The Integrated Satellite-Ground Network (ISGN) is regarded as a promising technology for future communication systems, which can provide ubiquitous connectivity for various communication scenes, such as remote areas, maritime, and emergency communications. This paper presents a novel downlink transmission framework for the cell-free multi-input multi-output (CFmMIMO) network-based ISGN system. The satellite serves a group of earth stations through multicast technology, while the ground network embraces the cell-free network strategy to serve multiple terrestrial users. Furthermore, the CFmMIMO-based ISGN under consideration operates at millimeter-wave frequencies to satisfy the high data rate demand of the next-generation communication system. In order to enhance the spectral efficiency of the proposed CFmMIMO-based ISGN system, we formulate two joint beamforming design problems, which are denoted as the maximize sum-rate (MSR) problem and the max-min fairness (MMF) problem. Then, to solve this mathematically intractable problem, we initially applied Fractional programming, Alternating optimization, and successive convex approximation to convert the MSR-based joint precoding problem into an equivalent quadratically constrained quadratic program (QCQP) problem. After that, the QCQP optimization problem is solved using an alternating direction method of multipliers based algorithm, where we dramatically simplify the algorithm’s computational complexity with several straightforward variable substitutions. Furthermore, we extend our proposed algorithm to address the MMF-based joint beamforming design problem. At the last, the simulation result demonstrates the superiority of the proposed downlink transmission framework as well as the proposed algorithm.

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.

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 twolayer 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.

In MIMO systems, hybrid digital and analog precoding structures are gaining attention for reducing power consumption and costs compared to fully digital precoding. However, deployment expenses and circuit power consumption still pose challenges. Nonideal hardware in practical applications further impacts energy efficiency (EE), making research into high-energy-efficiency MIMO structures under such conditions essential. To tackle this issue, an exceedingly effective hybrid precoding strategy is developed, incorporating the implications of nonideal hardware characteristics. Initially, the research models the aforementioned hardware characteristics, formulates the signal model for the hybrid-structured MIMO communication system, and discerns the nonconvex problem relative to energy efficiency optimization. Subsequently, the optimization problem is effectively resolved by choosing analog radio frequency (RF) precoding and digital baseband precoding designs, including the precoding design for array gain. Furthermore, fractional programming is utilized to achieve digital precoding. An iterative optimization method is utilized to solve this problem. Finally, the mixed precoding scheme is formulated to showcase the system’s energy efficiency performance through simulation and explore the impact of imperfect hardware on the system. Additionally, to further diminish design complexity, the implementation of various channels is analyzed to propose a low-complexity method via the approximation of the equivalent channel matrix. Simulation results show that the performance of this method achieves performance comparable to existing hybrid precoding methods, when the number of antennas is sufficiently large.

The rapid development of the Internet of Things (IoT) has led to exponential growth in wireless network traffic and the number of connected devices, thereby intensifying the demand for scarce spectrum resources. In this context, Wireless signal identification, a key technology in spectrum sensing, is crucial for enhancing spectrum utilization by mitigating interference and ensuring system security. In this study, we treat wireless signal identification as a time series classification task and propose a novel model based on Fourier-segmented attention. In our proposed model, instead of computing point-level attention, we extract sequence dependencies by computing segment-level attention. Moreover, we introduce a method based on the Fourier transform to determine the segment length, ensuring that each segment captures multi-scale features. Experimental results indicate that the proposed method outperforms existing models, achieving an accuracy of approximately 95% on our dataset and representing an improvement of around 1.6% in accuracy over competing approaches. Furthermore, experiments were conducted to evaluate the model’s effectiveness in detecting fake signals and its potential to enhance system security.

With the development of the sixth-generation wireless communication technology, the difficulty of covert communication in the satellite communications system (SCS) is further increased. In this paper, a satellite covert communication system based on reconfigurable intelligent surface (RIS) is studied, which consists of a satellite, a terrestrial receiver, RIS, and a warden. Specifically, the ground terminal simultaneously receives the signal transmitted by the satellite and the signal reflected by RIS, while the warden wants to overhear the transmitted signal from the satellite and that reflected by the RIS. The main target of this wort is to maximize the minimum covert rate to improve signal quality while considering the limited onboard resources, the hardware constraints of RIS, and the requirement to implement covert communication. To address the non-convex problem, we propose an effective alternating optimization scheme by jointly optimizing transmitter precoding and RIS to improve the covert communication performance of the considered system. In addition to ideal RIS, we also consider the non-ideal RIS, the angle of which is discrete in the actual case. In addition, several simplified methods are proposed to decrease the computational complexity of the covert communication optimal problem under multiple terminals. Finally, simulation results are provided to verify the superiority of the proposed scheme compared with the benchmark scheme, which suggests that the considered RIS-based satellite-terrestrial covert communication system can significantly improve the achievable masking.

