Unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) satellite Internet of Things (IoT) networks face severe physical-layer security challenges in the presence of eavesdroppers. To address this issue, this paper proposes a secure graph neural network (Sec-GNN) based multi-dimensional anti-eavesdropping optimization method. The approach models the node-spatial relationships among the UAV, legitimate users, and eavesdroppers as a graph structure. By leveraging the message-passing mechanism of graph neural networks—sequently performing message generation, message aggregation, and node update—it dynamically integrates network topology information and node interaction features. This enables end-to-end joint optimization of the UAV’s three-dimensional position, beamforming vectors, and multi-user power allocation strategies. The method does not rely on explicit channel state information and directly generates near-optimal resource allocation schemes based on node location information and observable signal features. Experimental results demonstrate the superiority of Sec-GNN across various scenarios, and ablation studies confirm that partial optimization leads to significant performance degradation, thereby verifying the necessity of multi-dimensional joint design. The proposed framework provides an efficient and scalable solution for secure resource management in dynamic space-air-ground integrated networks.

Certificate management is needed for securing certificates which has been widely deployed in satellite networks to support security-related services. However, directly utilizing existing certificate management mechanisms in satellite networks would cause critical issues in terms of security, privacy, and practicality. Typically, the trustworthiness of certificate revocation checking (CRC) cannot be guaranteed in the presence of active adversaries; The certificate to be checked contains the satellite’s identity, which is sensitive in some applications but could be exposed during CRC; CRC cannot be trivially launched when the satellite is under constrained networks (e.g., it enters dead zones where direct communication with base stations fails).Worse still, compromising the secret key of a single certificate authority (CA) leads to certificate forgery. In this paper, we propose a forward-secure and privacy-preserving certificate management scheme, dubbed SNCM, for satellite networks, where a forward-secure signature algorithm is used to issue certificates. We utilize a neighboring-assisted forwarding paradigm in SNCM to support CRC in constrained networks. SNCM is secure against adversaries who invalidate CRC results or violate related sensitive information about the satellite, which is achieved by utilizing authenticated encryption with associated data (AEAD). Furthermore, SNCM utilizes a 2-layer revocation checking protocol to perform lightweight CRC, where the CA and base stations handle CRC tasks from satellites in a cooperative way, which frees the CA from heavy costs and reduces CRC delay significantly. We analyze the security of SNCM, implement an SNCM prototype, and conduct a comprehensive performance evaluation, which demonstrates its security, efficiency, and practicality.

This paper investigates backdoor attacks in image oriented semantic communications. The threat of backdoor at tacks on symbol reconstruction in semantic communication (Sem Com) systems has received limited attention. Existing research on backdoor attacks targeting SemCom symbol reconstruction primarily focuses on input-level triggers, which are impractical in scenarios with strict input constraints. In this paper, we propose a novel channel-triggered backdoor attack (CT-BA) framework that exploits inherent wireless channel characteristics as activation triggers. Our key innovation involves utilizing fundamental channel statistics parameters, specifically channel gain with different fading distributions or channel noise with different power, as potential triggers. This approach enhances stealth by eliminating explicit input manipulation, provides flexibility through trigger selection from diverse channel conditions, and enables automatic activation via natural channel variations without adversary intervention. We extensively evaluate CT-BA across four joint source-channel coding (JSCC) communication system architectures and three benchmark datasets. Simulation results demonstrate that our attack achieves near-perfect attack success rate (ASR) while maintaining effective stealth. Finally, we discuss potential defense mechanisms against such attacks.

Six-dimensional movable antenna (6DMA) offers a potential solution to enhance wireless transmission performance by physically reconfiguring antenna positions and orientations. However, prevailing snapshot-based reactive methods are ill-suited for continuously tracking mobile user equipments (UEs) due to their neglect of antenna kinematic constraints and system latency. To address these limitations, in this paper, we propose a proactive approach by modeling UE tracking as a single, long-term 6DMA trajectory optimization problem to maximize sum spectral efficiency. Leveraging a channel knowledge map (CKM) for predictive data, our model holistically incorporates the system’s complex kinematics and physical constraints, including velocity limits and safety distances, to ensure a physically feasible trajectory. To solve this high-dimensional, non-convex problem, we develop a novel manifold optimization algorithm. This method maps the antenna’s rotational states onto the SO(3) Lie group and employs an adaptive penalty measure with tangent space backpropagation for an efficient solution. Simulation results demonstrate our approach significantly enhances sum spectral efficiency over benchmarks, while ensuring continuous and physically feasible antenna trajectories.

Low Earth Orbit (LEO) satellite communications have emerged as a promising paradigm for achieving ubiquitous coverage, driving the evolution of space-ground integrated networks (SGINs). The cell-free (CF) architecture has attracted significant attention in SGINs as the terrestrial segment for its potential to enhance capacity and connectivity. However, deploying CF necessitates numerous access points (APs), resulting in a prohibitive cost. To this end, we propose reconfigurable intelligent surface (RIS)- and simultaneous transmitting and reflecting (STAR)-RIS-assisted CF systems for SGINs, where part of the APs is replaced with cost-efficient RISs and STAR-RISs. Non-orthogonal multiple access (NOMA) is incorporated to improve connectivity under limited spectrum. We formulate transmit power minimization problems for both RIS- and STAR-RIS-assisted CF-NOMA in SGINs, jointly optimizing the active beamforming vectors of the satellite and APs, as well as the discrete passive beamforming (DPB) vectors of RISs/STAR-RISs. For the RIS-assisted scenario, a semi-definite programming (SDP)-based method is proposed to optimize the active beamforming vectors, while an enhanced integer linear programming (ILP) method is proposed to obtain the optimal DPB of RISs. To reduce complexity, we develop a low-complexity penalty-based SDP (PB-SDP) algorithm that achieves near-optimal DPB solutions. For the STAR-RIS-assisted scheme, both independent and coupled DPB for transmission and reflection are optimized alone with the active beamforming vectors. Numerical results demonstrate that: 1) The proposed systems outperform cell-based systems and heuristic optimization algorithms in terms of transmit power consumption; 2) The proposed PB-SDP algorithm achieves near-optimal performance with reduced complexity; 3) It is shown that DBP with 3 quantization bits achieves performance comparable to continuous passive beamforming (CPB) in both RIS- and STAR-RIS-assisted systems; 4) Also, it is shown that beyond a certain number of APs, further increasing the APs yields only limited transmit power consumption gains under a fixed total number of antennas.

This paper investigates the secrecy performance optimization of an uplink satellite communication system exposed to dual-domain eavesdropping threats. Specifically, a cooperative relaying architecture is considered, where a ground user (GU) transmits confidential information to a target satellite (TS), assisted by an amplify-and-forward (AF) relay satellite (RS). Simultaneously, a ground-based malicious node acts as an AF relay to enhance the interception capability of a satellite eavesdropper (SE). To improve secure transmission, a secrecy rate maximization problem is formulated by jointly optimizing the transmit powers of the GU and RS, subject to quality-of-service constraints at the TS. The resulting non-convex problem is solved efficiently using a successive convex approximation algorithm. Simulation results demonstrate that the proposed cooperative relaying scheme significantly enhances secrecy performance compared to traditional non-cooperative baselines. These findings highlight the potential of cooperative multi-satellite relaying as an effective approach to securing next-generation satellite communication networks against sophisticated eavesdropping threats.

