This article investigates the physical-layer security for user-centric cell-free massive multiple-input-multiple-output (UC-CF-mMIMO)-enabled Internet of Things (IoT) network with multiple active eavesdroppers (Eves). We assume that each Eve intends to decode the information of the user (UE) closest to it and is intelligent enough to eliminate interference of other UEs perfectly. Two eavesdropping modes of noncolluding and colluding Eves are considered. Additionally, access points (APs) inject artificial noise (AN) into confidential data signal to hamper the eavesdropping of Eves. To assess the secrecy performance of UC-CF-mMIMO-enabled IoT network, we derive the worst case secrecy rate expression. Then, a max-min secrecy rate (MMSR) problem is formulated via joint optimization of AP clustering, AN selection, and power allocation, which aims to maximize the minimal secrecy rate among attacked UEs while adhering to the Quality-of-Service requirement of UEs, the limitation on the number of UEs and AN sent by each AP, and maximum transmit power limitation of APs. Due to the mixed-integer and nonconvex nature, the formulated problem is tackled via employing the${\ell }_{1}$-norm relaxation and successive convex approximation approach. Finally, we obtain a near-optimal solution to the MMSR problem by iteratively solving a sequence of second-order cone programmings. Simulation results demonstrate the superiority of proposed approach by the comparison with some existing schemes.

This paper addresses the challenges of “double-fading” effect and coverage limitations encountered by passive intelligent reflecting surface (IRS) by introducing a novel IRS architecture, termed the dual-sided active-intelligent omni-surface (DSA-IOS). This architecture is capable of processing incident signals on both sides with controllable amplitudes and phases. Furthermore, the DSA-IOS is deployed in a multi-cell multiple-input single-output system to alleviate inter-cell interference and combat potential wiretapping from multi-antenna eavesdroppers. Considering eavesdroppers’ statistical channel state information, we introduce a system metric, the expected secrecy rate (ESR), to capture the tradeoff between secrecy rate (SR) and secrecy outage probability (SOP). Our objective is to maximize the system’s expected secrecy energy efficiency by jointly optimizing the beamformers and artificial noise at the base stations and the reflection and transmission coefficients for both sides at the DSA-IOS. To address the design problem, we propose a low-complexity alternating optimization scheme to acquire an effective suboptimal solution. Simulation results demonstrate that the proposed DSA-IOS outperforms other advanced IRS architectures in enhancing secure performance due to additional degrees of freedom for superior resource utilization. Our results also validate that the proposed ESR metric effectively balances the tradeoff between SR and SOP by customizing SOP thresholds for individual users.

In this paper, to address the “double fading” effect and coverage limitations encountered by conventional passive intelligent reflecting surface (IRS), we propose a novel IRS hardware architecture, termed the dual-sided active (DSA)-IRS, which is capable of simultaneously processing dual-sided incident signals with controllable both amplitude and phase. Furthermore, the DSA-IRS is deployed in a multi-cell multiple-input single-output (MISO) system to alleviate inter-cell interference. Our design objective is to maximize the weighted sum-rate (WSR) among all users by jointly optimizing the beamformers at the base stations (BSs) and the reflection and transmission coefficients for both sides at the DSA-IRS, which is formulated as a non-convex optimization problem. To address the problem, we propose an alternating optimization (AO) scheme to obtain an effective suboptimal solution. Simulation results demonstrate that the proposed DSA-IRS outperforms other advanced IRS architectures in enhancing system performance in multi-cell communications due to the additional degrees of freedom for superior resource utilization.

