Under non-independent and identically distributed (non-IID) data conditions, static weighted aggregation strategies in Federated Learning (FL) often fail to reflect each client's effective contribution to the global objective. This limitation may induce gradient conflicts, leading to unstable convergence and model performance degradation. To address this issue, we propose a Gradient Projection-guided Adaptive aggregation strategy for Federated Learning (GPAFed). We derive a novel convergence bound that explicitly incorporates the projection of local gradients onto the global gradient, revealing its quantitative influence on global loss reduction in FL. Building on this insight, we formulate a projection-based client contribution metric and design an adaptive aggregation weighting strategy inspired by the softmax mechanism. This approach strengthens the influence of high-contribution clients while attenuating the adverse effects of low-quality updates. Finally, we provide a theoretical guarantee that the proposed strategy achieves a tighter convergence bound. Extensive experiments under diverse non-IID conditions demonstrate that GPAFed consistently outperforms strong baselines in terms of model accuracy, convergence speed, and robustness.

Accurately predicting passenger flow distribution within a metro network during large-scale disruptions is crucial for maintaining the resilience of metro systems in megacities. Such disruptions not only risk causing severe station congestion but also exacerbate operational delays across the entire network, thereby affecting the overall efficiency of urban transportation systems. Thus, investigating passenger flow distribution under disruption conditions is essential for alleviating congestion and optimizing emergency responses. This study proposes a predictive framework based on a dynamic Bayesian network (DBN) path choice model and a spatio-temporal graph convolutional network (ST-GCN) to accurately forecast network-wide passenger flow distribution under disruption conditions in megacities. The framework leverages ST-GCN to capture the spatiotemporal dependencies and complex relationships among stations and integrates a DBN to simulate passengers’ path adjustment behavior, facilitating real-time updates of optimal travel paths during disruptions. Simulation results using real-world disruption data from the Shanghai Metro demonstrate that the proposed model outperforms benchmark models such as long and short-term time-series network (LSTNet), gated convolutional neural network (CNN), diffusion convolutional recurrent neural network (DCRNN), multi-task multi-graph neural network (MTMGNN), and convolutional long short-term memory (ConvLSTM) in terms of prediction accuracy across two major disruption scenarios. The findings indicate that the proposed framework significantly enhances the accuracy of passenger flow prediction under disruption conditions, providing operators with reliable early warnings and effective decision-making support, thereby improving the disaster resilience and operational robustness of metro systems.

Vehicular crowdsensing (VCS) leverages data exchange among intelligent connected vehicles (ICVs) to support diverse intelligent applications, making privacy protection essential due to the sensitivity of shared data. Federated learning (FL) combined with differential privacy (DP) has emerged as a powerful approach for safeguarding privacy in such distributed systems. Due to the unique characteristics of vehicular networks, many VCS tasks are inherently linked to spatial and temporal factors. This article focuses on these VCS tasks within the context of a complex system, where ICV mobility serves as a central factor that dynamically influences interconnected aspects such as task matching accuracy, participation rates, and DP noise consistency. The multifaceted impact of ICV mobility on these elements requires a comprehensive analysis to understand its cascading effects on privacy protection and model performance within FL-based VCS. To address these complexities, we propose DFed-ADP, a dynamic FL framework with an adaptive DP mechanism tailored for VCS. DFed-ADP includes a rigorous theoretical derivation to quantify the influence of ICV mobility on the DP noise scale and a dynamic ICV selection strategy that prioritizes data importance and adapts to mobility patterns. Specifically, we introduce an adaptive user-level DP and derive a closed-form expression to quantify the impact of ICV mobility on noise variance. Then, based on theoretical analysis, we propose an efficient ICV selection scheme that ensures participating ICVs can provide high-value data and complete training tasks on time. These designs ensure a robust balance between privacy and model performance. Experimental results demonstrate the adaptability and efficiency of DFed-ADP, achieving significant accuracy improvements under independent and identically distributed (IID) and non-IID settings in VCS.

