Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

IEEE Consumer Communications and Networking Conference

Artificial noise-aided secure beamforming for multigroup multicast

Wanjik Kim, Sukjong Ha, Jeongwan Koh, Joonhyuk Kang

Published Jan 1, 2018• 6 citations
Publisher

Abstract

We investigate the physical layer security for multiple-input single-output (MISO) multigroup multicast system in the presence of multiple eavesdroppers (Eves). To elaborate, we aim to tackle the secrecy rate maximization (SRM) problem of the multicast system by proposing an algorithm for secure beam-forming. Here, we adopt the concept of artificial noise (AN) which is used for disturbing Eves by sending noise deliberately. Thus, we simultaneously design beamforming vectors and AN vector to maximize the secrecy rate of the system. We formulate the SRM problem in the form of non-convex max-min fair problem which is hard to solve due to its non-convexity. Therefore, in order to tackle this problem, we first reformulate this SRM problem to epigraph form, and we utilize the minorization maximization (MM) algorithm and rank-1 reduction technique. Simulation results show that our proposed algorithm improves the secrecy rate of given system model and outperforms the conventional algorithm for secure beamforming.

DOI
10.1109/ccnc.2018.8319269

Related papers

Artificial Noise-Aided Secure Multicasting Design Under Secrecy Outage Constraint

Bo Wang, Pengcheng Mu

This paper investigates the physical layer security in a multiple-input-single-output multicast channel, where a common message is transmitted to multiple receivers and only statistical channel state information of the eavesdropper is available. We consider transmitting some artificial noise (AN) along with the information-bearing signal to improve secrecy. To find the optimal AN design, we formulate a secrecy outage probability (SOP) constrained secrecy rate maximization problem. For solving this problem, we adopt a novel transformation of the intractable SOP constraint and establish an efficient algorithm which solves the transformed problem to global optimality. We also propose a low-complexity refinement method to reduce the conservatism incurred by the transformation and an efficient algorithm to find a suboptimal solution when the information-bearing signal is transmitted using single-stream beamforming. Our solution is found to be a valuable generalization of the traditional null-space AN scheme. According to our simulation results, when the number of receivers is less than that of transmit antennas, distributing AN uniformly in the null space of the legitimate channel vectors is near-optimal. When there is no valid null space, transmitting AN will interfere with some legitimate receivers, and the secrecy performance can also be improved with carefully designed AN.

IEEE Transactions on Communications•Sep 1, 2017•27 citations

Robust Artificial Noise Aided Transmit Method for Multicast MISO Wiretap Channels with Imperfect Covariance-Based CSI

Lijian Zhang, Liang Jin, Wenyu Luo +2 more

In this work, a robust artificial noise (AN) aided transmit method is proposed for a multicast multiple-input single-output (MISO) system in the presence of multiple single-antenna eavesdroppers. With the imperfect covariance-based channel state information (CSI), we aim to minimize the transmit power at the transmitter under the multicast secrecy rate constraint and the maximum transmit power constraint. The resulting optimization problem involves the joint optimization of the beamforming vector and AN covariance matrix. After using the semidefinite relaxation (SDR) technique, we derive the exact reformulation of the original optimization through the Lagrange duality. Then this non-convex problem is transformed into a one-variable optimization problem, which can be handled by solving a series of semidefinite programs (SDPs). Simulation results demonstrate the effectiveness of the proposed robust transmit method.

IEEE Vehicular Technology Conference•May 1, 2015•4 citations

Joint Information and Jamming Beamforming for Securing IoT Networks With Ratesplitting

Bin Qiu, Wenchi Cheng, Wei Zhang

The goal of this paper is to address the physical layer (PHY) security problem for multi-user multi-input single-output (MU-MISO) Internet of Things (IoT) systems in the presence of passive eavesdroppers (Eves). To this end, we propose an artificial noise (AN)-aided rate-splitting (RS)-based secure beamforming scheme. Our design considers the dual use of common messages and places the research emphasis on hiding the private messages for secure communication. In particular, leveraging AN-aided RS-based beamforming, we aim to maximize the focused secrecy sum-rate (F-SSR) by jointly designing transmit information and AN beamforming while satisfying the desired received constraints for the private messages at IoT devices (IoDs), and per-antenna transmit power constraint at base station. Then, we proposed a two-stage algorithm to iteratively find the optimal solution. By transforming non-convex terms into linear terms, we first reformulate the original problem as a convex program. Next, we recast the optimization problem to an unconstrained problem to obtain the global optimal solutions. Utilizing the duality framework, we further develop an efficient algorithm based on a barrier interior point method to solve the reformulated problem. Simulation results validate the superior performance of our proposed schemes.

arXiv:2407.13973•IEEE Internet of Things Journal•Sep 11, 2023•9 citations

Multicast Secrecy Rate Maximization for MISO Channels with Multiple Multi-Antenna Eavesdroppers