Active reconfigurable intelligent surface (RIS) has attracted a lot of attention due to its ability to drastically change the communication environment by adjusting the phase shift and amplifying the amplitude of signals. In this paper, we consider to apply the active RIS to enhance the performance of the multi-cluster cooperative non-orthogonal multiple access (CNOMA) system. Specifically, in terms of the power consumption, we first formulate a transmit power minimization problem by jointly optimizing the beamforming at the base station, power splitting ratio at cluster heads, power allocation in each cluster, and RIS matrices in direct transmission and cooperative transmission phases. Then, to improve the fair energy efficiency (EE), we solve a minimum EE maximization problem. To tackle the coupling of optimization variables, we propose the block coordinates descent (BCD) based algorithms. By applying the successive convex approximation (SCA), semi-definite relaxation (SDR), arithmetic-geometric mean (AGM) inequality, Schur complement, and convex upper bound substitution methods, the developed approaches are guaranteed to converge to local optimal solutions. Simulations results demonstrate that the proposed algorithms outperform the baseline schemes under passive RIS aided case, non-cooperation case, and no-RIS case in terms of power consumption and energy efficiency. It is also revealed that active RIS is not always superior to passive RIS schemes with a large number of RIS elements in terms of the system power consumption.

Joint direction-of-arrival (DOA) and range estimation based on frequency diverse array (FDA) have been a critical issue in radar detection, tracking, and navigation. This paper first proposes a bidirectional extension frequency diverse coprime array (BE-FDCA) to obtain an extended 2D virtual array with a larger degree of freedom (DOF). Furthermore, on top of BE-FDCA, we introduce a sparse reconstruction algorithm, referred to as SR-BE-FDCA, to avoid time-consuming peak searches. The algorithm includes two key steps: sparse reconstruction of Decoupled Atomic Norm Minimization (DANM) and 2D ESPRIT estimation. With the aid of these designs, our approach achieves more accurate DOA and range estimations. Finally, numerical simulations are implemented to demonstrate the effectiveness of our method.

Recently, the Transformer-based technique has emerged as a promising solution for modeling contextual information in Remote Sensing (RS) scenes and has found widespread applications in RS scene classification. However, how to make full use of intermediate features learned in Transformers is of crucial importance in the RS scene classification tasks. Therefore, this paper proposes a Hierarchical Feature Fusion of Transformer with Patch Dilating (HFFT-PD), which aims to capture rich contextual information from hierarchical features to enhance the performance of RS scene classification. Specifically, the HFFT-PD model consists of a Hierarchical Transformer Merging (HTM) block and a Lightweight Adaptive Channel Compression (LACC) module, in which the HTM is specially designed for the Transformer architecture to bridge the semantic gaps between features from different hierarchical blocks, and the LACC accounts for the significance of distinct channels in the ultimate classification features. In addition, a brand-new Patch Dilating strategy is uniquely designed for the Transformer paradigm, functioning as a reassembly operator predicated on patch features. Contrasting with conventional upsampling techniques, Patch Dilating facilitates upsampling without requiring supplementary information, while concurrently preserving the semantic content of local spatial structure. Extensive and rigorous experiments conducted on the UCM, AID, and NWPU-45 datasets, with training ratios of 80%, 50%, and 20% respectively, demonstrate that our proposed HFFT-PD outperforms the baseline at least by 0.59%, 0.44%, and 0.99% respectively, showcasing the significant superiority of our HFFT-PD over contemporary state-of-the-art methodologies.

In this paper, we study the secrecy performance of non-orthogonal multiple access (NOMA) enabled visible light communication (VLC) broadcast channels in the presence of an active eavesdropper (Eve). The considered VLC system consists of multiple separately distributed light-emitting diodes arrays and multiple randomly located users (UEs) in an indoor room. User clustering is conducted to reduce the implementation complexity of successive interference cancellations. Two cooperative precoding strategies based on zero-forcing (ZF) and maximum ratio transmission (MRT) are designed using the effective channel of each cluster. Based on each precoding strategy, a sum secrecy rate maximization problem is developed to obtain the near-optimal power allocation (PA) to strengthen UEs’ confidential transmission and degrade Eve’s reception under minimum secrecy rate requirement, peak amplitude, non-negativity, and power constraints. To tackle the challenging non-convex problem for each precoding strategy, equivalent transformations and arithmetic-geometric mean approximation are conducted to convert the original problem into a series of geometric programming (GP) problems. Based on the reformulated problems, iterative algorithms are proposed to obtain near-optimal solutions by solving the GP problems through successive convex approximations. The convergence and complexity analysis of the proposed algorithms are studied. Simulation results show that the sum security performance of the proposed PA approach outperforms the conventional PA approaches in both ZF-based and MRT-based precoder schemes. The effectiveness of applying NOMA compared with the orthogonal multiple access-based scheme is also validated for the proposed system.

