Channel knowledge map (CKM) is a key enabler for environmental awareness in future wireless systems. However, reconstructing high-fidelity CKM from sparse and noisy measurements poses a significant challenge. While Graph Neural Networks (GNNs) have emerged as a potent tool for this task, existing methods often lack physical consistency and generalization due to reliance on single graph structures and purely data-driven approaches. To tackle this challenge, in this paper, we propose a physics-informed multi-view graph neural network (PIMV-GNN) framework. This framework innovatively integrates two mechanisms within a unified GNN backbone: a multi-view learning (MVL) module that builds a rich spatial representation of the complex environment by fusing two complementary graph structures, namely a "spatial line-of-sight" graph and a "physical proximity" graph; and a physics-informed neural network (PINN) module that enforces physical consistency by imposing constraints derived from the Helmholtz equation in the graph domain. Extensive simulations demonstrate that our proposed PIMV-GNN framework significantly outperforms baseline models under various levels of data sparsity and measurement noise. Furthermore, the results reveal a profound synergistic effect between the MVL and PINN modules, where high-quality multi-view features significantly improve the regularization efficiency of the physics-based constraints.

With the rapid development of low-altitude intelligent networks (LAINs), the growing demand for data services poses significant challenges for the existing networks. At the same time, the airspace becomes increasingly complex due to the escalating count of low-altitude users. Although unmanned aerial vehicles (UAVs) carrying mobile base stations to provide communication services can effectively alleviate pressure on existing network infrastructure, they unfortunately face the dual challenge of sustaining reliable data transmission and guaranteeing UAV flight safety. Therefore, in this paper, we propose a collision-free UAVs communication model specifically designed for the hybrid low-altitude environment, incorporating both static and dynamic, as well as known and unknown obstacles. To efficiently support safe flight operations of UAVs, an artificial potential field (APF)-based collision probability map is constructed, enabling the UAVs to dynamically evaluate and avoid obstacles while maintaining high communication performance constrained by limited energy resources. To maximize energy efficiency in low-altitude environments with hybrid obstacles, an adaptive association multi-agent deep deterministic policy gradient (AA-MADDPG) algorithm is proposed to enable collaborative trajectory planning among multiple UAVs. Simulation results confirm that the proposed strategy enhances energy efficiency by 58.06% and reduces collision probability by 86.18%, achieving significant improvements in both communication performance and flight safety.

NOMA-aided cognitive satellite aerial terrestrial integrated networks (CSATINs) are considered revolutionary and key technologies for 6G Internet of Things (6G-IoT), offering enhanced connectivity, high spectral efficiency, and broad coverage. In this article, we first establish trustworthy CSATINs with multiple aerial relays, aiming to achieve reliable communication in the presence of an eavesdropper. Then, to enhance the system’s covert performance, we propose an unmanned aerial vehicle scheduling scheme. Moreover, based on the established covert system model, we derive the closed-form expressions of detection error probability (DEP), covert outage probability (COP), and effective covert rate (ECR). Particularly, an optimization is proposed to enhance the covert performance of the considered system. Finally, Monte Carlo simulations are given to validate the correctness of the theoretical analysis, demonstrating that the reliability and covertness of the proposed system can be simultaneously enhanced by appropriately adjusting the power allocation coefficients, jamming power, and the transmission power of the satellite and UAVs.

This works investigates the covert performance for the satellite aerial ground integrated networks with imperfect limitations, i.e., channel estimation errors, non-ideal hardware and co-channel interference. To enhance the covert transmission, an unmanned aerial vehicle is applied to forward the signal from the satellite source to the destination. Particularly, the detection error probability, outage probability and the covert transmission rate are further studied in the presence of closed-form expressions and asymptotic expressions. Finally, some representative Monte Carlo simulations are proposed to verify our theoretical analysis. Derived from the results, the non-ideal hardware, the channel estimation errors and the co-channel interference have great impacts on the covert performance. Particularly, when the system is under non-ideal hardware, a suitable power allocation scheme is needed for the interference to gain the lowest detection error probability. Moreover, the outage probability has a lower bound in the case of non-ideal hardware. What’s more, the covert transmission rate also has an upper bound when the system is under non-ideal hardware. In addition, to have a better covert performance, the channel state information should be accurate enough by allocating much power for the channel estimations. The finding results are essential for the practical system for they can promote the engineering design.