In the Internet of Vehicles (IoV), data privacy concerns have prompted the adoption of Federated Learning (FL). Efficiency improvements in FL remain a focal area of research, with recent studies exploring model pruning to lessen both computation and communication overhead. However, in the IoV, model pruning presents unique challenges and remains underexplored. Pruning strategy design is critical as it directly impacts each vehicle's learning latency and capacity to participate in FL. Furthermore, FL performance and model pruning are intricately connected. Additionally, the fluctuating number and mobility states of vehicles per round complicate determining the optimal pruning ratio, closely intertwining pruning with vehicle selection. This study introduces Vehicular Federated Learning with Adaptive Model Pruning (VFed-AMP) to tackle these challenges by integrating adaptive pruning with dynamic vehicle selection and resource allocation. We analyze the impact of pruning ratios on learning latency and convergence rate. Then, guided by these findings, a joint optimization problem is formulated to maximize the convergence rate concerning optimal vehicle selection, bandwidth allocation, and pruning ratios. Finally, a low-complexity algorithm for joint adaptive pruning and vehicle scheduling is proposed to address this problem. Through theoretical analysis and system design, VFed-AMP enhances FL efficiency and scalability in the IoV, offering insights into optimizing FL performance through strategic model adjustments. Numerical results on various datasets show VFed-AMP achieves superior training accuracy (e.g., at least 13.4% improvement for BelgiumTS) and significantly reduces training time (e.g., at least up to 1.8x for CIFAR-10) compared to traditional FL methods.

Recently, Federated Learning (FL) has been advanced for Vehicular Crowdsensing (VCS), termed F-VCS, to enable collaborative learning without sharing private data, thus promoting privacy. However, the mobility of Intelligent Connected Vehicles (ICVs) leads to dynamic changes among FL participants, becoming one of the main challenges limiting F-VCS performance, as these dynamics can affect model training stability and accuracy. Current research primarily addresses these dynamics through ICV selection and resource optimization mechanisms. However, due to the lack of an effective method for characterizing ICV mobility, these approaches often rely on making binary judgments based on certain constraints (e.g., learning delay) rather than comparing the ICVs' mobility characteristics. As a result, they may not yield the most efficient outcomes for adapting to vehicular network dynamics, affecting overall system performance. This paper introduces a Grid-Assisted FL framework for VCS, named VCS_GAFL, which measures ICV mobility using a grid-cell model. Unlike traditional binary selection methods, our approach utilizes participation probabilities to capture the mobility differences among individual ICVs, guiding more precise and effective ICV selection and enhancing the VCS system's overall performance and adaptability. Specifically, we introduce an MMP-V model to predict ICV participation probabilities in F-VCS tasks. We then derive expressions for the impact of ICV participation probabilities on expected FL loss and ineffective energy consumption and formulate an optimization problem to jointly optimize ICV participation probabilities, wireless resource allocation, and ICV selection to minimize these impacts. By solving this problem, we derive a closed-form solution for wireless resource allocation and propose a probability-guided ICV selection strategy. Experimental results demonstrate that VCS_GAFL significantly enhances global accuracy, highlighting its potential and value in improving F-VCS services.

