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IEEE Access

Distributionally Robust Secure Transmission for MISO Downlink Networks With Assisting Jammer

Xiaochen Liu, Yuanyuan Gao, Guozhen Zang, Nan Sha, Mingxi Guo

Published Jan 1, 2019• 2 citations
Publisher

Abstract

In this paper, we propose the distributionally robust secure transmit schemes in multi-input single-output (MISO) downlink wireless networks which consist of a transmitter, a desired receiver, multiple eavesdroppers and an assisting jammer. The imperfect channel state information (CSI) is considered and the CSI errors are only captured by the mean and covariance. We first study the transmit power minimization by jointly designing the beamforming vector at transmitter and artificial noise(AN) covariance at jammer, while the lower bound of connection probability at desired receiver and lower bound of outage probability at eavesdroppers are guaranteed simultaneously. Since the chance-constrained problem is non-convex, we derive two safe convex approximations by exploiting the Conditional Value-at-risk (CVaR) based method and Bernstein-type inequality (BTiE) based method, respectively. Specifically, we extend the application of BTiE originally proposed with the Gaussianity assumption to other possible distributions which satisfy the proposed sufficient condition. Furthermore, the secrecy rate maximization is exploited under the constraint of total transmit power. The original problem is non-convex and fractional, hence we design the Bilevel Quick Search (BQS) method to make it tractable. Finally, the simulation results verify the effectiveness and the robustness of the proposed transmit schemes.

