Artificial Noise Research Articles

Spectral efficiency and secrecy enhancing scheme for IRS-aided SWIPT systems

Simultaneous wireless information and power transfer (SWIPT) has emerged as a promising technology for enabling efficient and scalable wireless communication within large-scale Internet of Things (IoT) networks. Despite its substantial potential, challenges related to spectral efficiency and overall system performance remain, underscoring the need for further advancements and innovations. Furthermore, the secure transmission of information is a crucial aspect that cannot be overlooked, as it guarantees the integrity and confidentiality of data exchanged within IoT networks. In this paper, we introduce a novel generalized spatial modulation (GSM) scheme specifically designed for intelligent reflecting surface (IRS)-Aided SWIPT systems. Moreover, the proposed scheme incorporates space-time block coding (STBC) to enhance performance. To strengthen the secrecy, a novel artificial noise (AN) generation strategy that effectively disrupts eavesdroppers while minimizing the impact on legitimate receivers is proposed. Additionally, the designed AN can be utilized by the energy-harvesting receiver to enhance its energy collection capabilities. We derived the average bit error probability (ABEP) of the system using the moment-generating function (MGF) approach and validated our analytical findings through rigorous simulations. The results obtained demonstrate convincingly that the proposed GSM scheme for IRS-Aided SWIPT systems significantly outperforms traditional methods, offering remarkable improvements in performance and efficiency.

The Impact of Campus Soundscape on Enhancing Student Emotional Well-Being: A Case Study of Fuzhou University

As the primary setting for students’ daily life and learning, university campuses are facing a growing concern about the impact of increased stress on students’ emotional well-being. The sound environment plays a critical role in affecting students’ mental health, learning efficiency, and overall well-being. However, research on the influence of campus soundscapes on students’ emotions is limited, and the mechanisms behind these effects remain to be explored. This study, using the Qishan Campus of Fuzhou University as a case, investigates the impact of campus soundscapes on students’ emotional perception and restorative effects. Four typical functional areas (academic zone (ACZ), residential zone (RDZ), recreational zone (RCZ), and administrative zone (ADZ)) were selected to analyze the effects of natural and artificial sounds on students’ emotions and physiological states. Based on EEG, eye tracking, sound level measurements, and questionnaire surveys, a one-way repeated measures ANOVA was used to assess students’ emotional arousal, valence, and physiological restoration under different soundscape conditions. The results showed that natural sounds, such as the sound of wind-blown leaves and flowing water, significantly improved students’ emotions and restorative effects, while artificial noises like construction sounds and traffic noise had negative impacts. Additionally, subjective perceptions of soundscape restoration were positively correlated with arousal, valence, and acoustic comfort, and negatively correlated with gaze frequency and pupil size. The findings provide a theoretical foundation for optimizing campus soundscape design and highlight the importance of natural sounds in enhancing students’ mental health and academic environment.

On Security Performance of SWIPT Multi-User Jamming Based on Mixed RF/FSO Systems with Untrusted Relay.

This paper presents research on the security performance of a multi-user interference-based mixed RF/FSO system based on SWIPT untrusted relay. In this work, the RF and FSO channels experience Nakagami-m fading distribution and Málaga (M) turbulence, respectively. Multiple users transmit messages to the destination with the help of multiple cooperating relays, one of which may become an untrusted relay as an insider attacker. In a multi-user network, SWIPT acts as a charging device for each user node. In order to prevent the untrusted relays from eavesdropping on the information, some users are randomly assigned to transmit artificial noise in order to interfere with untrusted relays, and the remaining users send information to relay nodes. Based on the above system model, the closed-form expressions of secrecy outage probability (SOP) and average secrecy capacity (ASC) for the mixed RF/FSO system are derived. The correctness of these expressions is verified by the Monte Carlo method. The influences of various key factors on the safety performance of the system are analyzed by simulations. The results show that the security performance of the system is considerably improved by increasing the signal-interference noise ratio, the number of interfering users, the time distribution factor and the energy conversion efficiency when the instantaneous signal-to-noise ratio (SNR) of the RF link instantaneous SNR is low.