We propose a novel cross-layer security design for wireless distributed coded computation that integrates intelligent reflecting surface (IRS)-based directional modulation at the physical layer with coded computation at the logic layer. This complementary integration leverages the IRS to shape the spatial transmission environment for signal confinement, while exploiting redundancy in coded computation to ensure confidentiality at the algorithmic layer, thereby addressing vulnerabilities of broadcast wireless channels. Specifically, we design an IRS-Assisted Selective Blocking Mechanism (IRS-ASBM) that dynamically orchestrates the IRS to selectively block non-target worker nodes, preventing any unintended receiver from gleaning useful information and preserving data secrecy. This forms a new defense strategy in which physical-layer signal suppression safeguards the logic-layer coding thresholds—no eavesdropper can accumulate enough encoded fragments to overcome the decoding threshold of the coded scheme. Building on IRS-ASBM, we formulate an optimization framework to maximize the Secrecy Energy Efficiency (SEE) under transmit power constraints, which strikes an optimal balance between secrecy rate and energy consumption. By applying the Charnes–Cooper transformation, Dinkelbach’s algorithm, and difference-of-convex programming, we convert the fractional SEE maximization problem into a convex form and efficiently obtain the optimal configuration. Simulation results confirm the feasibility and superior energy efficiency of the proposed cross-layer strategy for secure distributed computation in wireless environments.

Multi-location task allocation is one of the most crucial issues in vehicular crowdsourcing (VCS). To ensure service quality, the VCS service provider prefers to assign multi-location tasks to the workers whose future trajectories have high spatial proximity with the task locations. However, this process requires workers and task owners to upload their precise locations to a not-fully-trusted service provider, thereby raising location privacy concerns. Although several privacy-preserving trajectory similarity evaluation schemes have been proposed, they either fail to match the multi-location task allocation scenario, or incur nontrivial computational costs due to homomorphic encryption. To address these challenges, we propose a novel efficient privacy-preserving multi-location task allocation scheme in fog-assisted VCS. Specifically, we design a lightweight secure Euclidean distance computation protocol based on arithmetic secret sharing (ASS), which can compute Euclidean distance without revealing the two input locations. Then, based on this protocol, we build our scheme that supports multi-location task allocation based on Hausdorff semi-distance (HSD). Our security analysis demonstrates the location privacy preservation of our scheme, and the experiment results on a real dataset also validate the efficiency of our scheme.

The proliferation of intelligent connected vehicles (ICVs) has catalyzed the emergence of vehicular crowdsensing (VCS) applications, wherein sensing tasks are assigned to ICVs with abundant sensing resources and high mobility. To select workers whose future trajectories have sufficient spatio-temporal similarity with the target sensing area, workers unavoidably need to upload their trajectories to the VCS platform that is not fully trusted, thereby triggering location privacy concerns. Recently, numerous privacy-preserving worker selection schemes have been put forth. Nevertheless, they either fail to enable flexible arbitrary query ranges or incur substantial communication and computation costs, which severely limits their suitability for VCS applications. To tackle the above two issues simultaneously, we propose a novel efficient and privacy-preserving VCS worker selection scheme that supports flexible arbitrary spatial ranges. By utilizing the Bloom filter technique and lightweight cryptographic tools, our proposed scheme allows the VCS platform to efficiently collaborate with the fog server to compute the spatio-temporal similarity without leaking location-derived Bloom filters. Rigid security analysis shows that our scheme effectively preserves the location privacy of both workers and the query user. Extensive experiments are conducted and the results demonstrate that our scheme is significantly more efficient in both communication and computation compared with the state-of-the-art scheme.

The flourishing of intelligent connected vehicles (ICVs) has fostered the emergence of vehicular crowdsourcing (VCS) applications, in which ICVs function as workers to execute diverse spatio-temporal critical tasks. As a vital service of VCS, task scheduling aims to assign tasks to the most suitable workers. To cope with the escalation of service scale, the service provider tends to outsource the service to powerful cloud servers, which however triggers the privacy concerns of workers, task owners, and the service provider. Previously reported privacy-preserving task allocation schemes can mainly be divided into single-attribute-aware and multiattribute-aware schemes. Nevertheless, the former suffers from practicality issues, while the latter either fails to achieve single-dimensional privacy and access pattern privacy or introduces substantial computational costs. To tackle the above challenges, we propose an efficient privacy-preserving outsourced task scheduling scheme (EPTS) for VCS, in which two cloud servers can cooperate to efficiently and securely conduct multiattribute-aware task scheduling. To this end, we devise five lightweight secure two-party protocols under the additive secret sharing (ASS) setting, which enable cloud servers to obliviously filter workers that meet multiple constraints and traverse the candidate worker set to obtain the optimal worker without revealing the input and output. Rigorous security analysis proves that our EPTS scheme effectively preserves user privacy, single-dimensional privacy, and access pattern privacy. Extensive experimental results validate its superior efficiency compared with the state-of-the-art scheme.

Intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) are both powerful means to improve the spectrum efficiency and can assist the cognitive radio network (CRN) in achieving more effective spectrum allocation. However, spectrum sharing in CRNs and the utilization of the scarce bandwidth in NOMA make it vulnerable to security threats. The secure beamforming design in IRS-assisted CRNs with NOMA is complicated and non-convex. Traditional semidefinite relaxation along with Gaussian randomization (SDR-GR) algorithm can solve the non-convex problem, however a high computational complexity. This paper proposes an alternating direction method of multipliers (ADMM)-based sum secrecy rate maximization (A-SSRM) scheme to achieve secure transmission with low computation complexity in IRS-assisted uplink CRNs with NOMA. Specifically, we derive an appropriate transformation of the original problem based on the ADMM framework and decompose it into multiple sub-problems, which are carefully designed to have closed-form solutions. Furthermore, an eavesdropper zero forcing (EZF)-based sub-optimal scheme is presented. Simulation results reveal that our proposed A-SSRM scheme outperforms other benchmarks in terms of sum secrecy rate and reduces the average running time by 6 times compared with the traditional SDR-GR algorithm. This is attributed to our effective decoupling and decomposing of the original problem, which enables each sub-problem to obtain a simple closed-form solution through derivation.

Federated Learning (FL) in the Internet of Vehicles (IoV) is a distributed machine learning technology that allows Intelligent Connected Vehicles (ICVs) to collaboratively train models without sharing raw data. In vehicular FL, the selection of participating nodes directly impacts the efficiency and accuracy of the model. Specifically, selecting appropriate ICVs ensures that participating nodes provide high-quality data while possessing sufficient computational resources and communication capabilities. While some existing studies have focused on optimizing resource allocation, improving data quality, and proposing ICV selection strategies, research on the comprehensive impact of ICV mobility on these factors remains limited. This paper proposes a dynamic optimization-based ICV selection algorithm aimed at effectively addressing the impact of mobility on FL performance in IoV. Our strategy combines factors such as ICVs' geographic location, speed, and data quality, selecting the optimal subset of ICVs within learning time constraints to ensure that participating ICVs can provide high-quality data and complete training tasks on time. Simulation results show that this method significantly improves the convergence speed and accuracy of FL models, validating the effectiveness of the proposed approach.

Vehicular crowdsensing (VCS) has emerged as a promising paradigm, in which spatio-temporal-based sensing tasks are outsourced to intelligent connected vehicles (ICVs) carrying sensor-equipped devices. A critical issue of VCS is to guarantee the spatio-temporal sensing coverage by assigning tasks to appropriate vehicles, which inevitably requires vehicles’ precise locations or trajectories and thus raises location privacy concerns. To address this problem, we propose a novel secret sharing-based efficient privacy-preserving task allocation scheme for VCS, which can select sensing vehicles with approximately optimal total spatio-temporal coverage based on their future trajectories while achieving strong location privacy preservation for users (customers and sensing vehicles). With a grid-based region encoding method, a user’s location information is encoded as a binary array, termed as the region code. Based on the idea of secret sharing, we design a bit-wise XOR-based secret splitting method to split a user’s region code into two random shares and separately transmit them to two fog servers, thereby perfectly hiding the original location information. With a carefully-designed code permutation mechanism and a greedy task allocation algorithm, the cloud server and fog servers can efficiently collaborate and complete task allocation based on permuted region codes without revealing users’ location information. Detailed security analysis shows that our proposed scheme effectively preserves users’ location privacy. Extensive experiments conducted on a realistic traffic scenario dataset also demonstrate that it is efficient in communication and computation while achieving large total spatio-temporal coverage.

