Stacked intelligent metasurfaces (SIMs), which integrate multiple programmable metasurface layers, have recently emerged as a promising technology for advanced wave-domain signal processing. SIMs benefit from flexible spatial degree-of-freedom (DoF) while reducing the requirement for costly radio-frequency (RF) chains. However, current state-of-the-art SIM designs face challenges such as complex phase shift optimization and energy attenuation from multiple layers. To address these aspects, we propose incorporating meta-fibers into SIMs, with the aim of reducing the number of layers and enhancing the energy efficiency. First, we introduce a meta-fiber-connected 2-layer SIM that exhibits the same flexible signal processing capabilities as conventional multi-layer structures, and explains the operating principle. Subsequently, we formulate and solve the optimization problem of minimizing the mean square error (MSE) between the SIM channel and the desired channel matrices. Specifically, by designing the phase shifts of the meta-atoms associated with the transmitting-SIM and receiving-SIM, a non-interference system with parallel subchannels is established. In order to reduce the computational complexity, a closed-form expression for each phase shift at each iteration of an alternating optimization (AO) algorithm is proposed. We show that the proposed algorithm is applicable to conventional multi-layer SIMs. The channel capacity bound and computational complexity are analyzed to provide design insights. Finally, numerical results are illustrated, demonstrating that the proposed two-layer SIM with meta-fiber achieves over a 25% improvement in channel capacity while reducing the total number of meta-atoms by 59% as compared with a conventional seven-layer SIM.

Artificial noise (AN) has been recognized as an effective physical-layer security scheme impairing the eavesdropper (Eve). Recently, artificial noise elimination (ANE) has emerged as a promising strategy to mitigate the impact of AN at Eves. However, conventional ANE schemes rely on prior knowledge, such as legitimate channel state information (CSI) or classification information, which may limit their practical applicability. To address these practical challenges, we propose an ANE scheme beyond prior knowledge (BPK) by leveraging machine learning algorithms. Firstly, a coarse projection is applied to partially eliminate the impact of AN using maximum likelihood estimation on the equivalent AN matrix. Secondly, a density clustering algorithm is introduced to obtain classification information based on the coarsely-projected observed vectors. Thirdly, a generalized principal component analysis (PCA)-based ANE algorithm is developed to effectively mitigate the residual AN using the obtained classification information. Furthermore, the artificial-noise-to-signal ratio (ANSR) and computational complexity are analyzed for performance revaluation, and a redefinition of several AN design principles is provided for scenarios involving a powerful Eve equipped with the BPK-ANE scheme by deriving the validity boundary. Finally, numerical results reveal key insights into four principles of AN: 1) Allocating less power to AN; 2) Reducing the randomness of AN; 3) Increasing the number of transmit antennas; and 4) Increasing the modulation order.

The site-specific non-line-of-sight (NLoS) conditions and unpredictable transmission signals in urban areas complicate localization efforts. Radio-frequency fingerprinting (RFF) addresses this challenge by building a database of signal characteristics at various locations. However, transition from indoor to outdoor environments is difficult due to the vast physical area and inaccessible sites. In this paper, we propose an RFF-based passive localization approach enhanced by ray-tracing simulation. This simulation utilizes real geographic data, including all buildings and terrains, without making assumptions about NLoS error statistics. To improve localization accuracy, we investigate the optimal placement of a moving sensor by minimizing the average mean squared error (MSE) within a confidence interval. Simulation results indicate that RFF-assisted passive localization is effective in real-world scenarios, and the optimal placement of the moving sensor significantly enhances localization accuracy.

Directional modulation (DM) is a physical layer security (PLS) technique implemented at the transmitter, leveraging antenna arrays to ensure secure communications. Through a process of spatial precoding between transceivers to transmit signals in specific directions, DM is capable of disrupting communications in unintended directions to prevent eavesdropping. In general, recent progress in the development of multiple-input multiple-output (MIMO) systems, including advanced radio frequency (RF), antenna technologies, along with innovative precoding algorithms, has enhanced the capabilities of DM techniques, leading to a multitude of robust DM variants. Hence, this survey aims to offer a comprehensive overview of DM, covering its fundamentals, promising variants, applications, hardware implementations, and future trends. Initially, the basic principle of DM is outlined in a general manner for subsequent comprehension. Subsequently, the large family of DM techniques is categorized into distinct variants based on the types of transmitting arrays. Next, we give a comprehensive survey of DM in common wireless scenarios, including multi-user (MU), relay, Internet of Things (IoT), and non-orthogonal access (NOMA) networks. Furthermore, we provide an illustration of DM system implementations, encompassing foundational architectures and cost-effective hardware realizations. Finally, concerning the unresolved challenges and current research focal points in DM, we present future research directions that merit further exploration and reference.