Due to the broadcasting characteristics of satellite-terrestrial integrated networks (STINs), security vulnerabilities have emerged as a critical concern requiring urgent mitigation strategies. Unlike traditional security methods, federated learning (FL) enables a large number of participants to collaborate without disclosing actual privacy data. Its potential as a framework that combines collaborative model training and covert payload transmission in STINs represents a significant research gap. This paper proposes FedSAT, a novel FL-based covert communication scheme for STINs, in which each participant in the FL process can utilize the shared learning protocol as a covert medium for transmitting arbitrary information in privacy-preserving framework. Our framework leverages the dual capabilities of FL for collaborative model training and covert payload embedding, utilizing Geostationary Earth Orbit (GEO) satellites and distributed terrestrial nodes to embed sensitive data within FL parameter updates. The system maintains model convergence accuracy while implementing strategic encryption to achieve robust sharing and transmission of payloads within the FL framework. Comprehensive simulation tests demonstrate the framework significant efficacy, achieving a 98.7% communication coverage for covert payload transmission under monitoring by low Earth orbit (LEO) surveillance satellites, with only a 0.8% decrease in model accuracy. This breakthrough achievement paves the way for a transformative paradigm in covert cross-domain communication for next-generation networks.

With the development of unmanned aerial vehicles (UAVs) and the diversification of low-altitude applications, the cooperation among UAVs with different capabilities and objectives offers an exciting prospect for achieving efficient and ubiquitous communication coverage. However, coordinating the cooperation and competition among heterogeneous UAVs is an intractable challenge. In this paper, we propose a novel centralized-distributed heterogeneous-UAVs intelligent communication network system, which addresses the cooperation-competition issue among heterogeneous UAVs through reasonable task allocation. Specifically, a hub UAV makes ground users (GUs) scheduling decisions based on global information and provides backhaul link support through trajectory optimization. Meanwhile, high-mobility distributed UAVs cooperate to ensure fair, efficient communication for assigned GUs. Although centralized user scheduling offers greater flexibility and better performance, it also faces the serious problems which includes time-varying local observation spaces, hybrid action spaces, heterogeneous state spaces, and reward discrepancies. To solve these problems, we propose a convolutional neural network-assisted heterogeneous-UAVs proximal policy optimization algorithm, which aims to jointly optimize UAV trajectories and user scheduling, maximizing the system’s total fair energy efficiency. The simulation results demonstrate that the proposed CNN-HUPPO algorithm outperforms the four multi-agent deep reinforcement learning (MADRL) benchmark algorithms and two baseline algorithms in terms of fairness and accumulative fair energy efficiency.

Achieving real-time processing in integrated sensing and communication (ISAC) systems presents significant challenges due to the high computational burden of conventional optimization methods, particularly within intelligent transportation networks (ITN). This paper addresses these challenges by proposing lightweight supervised and unsupervised deep learning (DL) algorithms, respectively for quasi-static and dynamic environments, aiming to improve the secrecy energy efficiency (SEE) of ITN under the constraints of the Cram´er-Rao bound (CRB) for direction-of-arrival (DOA) estimation and the transmission rate of each user. By jointly optimizing power allocation and reconfigurable intelligent surface (RIS) phase shifts, the framework ensures robust physical layer security (PLS) alongside communication efficiency, aligning with defense-in-depth strategies for securing next-generation ITN. For quasi-static environments, a supervised deep neural network (DNN) algorithm leverages offline codebook-generated labels to achieve near-optimal channel state information (CSI) mapping, explicitly minimizing signal leakage to eavesdroppers. In dynamic scenarios, an unsupervised channel attention mechanism-based residual network (CAM-ResNet) eliminates labeling overhead through direct physics-informed SEE optimization with adaptive constraint enforcement, enabling real-time adaptation to rapidly varying channels and evolving security threats. Simulation results demonstrate that both algorithms achieve comparable SEE performance with the zero-forcing (ZF) method, while significantly reducing computational complexity, with the CAM-ResNet demonstrating superior resilience to dynamic security threats. This work contributes to advancing secure and efficient ISAC solutions, reinforcing multi-layered defense mechanisms critical for future ITN.