Qiang Li, Wing‐Kin Ma

Recently, there has been growing interest in secure communication via multi-antenna physical-layer designs. In this paper, we consider a transmit covariance design for a secrecy rate maximization problem under a multicast scenario, where a multi-antenna transmitter delivers a common confidential message to multiple single-antenna receivers in the presence of multiple multi-antenna eavesdroppers. This multicast secrecy rate maximization (SRM) problem is nonconvex by nature. By resorting to a convex approximation, we provide an upper bound and lower bounds of the multicast secrecy rate by solving a semidefinite program. In particular, for the case of either i) no more than three legitimate receivers and one eavesdropper, or ii) one legitimate receiver and arbitrary number of eavesdroppers, these bounds are shown to be tight and transmit beamforming is an optimal transmit strategy. We also demonstrate by simulations that the multicast SRM problem can be accurately approximated by the proposed method.

International Conference on Communications•Jun 1, 2011•34 citations

An Artificial-Noise-Based Approach for the Secrecy Rate Maximization of MISO VLC Wiretap Channel With Multi-Eves

Ge Shi, Yong Li, Wei Cheng +2 more

In this paper, we consider improving the secure performance of multiple-input single-output visible light communication channel in the presence of multiple eavesdroppers with multiple photodiodes. Our goal is to design an optimal artificial-noise (AN) aided transmission strategy to maximize the achievable secrecy rate subject to both sum power constraint and peak amplitude constraint. We consider a joint optimization of the transmit covariance and AN covariance for the non-convex secrecy rate maximization (SRM) problem. In order to solve it, the SRM problem is transformed into a series of single-variable semidefinite programming (SDP) problems without losing any optimality, and a one-dimensional search based algorithm is proposed to handle the converted problem, with polynomial complexity. By exploiting Karush-Kuhn-Tucker conditions of the problem, beamforming is found to be optimal for the confidential information transmission. Simulation results show the superior performance of the proposed AN-aided method compared with two other AN-aided methods and no AN-aided method.

IEEE Access•Dec 22, 2020•10 citations

A robust artificial noise aided transmit design for MISO secrecy

Qiang Li, Wing‐Kin Ma

This paper considers an artificial noise (AN) aided secrecy rate maximization (SRM) problem for a multi-input single-output (MISO) channel overheard by multiple single-antenna eavesdroppers. We assume that the transmitter has perfect knowledge about the channel to the desired user but imperfect knowledge about the channels to the eavesdroppers. Therefore, the resultant SRM problem is formulated in the way that we maximize the worst-case secrecy rate by jointly designing the signal covariance W and the AN covariance Σ. However, such a worst-case SRM problem turns out to be hard to optimize, since it is nonconvex in W and Σ jointly. Moreover, it falls into the class of semi-infinite optimization problems. Through a careful reformulation, we show that the worst-case SRM problem can be handled by performing a one-dimensional line search in which a sequence of semidefinite programs (SDPs) are involved. Moreover, we also show that the optimal W admits a rank-one structure, implying that transmit beamforming is secrecy rate optimal under the considered scenario. Simulation results are provided to demonstrate the robustness and effectiveness of the proposed design compared to a non-robust AN design.

