Integrated air-ground communication (IAGC) has emerged as a promising solution to deliver seamless wireless coverage and high-data-rate services. However, potential malicious eavesdroppers pose a serious threat to the confidential transmission in IAGC due to their non-cooperative behaviors and the inherent openness of communication channels. To tackle this problem, a dynamic spectrum control (DSC)-based transmission scheme is proposed to enhance covert performance and communication reliability in IAGC. With the proposed scheme, we apply the principles of block cryptography, perform adaptive iterative and orthogonal transformations to generate sequence sets that drive transmission decisions. Guided by these sequences, multiple legitimate users can dynamically occupy different frequency slots and transmit data simultaneously. In addition, we analyze the probability of frequency slot multiplexing when several data groups occupy the same frequency slot in a time slot, resulting in the closed-form expression for the detection error probability. We then derive the maximum reliable transmission probability and ergodic rate subject to the covert communication constraints. Simulation results demonstrate that the proposed scheme can achieve superior covert performance compared with benchmark schemes. Furthermore, we evaluate and discuss the effects of key parameters in the proposed DSC-based transmission scheme on communication security and reliability.

Sixth-generation (6G) networks are rapidly becoming a focal point of global technological innovation, driven by the need to support hyper-reliable, low-latency, and intelligent connectivity for applications such as immersive extended reality, autonomous systems, and ubiquitous sensing. While 6G promises transformative advancements in wireless communication, achieving its ambitious goals poses significant fundamental challenges. One natural direction is to scale multiple-input multiple-output (MIMO) technology to unprecedented levels; however, doing so introduces substantial hardware complexity and power consumption. To overcome these limitations, recent research has explored antenna reconfigurability as a novel degree of freedom (DoF) at the physical (PHY) layer. Among these efforts, the fluid antenna system (FAS) has emerged as a compelling concept, offering reconfigurability in both spatial positioning and physical structure. This idea has inspired related innovations, including movable antennas, flexible-position MIMO, reconfigurable MIMO architectures, and adaptive antenna arrays, collectively referred to as next-generation reconfigurable antenna (NGRA) systems. While prior work has primarily focused on spatial flexibility, this article introduces a generalized model of FAS that incorporates both structural and morphological fluidity, enabling the vision of “shapeless and formless” antennas in future wireless systems. We analyze FAS’s potential to enhance key performance metrics such as coverage, energy efficiency, reliability, and spectral capacity. In addition, we outline implementation challenges and explore synergies with key 6G enablers, including reconfigurable intelligent surfaces (RIS), non-terrestrial networks (NTN), integrated sensing and communication (ISAC), and artificial intelligence (AI). This survey provides a comprehensive overview of NGRA systems and identifies promising directions for future research in reconfigurable wireless technologies.

The proliferation of unauthorized Unmanned Aerial Vehicles (UAVs) poses significant security risks, necessitating robust detection systems. However, in practical long-range surveillance scenarios, UAV signals often deteriorate due to severe attenuation and complex environmental interference, rendering traditional detection methods ineffective. To address this, this letter proposes a novel signal enhancement framework comprising an improved adaptive Wiener filter. We introduce a dynamic noise spectrum estimation strategy coupled with a decision-directed phase compensation mechanism. This approach effectively suppresses non-stationary background noise while reconstructing high-fidelity time-frequency features by rectifying phase distortions. Experimental results on a proprietary real-world dataset demonstrate that the proposed method significantly improves signal quality, enabling high-accuracy detection using YOLO models even in ultra-low Signal-to-Noise Ratio (SNR) regimes compared to raw data-based baselines.

We propose an innovative physical layer authentication method, leveraging deep learning to robustly safeguard millimeter wave communications against pilot contamination and clone attacks. Unlike traditional upper-layer authentication mechanisms, our method capitalizes on the spatial-temporal characteristics of millimeter wave channels to extract unique fingerprints, thus establishing a lightweight channel-based authentication technique. Existing methods largely overlook pilot contamination attacks, which may severely degrade the performance of physical layer authentication. Furthermore, traditional threshold-based methods struggle to differentiate between multiple nodes, while supervised learning-based methods are practically constrained due to the unavailability of attackers’ instantaneous channel state information. Moreover, traditional real-valued deep neural networks are inefficient in utilizing the phase information of complex-valued channels, rendering them inadequate for designing practical physical layer authentication schemes. To address these challenges, we propose an autoencoder, empowered by an alternating direction method of multipliers, which can detect and mitigate pilot contamination attacks by exploiting the inherent sparsity of channels. Subsequently, we design a weighted loss function to optimize the proposed classifiable autoencoder to strike an effective balance between detecting clone attacks and authenticating multiple nodes. Finally, to further enhance feature extraction from complex-valued channels, we customize a complex-valued classifiable autoencoder incorporating an innovative complex-valued long short-term memory module. Our simulation results unveil that the proposed method significantly outperforms existing approaches in maintaining high authentication accuracy even under pilot contamination, achieving a desirable trade-off between false alarm and detection rates. Additionally, our proposed complex-valued neural networks further enhance the accuracy of clone attack detection and multiple legitimate nodes authentication.

