Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) systems have been widely applied in various scenarios recently. This paper aims to maximize the total secure communication rate (SCR) of multiple users while ensuring the minimum beamforming gain towards sensing targets under the surveillance of multiple UAV warden swarms. To reduce the risk of detection, a novel type of artificial noise (AN) is introduced to interfere with swarm wardens. We conduct an analysis of the detection error probability (DEP) of these wardens and subsequently establish a mathematical model. In this model, the SCR is maximized subject to power, trajectory, sensing performance, and secure communication constraints. Since the problem is non-convex and the variables to be optimized are numerous and complex, we decompose the problem into three sub-problems. Then, an overall algorithm is proposed to solve these sub-problems separately. Simulation results demonstrate that the proposed scheme leads to a significant increase in the SCR. Moreover, the system exhibits highly stable performance in both communication and sensing tasks over time, indicating its robustness and reliability. Additionally, communication fairness among users is ensured, and energy efficiency is enhanced.

Highlights What are the main findings? This study proposes a joint optimization framework for multi-UAV-relay-assisted aviation FSO/RF networks. The model jointly optimizes trajectory, ground station association, and power allocation, while systematically incorporating multi-dimensional practical constraints-including platform dynamics, information causality, co-channel interference, meteorological effects, and multi-UAV collision avoidance-thereby significantly enhancing its engineering applicability. This study introduces a dynamic power control strategy that achieves an optimal trade-off between desired signal enhancement and co-channel interference suppression. By flexibly adjusting power to partially substitute for spatial adjustments, this strategy proves effective in complex multi-station and multi-UAV scenarios. What are the implications of the main findings? This study addresses a critical gap in existing aviation FSO/RF research, which often relies on idealized models while overlooking practical engineering constraints. This work provides both a systematic theoretical framework and a practically implementable technical pathway for multi-UAV relay communications in complex aviation scenarios. The proposed "multi-node, multi-constraint, multi-variable" joint optimization approach can be extended to the trajectory and resource co-design of other space-based communication networks (e.g., high-altitude platforms and satellite relays), laying a theoretical and algorithmic foundation for high-capacity, high-reliability transmission in future integrated space-air-ground information networks.Highlights What are the main findings? This study proposes a joint optimization framework for multi-UAV-relay-assisted aviation FSO/RF networks. The model jointly optimizes trajectory, ground station association, and power allocation, while systematically incorporating multi-dimensional practical constraints-including platform dynamics, information causality, co-channel interference, meteorological effects, and multi-UAV collision avoidance-thereby significantly enhancing its engineering applicability. This study introduces a dynamic power control strategy that achieves an optimal trade-off between desired signal enhancement and co-channel interference suppression. By flexibly adjusting power to partially substitute for spatial adjustments, this strategy proves effective in complex multi-station and multi-UAV scenarios. What are the implications of the main findings? This study addresses a critical gap in existing aviation FSO/RF research, which often relies on idealized models while overlooking practical engineering constraints. This work provides both a systematic theoretical framework and a practically implementable technical pathway for multi-UAV relay communications in complex aviation scenarios. The proposed "multi-node, multi-constraint, multi-variable" joint optimization approach can be extended to the trajectory and resource co-design of other space-based communication networks (e.g., high-altitude platforms and satellite relays), laying a theoretical and algorithmic foundation for high-capacity, high-reliability transmission in future integrated space-air-ground information networks.Abstract The utilization of unmanned aerial vehicle (UAV) relays has significantly improved the availability and reliability of free-space optical (FSO) communication links within airborne communication backhaul networks. This paper proposes an FSO/RF dual-hop backhaul network employing multiple UAV relays and investigates a joint optimization scheme for three-dimensional (3D) trajectories and resource allocation of multiple UAVs. In this scheme, network throughput is maximized by jointly optimizing three variables: the association between the UAVs and the ground stations (GSs), power allocation, and the UAVs' trajectories. Moreover, to enhance the engineering applicability of this research, we systematically incorporate multi-dimensional practical constraints-including the motion of the AWACS, platform dynamics, information causality, co-channel interference, the influence of weather variations, and multi-UAV collision avoidance. Furthermore, to address this challenging mixed-integer non-convex optimization problem, an iterative algorithm is developed. This algorithm integrates the principles of block coordinate descent with successive convex approximation, thereby alternately optimizing the three variable blocks within each iterative cycle. Numerical simulations confirm that the proposed scheme achieves a substantial throughput improvement in the multi-UAV-assisted FSO/RF hybrid backhaul network in comparison with other benchmark schemes.