Certificate revocation checking (CRC) is a fundamental component for securing certificates which has been widely deployed in satellite networks to support security-related services. However, directly utilizing existing CRC mechanisms in satellite networks would cause critical issues in terms of security, privacy, and practicality. Typically, the trustworthiness of checking results cannot be guaranteed in the presence of active adversaries; the certificate to be checked contains the satellite’s identity, which is sensitive in some applications but could be exposed during CRC; CRC cannot be trivially launched when the satellite is being under constrained networks (e.g., it enters dead zones where direct communication with base stations fails). In this paper, we propose a privacy-preserving and lightweight CRC scheme, dubbed SNCRC, for satellite networks, where a neighboring-assisted forwarding paradigm is utilized to support CRC in constrained networks. SNCRC is secure against adversaries who invalidate checking results or violate related sensitive information about the satellite, which is achieved by utilizing authenticated encryption with associated data (AEAD). Furthermore, SNCRC utilizes a 2-layer revocation checking protocol to perform lightweight CRC, where the certificate authority (CA) and base stations handle CRC tasks from satellites in a cooperative way, which frees CA from heavy costs and reduces CRC delay significantly. We analyze the security of SNCRC, implement an SNCRC prototype, and conduct a comprehensive performance evaluation, which demonstrates its security, efficiency, and practicality.

In this article, we propose a hybrid-field-aware two-stage beamforming (HFA-TSB) scheme for extremely large-scale MIMO (XL-MIMO) systems. The HFA-TSB scheme can reduce the high pilot overhead associated with channel estimation by leveraging statistical channel state information (SCSI). Utilizing our derived ergodic spectral efficiency (SE), which relies solely on SCSI, we can eliminate the interference resulting from the nonorthogonality of near-field polar domain codewords. Specifically, the HFA-TSB scheme comprises two stages: 1) pre-beamforming and 2) precoding. In the pre-beamforming stage, we use SCSI to design the pre-beamforming matrix, focusing on eliminating interference among asymptotically orthogonal channels. This can establish an equivalent reduced-dimensional channel matrix, which can be estimated using less pilot overhead. Additionally, to address the interference caused by the nonorthogonality of near-field codewords, we derived an ergodic SE that relies solely on SCSI. Using this as an optimization objective, the pre-beamforming matrix design problem is formulated as a 0-1 integer nonlinear programming problem, which is challenging to solve. To address this, we propose a constraint-driven two-phase greedy beam selection algorithm for identifying effective beams. In the precoding stage, we design the precoder based on the estimated equivalent sparse channel to effectively mitigate any residual interuser interference. Simulations validate the superior performance of our proposed HFA-TSB scheme in terms of net SE.

The communication among nodes in the space–air–sea integrated network (SASIN) relies on collaborative multihop transmission. Hence, effective routing techniques should be designed to optimize multiple indicators. Routing optimization for multihop is usually focused on optimizing a single metric. Moreover, designing effective routing strategies for multihop networks with SASIN is challenging as balancing multiple performance metrics can lead to conflicts. In this article, we propose near-Pareto multiobjective routing optimization for SASIN, which adopts multiobjective combinatorial optimization (MOCOP) to strike a tradeoff among multiple objectives. We establish the SASIN system model, including channel models of communication links between satellites, aircraft, and ships. Furthermore, we use multiobjective optimization methods to formulate objective functions of spectral efficiency, energy efficiency, and delay. We employ the multiobjective evolutionary algorithms (MOEAs) for approximating the set of the Pareto optimal solutions. An improved nondominated sorting genetic algorithm II (INSGA II) and an improved strength Pareto evolutionary algorithm II (ISPEA II) are proposed to generate approximations of the Pareto optimal set. We evaluated the MOCOP formulation, and the SASIN network topology was built based on real data and simulated data. The simulation results indicate that a set of beneficial tradeoff solutions can be obtained for providing flexible selection of communication connections by addressing the multiobjective routing problem formulated. The results demonstrate that the MOEAs utilized have the potential to find Pareto-optimal solutions for SASIN.

Intelligent Reflecting Surfaces (IRS) enhance spectral efficiency by adjusting reflection phase shifts, while Non-Orthogonal Multiple Access (NOMA) increases system capacity. Consequently, IRS-assisted NOMA communications have garnered significant research interest. However, the passive nature of the IRS, lacking authentication and security protocols, makes these systems vulnerable to external eavesdropping due to the openness of electromagnetic signal propagation and reflection. NOMA’s inherent multi-user signal superposition also introduces internal eavesdropping risks during user pairing. This paper investigates secure transmissions in IRS-assisted NOMA systems with heterogeneous resource configuration in wireless networks to mitigate both external and internal eavesdropping. To maximize the sum secrecy rate of legitimate users, we propose a combinatorial optimization graph neural network (CO-GNN) approach to jointly optimize beamforming at the base station, power allocation of NOMA users, and phase shifts of IRS for dynamic heterogeneous resource allocation, thereby enabling the design of dual-link or multi-link secure transmissions in the presence of eavesdroppers on the same or heterogeneous links. The CO-GNN algorithm simplifies the complex mathematical problem-solving process, eliminates the need for channel estimation, and enhances scalability. Simulation results demonstrate that the proposed algorithm significantly enhances the secure transmission performance of the system.

The recently proposed TrackFormer has established a fully end-to-end framework with the concepts of object query and track query for multi-object tracking (MOT). TrackFormer, which is based on the deformable attention mechanism, heavily depends on the keypoint sampling, where a set of keypoints is sampled around the so-called reference point for the subtasks of object detection and data association in MOT. However, the keypoint sampling is still not effective due to the absence of prior position information and the inaccuracy of the reference point, which leads to degraded tracking performance. In this paper, we propose TrackFormer++ to address this issue of the ineffective keypoint sampling through the strategies of prior position embedding and reference point updating. In the proposed TrackFormer++, the reference point for object detection is utilized as the prior position and explicitly embedded into the object query. Similarly, the reference point for data association is adaptively updated according to a predicted offset relative to the object center in the previous frame. Extensive experiments by the public and private detection on the MOT17 and MOT20 datasets demonstrate that TrackFormer++ achieves superior or comparable performance to the state-of-the-art baselines. Our code is available at:.

As a malicious attack targeting on the GPS receiver, GPS spoofing attack interferes the normal received satellite signal by reproducing or relaying the signal, resulting in severe position deviation. Such attack has posed significant security threat to unmanned-aerial-vehicles (UAVs), especially in the era of low-altitude economics. However, due to the similarity of the spoofing and intended signal, and the presence of noise, accurate detection and recognition of GPS spoofing attack still remains a challenging issue, particularly in the case of limited samples. In this article, we apply the AdaBoost-CNN algorithm, which combines multiple weak convolutional neural network (CNN) classifiers into a strong classification model, to achieve GPS spoofing attack recognition. To further improve the recognition accuracy when there are very limited samples, we improve the AdaBoost-CNN algorithm by transferring previous network parameters to subsequent CNN. Both simulated and real measurement data are employed to verify the effectiveness of the proposed scheme. It is shown that the recognition accuracy can reach up to 93.75% and 95.83% with 160 simulated samples and 120 measured samples, respectively.