DOI
10.1109/access.2019.2933277

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In this paper, we propose the distributionally robust secure transmit schemes in multi-input single-output (MISO) downlink wireless networks which consist of a transmitter, a desired receiver, multiple eavesdroppers and an assisting jammer. The imperfect channel state information (CSI) is considered and the CSI errors are only captured by the mean and covariance. We first study the transmit power minimization by jointly designing the beamforming vector at transmitter and artificial noise(AN) covariance at jammer, while the lower bound of connection probability at desired receiver and lower bound of outage probability at eavesdroppers are guaranteed simultaneously. Since the chance-constrained problem is non-convex, we derive two safe convex approximations by exploiting the Conditional Value-at-risk (CVaR) based method and Bernstein-type inequality (BTiE) based method, respectively. Specifically, we extend the application of BTiE originally proposed with the Gaussianity assumption to other possible distributions which satisfy the proposed sufficient condition. Furthermore, the secrecy rate maximization is exploited under the constraint of total transmit power. The original problem is non-convex and fractional, hence we design the Bilevel Quick Search (BQS) method to make it tractable. Finally, the simulation results verify the effectiveness and the robustness of the proposed transmit schemes.
This letter investigates secure transmission with imperfect channel state information (CSI) in multi-input single-output downlink wireless networks that consist of a transmitter, a desired receiver, one eavesdropper, and an assisting jammer. Specifically, only rough first- and second-order moment information is obtained for the estimation errors of CSI. We aim to minimize the total transmit power by jointly designing the beamforming vector at transmitter and artificial noise covariance at jammer under the reliability and secrecy constraints for all the possible distributions of CSI errors. Since the chance-constrained optimization problem is non-convex, we reformulate it using a Lagrangian duality theory and semidefinite relaxation. The simulation results illustrate the validity of the proposed distributionally robust transmit scheme.
In this paper, we investigate the robust secure transmit beamformming design for multiple-input single-output wiretap channels overheard by multiple non-collaborating single-antenna eavesdroppers. Assuming that at the transmitter, the instantaneous legitimate receiver's channel state information (CSI) is imperfect and only the statistical eavesdroppers' CSI is known, we seek to maximize the target secrecy rate R with the total transmit power constraint and the secrecy outage probability constraint. The resulting outage-constrained secrecy rate maximization (O-SRM) is non-convex and difficult to solve. Thus, we develop an efficient algorithm to solve it and determine the robust secure beamforming design. Specifically speaking, we first prove that the original O-SRM problem can be solved by an equivalent outage-constrained power minimization problem (O-PMP), which is easy to perform semi-definite relaxation (SDR) and can be solved by semi-definite program (SDP) consequently. Second, we transform the non-convex secrecy rate outage constraints into deterministic ones by resorting to Bernstein-type inequality II. Finally, by solving the convex approximation of the O-PMP with different Rs and bisection search over R, we convert the original O-SRM problem into a sequence of solvable SDPs. Simulation results are provided to verify the secrecy-rate performance improvement of the proposed robust beamforming compared with the existing worst-case design proposed by Li and Ma and the simple non-robust maximum-ratio transmission.
Physical layer security has recently emerged as a promising approach to provide secure wireless communications. Physical layer security technology is the key means to achieve security and communication integration. The built-in security mechanism based on the characteristics of wireless channel provides a feasible idea for the realization of “one secret at a time”. It has important application prospects in integrity protection of service data, Internet of things (IoT) lightweight encryption and blockchain wireless security technology. In this paper, we consider the scenario of a unicast multiuser multiple-input single-output (MISO) downlink system in the presence of multiple single-antenna passive eavesdroppers. Instead of assuming perfect channel state information (CSI), we investigate the artificial noise (AN)-aided robust transmit beamforming scheme with imperfect CSI on legitimate receivers and no CSI regarding eavesdroppers at the transmitter. Under the fixed total transmit power, the information symbol and the AN are transmitted concurrently. We aim to guarantee each legitimate receiver's signal-to-interference-and-noise ratio(SINR) outage probability below a preset level with minimum power. Hence, as much power as possible can be allocated to AN to confuse the eavesdroppers. In order to reduce the negative impact on the legitimate receivers, the AN is transmitted in the null space of the estimated legitimate channels. To deal with this nonconvex optimization problem, a rank relaxed formulation is obtained by employing semidefinite relaxation (SDR). Then we propose two approaches by using Bernstein-type inequality and S-procedure, respectively, to transform the probabilistic constraints into deterministic constraints. The optimization problem is reformulated as a convex semidefinite program (SDP). Simulation results show that the proposed approaches have better performances than the non-robust approach.
In this letter, we investigate the simultaneous wireless information and power transfer in a multiple-input-single-output downlink system which consists of one multi-antenna transmitter, one single-antenna desired receiver, and multiple single-antenna eavesdroppers. Specifically, we investigate the outage-constrained secrecy rate maximization by jointly designing the transmit beamforming vector and the power splitting ratio with statistical channel uncertainties. To handle the formulated non-convex problem, we resort to semi-definite relaxation (SDR) and the Bernstein-type inequality to transform the outage constraints into a deterministic form. Then, the original problem can be efficiently solved by alternately optimizing two convex subproblems. In addition, we testify that the SDR can obtain the rank-one optimal solution. Finally, our proposed algorithm is validated by some representative numerical results.
We study robust secure transmit design in terms of input covariance matrix for Gaussian multiple-input single-output (MISO) wiretap channels in the presence of a single-antenna eavesdropper. Under the assumption that the transmitter knows partial channel state information (CSI) on the main and eavesdropper's channels, we jointly design the input covariance matrices of information-bearing signal and artificial noise (AN) to maximize the secrecy rate. Unfortunately, the secrecy rate maximization (SRM) problem is difficult to solve. We derive a safe approximation to the SRM problem and propose a tractable approach to solve it. To get more insights, we discuss the rank property of the input covariance matrix of the information-bearing signal. Finally, the simulation results are provided to verify the validity and robustness of our proposed transmit design.
This paper addresses robust designs of secure simultaneous wireless information and power transfer for multiple-input single-output (MISO) multiuser systems. By using a bounded uncertainty model for the channel state information (CSI) from transmitters to legitimate receivers and under the assumption of only available statistic knowledge of the CSI to eavesdroppers, we consider the robust designs with both worst-case and chance-constrained optimizations. To jointly optimize transmit beamforming vectors and power splitting factors, we formulate three problems aiming at minimizing the system power, maximizing the sum secrecy rate, and maximizing the minimal secrecy rate, respectively, subject to both energy harvesting and secrecy outage constraints. These problems are intractable in their original forms due to the CSI models and their non-convexity. To reach computationally efficient solutions, we first rewrite the original problems by semi-definite relaxation, then transform the CSI uncertainty related constraints by S-procedure, and finally approximate the non-convex constraints by the first-order Taylor expansion. By the reformulations, we present iterative successive convex approximation algorithm for approaching the optimized solutions. Simulation results under various setups of system parameters are provided to examine the system performance and validate the effectiveness of the proposed schemes.