To further enhance the covert communication capability of satellite-ground-integrated sensor networks, a dual-polarization constellation joint modulation scheme based on frequency-hopping double-layer multi-parameter weighted fractional Fourier transform (FH-DL-MPWFRFT) is proposed from the perspective of physical layer security. The proposed scheme integrates the constellation confusion property of weighted fractional Fourier transform (WFRFT) with the anti-interception capability of frequency-hopping (FH) phase scrambling. Specifically, the weighted parameters of conventional 4-WFRFT are extended to construct a multi-parameter and multi-layer signal representation, and FH phase scrambling is introduced to realize dynamic constellation rotation and phase-domain encryption. Furthermore, a secure transmission model for satellite-ground-integrated sensor networks is established, revealing the constellation optimization principle and the fission-fusion mechanism of dual-polarization signals. Simulation results show that, compared with the non-FH benchmark, the proposed scheme significantly improves waveform-level anti-interception performance; even when eavesdropper obtains the modulation scheme and partial transform parameters, the symbol error rate (SER) of quadrature phase shift keying (QPSK) and four-phase modulation (4PM) signals remains around 0.4 to 0.5 under parameter mismatch, indicating that effective demodulation is difficult to achieve.

Cell-free (CF) massive multiple-input multiple-output (MIMO) systems are regarded as a promising network architecture for future 6G communications. Although distributed structures can be used to enhance the spectral efficiency (SE) and energy efficiency (EE) of the system, the fronthaul capacity is a bottleneck limiting further improvement in communication performance. This paper proposes an RSMA-assisted CF massive MIMO scheme that integrates rate-splitting principles into the CF architecture. Specifically, the data is split at the central processing unit (CPU) into common coherent data and private non-coherent data, thereby reducing the burden on the fronthaul links. Furthermore, we derive novel expressions for the instantaneous downlink SE and the system EE of the proposed system. To maximize the system EE, we jointly optimize the coherent common beamforming and the non-coherent private beamforming under limited fronthaul capacity constraints. Since the proposed problem is dynamic and non-convex, traditional optimization algorithms are difficult to apply directly. Therefore, this paper proposes the Projection-Multi-Agent Deep Deterministic Policy Gradient (P-MADDPG) algorithm, where the beamforming scheme is designed based on weighted Maximum Ratio Transmission (MRT) to reduce action dimensions, and a projection layer is incorporated to satisfy the power constraints of weight allocation. Simulation results validate the effectiveness of the proposed scheme, and the proposed algorithm can achieve higher SE and EE compared to the benchmark algorithms.

With the continued deployment of low Earth orbit (LEO) satellite constellations, spectrum resources are becoming more limited. As a result, satellite-to-ground links face many types of strongly coupled interference in complex electromagnetic environments. This interference includes non-intentional interference caused by different systems and devices under spectrum sharing, and also malicious jamming with adversarial intent. The effects of interference exhibit multi-scale temporal and spatial evolution characteristics. Most existing surveys discuss mitigation methods for individual interference types separately, but lack systematic approaches to deal with complex environments where different types of interference exist together. This survey reviews interference mitigation techniques in LEO satellite communications from a novel perspective. Specifically, we first summarize the interference scenarios for LEO satellite communications based on the interference intent. Then, we propose a novel taxonomy based on the timescale at which a mitigation strategy takes effect. This taxonomy provides a unified framework to capture the multi-timescale nature of interference and helps to find solutions more systematically. With this taxonomy, existing methods can be grouped into three paradigms: long-term planning, short-term scheduling, and instantaneous decision-making. For each paradigm, we review representative methods, applicable conditions, and key limitations. In addition, this survey discusses recent advances in cross-timescale coordinated mitigation, as well as key challenges in multi-satellite coordination. Finally, we outline several promising directions for future research.