Multi-reconfigurable intelligent surfaces (RIS) has recently gained significant interest as emerging technology for exploiting Intelligent electromagnetic environment. Inspired by opportunistic communications, a low-complexity adaptive selection and training method for multi-RISs is proposed in this paper. Firstly appropriate number of the multi-RISs is selected to assist communication. The elements of the selected RIS are grouped, and the grouped elements share a common coefficient to reduce training overhead. Secondly, the communication performance is evaluated and other RIS will be selected to assist communication if the communication performance not meet user requirement. In this way, the system performance and the training complexity of multi-RISs can be trade-off efficiently. Simulation results show that the proposed scheme outperforms the state-of-the-art benchmarks in terms of training overhead and robustness in different channel condition, and the training overhead is 30% lower compared to the centralized deployment scheme proposed in [13].

Covert communication has become a hot topic in the wireless transmission field due to the ability to secure transmitted data by hiding the wireless transmissions. Given the extensive use of drone communications and its urgent demand for security, we investigate covert communications in aerial terrestrial integrated networks (ATINs), where an unmanned aerial vehicle (UAV) tries to send private messages to a remote user via multiple terrestrial relays under the supervisions of the warden. On this foundation, one covert scheme for joint power control and relay selection has been proposed. Subsequently, we derive the detection capabilities at warden, and the effective covert rate (ECR) of link from UAV to user. Furthermore, a power optimization problem is designed to maximize ECR with covertness constraint. Finally, numerical results are presented to verify the achievable covert performance of system and prove the effectiveness of the proposed scheme.

We investigate the performance of an active reconfigurable intelligent surface (RIS)-assisted mixed radio frequency (RF)-terahertz (THz) relaying system, where the RF signal reaches the relay through the active RIS and is then transmitted to the user via the THz channel. Under this scenario, we analyze the system performance with the relay employing amplify-and-forward (AF) and decode-and-forward (DF) protocols. More specifically, we derive the exact expressions for the cumulative distribution function (CDF) of the end-to-end signal-to-noise ratio (SNR) for both relaying protocols. Based on this, we obtain the exact expressions for the outage probability, average bit error rate (ABER), and average channel capacity (ACC). Furthermore, to gain deeper insights, we derive the asymptotic expressions at high SNRs and obtain diversity order (DO) of the system. Moreover, we extend the analysis to the variable gain relaying scheme. The findings reveal that the DOs for both relaying protocols are determined by the THz channel parameters, and the DO under the AF relaying protocol is twice that of the DF relaying protocol. Finally, we validate that active RIS (A-RIS) can more effectively assist the performance of the mixed RF-THz relaying system compared to passive RIS.

Given the rapid growth of diverse communication demands, future large-scale satellite-aerial-terrestrial integrated networks (SATINs) need to simultaneously provide services to users while guaranteeing spectral efficiency, secrecy and energy efficiency. This paper addresses the problem of maximising secrecy energy efficiency (SEE) in SATINs, which can accurately describe the effective trade-off between security, spectral efficiency and transmit power. Particularly, we investigate a secure beamforming (BF) scheme in cognitive SATINs that employs rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) in the presence of multiple eavesdroppers (Eves) in a UAV-aided secondary network (SN). To optimize the SEE for secondary vehicle users while satisfying the constraints of primary users (PUs), we utilize deep reinforcement learning (DRL) to address the coupling between different optimized parameters based on the improved long short-term memory proximal policy optimization (LSTM-PPO) algorithm. The main innovation of this paper is to design a sophisticated reward function, action space, and state space according to each constraint to speed up the convergence. In addition, simulation results show that the proposed DRL-based optimization scheme exhibits significant advantages in terms of SEE compared with benchmark schemes, validating the effectiveness of this work.

The unmanned aerial vehicle (UAV) aided mobile edge computing (MEC) network has attracted significant attention due to its enhanced computing power, reliable network connectivity, dynamic environment adaptability, which however still suffer from non-instantaneous channel reconstruction and wireless security threats. Therefore, this paper considers a reconfigurable intelligent surface (RIS) assisted UAV-MEC network, aiming to minimize UAV energy consumption by jointly optimizing task offloading rate, user scheduling coefficient, RIS phase and UAV trajectory with the constraints of secure offloading rate. Given the multi-variable coupling and non-convex nature of this optimization problem, we decouple it into three subproblems, which include the user scheduling and offloading ratio optimization, the RIS phase optimization, and the UAV trajectory optimization. For the fractional programming problem involving RIS phase optimization, the Dinkelbach algorithm is used to transform it into a parametric subtraction problem, thus obtaining a closed-form solution for the RIS phases. Furthermore, the successive convex approximation (SCA) algorithm is employed for the UAV trajectory optimization subproblem. Ultimately, a double-layer iterative optimization algorithm based on block coordinate descent (BCD) is proposed to solve the original non-convex optimization problem. Simulation results confirm its superior performance in saving UAV energy compared to other baseline schemes.