Leveraging its exceptionally wide bandwidth, Terahertz (THz) communication offers ultra-high-speed data transmission and remarkably precise sensing, establishing itself as a cornerstone technology for integrated sensing and communication (ISAC) systems. This paper investigates a multi-base-station (multi-BS) cooperative extremely-large-scale multiple-input multiple-output (XL-MIMO) orthogonal frequency division multiplexing (OFDM) near-field THz-ISAC system designed to guarantee secure downlink communication for multiple users, while simultaneously enhancing multi-target localization accuracy. The core challenge lies in optimizing the secrecy rate subject to the Cramer-Rao Bound (CRB) constraint to strike an´ effective balance between communication security and sensing precision. To this end, we propose an unsupervised learning-based complex-valued deep neural network (CVNN) that jointly optimizes hybrid beamforming and radar sensing signals in a data-driven manner. Simulation results unveil that the proposed approach outperforms the conventional alternating optimization-based hybrid beamforming benchmark in terms of secrecy rate and computation time. These results validate the practicality of data-driven joint optimization in THz-ISAC systems by demonstrating its effectiveness in achieving an efficient tradeoff between communication security and sensing accuracy, while also providing actionable design guidelines for scalable, low-latency system in extremely-large-scale deployment.

Fluid antenna is regarded as one of the most promising technology for next-generation wireless communication system due to its ultimate flexibility. Previous works have extensively studied fluid antenna multiple access (FAMA) in single-cell scenarios. However, in multi-cell environments, the centralized computation of transmission strategies for each base station (BS) is challenging due to inter-cell interference and user privacy concerns. Moreover, the large number of users is expected to introduce substantial protocol overhead, which may hinder the release of FAMA's full potential. In this paper, we propose a model-based multi-agent reinforcement learning algorithm, where each BS is treated as an independent agent and adopts a distributed approach to solve precoding and port selection problems. Using a world model, each BS can predict its future observations, enabling it to perform multi-step decisionmaking based on the current state, which has significant potential for reducing protocol overhead. Experimental results validate the superiority of FAMA over fixed antenna systems in multi-cell scenarios and demonstrate the potential of the proposed model-based decision mechanism in reducing transmission time.

With the explosive growth of multimedia content on the Internet, perceptual hashing has become a mainstream technique for efficient retrieval of similar content. Unfortunately, the robustness of perceptual hashing algorithms in this context is not well understood. Adversaries can deliberately manipulate the retrieval results by introducing slight perturbations to the image. However, existing attack methods often overlook the feature extraction scale of perceptual hashing, leading to lower attack success rates and higher computational costs. In this paper, we propose a frequency-domain adversarial attack method that aligns the added perturbations with the scale of perceptual hash feature extraction, thereby significantly altering the hash code. Meanwhile, the frequency-domain representation greatly reduces dimensionality compared to the pixel domain, which can effectively improve attack efficiency. Under a black-box setting, we conduct targeted attacks on the widely used pHash algorithm across two different datasets. The results show that our method can make two perceptually distinct images collide in the hash space with a high success rate, leading the retrieval system to mistakenly identify the adversarial example as the target image.

Reconfigurable intelligent surface (RIS) is a key potential technology for the sixth generation wireless communication. The deployment of RIS in wideband communication systems can effectively mitigate severe path loss and against the blockage of line-of-sight path, which can improve transmission gain and enhance communication quality. However, with the increase of RIS array and bandwidth, beam-squint effect will occur and seriously damage the performance of communication systems. In this paper, we first establish a holographic RIS-aided wideband communication system model from the perspective of the electromagnetic wave propagation theory. Then, we analyze the holographic RIS electromagnetic characteristics under the beam-squint effect. Further, we derive the angle spread range, 3dB beam bandwidth, and beam coverage range to analyze the regularities of beam offset. Besides, we propose a new codebook design scheme to address the impact of the beam-squint effect. Finally, we introduce the true-time-delay (TTD) lines into the holographic RIS structure to mitigate the beam-squint effect. The simulation results reveal the influence of the beam-squint, and also verify the mitigation effect of TTD lines on the beam-squint effect.

Generative artificial intelligence has shown great success in visual content synthesis such that humans struggle to distinguish between real and synthesized images. Forensic research seeks to reveal artifacts in such generated images, ensuring information security or improving generation capability. In this regard, the robustness and interpretability are important for the trustworthy purpose of forensic tasks. However, typical forensic models and their underlying data representations rely on empirical learning algorithms, which cannot effectively handle the high robustness and interpretability requirements beyond experience. As an effective solution, we extend the classical geometric invariants to the forensic research of large-scale generated images. Invariants are handcrafted representations with robust and interpretable geometric principles. However, their discriminability is far from the large scale of today's forensic tasks. We boost the discriminability by extending the classical invariants to the hierarchical architecture of convolutional neural networks. The resulting overcompleteness allows for an automatic selection of task-discriminative features, while retaining the previous advantages of robustness and interpretability. From generative adversarial networks to diffusion models, the forensic with our boosted invariants demonstrates state-of-the-art discriminability against large-scale content diversity. It also exhibits high efficiency on training examples, intrinsic invariance to geometric variations, and better interpretability of the forensic process.

A long-standing topic in artificial intelligence is the effective recognition of patterns from noisy images. In this regard, the recent data-driven paradigm considers 1) improving the representation robustness by adding noisy samples in training phase (i.e., data augmentation) or 2) pre-processing the noisy image by learning to solve the inverse problem (i.e., image denoising). However, such methods generally exhibit inefficient process and unstable result, limiting their practical applications. In this paper, we explore a non-learning paradigm that aims to derive robust representation directly from noisy images, without the denoising as pre-processing. Here, the noise-robust representation is designed as Fractional-order Moments in Radon space (FMR), with also beneficial properties of orthogonality and rotation invariance. Unlike earlier integer-order methods, our work is a more generic design taking such classical methods as special cases, and the introduced fractional-order parameter offers time-frequency analysis capability that is not available in classical methods. Formally, both implicit and explicit paths for constructing the FMR are discussed in detail. Extensive simulation experiments and robust visual applications are provided to demonstrate the uniqueness and usefulness of our FMR, especially for noise robustness, rotation invariance, and time-frequency discriminability.

