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arXiv:1606.07176

Secure MIMO Relaying Network: An Artificial Noise Aided Robust Design Approach

Jiaxin Yang, Qiang Li, Benoı̂t Champagne, Yulong Zou, Hanzo, Lajos

Published Jun 23, 2016• 0 citations
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Abstract

Owing to the vulnerability of relay-assisted and device-to-device (D2D) communications, improving wireless security from a physical layer signal processing perspective is attracting increasing interest. Hence we address the problem of secure transmission in a relay-assisted network, where a pair of legitimate user equipments (UEs) communicate with the aid of a multiple-input multiple output (MIMO) relay in the presence of multiple eavesdroppers (eves). Assuming imperfect knowledge of the eves' channels, we jointly optimize the power of the source UE, the amplify-and-forward (AF) relaying matrix and the covariance of the artificial noise (AN) transmitted by the relay, in order to maximize the received signal-to-interference-plus-noise ratio (SINR) at the destination, while imposing a set of robust secrecy constraints. To tackle the resultant nonconvex optimization problem, a globally optimal solution based on a bi-level optimization framework is proposed, but with high complexity. Then a low-complexity sub-optimal method relying on a new penalized difference-of-convex (DC) algorithmic framework is proposed, which is specifically designed for non-convex semidefinite programs (SDPs). We show how this penalized DC framework can be invoked for solving our robust secure relaying problem with proven convergence. Our extensive simulation results show that both proposed solutions are capable of ensuring the secrecy of the relay-aided transmission and significantly improve the robustness towards the eves' channel uncertainties as compared to the non-robust counterparts. It is also demonstrated the penalized DC-based method advocated yields a performance close to the globally optimal solution.