Due to the exposed orbits and open wireless channels, low Earth orbit (LEO) satellite communications are vulnerable to malicious jamming. Moreover, sensing devices inevitably suffer from measurement errors in practical systems. When the receiver inaccurately estimates jamming power, it may make unstable resource allocation decisions. This problem is even worse in deep reinforcement learning (DRL)-based methods, where even slight perturbations in the input state can cause severe policy deviations, thus degrading communication performance. In this paper, we propose a two-stage DRL method for robust resource allocation in LEO satellite communication scenarios. Specifically, we treat each beam as an agent to optimize beam hopping (BH) and frequency scheduling. In the offline training stage, we propose a nonlinear action-value correction mechanism to mitigate Q-value ambiguity. This method can improve decision stability under state perturbations. Furthermore, real-world environments are often unpredictable. In the online execution stage, we design a state confidence judgment module that adaptively switches to a conservative policy when it detects abnormal states, so the system can mitigate uncontrollable risks caused by neural networks. Simulation results demonstrate that the proposed method improves robustness under varying sensing error radii, while satisfying non-uniform traffic demands. This highlights its potential for practical implementation in uncertain electromagnetic environments.

Hyperspectral image super-resolution (HSI-SR) is vital for fine-grained earth observation but remains impractical on resource-constrained airborne and spaceborne platforms. While existing methods prioritize reconstruction fidelity and degradation robustness, they largely neglect hardware efficient. To address this gap, we propose the first unified compression framework for HSI-SR, featuring two hardware-friendly quantizers. For inference, the Blocked Base-Vector Quantizer (BBVQ) exploits the strong local spatial-spectral similarity inherent in HSIs by representing each spatial block with a shared spectral base and low-bit residuals. This enables nearly integer only arithmetic while preserving spectral fidelity. For backward propagation, the Channel-Aware Scale-Adaptive Quantizer (CA-SAQ) reduces quantization error of gradients under aggressive bit-width compression by dynamically rescaling channels through integer bit-shifts, which provide a hardware-efficient alternative to fine-grained quantization. This design maintains gradient accuracy without introducing memory rearrangement. Both quantizers are plug-and-play and compatible with mainstream HSI-SR models. With optimized FPGA and GPU kernels, our framework achieves 2.27× faster execution on GPU and 54.4% lower resource usage on FPGA at 8-bit precision, matching the spectral quality and degradation robustness of full-precision models, which will promote practical on-orbit deployment.

Owing to the openness of wireless channels, wireless communication systems are highly susceptible to malicious jamming. Most existing anti-jamming methods rely on the assumption of accurate sensing and optimize parameters on a single timescale. However, such methods overlook two practical issues: mismatched execution latencies across heterogeneous actions and measurement errors caused by sensor imperfections. Especially for deep reinforcement learning (DRL)-based methods, the inherent sensitivity of neural networks implies that even minor perturbations in the input can mislead the agent into choosing suboptimal actions, with potentially severe consequences. To ensure reliable wireless transmission, we establish a multi-timescale decision model that incorporates state uncertainty. Subsequently, we propose two robust schemes that sustain performance under bounded sensing errors. First, a Projected Gradient Descent-assisted Double Deep Q-Network (PGD-DDQN) algorithm is designed, which derives worst-case perturbations under a norm-bounded error model and applies PGD during training for robust optimization. Second, a Nonlinear Q-Compression DDQN (NQC-DDQN) algorithm introduces a nonlinear compression mechanism that adaptively contracts Q-value ranges to eliminate action aliasing. Simulation results indicate that, compared with the perfect-sensing baseline, the proposed algorithms show only minor degradation in anti-jamming performance while maintaining robustness under various perturbations, thereby validating their practicality in imperfect sensing conditions.

