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IEEE Transactions on Vehicular Technology

Optimal Secure Multicast With Simultaneous Wireless Information and Power Transfer in Presence of Multiparty Eavesdropper Collusion

Wenjun Xu, Shengyu Li, Chia‐Han Lee, Zhiyong Feng, Jiaru Lin

Published Jan 26, 2016• 17 citations
Publisher

Abstract

In this paper, secure transmission with simultaneous information and energy multicast in the presence of multiparty eavesdropper (EVE) collusion is investigated. In the considered system, legal users simultaneously decode information and harvest energy through dynamic power splitting (PS), whereas EVEs form multiple eavesdropping-collusion parties to cooperatively decode signals. A novel optimization problem is formulated to maximize the overall achievable secure multicast capacity while satisfying the maximum transmit power constraint at the transmitter, guaranteeing the minimum harvested energy requirements at each legal user and restricting the wiretapping capability of each EVE party. Since the formulated problem is nonconvex and hence difficult to solve, it is decomposed into a power minimization subproblem and a rate optimization subproblem and then solved in a nested manner. By leveraging the properties of the objective function, it is proved that through the proposed algorithm the optimal secure multicast capacity can be achieved, with the optimal transmission strategy, i.e., optimal beamforming matrix, optimal artificial noise (AN) covariance matrix, and optimal PS factors, obtained. Moreover, the case of imperfect channel state information (CSI) is considered in which a robust secure multicast scheme is designed by considering the channel uncertainty of EVEs and maximizing the worst-case achievable secure capacity. Simulation results confirm the optimality of the proposed algorithm, show its capability of adapting to the dynamic joining and departure of multicast users and EVE parties, and verify its superiority over existing schemes for both the perfect CSI and imperfect CSI cases.