Aggregation query, which is one of the most crucial data analysis tools applied to vehicular crowdsensed data, is expected to extract valuable insights and support smart decision-making. Meanwhile, the edge servers are deployed to cope with the escalation of service scale, which however raises privacy concerns about the query requests and reported data. Previously reported privacy-preserving aggregation query schemes are either tailored for static datasets or lack an index structure for deployed queries, which makes them impractical in vehicular crowdsensing (VCS) scenarios characterized by large volumes of real-time data and high data update frequencies. To tackle these challenges, we propose an efficient privacy-preserving edge-based dynamic aggregation query scheme with an encrypted tree-based index. Concretely, we first design two building blocks, namely the balanced spatial encoding quadtree (BSEQtree) and a predicate encryption scheme for membership testing (PEMT), which enable the edge servers to obliviously match data and queries by traversal over the encrypted BSEQtree. Based on the above blocks and arithmetic secret sharing (ASS), we construct our scheme, in which the edge servers can efficiently and securely aggregate newly reported data to related query results. Our security analysis shows the privacy preservation of our scheme, and the experiment results on a real dataset validate the efficiency of our scheme.

With the widespread deployment of multi-sensors on vehicles, a significant amount of data is generated that exhibits the characteristics of massive volume, wide variety, and privacy sensitivity. The private information protection poses immense challenges to intelligent applications in intelligent transportation systems (ITS). Federated learning (FL) has been introduced to support advanced ITS applications with minimum data exchanges and privacy disclosure. However, besides achieving data privacy in such a system, the malicious behavior or unintentional misbehavior of participating vehicles caused by high vehicle mobility and limited resources are critical considerations that may hinder the adoption of FL in ITS. In this paper, the concept of reputation is introduced as a metric to evaluate the reliability and trustworthiness of vehicles' behavior. Additionally, the reputation evaluation mechanism of the joint vehicle mobility metric and local model update performance metric is designed to calculate the reputation score of the vehicle at each model aggregation round. Furthermore, considering the case that well-behaved vehicles are misjudged due to mobility, we propose a spot-check strategy to verify and employ low-reputation but reliable misjudged vehicles with a certain probability based on their reputation value to improve the model training efficiency. Extensive experiments are conducted on real traffic signal datasets to demonstrate the effectiveness of our proposed scheme.

This research proposes a novel approach, namely the user fairness-enhanced beamforming (UFEB) scheme, to improve the user fairness in intelligent reflecting surface (IRS) assisted multiuser cognitive radio networks (CRNs). The proposed scheme takes into account the bounded channel state information (CSI) error of the primary user (PU) related channel and establishes a sum α-fair utility maximization problem, where α denotes the fairness adjustment index. This problem optimizes the system for fairness and rate, subject to constraints, such as the quality-of-service requirement of secondary users (SUs), limited interference on PUs, maximum transmit power of cognitive base station (CBS), and unit modulus of the IRS. Firstly, we design a prior judgment mechanism that makes preliminary assessments of the optimized requirements of the system in terms of fairness and rate, whose rationality can be proved in the simulation results. Then, with the prior selection of α, by exploiting the successive convex approximation and Taylor series expansion, we present an alternating iterative algorithm to solve the α-fair utility maximization problem. Simulation results show that the prior selection of α can accurately reflect the difference in the optimization requirements for sum rate and fairness of the system after the beamforming of CBS and the phase of IRS are determined, and validate the effectiveness and superiority of our proposed UFEB scheme in improving the fairness among SUs.