Industrial Internet of Things has significantly boosted predictive maintenance for complex industrial systems, where the accurate prediction of remaining useful life (RUL) with high-level confidence is challenging. By aggregating multiple informative sources of system degradation, information fusion can be applied to improve the prediction accuracy and reduce the uncertainty. It can be performed on the data-level, feature-level, and decision-level. To fully exploit the available degradation information, this article proposes a hybrid fusion method on both the data level and decision level to predict the RUL. On the data level, genetic programming (GP) is adopted to integrate physical sensor sources into a composite health indicator (HI), resulting in an explicit nonlinear data-level fusion model. Subsequently, the predictions of the RUL based on each physical sensor and the developed composite HI are synthesized in the framework of belief functions theory, as the decision-level fusion method. Moreover, the decision-level method is flexible for incorporating other statistical data-driven methods with explicit estimations of the RUL. The proposed method is verified via a case study on NASA's C-MAPSS data set. Compared to the single-level fusion methods, the results confirm the superiority of the proposed method for higher accuracy and certainty of predicting the RUL.

Secure signaling over multiple-input multiple-output (MIMO) wiretap channel (WTC) is studied under interference and transmit power constraints. The classical MIMO WTC model is extended to interference-limited scenarios, so that interference to other users does not exceed a given threshold while ensuring simultaneously no information leakage to an eavesdropper. The operational secrecy capacity of the Gaussian MIMO WTC under interference and transmit power constraints is rigorously established in two forms (a non-convex max problem and a convex-concave max-min problem), to which per-antenna power constraints can be added as well. Optimal signaling directions are characterized in the general case, from which (tight) upper bounds to the rank of optimal transmit covariance matrix are derived. A sufficient condition for the optimality of beamforming and a necessary condition for optimal full-rank signaling are given. Closed-form rank-1 and high-rank solutions are obtained in the case of zero interference constraints. Sufficient and necessary conditions for non-zero secrecy capacity are established. The results are extended to multi-user scenarios. A sufficient and necessary condition for the unbounded growth of the secrecy capacity with transmit power is obtained. The interplay between transmit and interference power constraints is studied, and its significant impact on optimal signaling is demonstrated (so that neither constraint can be absorbed into the other one in general, as was sometimes suggested in the literature). Overall, these results provide insights into fundamental information-theoretic limits and optimal signaling strategies for secure communications under interference constraints.

This paper presents a new general method for evaluating the reliability function and the mean residual life of degrading systems subject to condition monitoring and random failure. In the proposed method, the degradation process of the system is characterized by a continuous-time Markov chain, which is then incorporated into the proportional hazards model as a stochastic covariate process to describe the hazard rate of the time to system failure. Unlike the conventional method based on conditioning, which is applicable only for a small number of degradation states, the proposed method is capable of tackling the case with a general number of degradation states. Using the developed approximation techniques, closed-form formulas for related reliability characteristics are obtained in terms of the appropriate transition probability matrix. The proposed evaluation algorithm is computationally efficient and embeddable to support real-time reliability assessment of the system subject to condition monitoring for developing the optimal maintenance policy. The effectiveness and the accuracy of the method are validated by a numerical study and compared with the conventional method. A general case where the degradation path can be discretized up to ten states is also studied to illustrate the appealing general features.