The vulnerability of deep neural networks to adversarial perturbations has been widely perceived in the computer vision community. From a security perspective, it poses a critical risk for modern vision systems, e.g., the popular Deep Learning as a Service (DLaaS) frameworks. For protecting deep models while not modifying them, current algorithms typically detect adversarial patterns through discriminative decomposition for natural and adversarial data. However, these decompositions are either biased towards frequency resolution or spatial resolution, thus failing to capture adversarial patterns comprehensively. Also, when the detector relies on few fixed features, it is practical for an adversary to fool the model while evading the detector (i.e., defense-aware attack). Motivated by such facts, we propose a discriminative detector relying on a spatial-frequency Krawtchouk decomposition. It expands the above works from two aspects: 1) the introduced Krawtchouk basis provides better spatial-frequency discriminability, capturing the differences between natural and adversarial data comprehensively in both spatial and frequency distributions, w.r.t. the common trigonometric or wavelet basis; 2) the extensive features formed by the Krawtchouk decomposition allows for adaptive feature selection and secrecy mechanism, significantly increasing the difficulty of the defense-aware attack, w.r.t. the detector with few fixed features. Theoretical and numerical analyses demonstrate the uniqueness and usefulness of our detector, exhibiting competitive scores on several deep models and image sets against a variety of adversarial attacks.

The aim of this paper is to enhance the performance of an integrated sensing and communications (ISAC) system in the multiuser multiple-input multiple-output (MIMO) downlink in which a two-dimensional (2D) fluid antenna system (FAS) with multiple activated ports is employed at the base station (BS) to maximize the sum-rate of the downlink users subject to a sensing constraint. The unique feature of this setup is that the locations of the antenna ports at the FAS can be optimized jointly with the precoding design to achieve a higher sum-rate. The required optimization problem is however NP-hard. To overcome this, we start by considering the perfect channel state information (CSI) scenario where all the port CSI is available. Deep reinforcement learning is utilized to build an end-to-end learning framework for the joint optimization problem. In particular, by fixing the activated ports, we adopt a primal-dual based learning algorithm to design a constraint-aware neural network for optimizing the ISAC precoder. Then, by using the neural precoding network to calculate the reward, we adopt the deep reinforcement learning algorithm to design the port selection and precoder jointly. An advantage actor and critic (A2C) algorithm is proposed to train the policy, in which the actor network uses the pointer network to learn the stochastic policy and the critic network adopts the Long Short-Term Memory (LSTM) encoder architecture to learn the expected reward from the observations. Afterwards, the partial CSI case is addressed, where we propose a masked autoencoder (MAE) induced channel extrapolation for predicting all the CSI to facilitate the joint design. Simulation results demonstrate the promising performance of using FAS for multiuser MIMO and also validate the proposed learning-based scheme.

Automatic modulation recognition (AMR) plays a vital role in non-cooperative communication systems, which is an important technological component of blind signal processing. The application of deep learning (DL) methods in the field of modulation recognition has shown tremendous potential, greatly surpassing the performance of traditional methods. Existing DL-based AMR schemes employ modules of convolution and attentions to capture the local patterns and long-range dependencies for improving the performance of modulation classification, e.g., transformer-based methods. However, the complicated structure of existing artificial neural networks incur exceedingly long running time that hinders the practice implementation of these methods. Against this background, for the first time, this paper shows that the commonly adopted convolution and attentions are not necessary modules for the signal recognition. Specifically, we propose a lightweight architecture exploiting the multilayer perceptrons (MLP) for signal modulation classification, named SigMixer, which includes layers of token-mixing MLP and channel-mixing MLP that are interleaved for enabling spatial and temporal interaction. The proposed SigMixer not only has a simpler structure and lower computational complexity than the existing state-of-the-art approaches, but can also achieve a competitive performance with the formers. Experimental results show that compared with state-of-the-art approaches, SigMixer achieves better recognition accuracy, with 1.44%-10.65% improvement. Besides, the recognition accuracy can reach 89.36% for a low signal-to-noise ratio (SNR) = - 2 dB.

Though Deep Neural Networks(DNNs) have achieved excellent performance in computer vision(CV) tasks such as classification, they are vulnerable to adversarial examples which are generated by adding small-magnitude perturbation to inputs. Recently, different methods have been proposed to produce adversarial examples, most of them are white-box attacks, which assume the adversary knows the internal structure and parameters of the target model which is not practical in the real world. So we proposed using adversarial knowledge distillation to iteratively train a substitute model for the target black model to better learn its high-frequency information processing for giving input and then generate adversarial examples based on the substitute model to attack the target model. According to the experiments, our method can achieve nearly or even a higher attacking success rate than directly attacking the target model in a white-box setting. Besides, most white-box attack methods can achieve black-box attacks using our approach.