Reconfigurable Intelligent Surface-Aided Security Enhancement for Vehicle-to-Vehicle Visible Light Communications

Vehicle-to-vehicle (V2V) visible light communication (VLC) systems are increasingly being deployed for real-time data exchange in intelligent transportation systems (ITS). However, these systems are highly vulnerable to eavesdropping, especially in scenarios such as road intersections where signals may be exposed to unauthorized receivers. To address these security challenges, we propose a novel reconfigurable intelligent surface (RIS)-assisted security enhancement scheme for V2V VLC networks. The proposed scheme leverages RIS to improve the reception of legitimate signals at the destination vehicle while simultaneously introducing artificial noise (AN) to interfere with potential eavesdroppers. Optimization problems are formulated to maximize the SINR of the destination vehicle and simultaneously minimize the worst-case SINR of eavesdroppers. The simulation results demonstrate that the proposed scheme achieves a notable improvement in the system’s secrecy rate by 1.64 bit/s/Hz and enhances the overall security performance, offering a robust solution to the security challenges in V2V VLC systems.

Can We Realize Data Freshness Optimization for Privacy Preserving-Mobile Crowdsensing With Artificial Noise?

Can We Realize Data Freshness Optimization for Privacy Preserving-Mobile Crowdsensing With Artificial Noise?

Secrecy Capacity of SWIPT-Based Cognitive Satellite-Terrestrial Network With Artificial Noise and Noncolluding ERs

Secrecy Capacity of SWIPT-Based Cognitive Satellite-Terrestrial Network With Artificial Noise and Noncolluding ERs

Constellation Superposition and Artificial Noise Against Interior and Exterior Eavesdropping in Bidirectional Untrusted Relaying Systems

Constellation Superposition and Artificial Noise Against Interior and Exterior Eavesdropping in Bidirectional Untrusted Relaying Systems

Physical Layer Security Based on Non-Orthogonal Communication Technique with Coded FTN Signaling

In recent years, ensuring communication security at the physical layer has become increasingly important due to the transmission of sensitive information over various networks. Traditional approaches to physical layer security often rely on artificial noise generation, which may not offer robust solutions against advanced interception techniques. This study addresses these limitations by proposing a novel security technique based on non-orthogonal signaling using Faster-than-Nyquist (FTN) signaling. Unlike conventional FTN methods that utilize fixed symbol intervals, the proposed technique employs variable symbol intervals encoded as secure information, shared only with legitimate receivers. This encoding enables effective interference cancellation and symbol detection at the receiver, while preventing eavesdroppers from deciphering transmitted signals. The performance of the proposed technique was evaluated using the DVB-S2X system, a practical digital video broadcasting standard. Simulation results demonstrated that the proposed method maintains smooth communication with minimal performance degradation compared to traditional methods. Furthermore, eavesdroppers were unable to decode the transmitted signals, confirming the enhanced security. This research presents a new approach to physical layer security that does not depend on generating artificial noise, offering a path to more secure and efficient communication systems.

Reconfigurable Intelligent Surface‐Aided MIMO Secure Communication System With Finite‐Alphabet Inputs