In wireless communication adversarial scenarios, signals are easily intercepted by non-cooperative parties, exposing the transmission of confidential information. This paper proposes a true-and-false (T/F) frequency multiplexing based anti-intercepting transmission scheme capable of concealing truth while showing fake (CTSF), integrating both offensive and defensive strategies. Specifically, through multi-source cooperation, true and false signals are transmitted over multiple frequency bands using non-orthogonal frequency division multiplexing. The decoy signals are used to deceive non-cooperative eavesdropper, while the true signals are hidden to counter interception threats. Definitions for the interception and deception probabilities are provided, and the mechanism of CTSF is discussed. To improve the secrecy performance of true signals while ensuring decoy signals achieve their deceptive purpose, we model the problem as maximizing the sum secrecy rate of true signals, with constraint on the decoy effect. Furthermore, we propose a bi-stage alternating dual-domain optimization approach for joint optimization of both power allocation and correlation coefficients among multiple sources, and a Newton’s method is proposed for fitting the T/F frequency multiplexing factor. In addition, simulation results verify the efficiency of anti-intercepting performance of our proposed CTSF scheme.

Edge collaboration is expected to effectively relieve the load of base stations and enhance the driving experience of autonomous vehicles (AVs). However, in existing edge collaboration schemes, the frequent information exchange between AVs will consume a significant amount of resources. In addition, the existing schemes ignore the types of services, where services with different types may be combined into a composite service which affects the utility of AVs. To this end, we consider various types of services in autonomous vehicular networks (AVNs) and propose a digital twin (DT)-enabled edge collaboration scheme for composite services. Specifically, we first divide the DTs of service requesters (DT-SRs) into service request groups (SRGs) based on the same basic service requests and propose an architecture to facilitate the edge collaboration between the DTs of the leaders of SRGs (DT-L-SRGs) and the DTs of the service providers (DT-SPs). In this architecture, different service composition forms will result in different resource purchase strategies for DT-L-SRGs and different resource pricing strategies for DT-SPs. Therefore, we model the process of service composition as a coalition game to determine the optimal service composition form for each basic service. In the process of the coalition game, in order to obtain the optimal resource purchase strategy for each DT-L-SRG and the optimal resource pricing strategy for each DT-SP under different coalition structures, the interaction between the DT-L-SRGs and the DT-SPs is formulated as a Stackelberg game. By obtaining the game equilibrium, the optimal strategies of each DT-L-SRG and each DT-SP can be determined to measure the performance of the given coalition structure until a stable and optimal composite service structure is finally formed through multiple rounds of iterations. Compared with traditional schemes, the simulation results demonstrate that our scheme can bring the highest utilities to both the SRs and the SPs.

Satellite-terrestrial integrated networks based on edge computing can provide computation offloading service to terminal devices in remote areas. However, it faces various limitations, including satellite energy consumption, computation delay, and environmental dynamics, etc. In this paper, we propose a satellite-terrestrial integrated cloud and edge computing network (STCECN) architecture, including satellite layer, terrestrial layer and cloud center, where computing resources exist in multi-layer heterogeneous edge computing clusters. Optimization of system delay and energy consumption is defined as a mixed-integer programming problem. Moreover, we present a deep reinforcement learning-based computation offloading decision algorithm that can adapt to the dynamics and variability of satellite networks. A dual network computation offloading decision method is proposed for delay and energy consumption based on deep reinforcement learning offloading (DRLO), including deep convolutional network update method, quantization strategy, and bandwidth resource allocation. Meanwhile, the proposed method is based on previous experience and integrates deviation adjustment strategies for decision making to solve the problem of pseudo-patch loss caused by satellite network switching. The simulation results indicate that the proposed method performs almost consistently with traditional heuristic algorithms, with only 20% of the time consumption of the latter, and the number of pseudo packet loss also decreases to the original 10–20%.

Automatic Modulation Classification (AMC) plays a crucial role in non-cooperative communication systems and is an essential component of blind signal processing. The application of deep learning methods in modulation classification has shown tremendous potential, surpassing the performance of traditional methods by a large margin. However, the high storage and computational requirements of existing deep learning methods limit their practical applications. In this paper, we propose an AMC technique using an Adaptive Lightweight Wavelet Neural Network (ALWNN) that features a streamlined design and lower computational demands. This innovative model introduces an adaptive wavelet-based feature extraction method that effectively captures information at different frequencies in the input data, ensuring classification accuracy. Additionally, the model incorpo-rates depthwise separable convolution techniques, transforming traditional convolutions into depthwise convolutions and point-wise convolutions, Substantially diminishing the count of the model’s parameters and the complexity of its computations. The proposed ALWNN model strikes a balance between efficiency and accuracy. Simulation results demonstrate that with only 9899 and 9700 parameters, it achieves accuracies of 62.14% and 63.93% on the datasets known as RML2016.10a and RML2016.10b, respectively. Furthermore, we evaluate the model in terms of Floating Point Operations Per Second (FLOPS) and Normalized Multiply-Accumulate Complexity (NMACC) to provide a more comprehensive measure of computational complexity. Compared to existing methods, ALWNN reduces FLOPS by 1.25 to 1.91 orders of magnitude and NMACC by 0.81 to 1.6 orders of magnitude.

This paper introduces defensive adversarial attacks aimed at enhancing the security of semantic communication systems by confusing potential eavesdroppers. Existing research predominantly focuses on enhancing the accuracy of semantic communications while neglecting the security vulnerabilities posed by eavesdroppers. In this study, from the standpoint of physical layer security, defensive adversarial attacks are employed to introduce artificial noise into semantic communications, effectively concealing real information. This artificial noise is generated by deep neural networks to mislead eavesdroppers into perceiving the content of images as unrelated information, with little probability of disrupting normal semantic communications. Experimental results demonstrate that the proposed model can selectively mislead the decoding efforts of eavesdroppers, while ensuring uninterrupted decoding by legitimate receivers.

The advent of deep learning (DL) technology has increasingly captivated the research community’s interest in harnessing DL to enhance data transmission efficiency. Notwithstanding, prevalent methodologies often overlook the computation delay of DL processing data during the inferencing procedure, and fail to adjust intelligent algorithm complexity based on user features. To bridge this gap, we introduce $\mathbf{C o m}^{2}$ (Communication-Computation) framework, to synergize the optimization of communication and computational delays. $\mathrm{Com}^{2}$ adeptly navigates the trade-offs between communication and computation delays, facilitated by autoencoders of varying depths, thus one user can reduce communication delay through more computation latency, and vice versa. Further enhancing this framework, we present an optimization algorithm that marries QMIX with a cascaded graph neural network (GNN), designed to select the optimal autoencoder depth and optimize transmission resources in a distributed manner. This algorithm pioneers a label-free training regime, employing reinforcement learning and unsupervised learning to adaptively improve without the need for high-quality labels. Simulation results show that $\mathrm{Com}^{2}$, alongside the proposed optimization algorithm, maximizes the utility of users’ computing and transmission resources, significantly curtailing the overall data transmission delay by intelligently managing delay trade-offs.