Image forensics is a rising topic as the trustworthy multimedia content is critical for modern society. Like other vision-related applications, forensic analysis relies heavily on the proper image representation. Despite the importance, current theoretical understanding for such representation remains limited, with varying degrees of neglect for its key role. For this gap, we attempt to investigate the forensic-oriented image representation as a distinct problem, from the perspectives of theory, implementation, and application. Our work starts from the abstraction of basic principles that the representation for forensics should satisfy, especially revealing the criticality of robustness, interpretability, and coverage. At the theoretical level, we propose a new representation framework for forensics, called dense invariant representation (DIR), which is characterized by stable description with mathematical guarantees. At the implementation level, the discrete calculation problems of DIR are discussed, and the corresponding accurate and fast solutions are designed with generic nature and constant complexity. We demonstrate the above arguments on the dense-domain pattern detection and matching experiments, providing comparison results with state-of-the-art descriptors. Also, at the application level, the proposed DIR is initially explored in passive and active forensics, namely copy-move forgery detection and perceptual hashing, exhibiting the benefits in fulfilling the requirements of such forensic tasks.

Covert transmission is investigated for a cooperative deception strategy, where a cooperative jammer (Jammer) tries to attract a multi-antenna adversary (Willie) and degrade the adversary’s reception ability for the signal from a transmitter (Alice). For this strategy, we formulate an optimization problem to maximize the covert rate when three different types of channel state information (CSI) are available. The total power is optimally allocated between Alice and Jammer subject to the Kullback-Leibler (KL) divergence constraint, which can be expressed analytically and be widely used as a covertness measurement. Different from the existing literature, in our proposed strategy, we also determine the optimal transmission power at the jammer when Alice is silent, while existing works always assume that the jammer’s power is fixed. Specifically, we apply the S-procedure to convert infinite constraints into linear-matrix-inequalities (LMI) constraints. When statistical CSI at Willie is available, we convert double integration to single integration using asymptotic approximation and substitution method. Finally, our simulation results show that for the proposed strategy, the covert rate is increased with the number of antennas at Willie. Moreover, compared to the benchmark, our proposed strategy is more robust in the presence of imperfect CSI.

Copy-move forgery is a manipulation of copying and pasting specific patches from and to an image, with potentially illegal or unethical uses. Recent advances in the forensic methods for copy-move forgery have shown increasing success in detection accuracy and robustness. However, for images with high self-similarity or strong signal corruption, the existing algorithms often exhibit inefficient processes and unreliable results. This is mainly due to the inherent semantic gap between low-level visual representation and high-level semantic concept. In this paper, we present a very first study of trying to mitigate the semantic gap problem in copy-move forgery detection, with spatial pooling of local moment invariants for midlevel image representation. Our detection method expands the traditional works on two aspects: 1) we introduce the bag-of-visual-words model into this field for the first time, may meaning a new perspective of forensic study; 2) we propose a word-to-phrase feature description and matching pipeline, covering the spatial structure and visual saliency information of digital images. Extensive experimental results show the superior performance of our framework over state-of-the-art algorithms in overcoming the related problems caused by the semantic gap.

Considering a simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS)-aided dual-functional radar-communications (DFRC) system, this paper proposes a symbol-level precoding-based scheme for concurrent securing confidential information transmission and performing target sensing, where the public signals intended for multiple unclassified users are exploited to deceive the multiple potential malicious radar targets. Specifically, the STAR-RIS-aided DFRC system design is formulated as a joint optimization problem that determines the transmission waveform signal, the transmission and reflection coefficients of STAR-RIS. The objective is to maximize the average received radar sensing power subject to the quality-of-service constraints for multiple communication users, the security constraint for multiple potential eavesdroppers, as well as various practical waveform design restrictions. However, the formulated problem is challenging to handle due to its nonconvexity. Furthermore, the high dimensionality of the optimization variables also renders existing optimization algorithms inefficient. To address these issues, we propose a distance-majorization induced low-complexity algorithm to obtain an efficient solution, which converts the nonconvex joint design problem into a sequence of subproblems that can be solved in closed-form, relieving the required high computational burden of the conventional approaches, e.g., the interior point method. Simulation results confirm the effectiveness of the STAR-RIS in improving the DFRC performance. Besides, by comparing with the state-of-the-art alternating direction method of multipliers (ADMM) algorithm, simulation results validate the efficiency of our proposed optimization algorithm and show that it enjoys excellent scalability for different number of T-R elements equipped at the STAR-RIS.

Forwarding path selection for data traffic is one of the most fundamental operations in computer networks, whose performance drastically impacts both transmission efficiency and reliability in network domains. Although deep reinforcement learning (DRL) has attracted considerable attention for path selection instead of hand-tuned heuristics, few works have considered how to exploit graph-structured information in networks to improve routing and forwarding efficiency. In fact, generating routes is essentially a process for finding a subgraph in a graph-structured network. To this end, this paper proposes an effective and novel graph embedding-based DRL framework for adaptive path selection (termed GRL-PS), aiming at reducing end-to-end (E2E) latency and promoting network throughput while maintaining stability in dynamically changing environments. Specifically, graph representation learning (GRL) is deployed as an effective enabler for the DRL agent to learn the relational knowledge of interacting entities for route decisions in networks. However, training such an agent in a dynamically changing environment encounters a knowledge acquisition bottleneck, since the DRL agent is always forced to learn every task from scratch. To improve the adaptation of behaviors and acquire skills beyond what the source policy can teach, we introduce potential-based reward shaping as a means of knowledge transfer to guide the agent in unfamiliar conditions with sparse rewards. Experimental results show that compared with baseline methods, our solution can achieve nearly-optimal performance with both latency and throughput, especially in large-scale dynamic networks.