In overflying Low Earth Orbit (LEO) satellite beam hopping (BH), the rapid variation in topology, intermittent visibility, dynamic traffic evolution, and the large joint beam and power action space collectively lead to significant challenges in beam scheduling. Traditional deep reinforcement learning (DRL)-based methods require exhaustive exploration of beam positions and power combinations, resulting in excessive training time and high computational complexity, which limits their practical deployment during LEO satellite overflight. Future LEO satellite networks demand high throughput to meet the needs of various applications, but the intelligent beam scheduling schemes for overflying satellites face high training complexity. These challenges become more critical for satellite-assisted IoT services, where massive low-rate terminals require wide-area and efficient access. To address this issue, this paper investigates the BH problem for overflying LEO satellite clusters and proposes a complexity-efficient DRL framework. To mitigate the excessive action space and the difficulty of acquiring channel state information during satellite overflight, a statistical pre-association mechanism is first introduced to eliminate infeasible satellite-cell pairs based on long-term traffic and visibility information. On this structured decision space, a constrained scheduler based on Multi-Agent Proximal Policy Optimization performs coordinated beam selection. Furthermore, fractional programming (FP) based power control is embedded into the learning loop to generate high-reward guidance signals, improving policy learning efficiency. Simulation results demonstrate that the proposed framework outperforms conventional DRL-based methods in terms of throughput and delay. Compared to traditional DRL-based methods, the average throughput is increased by 5-30%, and the training time is reduced by 50-60%.

With the development of the sixth generation wireless communication networks, low latency is required to support its applications. In order to meet the low latency requirement, short packet communication is considered to be used, in which a ground sensor transmits the sensing information to a fixed-wing unmanned aerial vehicle (UAV). In this paper, we consider maximizing the energy efficiency of intelligent reflecting surface (IRS)-assisted UAV short packet communication by optimizing the UAV’s speed, trajectory, transmit power and passive beamforming of IRS. Since the maximization problem is nonconvex with respect to the system parameters, this problem is difficult to be solved. Therefore, the successive convex approximation method is employed and a joint iterative optimization algorithm is proposed to solve this problem. In the simulation parts, it is shown that the algorithm proposed in this paper has good convergence performance. And there exists an optimal value of flight speed for the UAV to minimize the energy consumption. In addition, it is found that the application of IRS can improve the energy efficiency effectively.

The rapid growth in wireless device usage has intensified the demand for spectrum resources, leading to inefficiencies in traditional resource allocation methods. Cognitive Radio Networks (CRNs) address this by enabling secondary users (SUs) to access licensed spectrum bands of primary users (PUs) without compromising their Quality of Service (QoS). However, CRNs face challenges such as limited battery life and potential interference with PUs. Energy Harvesting (EH) techniques, particularly RF-based EH, offer a solution by powering CRN terminals, thereby enhancing spectrum utilization efficiency. Simultaneously, Intelligent Reflecting Surfaces (IRSs) have emerged as a powerful technology to enhance wireless propagation environments and support RF-based EH in CRNs. Despite this potential, most existing IRS-assisted CRN frameworks assume ideal continuous phase shifts, an impractical assumption given hardware limitations that permit only discrete phase levels, leading to quantization errors and increased design complexity. In this paper, we establish a unified system model for IRS-assisted Multiple Input Single Output (MISO) EH-CRNs that formulates an optimization problem to maximize SU throughput under practical constraints, including discrete IRS phase shifts, beamforming design, false alarm control, energy causality, and SU Quality of Service (QoS) requirements. To solve the non-convex problem, we develop a quantization-aware alternating optimization algorithm that decomposes the problem into interrelated subproblems for detection probability maximization, false alarm minimization, energy harvesting optimization, and SU throughput enhancement. Advanced techniques such as semidefinite relaxation (SDR), Successive Convex Approximation (SCA), and Nearest Point Search with Penalty (NPSP) are utilized to address practical implementation constraints. Simulation results demonstrate the superior performance of the proposed framework and the novel resource allocation algorithm based on alternating optimization. These results highlight the transformative potential of IRS with discrete phase shifts in enhancing EH-CRN efficiency, particularly in improving energy harvesting and SU throughput under practical constraints.