This article proposes a two-stage beamforming (TSB) scheme with user scheduling for FDD massive MIMO. The developed TSB scheme designs the analog prebeamformer and schedules the users using statistical channel state information (S-CSI), reducing the overhead of the pilot and the feedback. Particularly, in the one-ring local scattering channel model, the prebeamformer design and user scheduling problem is formulated as a 0–1 quadratic constrained quadratic programming (QCQP), which is further linearized to a mixed integer linear programming (MILP). In the multiple scattering clusters channel model, we design the prebeamformer and schedule the users based on graph theory, where the chromatic number of the equivalent matrix represents the minimum number of orthogonal pilots. Then, we propose an iterative beam selection and user scheduling (I-BSUS) scheme that approximates the minimum pilot constraint by the maximum vertex degree. Moreover, the net spectrum efficiency (NSE) is improved using a multiuser digital precoder, which depends on the effective instantaneous CSI (EI-CSI). Simulation results validate the superiority of the proposed scheme in enhancing the NSE over the existing schemes.

As an integral component of the space-air-ground integrated network (SAGIN), the low Earth orbit (LEO) satellite network has displayed immense potential in providing ubiquitous connectivity and broadband mobile communication. However, the intrinsic dynamics of LEO satellites pose unprecedented challenges in network management and service delivery. In this paper, we investigate the service function chain (SFC) orchestration in dynamic LEO satellite networks to achieve flexible and efficient service provision. Considering the service requirements and the limitations of network resources, we formulate the SFC orchestration problem as the integer nonlinear programming (INLP) problem for maximizing the service acceptance and the load fairness of satellites. Then, an efficient heuristic algorithm is proposed to solve this problem. Addressing the situation with frequent service requests, a graph attention network (GAT)-based approach with low complexity is also presented. Simulation results demonstrate that our proposed approaches outperform the benchmarks by a substantial margin in terms of load fairness and service acceptance. Besides, the proposed GAT-based approach shows its advantage in computation complexity, and exhibits robustness in unstable network scenarios with intermittent link interruptions.

Sparse code multiple access (SCMA) has excellent application prospects in satellite-terrestrial links because of its high spectral efficiency and access capacity. In the end-to-end SCMA systems, channel modeling is a fundamental task for the communication algorithm design and performance optimization, which however is very challenging as it requires in-depth domain knowledge and technical expertise in radio signal propagations, especially for modeling satellite-terrestrial fading channels. In this article, a convolutional autoencoder-aided SCMA paradigm based on the stochastic channel modeling and autoencoder structure is developed. We are the first to exploit generative adversarial network to represent the satellite-terrestrial fading channel effects for the convolutional autoencoder-aided SCMA. Specifically, convolutional neural networks (CNNs) are employed to jointly construct the encoder and decoder for SCMA to alleviate the curse of dimensionality. Furthermore, we propose a conditional Wasserstein generative adversarial network with the gradient penalty (CWGAN-GP)-based channel modeling approach to achieve approximately accurate conditional channel distribution. Particularly, the received signal corresponding to the pilot symbol is used as a part of the condition information, and the Wasserstein distance is used as a measure of the distance between the distributions. Gradient penalty is adopted to solve the problem of weight pruning forcing Lipschitz constraints, which leads to some data being unable to converge. The numerical results demonstrate the effectiveness of the proposed approach in terms of the bit error rate (BER), block error rate (BLER), and complexity in satellite-terrestrial fading channels.

Relay and friendly interference collaboration are effective technologies within the physical layer security (PLS) domain for countering eavesdroppers in wireless transmissions. However, the dynamic nature of wireless channels means that the quality of legitimate channels may not consistently exceed that of eavesdropping channels, posing challenges in ensuring the security performance of wireless links. In this paper, we present a multi-destination node selection framework aimed at enhancing the security of wireless communication systems. The strategic selection of relays, in conjunction with the deployment of friendly interference, is designed to optimize the reliability and secrecy performance of wireless link transmissions. Specifically, we propose a collaboration-based node selection (CNS) strategy that leverages selection combining (SC) and maximal ratio combining (MRC) to enhance system reliability and security. Through theoretical analysis, we derive closed-form expressions for secrecy capacity, outage probability (OP), intercept probability (IP), and asymptotic outage probability. The simulation results validate our analytical conclusions and demonstrate the effectiveness of the CNS scheme. Importantly, the introduction of interference nodes strengthens system security and reliability, while increasing relays or destinations primarily optimizes system robustness.

The escalating growth of the Internet of Things (IoT) has intensified the demand for dependable and efficient communication networks to accommodate the massive data volumes produced by interconnected devices. Satellite networks have emerged as a promising alternative, particularly in remote and underserved regions where terrestrial communication infrastructures are inadequate. Nevertheless, guaranteeing secure uplink communications in satellite-based IoT networks is a daunting task due to similar satellite channels and limited resources at IoT nodes. In this article, we explore the potential of unmanned aerial vehicle (UAV) to improve the secrecy performance of uplink transmissions in satellite-supported IoT networks. Specifically, we first introduce a framework for UAV-aided secure uplink communications, presuming a secure UAV-to-satellite connection. To mitigate the risks of ground eavesdroppers intercepting uplink transmissions, we develop a max–min secrecy rate optimization problem with uplink power constraints. To address this nonconvex problem, a streamlined two-stage optimization approach is proposed. In the inner stage, we combine uplink power allocation and UAV beamforming and propose a successive convex approximation (SCA)-based joint optimization algorithm to address them. In the outer stage, we propose a synergized bisection and coordinate descent algorithm to optimize UAV positioning. Convergence is attained by alternating iterations between these two stages. Particularly, the secrecy fairness among IoT users is reached by solving the max–min problem. Additionally, we offer a complexity analysis of the proposed algorithm and validate the efficacy of the presented approach through comprehensive simulation results.

Low Earth orbit (LEO) satellite networks have shown strategic superiority in maritime communications, assisting in establishing signal transmissions from shore to ship through space-based links. Traditional performance modeling based on multiple circular orbits is challenging to characterize large-scale LEO satellite constellations, thus requiring a tractable approach to accurately evaluate the network performance. In this paper, we propose a theoretical framework for an LEO satellite-aided shore-to-ship communication network (LEO-SSCN), where LEO satellites are distributed as a binomial point process (BPP) on a specific spherical surface. The framework aims to obtain the end-to-end transmission performance by considering signal transmissions through either a marine link or a space link subject to Rician or Shadowed Rician fading, respectively. Due to the indeterminate position of the serving satellite, accurately modeling the distance from the serving satellite to the destination ship becomes intractable. To address this issue, we propose a distance approximation approach. Then, by approximation and incorporating a threshold-based communication scheme, we leverage stochastic geometry to derive analytical expressions of end-to-end transmission success probability and average transmission rate capacity. Extensive numerical results verify the accuracy of the analysis and demonstrate the effect of key parameters on the performance of LEO-SSCN. Notably, with common parameter settings, after incorporating the space link, the transmission success probability increases by 886% with a 13 dB predefined signal-to-noise ratio (or signal-to-interference-plus-noise-ratio) threshold. This superior performance is attributed to the fact that the space link uses a wider bandwidth and greater power for signal transmission compared to the maritime link. It’s undeniable that the integration of the space link inevitably incurs additional expenses.