DOI
10.48550/arxiv.1606.07176

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Owing to the vulnerability of relay-assisted and device-to-device (D2D) communications, improving wireless security from a physical layer signal processing perspective is attracting increasing interest. Hence we address the problem of secure transmission in a relay-assisted network, where a pair of legitimate user equipments (UEs) communicate with the aid of a multiple-input multiple output (MIMO) relay in the presence of multiple eavesdroppers (eves). Assuming imperfect knowledge of the eves' channels, we jointly optimize the power of the source UE, the amplify-and-forward (AF) relaying matrix and the covariance of the artificial noise (AN) transmitted by the relay, in order to maximize the received signal-to-interference-plus-noise ratio (SINR) at the destination, while imposing a set of robust secrecy constraints. To tackle the resultant nonconvex optimization problem, a globally optimal solution based on a bi-level optimization framework is proposed, but with high complexity. Then a low-complexity sub-optimal method relying on a new penalized difference-of-convex (DC) algorithmic framework is proposed, which is specifically designed for non-convex semidefinite programs (SDPs). We show how this penalized DC framework can be invoked for solving our robust secure relaying problem with proven convergence. Our extensive simulation results show that both proposed solutions are capable of ensuring the secrecy of the relay-aided transmission and significantly improve the robustness towards the eves' channel uncertainties as compared to the non-robust counterparts. It is also demonstrated the penalized DC-based method advocated yields a performance close to the globally optimal solution.
Owing to the vulnerability of relay-assisted communications, improving wireless security from a physical layer signal processing perspective is attracting increasing interest. Hence, we address the problem of secure transmission in a relay-assisted network, where a pair of legitimate user equipments (UEs) communicate with the aid of a multiple-input multiple output (MIMO) relay in the presence of multiple eavesdroppers (eves). Assuming imperfect knowledge of the eves' channels, we jointly optimize the power of the source UE, the amplify-and-forward relaying matrix, and the covariance of the artificial noise transmitted by the relay, in order to maximize the received signal-to-interference-plus-noise ratio at the destination, while imposing a set of robust secrecy constraints. To tackle the resultant non-convex optimization problem with tractable complexity, a new penalized difference-of-convex (DC) algorithm is proposed, which is specifically designed for solving a class of non-convex semidefinite programs. We show how this penalized DC framework can be invoked for solving our robust secure relaying problem with proven convergence. In addition, to benchmark the proposed algorithm, we subsequently propose a semidefinite relaxation-based exhaustive search approach, which yields an upper bound of the secure relaying problem, however, with significantly higher complexity. Our simulation results show that the proposed solution is capable of ensuring the secrecy of the relay-aided transmission and significantly improving the robustness toward the eves' channel uncertainties as compared with the non-robust counterparts. It is also demonstrated the penalized DC-based method advocated yields a performance close to the upper bound.
This paper addresses the problem of secure amplify-and-forward (AF) relaying for multiple-input multiple output (MIMO) relaying networks in the presence of multiple eavesdroppers. Assuming practical imperfect eavesdroppers' channel state information (ECSI), we propose a robust approach to optimize the relay AF matrix, subject to power constraint, in order to maximize the received signal-to-interference-plus-noise ratio (SINR) at the destination while satisfying a set of secrecy constraints. The ECSI errors are assumed to fall within some predefined bounded sets. Since the resultant optimization problem is non-convex and semi-infinite, we transform it into a form constituted by the differences of convex functions (DC) using suitable matrix transformation techniques. Then an algorithmic solution with proven convergence is proposed by resorting to the penalty-DC algorithm (P-DCA). Experimental results show the security of the proposed transceiver design against eavesdropping and the robustness against the channel uncertainties.
This paper investigates the artificial noise (AN)-aided robust secure beamforming design for multiple-input multiple-output (MIMO) two-way relaying (TWR) systems based on physical layer network coding (PNC). In terms of signal-to-interference-and-noise ratio (SINR), we propose two robust beamforming designs to optimize worst-case secrecy sum rate in the presence of an eavesdropper, where the eavesdropper's channel state information (ECSI) is imperfect. In low SINR regime, we give a robust joint beamforming design. Since the optimization problem is non-convex, we use a zero-forcing (ZF) constraint on AN beamforming, and after approximating the objective function, the non-convex problem is formulated into a semidefinite programming (SDP). On the other hand, in high SINR regime, we provide a quality-of-service (QoS)-based robust beamforming design, in which the optimal solution can be efficiently obtained by employing an iterative algorithm based on Taylor expansion and semidefinite relaxation (SDR) techniques. Numerical results show the efficiency of the proposed schemes.
Multiple-input multiple-output (MIMO) amplify-and- forward (AF) relaying is designed for secure communication between a source-destination pair in the presence of multiple eavesdroppers. Assuming statistical knowledge of the eavesdroppers' channel state information (ECSI) errors, we introduce a probabilistically robust design method, which aims to optimize the source transmission power and AF relaying matrix by maximizing the received signal-to- interference-plus-noise ratio (SINR) at the destination, while satisfying a set of intercept probability constraints. The resultant optimization problem becomes nonconvex, and hence we propose a conservative two-step solution, where the source transmission power and the relaying matrix are sequentially optimized. Our simulation results demonstrate the improved secrecy of the proposed relaying design against eavesdropping and its robustness against the channel uncertainties.
Multiple-input multiple-output (MIMO) amplify-and- forward (AF) relaying is designed for secure communication between a source-destination pair in the presence of multiple eavesdroppers. Assuming statistical knowledge of the eavesdroppers' channel state information (ECSI) errors, we introduce a probabilistically robust design method, which aims to optimize the source transmission power and AF relaying matrix by maximizing the received signal-to- interference-plus-noise ratio (SINR) at the destination, while satisfying a set of intercept probability constraints. The resultant optimization problem becomes nonconvex, and hence we propose a conservative two-step solution, where the source transmission power and the relaying matrix are sequentially optimized. Our simulation results demonstrate the improved secrecy of the proposed relaying design against eavesdropping and its robustness against the channel uncertainties.
Robust artificial noise (AN) aided secure precoding for an amplify-and-forward multiple-input multiple-output untrusted relay system is studied, where the relay is untrusted and willing to help forwarding multiple data streams from the source to destination. We consider that the available channel state information is imperfect and modeled by the worst case model. Our objective is to maximize the worst case secrecy rate under the robust transmit power constraints at the source and relay, by jointly designing the signal and AN precoding matrices at the source and the precoding matrix at the relay. The robust secure precoding problem is nonconvex and hard to solve. To overcome this difficulty, we propose the weighted minimum mean square error based method, where the signdefiniteness lemma is used to eliminate the channel uncertainties and an effective iterative optimization algorithm is developed. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.