Unmanned aerial vehicles (UAVs) have shown significant advantages in disaster relief, emergency communication, and Integrated Sensing and Communication (ISAC). However, the escalating demand for UAV spectrum is severely restricted by the scarcity of available spectrum, which in turn significantly limits communication performance. Additionally, the openness of the wireless channel poses a serious threat, such as wiretapping and jamming. Therefore, it is necessary to improve the security performance of the system. Recently, Reconfigurable Intelligent Surfaces (RIS), as a highly promising technology, has been integrated into Cognitive UAV Network. This integration enhances the legitimate signal while suppressing the eavesdropping signal. This paper investigates a RIS-assisted Cognitive UAV Network with multiple corresponding receiving users as cognitive users (CUs) in the presence of malicious eavesdroppers (Eav), in which the Cognitive UAV functions as the mobile aerial Base Station (BS) to transmit confidential messages for the users on the ground. Our primary aim is to attain the maximum secrecy bits by means of jointly optimizing the transmit power, access scheme of the CUs, the RIS phase shift matrix, and the trajectory. In light of the fact that the access scheme is an integer, the original problem proves to be a mixed integer non-convex one, which falls into the NP-hard category. To solve this problem, we propose block coordinate descent and successive convex approximation (BCD-SCA) algorithms. Firstly, we introduce the BCD algorithm to decouple the coupled variables and convert the original problem into four sub-problems for the non-convex subproblems to solve by the SCA algorithm. The results of our simulations indicate that the joint optimization scheme we have put forward not only achieves robust convergence but also outperforms conventional benchmark approaches.

Conventional multi-antenna technology with fixed position antenna (FPA) array cannot make full use of the spatial degree of freedom offered by wireless channels. This letter proposes a novel covert communication system empowered by movable-antenna (MA) array, in which a transmitter equipped with a linear MA array sends covert information to a legitimate user while being monitored by multiple single and FPA wardens. With the aim of maximizing the covert rate, we jointly design the transmit beamforming and antenna position considering the constraints of covertness, transmitter's power as well as antenna position. Despite the non-convexity of the optimization problem, we solve two subproblems in an iterative manner by means of the block coordinate descent algorithm. Numerical results indicate that the proposed MA array scheme can provide superior covertness performance in comparison with the conventional FPA array especially when a small number of antennas is adopted at the transmitter.

Deep learning (DL) has been widely applied in synthetic-aperture radar (SAR) imaging and recognition technology, including multiple operations such as image formation, enhancement, and recognition. Currently, DL methods are only utilized in conjunction with individual image processing operations to enhance corresponding computational performance. The limitations of resolution and data storage also pose significant challenges to imaging and recognition. These limit SAR’s ability to fully exploit the advantages of DL and deliver accurate and fast responses from echo signals to target categories. This article proposes a DL framework-based super-resolution imaging (SRIm) and recognition integrated method, called DL framework-based integrated network (DLIN). This method can simultaneously complete echo imaging and target recognition tasks, that is, to achieve SRIm from low data echoes and complete fast recognition. Specifically, DLIN is a novel SAR processing pipeline that includes an SRIm network (SRIm-Net), followed by a transformer-based recognition network (Recog-Net). SRIm-Net first uses an iterative shrinkage threshold algorithm-based 2-D deep unfolding network to achieve sparse imaging of echo signals. Then, it introduces a neural network architecture called image super-resolution network, which integrates ResNet and U-Net to obtain SRIm results. Finally, Recog-Net is used to complete the image classification task. DLIN can obtain real-time processing results of echo data, including high-quality super-resolution images and accurate target categories. The effectiveness of our proposed DLIN is validated through simulated and measured experiments. The experimental results have shown that DLIN can achieve SRIm, and its improved imaging resolution further enhances the recognition accuracy.

The flexibility and controllable mobility of unmanned aerial vehicles (UAVs) render them easier to become aerial platforms carrying out integrated sensing and communication (ISAC) functionality, and the cooperation among multiple UAVs is a promising way to achieve simultaneous multi-static radar sensing and coordinated multiple point (CoMP) transmission, leading to an enhanced ISAC service. However, due to the intrinsically limited resources that UAVs can utilize, it is challenging to achieve performance improvement for dual purposes. Toward this end, in this paper, an orthogonal frequency division multiple access (OFDMA) UAV-enabled ISAC system is investigated, and a joint trajectory planning and resource allocation problem is formulated to minimize the Cramér-Rao lower bounds (CRLB) for target location estimation while guaranteeing the communication quality-of-service (QoS) constraints. The formulated problem is non-convex and difficult to solve in general, and we first decompose the original problem into three sub-problems and then propose the corresponding algorithms to obtain the optimal solutions efficiently. The extensive simulations demonstrate the convergence of the proposed algorithm and the performance improvement on the localization with different communication requirements compared to conventional techniques.