This work investigates the covert information freshness in intelligent reflecting surface (IRS)-aided communications, where a public full-duplex user (Alice) and a private full-duplex user (Bob) exchange information in the presence of a watchful warden (Willie). In particular, with the help of Alice’s undisguised signal transmission, Bob can establish covert communications such that his transmission can be shielded from Willie. Considering both the non-retransmission protocol and the automatic repeat-request (ARQ) protocol for Bob’s transmission, we study the resource allocation design. By exploiting the channel statistics, the joint design of active beamforming at Alice and Bob, the passive beamforming at the IRS, and the packet length of the confidential data packet is formulated as a nonconvex optimization problem which minimizes the age of information (AoI) at Alice for the two considered protocols taking into account the quality of service in terms of the maximum tolerable AoI at Bob and communication covertness. To circumvent the non-convexity of the design problem, we propose alternating optimization algorithms to find effective solutions. Numerical results demonstrate the superiority of our proposed optimization algorithms over various benchmarks and unveil the decrease of the optimized packet length with the improved covert channel quality.

This paper investigates physical layer security for a large-scale WSN with random multiple access, where each fusion center in the network randomly schedules a number of sensors to upload their sensed data subject to the overhearing of randomly distributed eavesdroppers. We propose an uncoordinated random jamming scheme in which those unscheduled sensors send jamming signals with a certain probability to defeat the eavesdroppers. With the aid of stochastic geometry theory and order statistics, we derive analytical expressions for the connection outage probability and secrecy outage probability to characterize transmission reliability and secrecy, respectively. Based on the obtained analytical results, we formulate an optimization problem for maximizing the sum secrecy throughput subject to both reliability and secrecy constraints, considering a joint design of the wiretap code rates for each scheduled sensor and the jamming probability for the unscheduled sensors. We provide both optimal and low-complexity sub-optimal algorithms to tackle the above problem, and further reveal various properties on the optimal parameters which are useful to guide practical designs. In particular, we demonstrate that the proposed random jamming scheme is beneficial for improving the sum secrecy throughput, and the optimal jamming probability is the result of trade-off between secrecy and throughput. We also show that the throughput performance of the sub-optimal scheme approaches that of the optimal one when facing a stringent reliability constraint or a loose secrecy constraint.

The location information for tasks may expose sensitive information, which impedes the practical use of mobile crowdsensing in the industrial Internet. In this article, to our knowledge, we are the first to discuss the privacy protection of task locations and propose a codebook-based task allocation mechanism to protect it. Considering the cost of system utility caused by privacy protection technology, the tradeoff between local privacy and system utility is formalized a multiobjective optimization problem. The optimal solution is theoretically derived, and the optimal task allocation scheme is obtained. In addition, the selected allocation codebook (SAC) method is introduced to solve the problem of high computational resource consumption in the task allocation process and protect the task location privacy to some extent. The experimental results show that the SAC method sacrifices system utility but improves the privacy protection for task locations by 60% on average.

In this paper, we consider the problem of full-duplex covert millimeter wave (mmWave) communications, where a mmWave transmitter (Alice) sends information signals to its intended receiver (Bob) covertly in the presence of a watchful warden (Willie). For covering the presence of Alice, Bob operates in the full-duplex mode and generates jamming signals with a time-varying power. We investigate the covert rate optimization for both the single data stream case and the multiple data streams case under the constraints of the detection error probability at Willie. Specifically, for the single data stream case, we analytically characterize the minimum detection error probability at Willie and establish a framework for optimizing the analog beamforming, transmit power, and analog jamming jointly. As for the case of multiple data streams, we derive a tractable lower bound of the minimum detection error probability at Willie and formulate a joint optimization of the hybrid precoder and analog jamming design problem for the maximization of the achievable covert rate. Although the joint design problem is nonconvex, we adopt the penalty decomposition technique to handle the effect of the coupling between the analog precoder and digital precoder paving the way for the development of an iterative algorithm to locate its Karush-Kuhn-Tucker (KKT) solution. Finally, we show that our proposed joint design algorithm can be adapted to handle the multi-antenna Willie scenario and simulation results show that our proposed joint design algorithms can achieve significantly better performance as compared with some benchmark schemes.

Considering multi-user downlink ultra-high reliability and low latency communications (URLLC), this paper employs the artificial noise (AN) technique to establish a secure and covert broadcast communication paradigm for the first time. Specifically, a multi-antenna transmitter (Alice) broadcasts the confidential information to multiple legitimate users in the presence of a multi-antenna malicious warden (Willie) and a multi-antenna eavesdropper (Eve). It is well known that AN is an effective technique for securing the physical layer security (PLS) of signal transmissions. Nevertheless, AN emission also exposes the signal transmission and decreases the signal covertness. Taking into account the impact of short-packet URLLC transmissions, we investigate the joint optimization of the precoder and AN to maximize the secrecy rate under the covertness constraint. Although the considered problem is nonconvex, we propose a branch-reduce-and-bound (BRB)-based algorithm to solve it optimally. However, the nested-loop structure of the BRB-based algorithm incurs a high computational complexity. To strike a balance between the performance and computational complexity, we also propose a low-complexity penalty successive convex approximation (SCA)-based algorithm, whose performance approaches that of the optimal BRB-based algorithm, particularly in the low to medium transmit power regime. Simulation results demonstrate the excellent performance of our proposed optimization algorithms compared with various benchmark algorithms and unveil the importance of exploiting AN for secrecy provisioning.

