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IEEE Vehicular Technology Conference

Robust Beamforming Techniques for MISO Secrecy Communication with a Cooperative Jammer

Zheng Chu, Martin Johnston, Stephane Le Goff

Published May 1, 2015• 6 citations
Publisher

Abstract

This paper investigates robust beamforming techniques for a multiple-input-single-output (MISO) secrecy transmission with a cooperative jammer by incorporating two different types of channel uncertainties. We solve the worst-case signal-to-interference-plus-noise ratio (SINR) maximization of the legitimate user with the worst-case SINR constraint of the eavesdropper and the transmit power constraint. The original problems are not convex in terms of transmit covariance matrices, and cannot be solved directly. Thus, we present two approaches to convert these problems into semidefinite programmings (SDPs) by exploiting S-Procedure and deriving the corresponding Lagrangian dual problem. Numerical results are provided to validate the performance of the proposed robust schemes.

DOI
10.1109/vtcspring.2015.7146065

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This paper investigates robust beamforming techniques for a multiple-input-single-output (MISO) secrecy transmission with a cooperative jammer by incorporating two different types of channel uncertainties. We solve the worst-case signal-to-interference-plus-noise ratio (SINR) maximization of the legitimate user with the worst-case SINR constraint of the eavesdropper and the transmit power constraint. The original problems are not convex in terms of transmit covariance matrices, and cannot be solved directly. Thus, we present two approaches to convert these problems into semidefinite programmings (SDPs) by exploiting S-Procedure and deriving the corresponding Lagrangian dual problem. Numerical results are provided to validate the performance of the proposed robust schemes.
Robust quality-of-service (QoS)-based and secrecy rate-based secure transmission designs are investigated for a multiple-input single-output system with multiple eavesdroppers and a cooperative jammer. Two scenarios are considered: (a) eavesdroppers' channel state information (ECSI) is available and (b) ECSI is unavailable. In scenario (a), a QoS-based design is considered to minimize the worst case signal-to-interference-and-noise ratio at the eavesdroppers and to guarantee the QoS of the legitimate receiver. A secrecy rate-based design is also studied where the worst case secrecy rate is maximized. In scenario (b), a QoS-based design is considered to maximize the power of jamming signals under the QoS constraint of the legitimate receiver, and the secrecy rate-based design is not applicable. In all these designs, we jointly optimize the transmit beamforming vector and the covariance matrix of jamming signals under individual power constraints. As the optimization problems are non-convex, we propose an algorithm for each problem through semidefinite relaxation. Our analysis and simulation results show that, even though the linear precoding scheme in our designs is transmit beamforming rather than the general rank transmit covariance, this does not cause any loss of optimality.
This paper studies robust transmission schemes for multiple-input single-output (MISO) wiretap channels. Both the cases of direct transmission and cooperative jamming with a helper are investigated with imperfect channel state information (CSI) for the eavesdropper links. Robust transmit covariance matrices are obtained based on worst-case secrecy rate maximization, under both individual and global power constraints. For the case of an individual power constraint, we show that the nonconvex maximin optimization problem can be transformed into a quasi-convex problem that can be efficiently solved with existing methods. For a global power constraint, the joint optimization of the transmit covariance matrices and power allocation between the source and the helper is studied. We also investigate the robust wiretap transmission problem for the case with a quality-of-service constraint at the legitimate receiver. Numerical results show the advantage of the proposed robust design. In particular, for the global power constraint scenario, although cooperative jamming is not necessary for optimal transmission with perfect eavesdropper's CSI, we show that robust jamming support can increase the worst-case secrecy rate and lower the signal to interference-plus-noise ratio at the eavesdropper in the presence of channel mismatches between the transmitters and the eavesdropper.
