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IEEE Communications Letters

Robust Energy Efficiency Optimization for Secure MIMO SWIPT Systems With Non-Linear EH Model

Hehao Niu, Daoxing Guo, Yuzhen Huang, Bangning Zhang

Published Sep 13, 2017• 37 citations
Publisher

Abstract

In this letter, we investigate the energy efficiency optimization for simultaneous wireless information and power transfer in multiple-input-multiple-output wiretapping systems, which consist of one transmitter, multiple desired receivers (DRs), and multiple eavesdroppers. Specifically, considering non-linear energy harvesting model, we aim to maximize the ratio of the total harvested power at the DRs to the total power consumption at the transmitter, e.g., the harvested power efficiency, by jointly designing the transmit precoding matrix, the artificial noise covariance and the power splitting ratios, subject to the secrecy rate constraint. To solve the formulated highly non-convex non-linear single-ratio problem, we propose a successive convex approximation and constrained concave convex procedure-based iterative algorithm. Finally, our proposed algorithm is validated by some representative numerical results.

DOI
10.1109/lcomm.2017.2751603

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In this letter, we investigate the energy efficiency optimization for simultaneous wireless information and power transfer in multiple-input-multiple-output wiretapping systems, which consist of one transmitter, multiple desired receivers (DRs), and multiple eavesdroppers. Specifically, considering non-linear energy harvesting model, we aim to maximize the ratio of the total harvested power at the DRs to the total power consumption at the transmitter, e.g., the harvested power efficiency, by jointly designing the transmit precoding matrix, the artificial noise covariance and the power splitting ratios, subject to the secrecy rate constraint. To solve the formulated highly non-convex non-linear single-ratio problem, we propose a successive convex approximation and constrained concave convex procedure-based iterative algorithm. Finally, our proposed algorithm is validated by some representative numerical results.
In this letter, we consider a multiple-input-multiple-output (MIMO) downlink system with simultaneous wireless information and power transfer (SWIPT), where the information sent to the desired receiver (DR) may be wiretapped by the malicious energy harvesting (EH) receivers (potential eavesdroppers). Assuming the channel state information (CSI) of the EH receivers is not perfectly known at the transmitter, we aim to maximize the worst-case secrecy rate by jointly designing the precoding matrix, AN covariance matrix and power splsplitting ratio, under the constraints of total transmit power and harvested energyitting ratio, under the constraints of total transmit power and harvested energy at receivers. The formulated problem is non-convex and semi-infinite, which is hard to tackle. To solve it, we first deal with the non-convexity caused by CSI errors with the aid of the S-Procedure, and then employ the Taylor series approximation techniques to transfer the nonconvex problem into a convex optimization problem. Based on that, we propose an iterative algorithm with proved convergence to solve the original problem. Simulation results are finconvex optimization problem. Based on that, we propose an iterative algorithmally provided to show the effectiveness of our proposed robust transmit design.
In this paper, we study the secure transmission scheme in multiple-input multiple-output (MIMO) communication systems with simultaneous wireless information and power transfer (SWIPT), in which both power splitting (PS) receiver and artificial noise (AN) are taken into account. We aim to maximize the achievable secrecy rate at the desired receiver by jointly designing precoding matrix, AN covariance matrix and PS ratio at the desired receiver, under the total transmit power constraint and the minimum energy harvesting constraints at all the receivers. The formulated optimization problem is nonconvex and hard to tackle. Then, by transforming this problem into a canonical DC (difference of convex function) programming, we propose an iterative method to solve this problem, and analyze the convergence of the proposed algorithm. Simulation results are provided to show the efficiency of our proposed scheme.
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.
In this paper, we investigate the secure transmission in multiple-input multiple-output (MIMO) communication systems with simultaneous wireless information and power transfer (SWIPT), where the energy receivers (ERs) may eavesdrop the confidential information send to the desired receiver (DR). By taking into account both artificial noise (AN) and power splitting (PS), we aim to maximize the secrecy rate at the DR by jointly designing the precoding matrix, AN covariance matrix and PS ratio at the desired receiver, under the transmit power constraint and the energy harvesting (EH) constraints at all the receivers. To solve the formulated non-convex problem, we propose an alternating optimization (AO) algorithm through a judicious reformulation, wherein the problem is handled by solving a series of convex problems in an alternating method. Finally, numerical results are provided to validate our proposed algorithm.
In this paper, we consider a multiple-input-multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system, which consists of a transmitter, a full-duplex (FD) desired receiver (DR) and multiple energy receivers (ERs), i.e., potential eavesdroppers. The transmitter and DR can send artificial noise (AN) and jamming signals to interfere and carry the energy to the ERs, respectively. Considering the imperfect channel state information (CSI) related to ERs, we aim to maximize the worst-case secrecy rate by jointly optimize the information covariance matrix, AN covariance matrix at the transmitter as well as the jamming covariance matrix at the DR, subject to their individual transmit power constraints and the minimum required power transferred to the ERs. In order to solve this non-convex and semi-infinite problem, we employ the S-procedure and propose a successive convex approximation (SCA) scheme based on Taylor series approximation, which can be efficiently solved by the proposed iterative method. Simulation results are finally presented to show the effectiveness of the new robust secrecy design.
This letter investigates a multiple-input single-output secrecy channel, where the legitimate user and the eavesdroppers employ a power splitting scheme for information decoding and energy harvesting (EH), simultaneously. An artificial noise is embedded to the information bearing signal to interfere the eavesdroppers and to harvest power by all receivers (i.e., legitimate user and eavesdroppers). We consider the robust secrecy rate maximization problem subject to the transmit power and the EH constraints by incorporating the norm-bounded channel uncertainty. This robust optimization problem is not convex, and thus, it can be reformulated as a convex one along with matrix transformations and convex conic optimization techniques, which is iteratively solved based on constrained concave-convex procedure-based algorithm. Finally, numerical results are provided to validate our proposed algorithm.