This paper proposes a manifold optimization-based tensor recovery algorithm for channel estimation (MO-TRACE) in reconfigurable intelligent surface (RIS)-assisted millimeter wave (mmWave) MIMO-OFDM systems. Specifically, considering the inherent sparse scattering characteristics of mmWave channels, the multidimensional cascaded channel in the angular-delay domain is represented by a sparse and low-rank tensor, and we formulate the channel estimation problem as a sparse and low-rank tensor recovery problem. Then, we use the canonical polyadic (CP) decomposition technique to decompose the tensor into multiple factor matrices, where the factor matrices are related to the channel parameters. To account for the sparse factor matrices, we add the sparse regularization terms of the factor matrices to the optimization objective, which can also reduce the number of nonzero columns in factor matrices, i.e., the rank of the tensor. As the concatenation of multiple factor matrices can be seen as a point on a product manifold, we apply MO algorithms to search for the optimal sparse point with enhanced convergence speed. The proposed MO-TRACE algorithm provides a more precise description of channels and ensures that the iteration point always remains within the feasible domain, thereby enhancing solution accuracy. Simulation results validate the superiority of the proposed MO-TRACE in terms of estimation accuracy.

In Space-air-ground integrated networks (SAGIN), the inherent openness and extensive broadcast coverage expose these networks to significant eavesdropping threats. Considering the inherent co-channel interference due to spectrum sharing among multi-tier access networks in SAGIN, it can be leveraged to assist the physical layer security among heterogeneous transmissions. However, it is challenging to conduct a secrecy-oriented access strategy due to both heterogeneous resources and different eavesdropping models. In this paper, we explore secure access selection for a scenario involving multi-mode users capable of accessing satellites, unmanned aerial vehicles, or base stations in the presence of eavesdroppers. Particularly, we propose a Q-network approximation based deep learning approach for selecting the optimal access strategy for maximizing the sum secrecy rate. Meanwhile, the power optimization is also carried out by an unsupervised learning approach to improve the secrecy performance. Remarkably, two neural networks are trained by unsupervised learning and Q-network approximation which are both label-free methods without knowing the optimal solution as labels. Numerical results verify the efficiency of our proposed power optimization approach and access strategy, leading to enhanced secure transmission performance.

As an integral component of space-air-ground integrated networks (SAGINs), the low Earth orbit (LEO) satellite networks have displayed immense potential in providing ubiquitous connectivity and broadband mobile communication. However, the intrinsic dynamics of LEO satellites poses unprecedented challenges in network management, multi-dimensional resource scheduling, and service delivery. In this paper, we study the service function chain (SFC) orchestration in dynamic LEO satellite networks, with the aim of achieving flexible and efficient service provision. Considering the service requirements and the load fairness of LEO satellite networks, we formulate the SFC deployment problem as an integer nonlinear programming (INLP) problem. We then introduce a load-aware SFC orchestration algorithm to improve serving capacity and load fairness. Additionally, we address the issue of SFC migration in dynamic LEO satellite networks to ensure service continuity. To minimize the service interruption and network resource wastes, a Tabu search (TS)-based approach is presented to optimize the virtual network function (VNF) migration. Simulation results demonstrate that our proposed approaches outperform the benchmark by a substantial margin in terms of load fairness, without compromising service acceptance.

As a fundamental problem, many studies are dedicated to the optimization of signal-to-interference-plus-noise ratio (SINR), in a multi-user setting. Although traditional model-based optimization methods achieve strong performance, it has high complexity. To fully leverage the high performance of traditional methods and the low complexity of the neural network (NN) based method, a knowledge distillation (KD) based algorithm distillation (AD) method is proposed in this paper, where traditional optimization methods serve as "teachers" for NN "students", improving unsupervised and reinforcement learning. This approach tackles common issues: unattainable optimal labels, overfitting, and inefficient training. Simulations confirm the advantages of AD, paving the way for traditional optimization integration with NNs in wireless communication.

With the rapid development of the Internet of Things (IoT), there are a dramatically increasing number of devices, leading to the fact that only using terrestrial infrastructure can hardly provide high-quality services to all devices. Due to their flexibility, maneuverability, and economy, unmanned aerial vehicles (UAVs) are widely used to improve the performance of IoT networks. UAVs can not only provide wireless access to IoT devices in the absence of a terrestrial network but can also perform rich IoT services and applications such as video surveillance, cargo transportation, pesticide spraying, and so forth. However, due to the high complexity, dynamics, and heterogeneity of the UAV-assisted IoT networks, growing attention has focused on using artificial intelligence (AI)-based methods to optimize, schedule, and orchestrate UAV-assisted IoT networks. In this article, we comprehensively analyze the impact of applying advanced AI architectures, models, and methods to different aspects of UAV-assisted IoT networks, including key IoT technologies, tasks, and applications. In addition, this article also explores challenges and discusses potential research directions of AI-enabled UAV-assisted IoT networks.

The heterogeneous vehicular networks (HetVNets) can accelerate the deployment of Internet of Vehicles (IoV) and enrich the content distribution methods. However, the diverse requirements of vehicular users (VUs), the limited cache resources of roadside units (RUs), and the frequent interactions between VUs and RUs pose great challenges to efficiently distribute contents. To address these challenges, we propose an on-demand content delivery scheme in digital twin-enabled HetVNets (DT-HetVNets). Specifically, we first design an on-demand content delivery architecture in DT-HetVNets which uses DT communication mode to simplify the frequent interactions between VUs and RUs. With this architecture, by jointly considering the popularity of each content and the relevance between different contents, the personal content requirement of each DT of VU (DT-VU) can be perceived and the VUs within the coverage of the same RU can collaboratively request contents in groups. Then, we formulate the interaction between each group and the DT of the RU (DT-RU) as a double auction game to determine the transaction price of the perceived content, where the request information of the contents which are accepted by the groups can be shared between different DT-RUs based on the path of each group, enabling collaborative content recommendation between the RUs. After that, by jointly considering the contents recommended by different DT-RUs and the content popularity, the content caching model of each DT-RU is formulated as a knapsack problem, where a collaborative content caching algorithm is designed to obtain the optimal caching strategy with the target of making full use of the limited cache resources. Compared with the conventional schemes, the simulation results show that our scheme can not only bring the highest utility to the RUs but also lead to the highest hit ratio and the lowest delay.

