorithm for multi-user channel estimation in the wavenumber domain. The size of the conventional angular domain channel matrix increases with the number of antennas in densely-spaced HMIMO. Therefore, user channels are projected into the wavenumber domain via a Fourier harmonic transform, revealing their inherent clustered sparsity and exploiting common scatterer clusters among users. Subsequently, a joint graph-cut channel estimation (JGC-CE) algorithm based on multi-user common supports is designed. In each iteration, the algorithm first partitions user clusters to extract shared supports. Then for each user, it performs users' individual graph update and channel estimation to reconstruct the channel matrix. Simulation results demonstrate that the proposed method outperforms independent estimation schemes for individual users in accuracy while reducing pilot length.

Low Earth Orbit (LEO) satellites have recently emerged as promising candidates for secure Internet of Remote Things (IoRT). However, in scenarios involving miniaturized terminals, limited transmission power and long distances often lead to diminished Signal-to-Noise Ratio (SNR) at satellite receivers, resulting in degraded communication quality. To address this challenge, the implementation of cooperative satellite systems has been proposed, where signals from multiple satellites are combined to improve SNR. Achieving optimal gain necessitates precise synchronization of carrier frequency and phase across all received signals, particularly challenging under low SNR conditions, and in short burst transmissions without training sequences. We propose an iterative code-aided estimation algorithm for joint estimation of Carrier Frequency Offset (CFO) and Carrier Phase Offset (CPO), employing Iterative Cross Entropy (ICE) and Cooperative Taylor Expansion of Objective Function (CTEOF). Simulation results show that our algorithm achieves accurate estimation within the normalized frequency range that exceeds existing algorithms and phase interval (-pi, +pi]. Moreover, the algorithm demonstrates the ability to approach the Bit Error Rate (BER) performance bounds with deviations of 0.3 dB and 0.4 dB in dual-satellite and quad-satellite collaboration scenarios, respectively.

Atomic receivers, which leverage the quantum interference termed electromagnetically induced transparency (EIT) for radio-frequency (RF) to optical signal transduction, offer a revolutionary paradigm for next-generation wireless communications. However, current information-theoretic characterizations are predominantly restricted to the Ξ-type of EIT path and rely heavily on the weak-probe approximation, which fails to predict the behavior of the atomic receivers under high signal-to-noise ratio regimes. In this paper, we establish a unified analytical model for atomic receivers, and apply this model to three typical quantum interference paths, i.e., V -type, Λ-type, and Ξ-type configurations. To provide a universal characterization, we propose the quantum coherence transfer coefficient (QCTC) to model the equivalent channel response induced by atomic receivers, using a steady-state perturbation framework built on the three-level EIT solution. The closed-form expressions of equivalent channel gains are then derived for three paths. Our results provide an analytical foundation for future capacity analysis and waveform optimization in atomic radio communication.

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.

With the development of integrated space–air–land–underwater networks, cross-medium communication has become increasingly important for ocean exploration and monitoring. As a promising solution, dual-hop radio frequency (RF)-underwater wireless optical communication (UWOC) transmission can support long-distance communication with high bandwidth and low latency. This article investigates the secrecy performance of a dual-hop RF-UWOC downlink system, where the first hop is an RF link from a satellite to a multiantenna relay in the presence of eavesdroppers, and the second hop is a UWOC link with underwater turbulence modeled by a lognormal distribution. Specifically, the random distribution characteristics of each node’s signal-to-noise ratio are first studied. Then, approximate and closed-form expressions are derived for the first hop’s nonzero secrecy capacity and the second hop’s outage probability, respectively. The validity of the proposed model is validated, and the influence of assorted parameters on the system’s secrecy performance is examined through Monte Carlo simulation methods.

For modern radars with high resolutions, an extended target may generate more than one observations. The conventional point target-based tracking method can hardly be applied in such scenarios. Recently, the ET-GM-PHD approach has been presented for tracking these extended targets. The performance of such an approach has been influenced by the following disadvantages. First, it has been formulated under the linear Gaussian assumptions. When targets move with nonlinear models, the tracking performance may be rapidly decreased. Second, it neglects the time associations of the estimated states at different time steps, which makes it very challenging to manage targets for the radar systems. In this paper, we present a labeled ET-GM-PHD approach based on the square root cubature information filter (SRCIF) to solve such problems. To be more specific, we, first, utilize the SCRIF for predicting and updating the GM components of the ET-GM-PHD approach. For decreasing the computational cost, a candidate observation extracting method has been put forward in the GM component updating step. Thus, the ET-GM-PHD approach can be adopted to track extended targets with nonlinear motions. Second, a label-based trajectory constructing method has been proposed. By assigning the GM components with different labels before the GM component predicting step, we can obtain the estimated states with different labels. On this basis, the associations between the estimated states and trajectories can be modeled based on these labels. Thus, we can obtain the states and trajectories of multi extended targets simultaneously. The simulation results prove the effectiveness of our approach.