Recently, stacked intelligent metasurfaces (SIMs) have aroused widespread discussions as an innovative technology for directly processing electromagnetic (EM) wave signals. By stacking multiple programmable metasurface layers, an SIM has the ability to provide additional spatial degrees of freedom without the introduction of expensive radio-frequency chains, which may outperform reconfigurable intelligent surfaces (RISs) with single-layer structures. For the sake of alleviating information leakage risks in wireless communications, artificial noise (AN) has arisen as a physical-layer security technology with severe hardware constraints, which is impracticable in single-input single-output (SISO) systems. Therefore, we deploy an SIM at the transmitter (Alice) to accomplish joint modulation, beamforming, and AN in SISO systems. As such, an artificial neural network structured SIM aims to convert an input carrier signal into a desired output signal. Subsequently, we formulate the fitting problem between the actual output signal and the desired signal. Moreover, we introduce a regularization parameter to improve the energy efficiency. In order to tackle this resultant non-convex problem, we provide an alternating optimization algorithm to iteratively determine each variable. For the sake of reducing the computational complexity, we derive closed-form expressions for each phase shift and transmit power. Furthermore, we theoretically analyze the secrecy rate and computational complexity. By considering the signal deviation introduced by SIM, we derive upper and lower bounds of the secrecy rate to provide fundamental insights. Finally, simulation results demonstrate that the SIM-aided SISO system is capable of realizing secure communications efficiently, while the introduced power regularization parameter saved over 2 dB transmit power for a 5-layer SIM without amplifying the fitting error.

As mobile networks progress towards a unified framework for integrated sensing and communication (ISAC), it is foreseeable to introduce new privacy concerns, particularly the potential exposure of position information to unintended receivers. In other words, the scope of physical-layer security (PLS) needs to be expanded to encompass both communication and sensing privacy. Therefore, in contrast to conventional PLS schemes that focus predominantly on preventing eavesdropping, this paper proposes a novel physical-layer privacy (PLP) design within ISAC frameworks, in order to guarantee the secrecy of data transmission while obscuring transmitter’s directional information. Specifically, we introduce a metric termed angular-domain peak-to-average ratio (ADPAR) to assess sensing resistance (SR) performance. Subsequently, three fundamental optimization problems are formulated under such ADPAR constraints to enhance communication secrecy, depending upon the integrity of illegitimate channel state information. These problems are then tackled using advanced strategies such as null-space projection and the cooperation with artificial noise. Additionally, closed-form solutions are further derived in a few specific cases by leveraging singular value decomposition (SVD) and generalized SVD. Finally, simulation results affirm the effectiveness of our design in safeguarding the twofold privacy within ISAC networks.

Recently, the reconfigurable intelligent surface (RIS), capable of adjusting the phase shifts (PSs) of the reflecting signals through its low-cost elements, has emerged as a promising technology for next-generation wireless communications. However, the RIS may be manipulated by an illegal passive attacker (Wyn) due to the shared nature of wireless channels. In this paper, a Wyn-controlled RIS is considered to attack multiple-input single-output (MISO) communications via passive beamforming based on existing localization and Rician factor estimation techniques. Specifically, we propose an alignment cancellation (AC) scheme to minimize the achievable rate (AR), where the closed-form expressions for location, reflecting element number, and PSs are derived. Furthermore, the computational complexity is quantified to evaluate the low-cost characteristics of this algorithm. Simulation results demonstrate that the proposed AC scheme outperforms other benchmark schemes in degrading the AR with efficient and low-complexity designs.

Reconfigurable intelligent surface (RIS), a large array of passive scattering elements, is able to control the properties of electromagnetic waves, thereby enhancing the channel capacity, reducing the bit error rate, and enabling novel signal modulation methods. However, the promising gain of RIS depends on the precision of channel estimation. In this paper, we propose a three-step channel reconstruction framework to improve the channel estimation accuracy inspired by the concept of integrated sensing and communication scenario. Firstly, based on the coarse channel state information (CSI), the proposed dual one-dimensional multiple signal classification (D1D-MUSIC) algorithm improves the localization precision with a reduced complexity. Secondly, expectation maximization-based refined estimation (EMRE) algorithms are proposed to refine the CSI and estimate channel statistical properties (CSP), i.e., the shadow fading, the power of line-of-sight paths, and that of non-line-of-sight components. Thirdly, a gradient descent-based pilot optimization (GDPO) algorithm is further derived to improve the channel estimation precision on the basis of estimated CSPs. Finally, simulation results demonstrate that the developed D1D-MUSIC algorithm has lower localization error and complexity compared with conventional two-dimensional MUSIC algorithm. Moreover, the EMRE algorithms achieve the identical normalized mean square error (NMSE) performances as the ideal minimum mean square error estimator, while possessing robust resistance to the channel model mismatch. Furthermore, the developed GDPO technique is capable of providing an over 11 dB signal-to-noise ratio gain for channel estimation performance at NMSE$\bf = 10^{-2}$.