ABSTRACTExisting research in the field of reconfigurable intelligent surface (RIS)‐aided physical layer security assumed Gaussian signal inputs, which is inapplicable to practical communication systems, where finite‐alphabet inputs are used. This paper considers an RIS‐aided secure multiple‐input multiple‐output wireless communication system with finite‐alphabet inputs, where artificial noise (AN) is invoked at the transmitter to enhance the secure performance. In order to maximize the secrecy rate (SR), the data precoder, the AN precoder, and RIS's reflection coefficients are jointly optimized subject to the constraints of the maximum transmit power and the finite resolution of the phase shifts of RIS. Particularly, due to the finite‐alphabet input, the exact expression of the SR involves multiple integrals and lacks a closed‐form expression. To tackle this, a closed‐form lower bound of the SR is derived as the objective function, which is theoretically proved to be equal to the SR in the high signal‐to‐noise ratio region. Numerical results show that the RIS can significantly improve the secure performance, and the maximum possible SR (due to the finite‐alphabet inputs) can be achieved by increasing the number of the RIS's elements or by increasing the transmit power, which shows the performance advantage of the proposed optimization algorithm.

Urban and Rural Differences in the Efficacy of a Mobile Health Depression Treatment for Young Adults.

Depression among young adults represents a growing health problem in the U.S., but access to effective treatment remains a challenge. Mobile health (mHealth) approaches promise to deliver accessible and effective depression treatment; however, questions remain regarding how mHealth depression treatment efficacy may vary geographically based on urban and rural environmental contexts. The present study addresses this knowledge gap by leveraging data from a randomized clinical trial of an mHealth depression treatment called Cognitive Behavioral Therapy-text (CBT-txt) as applied to a sample of 103 U.S. young adults (ages 18-25). Prior research has demonstrated the efficacy of CBT-txt to reduce depressive symptoms. In the present study, we conduct an exploratory, post hoc analysis employing moderated growth curve modeling to investigate whether observed treatment efficacy differed between study participants residing in rural versus urban areas. The findings indicate that CBT-txt treatment effects in terms of reducing depression symptoms were significantly stronger for young adults residing in rural, as compared to urban, regions (β = 13.759, 95% CI = 0.796, 26.723, p < 0.038). We speculate that this is because of the lack of mental healthcare resources in rural, as compared to urban areas, as well as the greater level of environmental stressors, such as artificial light and noise, found in cities, which may mitigate treatment effects.

Optimized design for integrated sensing and communication in secure MIMO SWIPT systems

This paper investigates a secure multiple-input multiple-output integrated sensing and communication (MIMO ISAC)-enabled untrusted simultaneous wireless information and power transfer (SWIPT) system in which a base station is concurrently responsible for three different functionalities of communicating with users, sensing targets, and powering energy harvesting (EH) devices. These EH devices are assumed to be untrusted and potentially wiretap the information intended for legitimate users. This paper considers three different system design strategies: (i) communication-centric design, (ii) sensing-centric design, and (iii) EH-centric design. In each strategy, we aim to jointly design the transmit communication precoders and sensing precoder (which also acts as both the energy beam for EH and the artificial noise for securing communication) subject to requirements corresponding to the transmit power budget, minimum achievable secrecy rate, minimum amount of harvested energy, and maximum beampattern similarity constraint. The optimization design problems are highly non-convex, and thus it is mathematically challenging to directly find the optimal solutions. To overcome this issue, we first derive convex inner approximations, and then utilize the sequential convex programming (SCP) approach to develop efficient iterative algorithms which converge at least to a locally optimal solution. Extensive numerical results are simulated to verify the optimality of developed algorithms and highlight the impact of sensing on the system secrecy rate and harvested energy.

Mode shape area difference method for sparse damage detection in beam‐like structures

AbstractThis article develops the mode shape area difference (MSAD) method for detecting sparse damage in beam‐like structures under serviceability conditions. MSAD enhances the static deformation area difference (DAD) damage detection method by extending its application to the modal domain. MSAD leverages the sensitivity of mode shapes to the damage, enabling damage detection and localization through the quantification of area differences between functions of modal displacements and their derivatives. To test the applicability of the method to real modal data that is affected by measurement noise resulting in different accuracies on which the modal displacements are estimated, method is evaluated under different levels of artificial noise added to the theoretical mode shapes derived from numerical model of a simply supported reinforced concrete beam. The noise level is defined by a maximum noise amplitude representing the maximal permutation in each component of normalized mode shape. Additionally, the threshold value of noise level from within the reliable estimation of damage based on MSAD method is determined. It is confirmed that MSAD method requires mode shapes with exceptionally low noise levels and demands precise estimation of all mode shape components, thereby reducing its applicability to real experimental data.