The sixth-generation (6G) network integrates communication, sensing, and computation into a synergetic system. Device-to-device (D2D) communication has also received widespread attention, and to improve the performance of D2D communications with limited network edge resources, we propose the use of the generative pretrained transformer (GPT) agent. Specifically, we use GPT to achieve resource-optimized quality of service (QoS) and energy consumption, forming the QoS-CNNGPT method. The simulation results show that the proposed system can support multiple edge users with a 28.7% improvement in spectral efficiency compared with the weighted minimum mean square error (WMMSE) method and a 79.8% reduction in computation time compared with the overhead of reinforcement learning (RL)-based techniques. The proposed system can also meet the deployment and individualization requirements of different users in resource-limited D2D systems with strong robustness, which will help improve 6G networks.

Millimeter wave (mmWave) based integrated sensing and communication (ISAC) enables high-speed communication and accurate sensing, yet it is highly susceptible to blockages that could lead to interrupted communication and sensing services. In this paper, we investigate blockage-aware mmWave orthogonal frequency division multiplexing (OFDM) waveform design for ISAC, where we exploit multi-base station (BS) collaboration to enhance resilience for dynamic blockage, and leverage dual-functional radar-communication (DFRC) waveform to simultaneously perform data transmission and target localization. To account for the dynamic blockage, the communication and sensing performance is characterized in a probabilistic fashion. Then, we formulate a chance-constrained max-min optimization problem, which can be efficiently solved by the proposed stochastic successive convex approximation (SCA)-based blockage-aware beamforming (SSBAB) scheme. Simulation results verify the robustness of the SSBAB scheme in the presence of dynamic blockage.

The identification and clustering of new wireless devices outside known categories are crucial for radio frequency fingerprint identification (RFFI), particularly important for device authentication and intrusion detection. However, most existing RFFI schemes have low robustness to environmental changes, limited scalability, and poor generalization across different scenarios. To overcome these shortcomings, we propose an end-to-end RFFI approach with signal-augmented entropy regularization (E2E-SAER). Specifically, we first construct a feature extractor based on a multi-block mixture of experts to capture condition-specific and radio frequency fingerprint-related features under inherent channel/signal diversity. We then propose an end-to-end approach for fingerprint classification/clustering to further optimize the feature extractor for more effective RFFI. Since the end-to-end training may result in model overfitting to labeled data and the biased predictions towards known devices, our E2E-SAER enhances the prototype classifier with a signal-augmented entropy regularization to achieve more uniformly distributed predictions between known and unknown categories for effective known class identification and unknown class clustering. We conduct extensive experiments on seven open-source radio frequency fingerprint datasets with six benchmarks and demonstrate that our proposed E2E-SAER significantly outperforms existing algorithms.

The terahertz frequency band has unique advantages such as large bandwidth and short wavelength, which can significantly enhance the transmission rate and capacity of satellite communication payloads, making it a candidate frequency band for future space information networks and providing new ideas for the design of satellite communication payloads. However, the high transmission loss of terahertz waves and the requirement for high-precision manufacturing pose contradictions with the current level of technology, which limit its large-scale practical application. To fully explore the value of terahertz in 6G era, there is an urgent need to propose innovative terahertz architectures. This paper proposes a terahertz communication payload architecture for space information networks and analyzes its advantages and challenges. On this basis, it further analyzes some of the key technologies involved. Finally, it discusses four promising development directions for space information networks based on terahertz communication payloads.

Superposition transmissions can simultaneously accommodate multiple users, while incorporating pilot-free design can further reduce the signaling overhead. Achieving reliable communication for pilot-free superposition transmissions faces previously unaddressed challenges not only due to unignorable inter-user interference (IUI) but also to unknown channel state information (CSI). Considering such difficulties, we first propose a dynamic 3D trellis diagram to effectively characterize the pilot-free superimposed signals, where IUI is introduced as an additional dimension, and a notion of updatable path metric is proposed to adapt to instantaneous CSI estimation. An interference-aware forward-backward Viterbi algorithm is then proposed for pilot-free joint channel estimation and symbol detection, with forward path selection using Gaussian mixture model-based piecewise metric calculation and backward CSI update using survivor path metric maximization. Interference reconstruction and cancellation are further developed for iterative interference suppression among users. The convergence of the proposed algorithm is also theoretically proved. Simulation results indicate that our method achieves an order-of-magnitude frame error rate improvement over state-of-the-art methods.