This paper researches the system evaluations of the reconfigurable intelligent surface (RIS)-assisted satellite aerial terrestrial integrated systems. To ensure the stability of the regarded network, a link selection scheme is presented to get the balance between the system performance and the system efficiency. Besides, in order to build a practical environment of the transmission networks, the imperfect hardware, channel estimation errors and co-channel interference are both considered in the networks. Relied on the above considerations, the detailed analysis for the outage behaviors is shown along with the asymptotic outage probability in high signal-to-noise ratio scenarios. Moreover, the diversity order and coding gain are also provided to give fast methods to confirm the system evaluation. Finally, some re-presentative simulations are provided to confirm the efficiency and advantage of analytical results and the proposed link selection scheme.

This paper researches the secrecy outage probabili-ty for the rate splitting multiple access-based integrated satellite-aerial-terrestrial networks. Specially, owing to some practical reasons, imperfect hardware is further analyzed for all the network nodes. Moreover, a UAV is utilized to help the signal transmitting from the satellite to the ground destination in the presence of an eve. Besides, by considering these limitations, the detailed analysis for the secrecy outage probability are gotten, which offer a good way to calculate the impacts of channel parameters and system factors on the secrecy networks. Finally, several representative Monte Carlo simulations are presented to confirm the rightness of the analytical results.

The performance of integrated satellite-aerial-terrestrial relay networks (ISATRNs) faces two main challenges, severe signal strength degradation over long transmission distances and limited spectrum resources. To address these issues, we consider the introduction of high altitude platforms (HAPs) and unmanned aerial vehicles (UAVs) carrying reconfigurable intelligent surface (RIS) as relays during transmission from satellites to the ground. Additionally, we employ rate splitting multiple access (RSMA) at HAPs to improve signal transmission robustness. To optimize system energy efficiency, we formulate a multi-objective problem that considers the active transmit beamforming vector, RIS phase shift, power splitting ratio, and UAV trajectory. To tackle the non-convex problem involving both discrete and continuous variables, we introduce a novel approach called access-free federated deep reinforcement learning (AF-DRL). The optimal transmit beamforming and power splitting ratio are obtained by allowing the UAV to plan its path and locally train, reducing computational overhead caused by high-dimensional UAV movement. Simulation results demonstrate that the proposed RSMA-based enhancement scheme achieves higher energy efficiency compared to the comparison scheme.

Satellite and high-altitude platform (HAP) are considered as the key parts of the next generation networks, and specifically for the Internet-of-Things networks, which are utilized to provide unobstructed connections and massive user access for terrestrial networks. In this article, we investigate the power allocation (PA) and system performance of nonorthogonal multiple access (NOMA)-enabled integrated satellite-HAP-terrestrial systems under practical limitations. Particularly, a practical system model is established by considering the channel estimation errors and imperfect successive interference cancelation at the receiver. To achieve the different quality of service requirements among multiple served users, we propose a novel NOMA-based PA scheme. In addition, the analytical and asymptotic expressions for the outage probability of NOMA users are obtained to verify the proposed scheme as well as the ergodic capacity. Finally, numerical results are corroborated with Monte Carlo simulations, which show the correctness of our analytical results, and the benefits of our proposed scheme. The proposed scheme indicates that the HAP relay link plays a significant role in the system performance.