Multi-Intelligent Reflecting Surfaces and Artificial Noise-Assisted Cell-Free Massive MIMO Against Simultaneous Jamming and Eavesdropping.

In a cell-free massive multiple-input multiple-output (MIMO) system without cells, it is assumed that there are smart jammers and disrupters (SJDs) that attempt to interfere with and eavesdrop on the downlink communications of legitimate users. A secure transmission scheme based on multiple intelligent reflecting surfaces (IRSs) and artificial noise (AN) is proposed. First, an access point (AP) selection strategy based on user location information is designed, which aims to determine the set of APs serving users. Then, a joint optimization framework based on the block coordinate descent (BCD) algorithm is constructed, and a non-convex optimization solution based on the univariate function optimization and semi-definite relaxation (SDR) is proposed with the aim of maximising the minimum achievable secrecy rate for users. By solving the univariate function maximisation problem, the multi-variable coupled non-convex problem is transformed into a solvable convex problem, obtaining the optimal AP beamforming, AN matrix and IRS phase shift matrix. Specifically, in a single-user scenario, the scheme of multiple intelligent reflecting surfaces combined with artificial noise can improve the user's achievable secrecy rate by about 11% compared to the existing method (single intelligent reflective surface combined with artificial noise) and about 2% compared to the scheme assisted by multiple intelligent reflecting surfaces without artificial noise assistance.

A filter calibration method for laser-scanned weld toe geometries

The scanning of weld seams can be used to evaluate the local weld toe geometry for fatigue assessments. Laser scanned weld seam profiles often contain noise which complicates the accurate measurement of the weld toe geometry. For that reason, filtering of the scanned data is necessary. The issue at hand is that a filtering method can significantly affect the measurement results. Therefore, a calibration of the filter input parameters is needed. In this study, a calibration method for filtered laser-scanned weld profiles is presented by using artificial weld toe geometries. The adjustment of different filter functions is achieved by using an optimization method on predefined weld toes with an artificial noise. The resulting input data for the filter functions is tested on a real specimen to verify the method. Through the calibration method it is possible to achieve satisfactory measurement results with precisely set input parameters for the filter functions. The most suitable filter functions for the measurement of the weld toe are the Gaussian and the Lowpass filter. Both functions are adequate as a universally applicable filter. For the evaluation of the measurement results of the radii and angles, a tolerance range is introduced, which is defined by the theoretically minimum measurable radii and angles. Using an adjusted Lowpass filter and a point distance of 0.07 mm set by the laser scanner, a measurement within the tolerance range of 0.2 mm is achievable for the weld toe radius. For the weld toe angle, the tolerance range of 1.5° is achieved for the majority of measurements.

A hybrid uplink-downlink secure transmission scheme for UAV-aided coordinated multi-point networks

Recognized as a pivotal facet in Beyond Fifth-Generation (B5G) and the upcoming Sixth-Generation (6G) wireless networks, Unmanned Aerial Vehicle (UAV) communications pose challenges due to limited capabilities when serving as mobile base stations, leading to suboptimal service for edge users. To address this, the collaborative formation of Coordinated Multi-Point (CoMP) networks proves instrumental in alleviating the issue of the poor Quality of Service (QoS) at edge users in the network periphery. This paper introduces a groundbreaking solution, the Hybrid Uplink-Downlink Non-Orthogonal Multiple Access (HUD-NOMA) scheme for UAV-aided CoMP networks. Leveraging network coding and NOMA technology, our proposed HUD-NOMA effectively enhances transmission rates for edge users, notwithstanding a minor reduction in signal reception reliability for strong signals. Importantly, the system's overall sum rate is elevated. The proposed HUD-NOMA demonstrates resilience against eavesdroppers by effectively managing intended interferences without the need for additional artificial noise injection. The study employs a stochastic geometry approach to drive the Secrecy Outage Probability (SOP) for the transmissions in the CoMP network, revealing superior performance in transmission rates and lower SOP compared to existing methods through numerical verification. Furthermore, guided by the theoretical SOP derivation, this paper proposes a power allocation strategy to further reduce the system's SOP.