arXiv:1104.4260•IEEE International Conference on Acoustics Speech and Signal Processing•May 1, 2011•20 citations
We investigate the physical layer security for multiple-input single-output (MISO) multigroup multicast system in the presence of multiple eavesdroppers (Eves). To elaborate, we aim to tackle the secrecy rate maximization (SRM) problem of the multicast system by proposing an algorithm for secure beam-forming. Here, we adopt the concept of artificial noise (AN) which is used for disturbing Eves by sending noise deliberately. Thus, we simultaneously design beamforming vectors and AN vector to maximize the secrecy rate of the system. We formulate the SRM problem in the form of non-convex max-min fair problem which is hard to solve due to its non-convexity. Therefore, in order to tackle this problem, we first reformulate this SRM problem to epigraph form, and we utilize the minorization maximization (MM) algorithm and rank-1 reduction technique. Simulation results show that our proposed algorithm improves the secrecy rate of given system model and outperforms the conventional algorithm for secure beamforming.
This paper investigates the physical layer security in a multiple-input-single-output multicast channel, where a common message is transmitted to multiple receivers and only statistical channel state information of the eavesdropper is available. We consider transmitting some artificial noise (AN) along with the information-bearing signal to improve secrecy. To find the optimal AN design, we formulate a secrecy outage probability (SOP) constrained secrecy rate maximization problem. For solving this problem, we adopt a novel transformation of the intractable SOP constraint and establish an efficient algorithm which solves the transformed problem to global optimality. We also propose a low-complexity refinement method to reduce the conservatism incurred by the transformation and an efficient algorithm to find a suboptimal solution when the information-bearing signal is transmitted using single-stream beamforming. Our solution is found to be a valuable generalization of the traditional null-space AN scheme. According to our simulation results, when the number of receivers is less than that of transmit antennas, distributing AN uniformly in the null space of the legitimate channel vectors is near-optimal. When there is no valid null space, transmitting AN will interfere with some legitimate receivers, and the secrecy performance can also be improved with carefully designed AN.
In this work, a robust artificial noise (AN) aided transmit method is proposed for a multicast multiple-input single-output (MISO) system in the presence of multiple single-antenna eavesdroppers. With the imperfect covariance-based channel state information (CSI), we aim to minimize the transmit power at the transmitter under the multicast secrecy rate constraint and the maximum transmit power constraint. The resulting optimization problem involves the joint optimization of the beamforming vector and AN covariance matrix. After using the semidefinite relaxation (SDR) technique, we derive the exact reformulation of the original optimization through the Lagrange duality. Then this non-convex problem is transformed into a one-variable optimization problem, which can be handled by solving a series of semidefinite programs (SDPs). Simulation results demonstrate the effectiveness of the proposed robust transmit method.
The goal of this paper is to address the physical layer (PHY) security problem for multi-user multi-input single-output (MU-MISO) Internet of Things (IoT) systems in the presence of passive eavesdroppers (Eves). To this end, we propose an artificial noise (AN)-aided rate-splitting (RS)-based secure beamforming scheme. Our design considers the dual use of common messages and places the research emphasis on hiding the private messages for secure communication. In particular, leveraging AN-aided RS-based beamforming, we aim to maximize the focused secrecy sum-rate (F-SSR) by jointly designing transmit information and AN beamforming while satisfying the desired received constraints for the private messages at IoT devices (IoDs), and per-antenna transmit power constraint at base station. Then, we proposed a two-stage algorithm to iteratively find the optimal solution. By transforming non-convex terms into linear terms, we first reformulate the original problem as a convex program. Next, we recast the optimization problem to an unconstrained problem to obtain the global optimal solutions. Utilizing the duality framework, we further develop an efficient algorithm based on a barrier interior point method to solve the reformulated problem. Simulation results validate the superior performance of our proposed schemes.
Recently, there has been growing interest in secure communication via multi-antenna physical-layer designs. In this paper, we consider a transmit covariance design for a secrecy rate maximization problem under a multicast scenario, where a multi-antenna transmitter delivers a common confidential message to multiple single-antenna receivers in the presence of multiple multi-antenna eavesdroppers. This multicast secrecy rate maximization (SRM) problem is nonconvex by nature. By resorting to a convex approximation, we provide an upper bound and lower bounds of the multicast secrecy rate by solving a semidefinite program. In particular, for the case of either i) no more than three legitimate receivers and one eavesdropper, or ii) one legitimate receiver and arbitrary number of eavesdroppers, these bounds are shown to be tight and transmit beamforming is an optimal transmit strategy. We also demonstrate by simulations that the multicast SRM problem can be accurately approximated by the proposed method.
In this paper, we consider improving the secure performance of multiple-input single-output visible light communication channel in the presence of multiple eavesdroppers with multiple photodiodes. Our goal is to design an optimal artificial-noise (AN) aided transmission strategy to maximize the achievable secrecy rate subject to both sum power constraint and peak amplitude constraint. We consider a joint optimization of the transmit covariance and AN covariance for the non-convex secrecy rate maximization (SRM) problem. In order to solve it, the SRM problem is transformed into a series of single-variable semidefinite programming (SDP) problems without losing any optimality, and a one-dimensional search based algorithm is proposed to handle the converted problem, with polynomial complexity. By exploiting Karush-Kuhn-Tucker conditions of the problem, beamforming is found to be optimal for the confidential information transmission. Simulation results show the superior performance of the proposed AN-aided method compared with two other AN-aided methods and no AN-aided method.
This paper considers an artificial noise (AN) aided secrecy rate maximization (SRM) problem for a multi-input single-output (MISO) channel overheard by multiple single-antenna eavesdroppers. We assume that the transmitter has perfect knowledge about the channel to the desired user but imperfect knowledge about the channels to the eavesdroppers. Therefore, the resultant SRM problem is formulated in the way that we maximize the worst-case secrecy rate by jointly designing the signal covariance W and the AN covariance Σ. However, such a worst-case SRM problem turns out to be hard to optimize, since it is nonconvex in W and Σ jointly. Moreover, it falls into the class of semi-infinite optimization problems. Through a careful reformulation, we show that the worst-case SRM problem can be handled by performing a one-dimensional line search in which a sequence of semidefinite programs (SDPs) are involved. Moreover, we also show that the optimal W admits a rank-one structure, implying that transmit beamforming is secrecy rate optimal under the considered scenario. Simulation results are provided to demonstrate the robustness and effectiveness of the proposed design compared to a non-robust AN design.