The autonomous vehicles (AVs), as intelligent mobile robots, can undertake tasks to facilitate various computation-intensive services in intelligent transportation system (ITS). Due to hardware device failures or environmental identification errors, the AVs controlled by intelligent algorithms may cause accidents during driving. However, the existing studies in the post-accident stage lack the analysis of the impact degree of the accidents and the computing tasks undertaken by the AVs to determine the optimal maintenance strategy. In this article, we consider the accidents in a continuous period of time and design a digital twin (DT)-enabled post-accident maintenance scheme. Specifically, by considering the computing tasks undertaken by the AVs and the impact degree of the accidents, we first design a DT-enabled post-accident maintenance architecture. With the designed architecture, an optimal maintenance method under an incomplete information scenario is then proposed to help each accident AV decide its optimal maintenance strategy. Besides, based on the maintenance strategies of the AVs and the capacities of the maintenance service providers (MSPs), the two-way selection problem between the AVs and the MSPs in the continuous period of time is modeled as a dynamic matching game to obtain the optimal AV-MSP pairs. Simulation results demonstrate that the proposed scheme outperforms the benchmark schemes in terms of the maintenance rate of the accident AVs, the average utility of the MSPs, and the average social welfare.

In this paper, we investigate the secure transmission of multi-resource heterogeneous radio access networks (RANs) in space-air-ground integrated network (SAGIN) from the perspective of physical layer security. Considering the network heterogeneity, resource constrain, and channel similarity, it is challenging to implement the physical layer security in SAGIN. Particularly, digital twin (DT) is considered in the cyberspace of SAGIN to reflect the physical network entities (i.e., satellite, unmanned aerial vehicle (UAV), and terrestrial base station), which is assumed to comprehensively control and manage the heterogeneous RANs’ resources. To ensure secure transmissions of multi-tier heterogeneous downlink communications in SAGIN, a multi-point symbiotic security scheme is proposed through DT-assisted multi-dimensional domain synergy precoding, where the co-channel interference due to spectrum sharing among these heterogeneous RANs is recast to unevenly corrupt the main and wiretap channels of each legitimate user. Specifically, to realize the multi-point symbiotic security, a max-min problem is formulated to maximize the minimum secrecy rate of three heterogeneous downlinks. Since this problem is non-convex and challenging, a list of mathematical reformulations is derived and the successive convex approximation (SCA) based multi-dimensional domain synergy precoding algorithm is proposed to solve it. Moreover, the computational complexity of our proposed approach is analyzed and meaningful discussions are made. In addition, extensive simulations are carried out to evaluate the secrecy rate performance and verify the efficiency of our proposed approach.

Due to the wireless broadcasting and broad coverage in satellite-supported Internet of things (IoT) networks, the IoT nodes are susceptible to eavesdropping threats. Considering the distance difference between satellite and nearby destinations is negligible, the main and wiretapping channels between satellite and IoT node are similar, it poses great challenges to reach physical layer security in satellite-assisted IoT networks. In this paper, to guarantee secure transmissions for satellite-assisted IoT downlink communications, the multi-domain resource multiplexing based secure approach is proposed. Particularly, the self-induced co-channel interference between adjacent nodes is leveraged to increase the difference of signal transmission quality over both main and wiretapping channels. By comprehensively optimizing multi-domain resources, i.e., frequency, power, and spatial domains, secure transmissions from satellite to IoT nodes are reached. Specifically, the problem to maximize the sum secrecy rate of IoT nodes is formulated with a constraint of common communication rate of IoT nodes. To solve this non-convex problem, an alternating optimization (AO) algorithm with two inner successive convex approximation (SCA) algorithms are executed to solve the power allocation, spectral multiplexing, and precoding. In addition, simulation results are carried out to evaluate the secrecy rate performance and verify the efficiency of our proposed approach.

Integrated satellite-terrestrial network (ISTN) can provide a continuous service for vehicular users in remote areas with a seamless network coverage. However, considering the difference in the usage costs between satellite and terrestrial networks and the variability of services for latency requirements, it is of great significance to design a cost-effective data offloading decision for reducing network overhead and ensuring task delay requirements. In this paper, we design a cost-effective data offloading mechanism for vehicles in ISTN. The default transmission for remote areas is via the satellite, where the terrestrial networks can offload the data with intermittent coverage in an opportunistic manner due to the vehicle mobility. To model the diversity in service delay requirements, a virtual queue is exploited to capture the residual maximum delay tolerance of each service as time elapses. We formulate the satellite-terrestrial collaborative transmission as a non-linear programming (NLP) problem. To solve the problem, we propose a reinforcement learning (RL)-based data offloading algorithm for real-time decision making. Simulation results show that the RL-based data offloading algorithm reduces the network overhead and outperforms other baseline schemes we proposed.

With the advancement of smart cities and the development of heterogeneous vehicular networks (HetVNets), vehicles can collect data and generate valuable information to obtain profits, thus forming a new vehicular self-media paradigm in HetVNets. However, in the HetVNets with potential security risks, the vehicular self-media market lacks the consideration of the values of the data owned by the media data producers (MDPs) and the capabilities of the media data sellers (MDSs) to improve their utilities. To this end, we propose a value-based secure self-media data trading scheme in the HetVNets. Specifically, we first design a vehicular self-media trading mechanism based on smart contracts to provide participants with a safe and reliable transaction environment. Then, we model the interactions between the MDPs and the MDSs as a Stackelberg game by considering the values of various media data and the sales capabilities of different MDPs. After that, we design an iterative method to obtain the optimal game strategies for the MDPs and the MDSs to maximize their utilities. Compared with the traditional schemes, the simulation results show that our scheme can obtain the optimal strategies for the MDPs and the MDSs and bring them the highest utilities.

In the heterogeneous vehicular networks (HetVNets), the base stations (BSs) can exploit the massive amounts of valuable data collected by vehicles to complete federated learning tasks. However, most of the existing studies consider the scenario of one task requester (TR) and ignore the fact that multiple TRs may concurrently generate their model training requests in the HetVNets. In this paper, we consider the scenario of multi-TR and multi-BS and propose a digital twin enabled scheme for multitask federated learning to address the two-way selection problem between the TRs and the BSs. We first analyze the diversified requirements of the TRs in the HetVNets. Then, we develop a novel model that jointly considers the available training data, the declared price, and the training experience to evaluate the differentiated training capabilities of the BSs. After that, based on the requirements of the TRs and the training capabilities of the BSs, the two-way selection problem between the TRs and the BSs is formulated as a matching game in the digital twin networks, where a matching algorithm is designed to obtain their optimal strategies. The simulation results demonstrate that the proposed scheme can obtain the highest model accuracy and bring the highest utility to the TRs compared with the conventional schemes.

The space-air-ground integrated vehicular networks (SAGIVNs) can efficiently accelerate the deployment of the Internet of Vehicles (IoV) and enrich the content distribution methods in the networks. In this paper, we propose a content delivery scheme in SAGIVNs that integrates sensing, communication, and caching. Specifically, we first perceive the content requests of the vehicles through which the vehicles covered by the same roadside unit (RU) can be facilitated to request the contents collaboratively. Then, based on the location and path of each vehicle, the perceived request information can be transmitted between different RUs, enabling efficient collaborative content recommendation between the RUs. After that, by jointly considering the contents recommended by different RUs, the popularity of each content, and the limited cache resources, the content caching model of each RU is formulated as a knapsack problem, where a dynamic programming method is designed to obtain the optimal caching strategy. Compared with the conventional schemes, the simulation results show that the proposed scheme can lead to the highest hit ratio and the lowest transmission delay.