This paper considers the physical layer security (PLS) and covertness of the signal transmission in a multiple-input single-output downlink adopting ultra-high reliability and low latency communication (URLLC). In the considered system, Alice transmits confidential signals to Bob in the presence of a multi-antenna eavesdropper (Eve) and a multi-antenna watchful adversary (Willie). Although artificial noise (AN) is a common PLS technique for protecting the confidential signal from wiretapping, it may reduce the communication covertness due to the additional signal emission. For maximizing the achievable secrecy rate, we propose an AN-aided secure and covert communication strategy through optimizing the information carrying beamformer and AN jointly subject to the covertness constraint. To tackle the formulated non-convex design problem, we propose a branch-reduce-and-bound (BRB)-based algorithm to solve the considered problem globally. Simulation results validate its efficiency compared with a benchmark algorithm and unveil the importance of exploiting AN.

Hybrid precoding architecture, as a cost-effective approach for millimeter-wave (mmWave) communications, can achieve an excellent tradeoff between spectrum efficiency and hardware implementation complexity. However, the design of a robust hybrid precoding for improving the physical layer security (PLS), which is insensitive to the uncertainty of eavesdropper's channel state information (CSI), has not been well studied. This article for the first time designs a probabilistically robust hybrid precoding scheme for securing broadcast communications in Internet of Things (IoT) with eavesdropper's imperfect CSI. Specifically, considering the Gaussian CSI error model, we maximize the minimum secrecy rate of multiple IoT devices (IoDs) by jointly designing analog and digital precoders under the constraints in terms of secrecy outage probability and per IoD's information rate. The optimization problem is challenging due to the coupling of the analog and digital precoders, and the secrecy outage constraint. To handle these challenges, we first employ a conservative probability inequality to transform the secrecy outage probability constraint into a deterministic one. Then, by employing the penalty dual decomposition (PDD) method, we develop a novel iterative algorithm to convert the resultant nonconvex problem into a sequence of convex problems, which can guarantee the convergence to its Karush-Kuhn-Tucker (KKT) solution. Simulation results show that the proposed algorithm can achieve significant secrecy performance gains compared with the benchmark algorithm.

This article investigates the intelligent reflecting surface (IRS)-aided multi-antenna covert communications. In particular, with the help of an IRS, a favorable communication environment can be established via controllable intelligent signal reflection, which facilitates the covert communication between a multi-antenna transmitter (Alice) and a legitimate full-duplex receiver (Bob) in the existence of a watchful warden (Willie). In order to shelter the desired communication, Bob generates jamming signals with a varying power to confuse Willie. The beamforming vector employed by Alice and the passive phase shifts of the IRS are optimized jointly to maximize the covert rate under the constraints of the successful detection probability at Willie and the communication outage experienced by Bob. We focus on the worst case by characterizing the minimum successful detection probability at Willie. The formulated problem is non-convex, due to the coupling between the beamforming vector of Alice and the phase shifts of the IRS, and the unit modulus constraint on the phase shifts of the IRS. To tackle the above issues, we first employ the penalty dual decomposition (PDD) method to handle the coupling effect. After that, we apply the successive convex approximation (SCA) method to develop an iterative algorithm for locating a Karush-Kuhn-Tucker (KKT) solution of the joint design problem. Moreover, we show that our proposed iterative algorithm can be adapted to handle the multi-antenna Willie case. Simulation results validate the effectiveness of the proposed iterative algorithm and show the great potential brought by the IRS for covert communications.

Millimeter wave (mmWave) communications and cognitive radio technologies constitute key technologies of improving the spectral efficiency of communications. Hence, we conceive a hybrid secure precoder for enhancing the physical layer security of a cognitive mmWave wiretap channel, where a secondary transmitter broadcasts confidential information signals to multiple secondary users under the interference temperature constraint of the primary user (PU). The optimization problem is formulated as jointly optimizing the analog and digital precoder for maximizing the minimum secrecy rate of all the secondary users under practical constraints. In particular, our design satisfies the constraint on the maximum interference power received by multiple PUs, as well as the secondary users’ minimum quality-of-service (Qos), and the unit-modulus constraint on the analog precoder. Due to the non-convexity of the resultant objective function and owing to the coupling between the analog and digital precoder, the optimization problem formulated is nonconvex and nonlinear, hence it is very challenging to solve directly. Hence, we first transform it into a tractable form, and develop a penalty dual decomposition (PDD) based iterative algorithm to locate its Karush-Kuhn-Tucker (KKT) solution. Finally, we generalize the proposed PDD algorithm to a secure hybrid precoder design relying on practical finite-resolution phase shifters and show that the proposed PDD algorithm can be straightforwardly adapted to handle the scenario, where each PU is equipped with multiple antennas and the CSI of multiple eavesdroppers (Eves) is imperfectly known. Our simulation results validate the efficiency of the proposed iterative algorithm.

In this paper, we study covert communications between a pair of legitimate transmitter-receiver against a watchful warden over slow fading channels. There coexist multiple friendly helper nodes who are willing to protect the covert communication from being detected by the warden. We propose an uncoordinated jammer selection scheme where those helpers whose instantaneous channel gains to the legitimate receiver fall below a pre-established selection threshold will be chosen as jammers radiating jamming signals to defeat the warden. By doing so, the detection accuracy of the warden is expected to be severely degraded while the desired covert communication is rarely affected. We then jointly design the optimal selection threshold and message transmission rate for maximizing covert throughput under the premise that the detection error of the warden exceeds a certain level. Numerical results are presented to validate our theoretical analyses. It is shown that the multi-jammer assisted covert communication outperforms the conventional single-jammer method in terms of covert throughput, and the maximal covert throughput improves significantly as the total number of helpers increases, which demonstrates the validity and superiority of our proposed scheme.