In this work, a reconfigurable intelligent surface (RIS)-based multi-input–multi-output (MIMO) covert communication system is considered and studied for Internet of Things (IoT) networks, while the joint optimization of precoder and RIS reflection phase is carried out to improve the covert communication performance. To solve this problem, we first derive the optimal signal-interference-to-noise ratio under covert communication and transmit power constraints. Then, we simplify the optimized function and use an iterative optimization algorithm to determine the optimal phase shift and precoding in continuous and discrete cases. Simulation results show that the RIS-aided MIMO covert communication system proposed in this article can significantly improve the invisibility of the implementation from Willie. In addition, for covert communication, the performance of the continuous phase shift case outperforms that of the discrete phase shift and fixed phase.

Hybrid energy supply composed of batteries and radio frequency (RF) signals has been anticipated to be a prominent solution for balancing the reliability and self-sustainability of future IoT networks. The newly emerging reconfigurable intelligent surface (RIS) is also capable of greatly enhancing spectral and energy efficiencies. In this paper, by considering an asynchronous transmission protocol among all energy receivers (ERs) and assuming the perfect self-interference cancellation (SIC) at the hybrid access point (HAP), we aim to maximize sum throughput in the RIS-aided hybrid powered communication networks (HPCNs) by jointly optimizing the transmit covariance matrices of the HAP and all ERs, the RIS reflection matrix and the downlink/uplink (DL/UL) time allocation. Generally, this optimization problem is intractable to solve due to strongly coupled variables and nonconvex unit-modulus constraints. To draw more insights into this joint design, we firstly carry out feasibility analysis on this problem, and then develop a 2-block alternating optimization algorithm, which consists of the semi-closed-form solution based iterative algorithm for deriving the optimal MIMO transceivers together with the DL/UL time allocation and the alternating direction method of multipliers (ADMM) based algorithm for the RIS design. To avoid the potential high complexity of alternating optimization, we also propose a two-stage scheme, where the RIS design is independent of the others and aims to create favorable DL/UL channels. The extension of our proposed algorithms to the practical imperfect SIC case is then discussed. Numerical results illustrate the superior performance of our proposed algorithms over the baselines in terms of the achievable sum throughput, and their time effectiveness in solving large-scale problems.

Intelligent reflecting surface (IRS) is capable of constructing the favorable wireless propagation environment by leveraging massive low-cost reconfigurable reflect array elements. In this article, we investigate the IRS-aided multiple-input-multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) for Internet-of-Things (IoT) networks, where the active base station (BS) transmits beamforming and the passive IRS reflection coefficients are jointly optimized for maximizing the minimum signal-to-interference-plus-noise ratio (SINR) among all information decoders (IDs), while maintaining the minimum total harvested energy at all energy receivers (ERs). Moreover, the IRS with practical discrete phase shifts is considered, and thereby the max-min SINR problem becomes an NP-hard combinatorial optimization problem with a strong coupling among optimization variables. To explore the insights and generality of this max-min design, both the single-ID single-ER (SISE) scenario and the multiple-IDs multiple-ERs (MIME) scenario are studied. In the SISE scenario, the classical combinatorial optimization techniques, namely, the special ordered set of type 1 (SOS1) and the reformulation-linearization (RL) technique, are applied to overcome the difficulty of this max-min design imposed by discrete optimization variables. Then, the optimal branch-and-bound algorithm and suboptimal alternating optimization algorithm are, respectively, proposed. We further extend the idea of alternating optimization to the MIME scenario. Moreover, to reduce the iteration complexity, a two-stage scheme is considered aiming to separately optimize the BS transmit beamforming and the IRS reflection coefficients. Finally, numerical simulations demonstrate the superior performance of the proposed algorithms over the benchmarks in both the two scenarios.