Recently, reconfigurable intelligent surface (RIS) has arisen as an excellent technology for assisting wireless communications. In order to handle the intractable non-convex problem for jointly optimizing beamforming and PSs in multiple-input multiple-output (MIMO) transmission, we propose a novel alternating direction (AD) method by maximizing the achievable rate (AR) at the receiver. Specifically, the initial problem is divided into the following two processes: i) optimizing the beamforming vector with fixed PSs, ii) determining a specific PS based on a closed-form solution when the other PSs and beamforming are fixed. Simulation results corroborate that the proposed AD method provides robust attainable performance with reduced computational complexity compared to its traditional counterparts.

Artificial noise elimination (ANE) has arisen as a possible countermeasure for mitigating the influence of artificial noise (AN) at the eavesdropper (Eve). However, conventional ANE schemes require the attainable channel state information (CSI) between the transmitter (Alice) and legitimate receiver (Bob), which reduces the feasibility of this proposal. In this paper, we investigate the issue of ANE without the CSI of Alice-Bob link by minimizing the artificial-noise-to-signal ratio (ANSR). Moreover, the detailed minor component analysis (MCA) algorithm is presented, and the computational complexity is quantified. Simulation results demonstrate that MCA can effectively degrade the influence of AN without the knowledge of CSI.

In this contribution, for enhancing the secure transmission of space-time block coded spatial modulation (STBCSM) systems, we conceive a minimum Euclidean distance (ED) transmit antenna selection (MED-TAS) method in conjunction with a new artificial noise (AN) design based on the optimized ED criterion (ED-AN). Specifically, we select the antennas by taking both legitimate receiver and eavesdropper into consideration, while proposing optimized ED-AN by minimizing the ED between the two specified signals, in order to enhance the performance at the legitimate receiver while degrading that at the eavesdropper. Simulation study demonstrates that the proposed technique is capable of achieving better security performance compared to its counterpart as conventional AN-aided STBC-SM design.

Abstract Recently, the physical limitation of range‐domain security guarantee for directional modulation with frequency diverse array was disclosed in Ding et al. ( IEEE Access 8, 63302–63309 (2020)). Therefore, to recreate this significant secrecy realisation in both direction and range domain, the authors conceive an artificial noise aided directional modulation scheme via reconfigurable intelligent surface. Specifically, the angle of departure from the reconfigurable intelligent surface to co‐direction receivers varies with distance, which provides the freedom to distinguish users in the range domain. With locations of the active eavesdroppers, an optimisation problem is further formulated to maximise the secrecy rate, which is then solved by applying genetic algorithm and alternating optimisation. On the other hand, when the eavesdroppers are silent, we employ maximum‐ratio transmission precoding and artificial noise to guarantee information security. Finally, simulation results demonstrate that, due to the aid of reconfigurable intelligent surface, the developed directional modulation structure can robustly guarantee the information security in the range domain, whether the information of eavesdroppers is achieved.

Artificial noise (AN), aiming to disturb the eavesdropper while avoiding the influence on the legitimate receiver, has arisen as an excellent technology for improving the physical-layer security of wireless communications. In order to challenge AN, zero-forcing elimination (ZFE) has been introduced as a possible countermeasure to mitigate the AN for the eavesdropper at the cost of more available receive antennas. In this contribution, from the perspective of eavesdroppers, we further conceive a class of efficient null-space elimination (NSE) schemes in order to reduce the number of receive antennas while enhancing the detection quality compared to original ZFE. Furthermore, the performance of secrecy rate as well as bit-error rate (BER) is quantified for both ZFE and NSE schemes through theoretical derivation, while the influence of imperfect channel state information (CSI) is also evaluated. The performance comparison of the above-mentioned schemes illustrates that NSE can provide more robust performance for eavesdroppers over ZFE, with lower hardware requirements as well as moderate complexity increase.

Artificial noise (AN) is considered as a new physical layer technology to improve the security of wireless systems. In this paper, we investigate secure transmission of AN-aided generalized spatial modulation (GSM), which maintains the same hardware requirements at the transmitter as the conventional GSM. In order to further improve the jamming intensity of conventional AN scheme, we propose an Euclidean distance optimized AN (ED-AN) scheme by minimizing the Euclidean distance between the transmit signal and the jamming signal, which also avoids the power waste of conventional AN scheme. The secrecy capacities of both the AN-GSM and EDAN-GSM schemes are analyzed, and the optimal power allocation of AN-GSM is further investigated by maximizing the secrecy capacity. Furthermore, the upper bounds of the theoretical bit error rates (BERs) of both the legitimate receiver and the illegal eavesdropper over the Rayleigh fading channel are derived. Simulation results validate our derived analysis and demonstrate that the ED-AN scheme offers better secrecy and BER performance.