This paper studies covert millimeter-wave (mmWave) communications, where a multi-antenna transmitter (Alice) sends information signals to a full-duplex receiver (Bob) covertly, in the presence of a warden (Willie). For covering the communication by Alice, Bob operates in full-duplex mode which generates jamming signals with a transmit power varying across different time slots. Assuming that Willie adopts a radiometer as its detector, we first derive the optimal detection threshold for Willie. Next, we jointly design the analog beamforming at Alice and the analog jamming at Bob for maximizing the covert rate taking into account the use of optimal detecting at Willie and the communication outage probability experienced by Bob. Although the joint design is nonconvex that is challenging to solve directly, a successive convex approximation algorithm-based algorithm is developed to address the design problem. Simulation results validate the efficiency of the proposed algorithm, compared to some baseline scheme.

Covert communication provides high-level security for protecting users' privacy information. In this paper, we analyze the joint impact of an external jammer and channel uncertainty on covert communication in multi-user cognitive radio networks. Meanwhile, to fairly schedule the covert communication over multi-user cognitive radio networks, we propose a fairness secondary user (SU) scheduling scheme, which enables each SU to have the same probability for sending information covertly with the aid of an external jammer. Then, the closed-form expression for the covert rate of the scheduled SU can be obtained. Our results show that the minimal detection error probability and covert rate of the scheduled SU can be significantly improved by exploiting the channel uncertainty and random variation of interference power. Moreover, the impact of interference power on the probability of detection error and the covert rate is noticeable when channel uncertainty is large.

This paper studies the dirty-paper coding (DPC) based secure transmission in a multiuser broadcast channel. Since the encoding order of DPC determines which information-bearing signals must be treated as noise by potential eavesdroppers, adopting DPC enables the accurate characterization of the intrinsic secrecy as well as secrecy outage of multiuser broadcasting. Furthermore, the information-bearing signals can be designed to provide secrecy in addition to supporting normal (unclassified) transmission. To show this, we consider the scenario where one user requests secure transmission and the other users request normal transmission, and propose a hybrid secure transmission scheme which combines zero-forcing DPC and artificial noise (AN). By solving the secrecy rate maximization problem under constraints on the secrecy outage probability and the normal communication rates, we find that in addition to supporting the normal transmission, the proposed scheme has the potential to achieve a secrecy rate close to that of the traditional AN-based beamforming.

Recent studies show that millimeter wave (mmWave) communications can offer orders of magnitude, which increases in the cellular capacity. However, the secrecy performance of an mmWave cellular network has not been investigated so far. Leveraging the new path-loss and blockage models for mmWave channels, which are significantly different from the conventional microwave channel, this paper comprehensively studies the network-wide physical layer security performance of the downlink transmission in an mmWave cellular network under a stochastic geometry framework. We first study the secure connectivity probability and the average number of perfect communication links per unit area in a noise-limited mmWave network for both non-colluding and colluding eavesdroppers scenarios, respectively. Then, we evaluate the effect of the artificial noise (AN) on the secrecy performance, and derive the analysis result of average number of perfect communication links per unit area in an interference-limited mmWave network. Numerical results demonstrate the network-wide secrecy performance, and provide interesting insights into how the secrecy performance is influenced by various network parameters: antenna array pattern, base station intensity, and AN power allocation.

This paper studies the impact of artificial noise (AN) on the secrecy performance of a target cell in multi-cell cellular networks. Although AN turns out to be an efficient approach for securing a point-to-point/single-cell confidential transmission, it would increase the inter-cell interference in a multi-cell cellular network, which may degrade the network reliability and secrecy performance. For analyzing the average secrecy performance of the target cell which is of significant interest, we employ a hybrid cellular deployment model, where the target cell is a circle of fixed size, and the base stations outside the target cell are modeled as a homogeneous Poisson point process. We investigate the impact of AN on the reliability and security of users in the target cell in the presence of pilot contamination using a stochastic geometry approach. The analytical results of the average connection outage and the secrecy outage of its cellular user (CU) in the target cell are given, which facilitates the evaluation of the average secrecy throughput of a randomly chosen CU in the target cell. It shows that with an optimized power allocation between the desired signals and AN, the AN scheme is an efficient solution for securing the communications in a multi-cell cellular network.

In this paper, we focus on the physical-layer security of a multiuser cellular downlink system. A multi-antenna base station (BS) communicates with several legitimate users, where a target user is overheard by an eavesdropper in the downlink transmission. We aim at designing linear precoders for the BS to maximize the secrecy rate of target user under the condition that a certain Quality-of- Service (QoS) is guaranteed for the other legitimate users. To solve the non-convex optimization problem, we propose an iterative algorithm to transform the original problem into a sequence of approximate convex problems, which can be solved using interior point method. Simulation results reveal that compared with two other methods dealing with the similar optimization problem in previous works, our proposed method can achieve a higher secrecy rate.

In this letter, we propose a distributed jamming strategy to maximize the achievable ergodic secrecy rate (ESR) of a single-input multi-output (SIMO) transmission with a multiple-antenna eavesdropper based on only the statistical channel state information (CSI). In the scheme, exploring the heterogeneous large-scale fading effects, multiple geographically distributed single-antenna jammers transmit independent and uncoordinated jamming signals to confound the eavesdropper. We derive a large-scale asymptotic approximation of the achievable ESR, and optimize the transmit power of each jammer for maximizing the asymptotic ESR by geometric programming (GP). The proposed scheme does not require signal coordination among jammers and greatly reduces the training/synchronization overhead, which is applicable when the receiver has a large number of antennas, i.e., massive MIMO system.