Status quo autonomous driving mechanisms rely on fusing the multimodal sensing data to integrate the information from onboard units of a vehicle, e.g., lidar, camera, etc., and have yet to consider the information obtained via the inter-vehicle communication, such as the status of neighboring peers. In this paper, we consider to integrate not only the local onboard sensing data, but also the neighboring vehicle information from the vehicle-to-vehicle (V2V) data pipe, which is demonstrated to improve the autonomous driving performance significantly. Specifically, the opportunistic V2V messages are input to a transformer based fusing framework to improve the driving accuracy in both short and long routes in CARLA environment. Unlike previous rule-based mechanisms of dealing with the V2V messages, to the best of our knowledge, the proposed method is the first to integrate the V2V data to the neural network which implicitly induce the waypoints for accurate end-to-end autonomous driving. We conduct extensive experiments, whose results well demonstrate the utility of the V2V information, and can provide useful inspirations for future driving system design.

Task scheduling is a critical problem when one user offloads multiple different tasks to the edge server. When a user has multiple tasks to offload and only one task can be transmitted to server at a time, while server processes tasks according to the transmission order, the problem is NP-hard. However, it is difficult for traditional optimization methods to quickly obtain the optimal solution, while approaches based on reinforcement learning face with the challenge of excessively large action space and slow convergence. In this paper, we propose a Digital Twin (DT)-assisted RL-based task scheduling method in order to improve the performance and convergence of the RL. We use DT to simulate the results of different decisions made by the agent, so that one agent can try multiple actions at a time, or, similarly, multiple agents can interact with environment in parallel in DT. In this way, the exploration efficiency of RL can be significantly improved via DT, and thus RL can converges faster and local optimality is less likely to happen. Particularly, two algorithms are designed to made task scheduling decisions, i.e., DT-assisted asynchronous Q-learning (DTAQL) and DT-assisted exploring Q-learning (DTEQL). Simulation results show that both algorithms significantly improve the convergence speed of Q-learning by increasing the exploration efficiency.

Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges between AVs for determining the members in each collaborative group will consume a lot of time and resources. In addition, since AVs have different computing capabilities and costs, the collaboration types of the AVs in each group and the distribution of the AVs in different collaborative groups directly affect the performance of the cooperative driving. Therefore, how to develop an efficient collaborative autonomous driving scheme to minimize the cost for completing the driving process becomes a new challenge. To this end, we regard collaboration as a service and propose a digital twins (DT)-based scheme to facilitate the collaborative and distributed autonomous driving. Specifically, we first design the DT for each AV and develop a DT-enabled architecture to help AVs make the collaborative driving decisions in the virtual networks. With this architecture, an auction game-based collaborative driving mechanism (AG-CDM) is then designed to decide the head DT and the tail DT of each group. After that, by considering the computation cost and the transmission cost of each group, a coalition game-based distributed driving mechanism (CG-DDM) is developed to decide the optimal group distribution for minimizing the driving cost of each DT. Simulation results show that the proposed scheme can converge to a Nash stable collaborative and distributed structure and can minimize the autonomous driving cost of each AV.

In this paper, we investigate unmanned aerial vehicle (UAV) assisted physical layer security in multi-beam satellite enabled vehicle communications. Particularly, the UAV is exploited as a relay to improve the secure satellite-to-vehicle link, and simultaneously serves as a jammer by deliberately generating artificial noise (AN) to confuse Eve. The satellite beamforming (BF) and UAV power allocation (PA) are jointly optimized to maximize the secrecy rate of the legitimate user within a target beam while guaranteeing the quality of service (QoS) of users within other beams. Since the problem is nonconvex, we first convert it into an equivalent two-stage problem. Then, the outer-stage problem is solved by using one-dimensional search, and the inner-stage problem is transformed to a bi-convex problem by using the semi-definite relaxation (SDR) and Charnes Cooper transformation. To solve the inner-stage bi-convex problem, we propose an iterative alternating optimization algorithm, where the optimal BF is obtained by semi-definite programming (SDP), and the optimal UAV PA is subsequently obtained by solving the reformulated fractional programming problem with an iterative Dinkelbach method. The tightness of SDR and the complexity of our proposed approach are analyzed, and extensive simulations are carried out to evaluate the effectiveness of our proposed approach.

In this paper, we investigate secure transmissions in integrated satellite-terrestrial communications and the green interference based symbiotic security scheme is proposed. Particularly, the co-channel interference induced by the spectrum sharing between satellite and terrestrial networks and the inter-beam interference due to frequency reuse among satellite multi-beam serve as the green interference to assist the symbiotic secure transmission, where the secure transmissions of both satellite and terrestrial links are guaranteed simultaneously. Specifically, to realize the symbiotic security, we formulate a problem to maximize the sum secrecy rate of satellite users by cooperatively beamforming optimizing and a constraint of secrecy rate of each terrestrial user is guaranteed. Since the formulated problem is non-convex and intractable, the Taylor expansion and semi-definite relaxation (SDR) are adopted to further reformulate this problem, and the successive convex approximation (SCA) algorithm is designed to solve it. Finally, the tightness of the relaxation is proved. In addition, numerical results verify the efficiency of our proposed approach.

Incident scene text detection, as the most crucial step of an incident scene text recognition system, has received increasing research attention. In this paper, a two-layer mobile federated learning model (TMFL) is proposed to protect data privacy and improve training efficiency. Particularly, a fast scene text detector is proposed to detect the multi-directional and multi-scale text by using an asymmetric convolution based feature pyramid network (AC-FPN). Compared with the traditional feature pyramid, asymmetric convolutions can effectively extract rotation-invariant features to improve the model's robustness to directed text. Moreover, in order to achieve a balance between the detection accuracy and efficiency, we modify the lightweight backbone of mobilenetv3, and integrate it with the asymmetric convolution based feature pyramid. In addition, we evaluate the performance of our detector on three benchmark datasets, where the results show that both the accuracy and the speed can be improved. Our detector can achieve an F-measure of 87.8 on the ICDAR2013, 80.5 on the MSRA-TD500 and 84.1 on the ICDAR2015 dataset, running at 32.5 FPS.

This paper investigates the impact of communication factors on the convergence performance of federated learning (FL) in wireless networks. Considering the limited communication resources in wireless networks, it is difficult to schedule all users to participate in a comprehensive training and the convergence performance of training model relies much on the user scheduling scheme. To minimize the maximum update delay of user training, we propose a joint resource allocation and user scheduling scheme in this paper. Particularly, the user communication delay and user training results are jointly considered to dynamically schedule users and allocate communication resources. Simulation results show that the convergence time can be reduced by 41.6% compared with the random scheduling allocation scheme.