This letter addresses the robust optimization problem of downlink coordinated beamforming over the multiple-input single-output (MISO) interference channel with confidential messages (IFC-CM) exploiting imperfect channel state information (CSI) at the transmitters. Our aim is to minimize the total transmitted power subject to the worst-case individual signal-to-interference-plus-noise-ratio (SINR) and equivocation rates. To model the imperfect CSI, bounded ellipsoidal uncertainties for channel vector and covariance are both considered. By deriving the equivalent forms for the SINR and secrecy constraints, the worst-case robust problem can be reformulated as a semi-definite programming (SDP) with the help of rank relaxation. Simulation results show the effectiveness of the proposed algorithm.
In this paper, a robust quality-of-service (QoS) based secret transmit beamforming design problem is discussed for a multiple-input single-output (MISO) system which works under cooperative jamming (CJ) scheme with a helper transmitting jamming signals to confuse the eavesdropper. For the channel state information (CSI), we assume that the CSI for the legitimate link is available and the CSI for the wiretap channel is imperfect. Our system design goal is to optimize the worst-case performance of the network under the situation: minimizing the signal-to-interference-and-noise ratio (SINR) at the eavesdropper and meanwhile guaranteeing the SINR at legitimate receiver. Thus the spatial distribution of jamming signals and the beamforming vector of source are jointly optimized under a total power constraint. As this optimization problem is difficult to tackle, a method is proposed with proofs to obtain exact solver of it. Our analysis reveals that optimizing the beamforming vector of source is a better strategy compared to optimizing the spatial covariance matrix of information signals. Numerical results affirm this and show the advantage of robust transmit designs compared to a non-robust approach in lowering the SINR at the eavesdropper in the presence of channel uncertainties.
In this paper, we study beamforming techniques for secrecy with worst case signal-to-interference-plus-noise ratio (SINR) guarantees in cooperative communications with imperfect channel state information (CSI). The worst case SINRs are the minimum SINR at the intended destination and the maximum SINR at the eavesdroppers. We consider a norm bounded CSI error model. With the available imperfect channel estimates, we design beamformers such that the minimum SINR receivable by the intended destination is above a certain threshold, while the maximum value of SINRs receivable at the eavesdroppers are below another lower threshold. We consider amplify-and-forward (AF) and decode-and-forward (DF) relaying schemes. The relays do the worst case transmit beamforming and injection of artificial noise (AN). We jointly optimize the beamformers and the AN for the AF and DF schemes subject to the worst case SINR constraints at the destination and the eavesdroppers. We solve the corresponding optimization problems as semi-definite programming (SDP) problems by rank relaxation and S-procedure. Numerical results show that AN injection at the relays significantly reduces the average total transmit power required to meet the worst case SINR constraints, and improves the solution feasibility of the optimization problems.
In this paper, we study different secrecy rate optimization techniques for a multiple-input–multiple-output (MIMO) secrecy channel, where a multiantenna cooperative jammer is employed to improve secret communication in the presence of a multiantenna eavesdropper. Specifically, we consider two optimization problems, namely, power minimization and secrecy rate maximization. These problems are not jointly convex in terms of the transmit covariance matrices of the legitimate transmitter and the cooperative jammer. To circumvent these nonconvexity issues, we alternatively design the transmit covariance matrix of the legitimate transmitter and the cooperative jammer. For a given transmit covariance matrix at the cooperative jammer, we solve the power minimization and secrecy rate maximization problems based on a Taylor series expansion. Then, we propose two iterative algorithms to solve these approximated problems. In addition, we develop a robust scheme by incorporating channel uncertainties associated with the eavesdropper. By exploitingS-Procedure, we show that these robust optimization problems can be formulated into semidefinite programming. Moreover, we consider the secrecy rate maximization problem based on game theory, where the jammer introduces charges for its jamming service based on the amount of the interference caused to the eavesdropper. This secrecy rate maximization problem is formulated into aStackelberggame where the jammer and the transmitter are the leader and the follower of the game, respectively. For the proposed game,Stackelbergequilibrium is analytically derived. Simulation results have been provided to validate the convergence and performance of the proposed algorithms. In addition, it is shown that the proposed robust scheme outperforms the nonrobust scheme in terms of the achieved secrecy rate and the worst-case secrecy rate. Finally, theStackelbergequilibrium solution has been validated through numerical results.