DOI
10.1109/tvt.2016.2519438

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In this paper, secure transmission with simultaneous information and energy multicast in the presence of multiparty eavesdropper (EVE) collusion is investigated. In the considered system, legal users simultaneously decode information and harvest energy through dynamic power splitting (PS), whereas EVEs form multiple eavesdropping-collusion parties to cooperatively decode signals. A novel optimization problem is formulated to maximize the overall achievable secure multicast capacity while satisfying the maximum transmit power constraint at the transmitter, guaranteeing the minimum harvested energy requirements at each legal user and restricting the wiretapping capability of each EVE party. Since the formulated problem is nonconvex and hence difficult to solve, it is decomposed into a power minimization subproblem and a rate optimization subproblem and then solved in a nested manner. By leveraging the properties of the objective function, it is proved that through the proposed algorithm the optimal secure multicast capacity can be achieved, with the optimal transmission strategy, i.e., optimal beamforming matrix, optimal artificial noise (AN) covariance matrix, and optimal PS factors, obtained. Moreover, the case of imperfect channel state information (CSI) is considered in which a robust secure multicast scheme is designed by considering the channel uncertainty of EVEs and maximizing the worst-case achievable secure capacity. Simulation results confirm the optimality of the proposed algorithm, show its capability of adapting to the dynamic joining and departure of multicast users and EVE parties, and verify its superiority over existing schemes for both the perfect CSI and imperfect CSI cases.
This paper studies the secure beamforming design for simultaneous wireless information and power transfer in a multiple-input single-output broadcast channel with confidential messages and external eavesdroppers, where each receiver adopts the power splitting (PS) scheme to decode information and harvest energy concurrently, and it is also seen as a potential eavesdropper for messages not intended for it. Our objective is to minimize the total transmit power while guaranteeing the individual secrecy rate and energy harvesting constraints at each receiver by jointly optimizing transmit beamforming vectors, artificial noise covariance, and receive PS ratios. Both scenarios of perfect and imperfect channel state information (CSI) at the transmitter are considered. For the perfect CSI case, we propose a two-stage optimization approach to solve the original non-convex problem global optimality, and also provide a low-complexity suboptimal solution based on the particle swarm optimization algorithm. Furthermore, we also extend the above result to the colluding eavesdroppers scenario. For the imperfect CSI case, we propose a worst-case-based robust formulation, where the CSI errors are norm-bounded. With the aid of S-Procedure, we derive the equivalent forms for constraints and then transform the non-convex robust design into a convex optimization problem. Simulation results are finally presented to demonstrate the performance of our proposed schemes.
In this paper, a robust transmission scheme for simultaneous wireless information and power transfer (SWIPT) in the presence of an eavesdropper is proposed. With imperfect channel state information (CSI) at the transmitter, the optimal transmit covariance matrix is obtained based on the maximization of the worst case harvested energy for the energy receiver (ER) while guaranteeing the achievable secrecy rate constraint for the information receiver (IR). To solve such a challenging nonconvex problem, in this paper, a two-stage optimization approach is proposed. In the first stage, the original problem is transformed into a robust design problem, which can be further converted into a convex semidefinite program (SDP) problem by using S-procedure as a tool. In the second stage, the optimal transmit covariance matrix is obtained via the aid of one-dimensional search algorithm. Finally, simulation results are provided to illustrate the robustness and effectiveness of the proposed method.
This paper considers transmit covariance matrix design for secrecy rate maximization problem in a multiple-input single-output (MISO) multicasting simultaneous wireless information and power transfer (SWIPT) system. In order to enhance the performance of the system, artificial noise (AN) is added to the transmit signal in the design for the following purposes: to reduce the received signal-to-noise ratio (SNR) at the eavesdroppers and increase the harvested energy. We assume that all the channel-state-information (CSI) is perfectly known at the transmitter and all legitimate users are capable of simultaneously receiving information and harvesting energy. In addition, all the eavesdroppers are passive and they can harvest energy only when they are not intercepting or eavesdropping the messages intended for the legitimate users. The original secrecy rate maximization problem is not convex in terms of transmit and artificial covariance matrices as well as the power splitting (PS) ratio. In order to circumvent this non-convexity issue, we exploit the \emph{Charnes-Cooper} Transformation and semidefinite relaxation (SDR) to convert this original problem into a convex one. However, this convex problem does not always yield the rank-one transmit and AN covariance matrices to obtain the solution of the original problem. Therefore, we analyze the optimal conditions and utilize a Gaussian randomization (GR) method to construct the rank-one solutions from the non-rank one results. Simulation results have been provided to demonstrate the performance of the proposed transmit covariance matrices design for MISO multicasting SWIPT system.
In this paper, we consider a multiuser multiple-input multiple-output (MIMO) downlink communication system with simultaneous wireless information and power transfer (SWIPT). In particular, we focus on a realistic and efficient multi-receiver multi-eavesdropper MIMO SWIPT system, in which the channel state information (CSI) of each legitimate receiver and energy receiver (i.e., potential eavesdropper) is partially known to the transmitter. Based on this, we propose a robust artificial noise (AN)-aided secure transmission scheme for the system, where the channel uncertainties are modeled by the worst-case model. In the proposed scheme, we aim to maximize the worst-case achievable secrecy rate under the transmit power constraint and the energy harvesting (EH) constraint, by jointly optimizing the transmit precoding matrix and the AN covariance matrix. We utilize the S-Procedure and Taylor series approximation to transform the non-convex problem. Then, we apply the interior point method to tackle the transformed convex problem, obtaining the approximate optimal matrices and the corresponding maximum worst-case secrecy rate. Simulation results show that our proposed scheme achieves significant performance improvements in terms of convergence and the worst-case achievable secrecy rate.
Considering simultaneous wireless information and power transfer (SWIPT), we investigate cooperative-jamming (CJ) aided robust secure transmission design in multiple-input-single-output channels, where a cooperative jammer introduces jamming interferences and assists a source to supply wireless power for both an energy receiver and a legitimate destination. The destination employs a power splitting (PS) scheme to split the received signals for both information decoding and energy harvesting (EH). Compared with conventional transmission without SWIPT, the transmission with SWIPT should satisfy additional worst-case EH constraints. Furthermore, the PS scheme introduces an additional multiplicative optimization variable, i.e., the PS factor. Our objective is to maximize worst-case secrecy rate under transmit power constraints and worst-case EH constraints. We propose to decouple the problem into three optimization problems and employ alternating optimization algorithm to obtain the locally optimal solution. For the optimization of transmit covariance matrices and PS factor, we propose to employ the S-procedure and its extension to reformulate it as a convex semidefinite programming. It is shown through the simulation results that our proposed CJ aided robust secure transmission scheme outperforms the robust direct transmission scheme without CJ and the CJ aided non-robust scheme.
This paper considers transmit covariance matrix design for secrecy rate maximization problem in a multiple-input single-output (MISO) multicasting simultaneous wireless information and power transfer (SWIPT) system. In order to enhance the performance of the system, artificial noise (AN) is added to the transmit signal in the design for the following purposes: to reduce the received signal-to-noise ratio (SNR) at the eavesdroppers and increase the harvested energy. We assume that all the channel-state-information (CSI) is perfectly known at the transmitter and all legitimate users are capable of simultaneously receiving information and harvesting energy. In addition, all the eavesdroppers are passive and they can harvest energy only when they are not intercepting or eavesdropping the messages intended for the legitimate users. The original secrecy rate maximization problem is not convex in terms of transmit and artificial covariance matrices as well as the power splitting (PS) ratio. In order to circumvent this non-convexity issue, we exploit the Charnes-Cooper Transformation and semidefinite relaxation (SDR) to convert this original problem into a convex one. However, this convex problem does not always yield the rank-one transmit and AN covariance matrices to obtain the solution of the original problem. Therefore, we analyze the optimal conditions and utilize a Gaussian randomization (GR) method to construct the rank-one solutions from the non-rank one results. Simulation results have been provided to demonstrate the performance of the proposed transmit covariance matrices design for MISO multicasting SWIPT system.