On the Design of Artificial Noise for Physical Layer Security in Visible Light Communication Channels With Clipping

On the Design of Artificial Noise for Physical Layer Security in Visible Light Communication Channels With Clipping

Securing the Sensing Functionality in ISAC Networks: An Artificial Noise Design

Securing the Sensing Functionality in ISAC Networks: An Artificial Noise Design

Centralised vs. decentralised federated load forecasting in smart buildings: Who holds the key to adversarial attack robustness?

The integration of AI and ML into energy forecasting is crucial for modern energy management. Federated Learning (FL) is particularly noteworthy because it enhances data privacy and facilitates collaboration among distributed energy resources. It enables model training across multiple locations while minimizing reliance on centralized servers and data transfers. However, FL faces significant security challenges, particularly from adversarial attacks that can undermine the models' integrity and reliability. This paper addresses these security concerns by evaluating the effectiveness of Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL) in distributed load forecasting. We conducted a comparative analysis using two publicly available datasets, household data and smart grid data, for short-term load forecasting. Our study finds that DFL is more robust against adversarial attacks and artificial noise compared to CFL. Specifically, adversarial model poisoning attacks impact only the targeted client in DFL, while CFL is more broadly affected. For instance, in the presence of attacks, CFL's average client Mean Absolute Error (MAE) increased from 0.084 to 0.78 kWh for Dataset 1 and from 0.475 to 11.159 MW for Dataset 2, whereas DFL maintained relatively low error rates. Conversely, artificial noise had a less pronounced effect on CFL compared to DFL. Additionally, we introduce Decentralized Random Layer Aggregation (DRLA) to enhance DFL's robustness, offering further insights into improving FL methodologies in the energy sector. Our findings also indicate that only a single layer of the neural network is required for local updates during the aggregation process in DFL.

Energy-efficient optimization for secure wireless-powered backscatter communications with imperfect CSI and artificial noise

Energy-efficient optimization for secure wireless-powered backscatter communications with imperfect CSI and artificial noise

Improved PHY-layer authentication utilizing multi-modal features for mmWave MIMO UAV-enabled systems

This paper exploits multi-modal Physical (PHY)-layer features in terms of artificial fingerprint, In-phase/Quadrature (IQ) imbalance and Angle of Arrival (AoA) to propose a novel PHY-layer authentication framework for a Millimeter Wave (mmWave) Multiple-Input Multiple-Output (MIMO) Unmanned Aerial Vehicle (UAV)-enabled communication system. First, we resort to the AoA-based spatial fingerprint to effectively address the challenge of channel fingerprint instability induced by high-speed UAV mobility. To further enhance the low discriminability of hardware fingerprints caused by refined manufacturing techniques, artificial Gaussian noise is injected into the transmission signals to assist the receiver in better distinguishing between legitimate and illegitimate UAVs. Then, we jointly combine with inherent IQ imbalance and AoA features to design a hybrid authentication scheme and thus construct a multi-dimensional fingerprint space for a comprehensive characterization of UAV identities. To theoretically evaluate the effectiveness of the proposed authentication framework, the analytical closed-form expressions of performance metrics like false alarm and detection probabilities are also exactly derived based on the statistical signal processing technology and composite hypothesis testing. Finally, we provide large simulation results to validate the correctness and feasibility of the proposed theoretical models, and also discuss the relation between system security and communication service quality under different artificial fingerprint level.