Federated learning (FL) as an emerging distributed machine learning (ML) paradigm enables participants to train their on-device data locally and share model parameters with others by the parameter server. Differing from the centralized ML, FL splits the high requirements of training data and computing power from the server to clients, which is well adapted to unmanned aerial vehicle (UAV) swarms with scattered nodes, heterogeneous data, and limited computing power. However, pre-trained models are unsatisfactory in unfamiliar scenes and most existing approaches fail to concentrate on the communication-sensitivity and real-time requirements in UAV-enabled FL scenarios. To address this problem, this paper proposes participant and sample selection for efficient online federated learning in UAV swarms (FedOL). Through the combination of online learning and FL, UAVs can supplement real-time samples and quickly improve the model accuracy in unfamiliar scenes. Meanwhile, to reduce the training latency with expected model accuracy, FedOL allows the server UAV to select participants with high training utility, while the client UAVs select more important samples. We implement FedOL and deploy it on UAV embedded devices. Experimental results show that compared with existing FL approaches, FedOL speeds up by about 2.61× and reaches the final accuracy about 1.02× higher.

In this work, we study the performance of an unmanned aerial vehicle (UAV)-aided mixed radio frequency (RF)/underwater acoustic communication (MRFUAC) transmission system, where the UAV transmits signals to an underwater node through the amplify-and-forward (AF) relay, such as a buoy located on the sea surface. In particular, the UAV-relay RF channel follows a Rician distribution, while κ-μ shadowed fading distribution is applied to model the UAC link. For this considered system with the fixed-gain AF relay, we obtain the statistical distributions of the end-to-end signal-to-noise ratio. To demonstrate the performance of the MRFUAC system, formulae for the outage probability and average bit-error rate are further obtained in closed form. Moreover, to obtain some interesting insights, we present the asymptotic analyses of these performance metrics. To show the validity of our analysis, the truncation error analysis is also provided and the results show that our proposed method has a smaller error than that in the previous literature. In addition, we present the analysis of the optimal height of the UAV at different horizontal distances. In addition to the fixed-gain analysis, we also provide few results for the variable-gain AF relaying MRFUAC system. Finally, the validity of the theoretical analysis is confirmed by Monte Carlo simulations.

In this article, we investigate covert communication in simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted networks with rate-splitting multiple access (RSMA). Specifically, a legitimate transmitter employs RSMA to send messages over the STAR-RIS to a pair of legitimate users located on both sides of the STAR-RIS, where the user on the transmitting surface is equipped with a full-duplex (FD) receiver with two antennas. In particular, one of the receiver’s antennas is used to receive signals and the other is used to transmit jamming signals to confuse the warden’s detection. In order to evaluate the covert performance of the considered system, we derive closed-form expressions for the detection error probability (DEP), the outage probability, and the covert rate. Then, the effects of the maximum signal interference power of the FD, the power distribution coefficient, and the reflection coefficient of the STAR-RIS elements on the covert performance are presented. The numerical results indicate that increasing the maximum signal interference power of the FD enhances the value of DEP, but reduces its outage performance. Additionally, it is observed that the impact of the reflection coefficient of the STAR-RIS elements on the covert performance is related to the transmit power, and the DEP is a monotonically increasing function of the reflection coefficient when the transmit power is sufficiently high.

Integrated satellite-aerial-terrestrial relay networks (ISATRNs) have been considered as a promising architecture for next-generation networks, where high altitude platform (HAP) is pivotal in these integrated networks. In this paper, we introduce a novel model for HAP-based ISATRNs with mixed FSO/RF transmission mode, which incorporates unmanned aerial vehicles (UAVs) equipped with reconfigurable intelligent surfaces (RISs) to dynamically reconfigure the propagation environment and fulfill the massive access requirements of ground users. Our aim is to maximize the system ergodic rate by joint optimizing the UAV trajectory, RIS phase shift, and active transmit beamforming matrix under the constraint of UAV energy consumption. To solve this intractable problem, a deep reinforcement learning (DRL)-based energy efficient optimization scheme by utilizing an improved long short-term memory (LSTM)-double deep Q-network (DDQN) framework is proposed. Numerical results demonstrate the superiority of our proposed algorithm over the traditional DDQN algorithm, on single-step exploration average reward values and other evaluation metrics.