With the rapid development of global satellite Internet and the growing demand for seamless connectivity, the Satellite-Terrestrial Integrated Network (STIN) has become a key architecture for achieving ubiquitous communication coverage. However, STIN faces critical energy efficiency challenges, including the high peak-to-average power ratio (PAPR) that degrades high power amplifier efficiency at the physical layer and frequent satellite handovers that increase network overhead. To address these issues, this paper proposes a mobility-aware model of Multicarrier Direct-Sequence Spread Spectrum (MC-DSSS) STIN. At the physical layer, the Orthogonality-Based Generalized Multicarrier Constant Envelope Multiplexing (CEMIC) technique is adopted, and an energy efficiency maximization problem is formulated under constant-envelope constraints. A joint power allocation algorithm is developed based on the Dinkelbach method and the Alternating Direction Method of Multipliers (ADMM) to solve the non-convex problem efficiently. To overcome the limitations of single-layer optimization, a two-layer collaborative framework is further proposed. At the network layer, an improved binary particle swarm optimization (IBPSO-HO) algorithm is employed to optimize satellite handovers. This joint design enables two-layer energy efficiency optimization. Simulation results demonstrate that the proposed scheme significantly enhances overall energy efficiency across both layers, providing robust theoretical support for the large-scale green deployment of STIN.

Satellite-terrestrial integrated networks (STINs) are key enablers for achieving all-time, all-weather connectivity, yet they face critical challenges in spectrum management and interference mitigation. To address these issues, this article proposes a downlink high-secrecy-rate cell-free massive multiple-input-multiple-output transmission scheme for STINs under active interference. An optimal beamforming design problem is formulated to maximize the system secrecy rate while satisfying the quality of service requirements of both satellite and terrestrial users, explicitly targeting the cell-free security challenges in this integrated framework. A nonsemidefinite relaxation (non-SDR) algorithm based on successive convex approximation is developed to achieve low computational complexity while maintaining near-optimal performance. Moreover, the security performance of the proposed cell-free (CF)-STIN system is analyzed under cross-layer eavesdropping scenarios, providing valuable insights into its antieavesdropping robustness in complex network environments. The structure of this article is shown as follows: the system model and problem formulation are presented first, followed by the proposed non-SDR optimization algorithm. Simulation evaluation is then discussed to validate the effectiveness of the proposed method, and the main findings are summarized in the conclusion.

To address the demand for integrated sensing and communication (ISAC) on unmanned aerial vehicle (UAV) platforms, this paper presents a conformal aperture-shared dual-band dual-mode antenna system. A shared-aperture radiating element is developed to integrate lower-band communication in the dipole mode and higher-band sensing in an embedded dual-Vivaldi bidirectional end-fire mode within the same physical aperture. Different from conventional dual-Vivaldi configurations, a modified feeding scheme with a shared tapered transition and simplified excitation structure is introduced, which lowers the minimum operating frequency, improves the realized gain, and enables higher-band array realization under the element-spacing constraint imposed by the lower band. Furthermore, a distributed fuselage-wing conformal array architecture is developed for UAV deployment, where multiple subarrays are coordinated to simultaneously provide wide-coverage communication and directional multi-polarization sensing under limited platform space. A prototype was fabricated and measured. Experimental results show the two modes achieve relative impedance bandwidths of 105% and 103.9%, with peak gains of 9.06 dBi and 14.23 dBi, respectively. The dipole mode provides 76° × 360° quasi-omnidirectional coverage, while the Vivaldi mode supports bidirectional radiation with horizontal, vertical and ±45° linear polarizations. The proposed antenna system therefore offers a compact and effective solution for UAV-based ISAC applications.

Low Earth Orbit (LEO) satellite communication systems face significant challenges in multiuser acquisition due to the coexistence of low Signal-to-Noise Ratio (SNR), Doppler effects, and Multiple Access Interference (MAI). To address these issues, this paper proposes a compressive sensing-based solution for the multiuser acquisition of coherent Multi-Carrier Direct Sequence Spread Spectrum (MC-DSSS) signals in LEO uplinks. We intro duce the Shape-Aware and Interference Cancellation Compressive Sampling Matching Pursuit (SAIC-CoSaMP) algorithm, which is specifically designed to determine the actual number of users from non-ideal correlation peaks, and to estimate their signal parameters when correlation peaks overlap. Simulation results demonstrate that the proposed algorithm achieves robust multiuser acquisition performance under low SNR conditions, significantly outperforming the conventional Successive Interference Cancellation (SIC) method under small user-delay intervals.

Traditional antenna systems, constrained by static architectures, are increasingly unable to support the reliable, flexible, and resilient communications demanded in dynamic environments. This article introduces the Distributed Movable Antenna System (DMAS), a novel architecture that combines local antenna mobility with distributed Remote Movable Antenna Units (RMAUs) to exploit spatial degrees of freedom (DoF) across large geographical areas. By extending coverage through distributed deployment and enhancing channel quality via antenna adjustments, DMAS enables more adaptive and robust wireless transmissions under intricate propagation conditions. In this article, we initially introduce a representative architecture of DMAS. To illustrate the advantages of DMAS, we compare it to existing antenna systems and propose its potential application scenarios. We then examine the main design considerations, including RMAU deployment, synchronization, channel estimation, and movable antenna optimization. The simulation results demonstrate the improvements in signal strength and system capacity with DMAS. Furthermore, a general optimization framework is outlined and discussed to guide the optimization of DMAS. Ultimately, potential integration prospects are investigated, significant deployment obstacles, alongside viable solutions, are addressed for the practical implementation of DMAS.