This paper presents a novel approach to consider trade-off in secrecy rate and power consumption in intelligent reflecting surface (IRS)-assisted cognitive radio networks (CRNs). In CRNs, the objective of enhancing secrecy rate (SR) conflicts with that of reducing total power consumption (TPC), creating a trade-off that should be flexibly considered due to dynamic environmental requirements. Whereas IRS act as an essential role that helps enhance performance and facilitate trade-offs, but does not change the fundamental conflict between these two objectives. Investigating the relationship between environmental parameters and both SR and TPC is crucial for achieving optimal performance. To address this issue, we propose a weighting-based trade-off (WBTO) algorithm that leverages a multi-objective optimization framework to optimize SR and TPC simultaneously. Specifically, we formulate a nonconvex problem with coupled variables and use convex approximation methods such as the penalty function method and biconvex function difference method to solve two subproblems iteratively. Simulation results demonstrate that the proposed algorithm can effectively adjust the optimization trend by changing the weighting factor, achieving up to nearly a 1-fold performance improvement compared to the SR maximization algorithm and nearly half reduction in time consumption compared to another baseline algorithm. Our approach enables flexible adjustment of the weighting factor to efficiently adapt to new metrics and achieve optimal trade-offs between SR and TPC.

Cognitive radio networks (CRNs) and millimeter wave (mmWave) communications are two major technologies to enhance the spectrum efficiency (SE). Considering that the SE improvement in the CRNs is limited due to the interference temperature imposed on the primary user (PU), and the severe path loss and high directivity in mmWave communications make it vulnerable to blockage events, we introduce an intelligent reflecting surface (IRS) into mmWave CRNs. Due to the estimation mismatch and the passivity of Eavesdroppers (Eves), perfect channel state information (CSI) of wiretap links is challenging to obtain, which promotes our research on robust secure beamforming (BF) design in the IRS-assisted mmWave CRNs. This paper considers the collaborate scenario of Eves, which allows us to investigate the BF design in the harsh eavesdropping environment. Specifically, by using a uniform linear array (ULA) at the cognitive base station (CBS) and a uniform planar array (UPA) at the IRS, and supposing that imperfect CSIs of angle-of-departures for wiretap links are known, we formulate a constrained problem to maximize the worst-case achievable secrecy rate (ASR) of the secondary user (SU) by jointly designing the transmit BF at the CBS and reflect BF at the IRS. To solve the non-convex problem with coupled variables, an efficient alternating optimization algorithm is proposed. As for the transmit BF at the CBS, we propose a heuristic robust transmit BF algorithm to attain the BF vectors analytically. As for the reflect BF at the IRS, by means of an auxiliary variable, we transform the non-convex problem into a semi-definite programming problem with rank-1 constraint, which is handled with the help of an iterative penalty function, and then obtain the optimal reflect BF through CVX. Finally, simulation results indicate that the ASR performance of our proposed algorithm has a small gap with that of the optimal solution with perfect CSI compared with the other benchmarks.

This paper studies the security and communication efficiency problem of a reconfigurable intelligent surface (RIS) assisted cognitive radio network, in which we proposed a scheme where secrecy rate (SR) and transmission rate are jointly considered. In specific, with the help of RIS, we exploit the green interference caused by the secondary network to ensure the physical layer security of the primary network. By taking the mutual interference between these two networks into account, we establish a problem to minimize the total transmit power subject to the quality-of-service requirement of secondary user (SU), SR requirement of the primary users (PUs) and limited interference temperature of PUs. In our proposed iterative algorithm, the transmit and reflect beamforming are alternatively optimized by transforming PUs' SR constraint into a second order cone (SOC) form, and the optimal transmit and reflect beamforming are achieved based on the iterative penalty function. Simulation results show the superiority of our proposed algorithm in terms of the total transmit power requirement compared with other benchmarks, and the total transmit power of our proposed algorithm is not very susceptible to the number of eavesdroppers and PUs, which is conducive to serving multiple users in the wireless communication system.