In this paper, we study the performance of a cooperative underwater acoustic communication/free-space optical communication (UAC-FSO) transmission system supported by a fixed-gain amplify-and-forward (AF) relay, where an underwater source transmits signals to a satellite through the help of a buoy functioning as a relay located on the sea surface. In particular,Kand κ - μ shadowed fading distribution models are used to characterize the UAC link, while the FSO channel follows a unified Málaga distribution existing pointing errors. Based on the above considerations, we calculate the cumulative distribution function (CDF) and probability density function (PDF) of the end-to-end (e2e) signal-to-noise ratio (SNR). To assess the system performance, expressions for the outage probability (OP) and average bit-error rate (BER) are further obtained in closed-form. Furthermore, driven by a desire for more explicit insights, the high SNR analyses of the OP and average BER, and upper and lower bounds on the average capacity are presented. Finally, through Monte Carlo simulations, the correctness of the theoretical analysis is verified.

In this paper, we consider a simultaneously transmitting and reflecting reconfigurable intelligent surface (STARRIS)-assisted Internet of Vehicles non-orthogonal multiple access network. For practical considerations, the impacts of residual hardware impairments and imperfect channel state information are investigated. For such a setup with three different protocols, including time switching (TS), energy splitting, and mode switching (MS), we present the outage probability (OP) analysis for the network. Moreover, the probability of signal-to-noise ratio (SNR) gain and the delayed outage probability are further investigated. Compared with the traditional relaying scheme, the obtained results show that the STARRIS-assisted system achieves better delay performance. Furthermore, for the STARRIS-assisted system, the TS protocol achieves the best performance, while the MS protocol has the worst performance. In addition, considering the base station with multiple antennas, the expression for the OP is derived and error floors at high SNRs due to imperfect channel state information constraints exist. Finally, one can readily observe that increasing the number of STARRIS elements and antennas of the source can improve the outage performance.

To overcome the long transmission distances and limited spectrum resources issues, both the space-aerial-terrestrial relay networks (SATRNs) and hybrid-free space optical/radio frequency (FSO/RF) mode have attracted significant attentions. Specifically, high-altitude platform (HAP) and unmanned aerial vehicle (UAV) are employed in this paper to enhance the transmission reliability and improve the resource utilization along with the reconfigurable intelligent surface (RIS) and rate splitting multiple access (RSMA) techniques. Besides, we propose a novel access-free federated deep reinforcement learning (DRL) framework, which exploits the privacy-preserving security features of federated learning (FL) and DRL, to optimize active beamforming vectors, RIS reflection coefficients, UAV trajectory, and power splitting ratio. The learning process of the algorithm is performed locally which significantly reduces the computational overhead compared to traditional algorithms. Simulation results demonstrate that the proposed federated DRL-aided framework achieves higher energy efficiency compared to the reference schemes.

The trend of integrated satellite-HAP-ground networks (IS-HAP-GNs) as an critical directions for the future development of next generation network technology is widely recognized by academia and industry. Besides, utilizing reconfigurable intelligent surfaces (RIS) as a green paradigm, unmanned aerial vehicles (UAVs) can be equipped to reflect uplink signals from vehicle transmitters (VTs) to high altitude platforms (HAPs). Acting as relays, HAPs then forward these signals to satellites via hybrid free-space optical (FSO) links to enable rapid link deployment. In this paper, we firstly investigate a novel uplink signal transmission mode to maximize the system ergodic sum rate. Then, to tackle the high-dimensional non-convex optimization problems, we propose an asymmetric long short-term memory (LSTM)-deep deterministic policy gradient (DDPG) (AL-DDPG) algorithm builds on the deep reinforcement learning (DRL) framework. The numerical results demonstrate the superiority of the AL-DDPG algorithm over traditional optimization algorithms and reveal the effect of different system parameter settings on the performance.

Integrated space-air-ground network (ISAGN) has been regarded as an important infrastructure of the next-generation network, which can offer massive access and seamless connections for users in a wide coverage area. This paper utilizes non-orthogonal multiple access (NOMA) technique to improve the spectrum efficiency of the ISAGN. Besides, two-way relay technique is introduced in ISAGN to boost the spectrum efficiency. Then, we conducted the ergodic capacity of two-way unmanned aerial vehicle (UAV)-aided ISAGN with NOMA. We first briefly establish a two-way UAV-aided ISAGN, by considering the imperfect channel state information (CSI) and successive interference cancellation (SIC). To obtain deeper insights, the closed-form expression of ergidic capacity for the considered system is derived. Finally, numerical simulations are provided to evaluate the performance of the system and reveal the impacts of imperfect factors.