This paper investigates the physical-layer security of a multiuser peer-to-peer (MUP2P) relay network for amplify-and-forward (AF) protocol, where a secure user and other unclassified users coexist with a multi-antenna eavesdropper and the eavesdropper can wiretap the confidential information in both two cooperative phases. Our goal is to optimize the transmit power of the source and the beamforming weights of the relays jointly for secrecy rate maximization subject to the minimum signal-to-interference-noise-ratio (SINR) constraint at each user, and the individual and total power constraints. Mathematically, the optimization problem is non-linear and non-convex, which does not facilitate an efficient resource allocation algorithm design. As an alternative, a null space beamforming scheme is adopted at the relays for simplifying the joint optimization and eliminating the confidential information leakage in the second cooperative phase, where the relay beamforming vector lies in the null space of the equivalent channel of the relay to eavesdropper links. Although the null space beamforming scheme simplifies the design of resource allocation algorithm, the considered problem is still non-convex and obtaining the global optimum is very difficult, if not impossible. Employing a sequential parametric convex approximation (SPCA) method, we propose an iterative algorithm to obtain an efficient solution of the non-convex problem. Besides, the proposed joint design algorithm requires a feasible starting point, we also propose a low complexity feasible initial points searching algorithm. Simulations demonstrate the validity of the proposed strategy.

This paper studies the cooperative transmission for securing a decode-and-forward (DF) two-hop network where multiple cooperative nodes coexist with a potential eavesdropper. Under the more practical assumption that only the channel distribution information (CDI) of the eavesdropper is known, we propose an opportunistic relaying with artificial jamming secrecy scheme, where a “best” cooperative node is chosen among a collection of N possible candidates to forward the confidential signal and the others send jamming signals to confuse the eavesdroppers. We first investigate the ergodic secrecy rate (ESR) maximization problem by optimizing the power allocation between the confidential signal and jamming signals. In particular, we exploit the limiting distribution technique of extreme order statistics to build an asymptotic closed-form expression of the achievable ESR and the power allocation is optimized to maximize the ESR lower bound. Although the optimization problems are non-convex, we propose a sequential parametric convex approximation (SPCA) algorithm to locate the Karush-Kuhn-Tucker (KKT) solutions. Furthermore, taking the time variance of the legitimate links' CSIs into consideration, we address the impacts of the outdated CSIs to the proposed secrecy scheme, and derive an asymptotic ESR. Finally, we generalize the analysis to the scenario with multiple eavesdroppers, and give the asymptotic analytical results of the achievable ESR. Simulation results confirm our analytical results.

This paper studies a single-input multi-output multi-eavesdropper (SIMOME) wiretap channel with multiple friendly single-antenna jammers. We consider random networks where the jammers and the eavesdroppers are distributed according to independent two-dimensional homogeneous Poisson point processes (PPP). We propose an opportunistic jammer selection scheme for the physical layer security enhancement, where the jammers whose channels are nearly orthogonal to the channel direction information (CDI) of the legitimate channel are selected to transmit independent and identically distributed (i.i.d.) Gaussian jamming signals to confound the eavesdroppers. The proposed scheme does not require a centralized design and signal coordinations among multiple jammers are not needed anymore. Furthermore, we analyze both the achievable secrecy throughput and the ergodic secrecy rate of the proposed jammer selection scheme. Based on the analysis results, we optimize the selection threshold for the secrecy throughput maximization and ergodic secrecy rate maximization. Simulation results show that the proposed jammer selection scheme can achieve a substantial performance gain.

This paper studies the secure multiple-antenna transmission in slow fading channels for the cognitive radio network, where a multiple-input, single-output, multieavesdropper (MISOME) primary network coexisting with a multiple-input single-output secondary user (SU) pair. The SU can get the transmission opportunity to achieve its own data traffic by providing the secrecy guarantee for the PU with artificial noise. Different from the existing works, which adopt the instantaneous secrecy rate as the performance metric, with only the statistical channel state information (CSI) of the eavesdroppers, we maximize the secrecy throughput of the PU by designing and optimizing the beamforming, rate parameters of the wiretap code adopted by the PU, and power allocation between the information signal and the artificial noise of the SU, subjected to the secrecy outage constraint at the PU and a throughput constraint at the SU. We propose two design strategies: 1) nonadaptive secure transmission strategy (NASTS) and 2) adaptive secure transmission strategy, which are based on the statistical and instantaneous CSIs of the primary and secondary links, respectively. For both strategies, the exact rate parameters can be optimized through numerical methods. Moreover, we derive an explicit approximation for the optimal rate parameters of the NASTS at high SNR regime. Numerical results are illustrated to show the efficiency of the proposed schemes.

In this paper, interference alignment (IA) achieved from one side is considered in the K-user constant multiple-input multiple-output (MIMO) interference channel (IC). Different from most existing algorithms which need alternating between the transmitter and receiver, our algorithm runs at transmitters only, thereby avoiding vast information exchanged between the transmitter and receiver. The essential aim of IA is overlapping the interference subspaces and maximizing the dimensionality of interference-free subspaces reserved for the desired signals. Based on this, we design the precoders by the steepest descent algorithm at transmitters only to minimize the spatial distance between different interference subspaces. Different interference subspaces are overlapping when the spatial distance between them is small enough. The simulation results illustrate that compared with existing alternating algorithm, the performance of the proposed one-sided algorithm does not degrade in a proper system, moreover, in a improper system, the proposed algorithm achieves better performance. Furthermore, compared with “Least Squares” and “One-sided Algorithm” which are existing one-sided approaches for IA, the proposed algorithm is more robust dealing with different systems.