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Mobility Aware Privacy-Preserving Federated Learning with Hierarchical Aggregation for Internet of Vehicles 44 Pages Posted: 14 Nov 2022 See all articles by Xing ChangXing ChangTongji UniversityXiaoping XueTongji UniversityJingxiao MaTongji UniversityYantao YuTongji UniversityTao SongTongji University Abstract With the application and development of intelligence and networking in the automobile industry, the Internet-of-Vehicles (IoV) can be empowered with collaborative and distributed intelligence using massive and geographically dispersed data, e.g., location and trajectory data, which have considerable privacy risks due to the privacy-sensitive nature of user data. Federated Learning (FL) has been regarded as an effective technique to preserve privacy for various intelligent applications in the IoV. However, when FL is applied in the IoV, the mobility nature of IoV brings specific challenges in privacy preservation. In this paper, we propose a mobility-aware privacy-preserving FL with hierarchical aggregation scheme to efficiently reduce the risk of privacy leakage and to enhance the privacy-preserving strength of FL, through a comprehensive resource scheduling design that optimizes communication overhead and learning efficiency under certifiable user-level privacy constraints. First, we formulate a vehicle selection optimization problem by comprehensively considering some important factors related to mobility and heterogeneity, such as vehicle location, vehicle speed, limited communication resources, and data heterogeneity, and a dynamic adaptive vehicle selection algorithm is proposed to dynamically select the suitable vehicles to participate in the learning task to improve the training efficiency of FL. Further, we provide the theoretical optimal noise addition algorithm and carry out a complete theoretical derivation, where local model updates are perturbed with additive Gaussian noise. Extensive experiments demonstrate that the proposed scheme can effectively improve the learning effectiveness of FL to reduce the risk of privacy leakage and improve the privacy preserving strength of the FL, thereby improving the ability to protect privacy in the IoV. Keywords: Federated Learning, Internet of vehicles, Participant Selection, Privacy-Preserving, Hierarchical Aggregation Suggested Citation: Suggested Citation Chang, Xing and Xue, Xiaoping and Ma, Jingxiao and Yu, Yantao and Song, Tao, Mobility Aware Privacy-Preserving Federated Learning with Hierarchical Aggregation for Internet of Vehicles. Available at SSRN: https://ssrn.com/abstract=4276311 Xing Chang Tongji University ( email ) ShanghaiChina Xiaoping Xue Tongji University ( email ) ShanghaiChina Jingxiao Ma (Contact Author) Tongji University ( email ) ShanghaiChina Yantao Yu Tongji University ( email ) ShanghaiChina Tao Song Tongji University ( email ) ShanghaiChina Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 1 Abstract Views 3 PlumX Metrics Related eJournals Data Science & Analytics eJournal Follow Data Science & Analytics eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 94 PAPERS 2,142 Information Policy & Ethics eJournal Follow Information Policy & Ethics eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 57 PAPERS 1,817 Libraries & Information Technology eJournal Follow Libraries & Information Technology eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 45 PAPERS 3,855 Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo-4dc in 0.224 seconds

This paper proposes a secure beamforming scheme to enhance the physical layer security (PLS) in the intelligent reflecting surface (IRS) assisted cognitive radio networks (CRNs). Taking the statistical channel state information (CSI) error of eavesdroppers (Eves) related channels into account, we jointly optimize the transmit beamforming at the cognitive base station (CBS) and reflect beamforming at the IRS to minimize the transmit power subject to the quality of service of secondary user (SU), the limited interference on the primary users (PUs), the secrecy rate (SR) outage probability constraint of SU and unit modulus of the IRS. To tackle this mathematically intractable problem, we first transform the non-convex outage constraint into deterministic form by using the Bernstein-type inequality and then exploit the alternating optimization method with the help of semi-definite relaxation (SDR) and Gaussian randomization method. Simulation results show that our proposed algorithm can significantly reduce the transmit power compared with that without IRS, and allows us to use fewer antenna number of CBS.