In this paper, we discuss the co-optimized performance of multi-reconfigurable intelligent surface (RIS)-assisted integrated satellite-unmanned aerial vehicle-terrestrial network (IS-UAV-TN), where the multiple vehicle users are applied to the network under consideration. The performance optimization of IS-UAV-TNs faces two major challenges: one is the obstacles in the transmission path and the other is the highly dynamic communication environment caused by the UAV movement for the multiple ground vehicle users. To tackle these above issues efficiently, we will install RIS on the UAV for the purpose of reshaping the wireless transmission path. In addition, non-orthogonal multiple access (NOMA) protocols are considered as a new paradigm to address spectrum shortage and enhance connection quality. Considering the UAV energy consumption, the satellite transmission beamforming matrix and RIS phase shift configuration, a multi-objective optimization problem is proposed to maximize the system achievable rate and minimize the UAV energy consumption during a specific mission. On this foundation, to facilitate the online decision problem, the deep reinforcement learning (DRL) algorithm is utilized to achieve real-time interaction with the communication environment. A multi-objective deep deterministic policy gradient (MO-DDPG) algorithm is proposed to search for sub-optimal solutions about the learning problem of multi-objective control policies in IS-UAV-TNs. Experimental results show that the method can simultaneously consider three optimization objectives and effectively adjust the optimal update policy according to the settings of different weight parameters.

Nonorthogonal multiple access (NOMA) and cognitive integrated satellite terrestrial relay networks are the promising and key part for the next-generation wireless networks. This article researches the joint effects of channel estimation errors (CEEs) and hardware impairments on the secrecy performance of cognitive integrated satellite terrestrial relay networks. The noncolluding eavesdropping scheme is applied in the multiple eavesdroppers, where the eavesdropper with the highest eavesdropping capacity is selected to overhear the legitimate transmission signal. Moreover, the detailed analysis for the secrecy outage probability (SOP) is obtained based on the utilized partial terrestrial relay selection strategy. To obtain the insightful conclusions, the asymptotic analysis along with the secrecy coding gain and secrecy diversity order for the SOP are further derived, which gives the effective methods to valuate the impacts of CEEs and hardware impairments on the considered system with the NOMA scheme in high signal-to-noise ratio regime. Moreover, simulations are derived for the secrecy energy efficiency. Finally, Monte Carlo simulations are given to prove the correctness of the theoretical SOP analysis.

With the extensive research of multiantenna technology, beamforming (BF) will play an important role in the future communication systems due to its high transmission gain and satisfying directivity. If we can detect the non-cooperative beams, it is of great significance in counter reconnaissance, beam tracking, and spectrum sensing of multiantenna transmitters. This paper investigates the wireless sensor networks (WSNs), which is used to detect the unknown non-cooperative beam signal. In order to perceive the presence of beam signals without the prior information, we first derive the detection probability based on the sensors’ received signal strength (RSS). Then, based on the strong directivity of the beam signal, we propose an improved “k rank” fusion algorithm by jointly exploiting the energy detection (ED) information and location information of the sensors. Finally, the beam detection performance of different fusion algorithms is compared in simulation, and we find that our proposed algorithm showed better detection probability and lower error probability. The simulation results verify the correctness and effectiveness of the proposed algorithm.

This paper investigates the performance of a dual-hop decode-and-forward (DF) relaying-aided land mobile satellite communication over Shadowed-Rician (SR) fading channels. A practical model for the satellite relaying system is first developed, where the impacts of satellite multi-beam antenna, radio propagation loss, and random shadowing are taken into account. Next, by assuming that the multi-beam satellite suffers from hardware impairments (HIs) and is perturbed by interference signals, we derive an equivalent end-to-end signal-to-interference-plus-noise-and-distortion-ratio of the system, and justify that the maximum ratio transmission at the source and the maximum ratio combining at the destination are the optimal transmit-receive beamforming schemes on the proposed HIs model. Then, closed-form expressions for the probability density function (PDF) of the sum of independent and identically distributed (i.i.d) squared SR random variables in the case of integer and rational Nakagami-m fading parameters are derived. Based on the derived PDF, new analytical expressions for the outage probability (OP) and average throughput are obtained in the presence of HIs and interference. Moreover, the asymptotic OP and average throughput at high signal-to-noise ratio are investigated to reveal the achievable diversity order of the system. Finally, Monte Carlo simulation results are provided to corroborate the analytical results.