Recently, the abnormal intrusion detection has become an urgent problem in high-speed railway system. One way to solve this problem is the optical fiber distributed acoustic sensing (DAS) system that can monitor the intrusion events and provide early warning. However, most long-distance DAS systems are unable to distinguish signal types to improve the detection performance. Moreover, the traditional fiber optic sensing system is susceptible to interference from environmental factors, resulting in false detections and alarms. To this end, with the adoption of DAS system, we propose a railway intrusion detection system based on Faster R-CNN. In our system, we first design the DAS system to collect the optical fiber acoustic signals. Then, the collected signals are normalized in temporal and spatial dimensions and converted into Spatio-temporal images. After that, we design the Faster R-CNN algorithm to extract the Spatio-temporal features to detect and classify five types of abnormal intrusion events. The experimental results demonstrate that the average detection precision of our system for all abnormal intrusion events is above 89%. In addition, compared with the conventional methods, our system achieves the highest detection precision. Meanwhile, the system can distinguish the non-threatening background noise, which is of great help to reduce the system false positive rate.

In this paper, we study the secrecy provisioning downlink transmission in an aerial-assisted network, where the unmanned aerial vehicle (UAV) serves as an aerial platform to provide secure transmission for the mobile users (MUs) with coexist of Internet of Things (IoT) nodes (INs). Specifically, secure transmission is required for MUs to combat eavesdropping attacks and a desired successful transmission probability should be ensured for INs to receive the public instruction massages. To improve the secrecy rates (SRs) for MUs, we consider an eavesdropper-free area, i.e., protected zone, surrounding the UAV. With non-orthogonal multiple access (NOMA) for MUs, the power allocation to each MU is optimized to maximize the minimum secrecy rate of MUs within the protected zone, under the constraints of successful receiving probability requirements for INs. To solve this problem, we first prove that the max-min SR can be obtained when SRs of all users are equal, and then a dichotomy-based successive power allocation policy is proposed. Numerical results show that higher max-min secrecy rate can be achieved by our proposed power allocation policy than the traditional policy.

To combat the pilot spoofing attack in non-orthogonal multiple access (NOMA) systems, we propose a double channel training scheme in this paper. Specifically, we consider two users in each cluster and both users send the training sequence in the first uplink training phase, while one of them keeps silent in the second phase. By exploiting channel estimation results in the two phases, more accurate legitimate channel estimation can be obtained by removing the contamination from the eavesdropping channel. Thus, the pilot spoofing attack can be mitigated effectively. We then analyze the achievable downlink secrecy rate with matched filter precoding scheme. Simulation results demonstrate that the achievable secrecy rate can be improved dramatically with the proposed scheme even under very strong pilot attack power.

Spectrally efficient frequency division multiplexing (SEFDM) is a promising non-orthogonal multi-carrier technique to improve spectral efficiency, by compressing the inter-carrier interval relative to orthogonal frequency division multiplexing (OFDM) systems. However, by breaking the orthogonality among subcarriers, the self- introduced inter-carrier interference (ICI) severely restrains the achievable transmission rate and poses great challenges in designing the receiver with ICI cancellation. In this paper, we first characterize the statistical distribution of ICI and then derive a closed-form expression of the signal-to- interference-plus-noise ratio (SINR). After that, an efficient time-domain ICI mitigation approach is proposed to improve the achievable SINR and the spectral efficiency of the SEFDM system. Numerical results verify the analytical expressions for the cumulative distribution function (CDF) of ICI and the achievable SINR. In addition, it is shown that the spectral efficiency can be significantly improved by adopting our proposed ICI mitigation approach.

Internet of Things as an essential integrated part of the future wireless communication system provides ubiquitous connectivity and information exchange to enable a range of applications and services, which has triggered spectrum resource pressure, multiple access, bandwidth efficiency, and security issues. Focusing on these issues, a high spectral efficiency secure access (HSESA) scheme based on dual nonorthogonal is proposed first in this paper. The scheme which can be recognized as a dual nonorthogonal scheme is designed by the nonorthogonal multiplexing and nonorthogonal multiple access. Particularly, HSESA scheme is equipped with secure multiplexing by using security matrix to improve physical layer security. Moreover, spectral efficiency analysis is given and the throughput of HSESA has been derived. Moreover, iterative detection (ID) and maximum likelihood (ML) are, respectively, combined with message passing algorithm (MPA) as detection schemes, and their respective performance advantages are analyzed. Simulation results show that the detection scheme using ID combined with MPA has lower complexity, while ML combined with MPA has better bit error rate performance, and the spectral efficiency is also enhanced by the proposed HSESA.

Cloud computing provides powerful computing ability of mobile devices in the Internet of Things (IoT) networks. However, the large amounts of data interaction with cloud suffers bandwidth limit and energy efficiency for data processing and transmission, and the energy consumption of data processing is far less than data transmission. In this paper, offloading is considered in transmission to improve the battery lifetime by employing a spectral-energy efficient transmission scheme with efficient computing in IoT-Cloud. The offloaded resource can be saved to serve more services if the physical air interface is designed efficiently. In addition, many personal things are unloaded to the IoT-Cloud which creates a risk of privacy and security. The improved offloading efficiency of data transmission scheme secure truncating orthogonal frequency division multiplexing (STOFDM) is generated by deliberately truncating the orthogonal frequency division multiplexing signal in time domain. Particularly, the truncations are selected by a dynamic random private matrix based on the proposed offloading power amplifier theorem. The corresponding legitimate receiver is designed with private mapping using the efficient fast Fourier transformation (FFT) for offloading computation. Moreover, the closed-form expression for the FFT-based STOFDM system is analyzed and be verified by simulation results. In light of the analysis, the STOFDM performs intercarrier interference as an orthogonal sequence is partially transmitted, which degrades the reliability of transmission link. Further, two enhanced detectors with low computing-complexity is also given to improve the performance of Bob while restrict eavesdropper's reception and further provides offloading computation.

The Internet of Things (IoT) is a new network that connects massive devices which have the communication ability. With the requirement of various services and the wireless spectrums becoming increasingly scarce, we consider a nonorthogonal multicarrier transmission scheme that is spectrum efficient frequency division multiplexing (SEFDM) as the downlink transmission scheme for IoT network. SEFDM has the merit of improved bandwidth usage efficiency, but the serious inter carrier interference (ICI) will be introduced by multiplexing overlapped carriers. Thus, the signal detection is challenged for recovering the signal which is suffered from ICI due to the loss of the orthogonality. In this paper, a low complexity detector based on quasi-orthogonality compensation (QOC) is proposed in the downlink receiver. And the complexity of the QOC detector is also analyzed. Moreover, a novel detector which joint QOC and fixed sphere decoding (FSD) algorithm is proposed. The bit error rate performances of QOC and QOC-FSD detectors are evaluated by numerical simulations. Numerical results show that the QOC and QOC-FSD detectors can achieve better performance than conventional iterative detection (ID) and ID-FSD, respectively. Furthermore, QOC-FSD detector performs a lower complexity than ID-FSD detector.