Next-generation wireless networks require the integration of cognitive networks (CNs) and decision-making techniques to improve the spectrum efficiency. The conventional spectrum sharing schemes require full channel state information of CNs and cannot satisfy the low-latency requirement of nextgeneration wireless networks. Artificial intelligence has shown its high potential to perform decision-making and improve resource utilization efficiency. Hence, we propose a multi-agent reinforcement learning (MARL)-based scheme for spectrum sharing, which is a promising self-decision technique in highly dynamic and complex wireless networks. We analyze several key challenges when MARL is applied to spectrum sharing, such as multi-objective function formulation, multi-dimensional action space, and partial channel state information. Then, we propose efficient solutions and apply the explainable DRL to improve the convergence efficiency in spectrum sharing. The proposed architecture, fundamentals, and challenges provide a clear vision for MARL in CNs.

To solve the bottleneck problem of constrained spectrum resource for Unmanned Aerial Vehicles (UAVs) in unlicensed bands, a co-optimization scheme high spectral efficiency in underlay mechanism is proposed for UAV-assisted monitoring communication networks in urban environment. Considering the high maneuverability of UAVs, the air-to-ground channel is modeled as a probabilistic Line-of-Sight (LoS) channel, and the co-channel interference and maximum speed constraints are adopted to formulate a hybrid resource optimization model for power allocation and trajectory planning, enabling UAVs to construct the fast transmission scheme for monitoring data with occupied spectrum within the given time. The original problem is an NP-hard and non-convex integer problem, which is first decomposed into a two-layer programming problem, and then solved by applying the slack variable and Successive Convex Approximation (SCA) technologies to transform the trajectory design problem into a convex programming problem. Compared with the Particle Swarm Optimization (PSO) algorithm, the proposed joint optimization scheme is verified to improve the spectral efficiency by up to about 19% in simulations. For high-dimensional trajectory planning problems, the SCA-based algorithm is proved to have lower complexity and faster convergence.

The multi-satellite multiple-input multiple-output (MIMO) communication system with full frequency reuse (FFR) has high spectral efficiency. However, satellite communications are highly susceptible to interference, and the communication quality of multi-satellite MIMO communication systems with FFR will be seriously affected when suffering from close-range interference from mobile unmanned aerial vehicle (UAV) Cluster. Therefore, we utilize an online blind source separation (BSS) algorithm without the aid of a priori information for the anti-jamming of mobile UAV clusters, which can avoid the additional occupation of channel resources, as well as process the mixed signals of time-varying systems and output separated signals in real-time. In this paper, the R-V-M-EASI algorithm is proposed, which adjusts the adaptive step size and momentum term factor using the crosstalk index as a separation performance index. The mutation point of the time-varying mixing matrix can be determined by detecting the change of the error function and then using a better converged separation matrix before the mutation to separate the observation signal retrospectively. Simulation results show that the R-V-M-EASI algorithm can effectively separate the mixed signals in both stationary and non-stationary environments, and it has faster convergence speed and lower steady error. For scenarios where the mixed matrix mutates, the proposed algorithm is effective in improving the separation accuracy of the separation matrix at the initial stage.

In order to achieve the long-term work of the wireless sensor network in the border area,a network combining UAV and NOMA(Non-Orthogonal Multiple Access)technology is used to collect data from sensors distributed along the narrow border,so as to effectively reduce the energy consumption of WSNs(Wireless Sensor Networks).The sensor user group uploads data to UAV in a multi-carrier NOMA mode to minimize the total energy consumption of all sensors by optimizing sensor user scheduling and grouping,transmit power and UAV trajectory under the constraints of maximum transmit power,user QoS(Quality of Service)and UAV mobility.The optimization problem is a mixed integer nonlinear optimization problem,which is decomposed into three subproblems to solve.By using exhaustive search,Lagrange duality decomposition and SCA(Successive Convex Approximation),the subproblems are solved successively,and the minimum energy consumption is achieved.Simulation results indicate that the proposed scheme can effectively reduce the total energy consumption of the WSNs.