This letter studies transmit optimization in three-node multiple-input-multiple-output wiretap channels with simultaneous wireless information and power transfer. In particular, we address the transmit covariance optimization for the energy-harvesting maximization (EHM) problem under the constraint of secrecy rate and transmit power. The problem is nonconvex and hard to tackle. To circumvent it, we first resort to the Taylor series expansion to convert the problem into a convex optimization problem. Then, based on the analysis of its dual problem, an iterative algorithm, which includes a semiclosed-form solution, is proposed to handle this EHM problem. The scenario of imperfect channel state information of an energy receiver (the potential eavesdropper) is also considered. In this scenario, a Bernstein-type inequality-based iterative algorithm is proposed to deal with the challenging outage-constrained EHM problem. Simulation results demonstrate the efficiency of our proposed designs.
This letter studies transmit optimization in three-node multiple-input-multiple-output wiretap channels with simultaneous wireless information and power transfer. In particular, we address the transmit covariance optimization for the energy-harvesting maximization (EHM) problem under the constraint of secrecy rate and transmit power. The problem is nonconvex and hard to tackle. To circumvent it, we first resort to the Taylor series expansion to convert the problem into a convex optimization problem. Then, based on the analysis of its dual problem, an iterative algorithm, which includes a semiclosed-form solution, is proposed to handle this EHM problem. The scenario of imperfect channel state information of an energy receiver (the potential eavesdropper) is also considered. In this scenario, a Bernstein-type inequality-based iterative algorithm is proposed to deal with the challenging outage-constrained EHM problem. Simulation results demonstrate the efficiency of our proposed designs.
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.
This paper studies the secure transmission issue for simultaneous wireless information and power transfer in a multiuser multiple-input-multiple-output system with multiple external eavesdroppers, where the transmitter broadcasts independent confidential messages to different legitimate receivers. Each receiver can be seen as an internal eavesdropper intended by other receivers. Our objective is to achieve the robust beamforming design under imperfect channel state information, in which the total transmission power is minimized with constraints on the achievable secrecy rate and the energy harvesting. Since the problem is nonconvex, we propose a two-level optimization scheme. For the inner problem, we investigate two conservative relaxation approaches, large-deviation inequality and Bernstein-type inequality (BTI), to reformulate the outage secrecy rate constraints into some convex ones, yielding a convex optimization by semidefinite programming (SDP) relaxation. For the outer problem, it is a K-variable optimization problem, which can be solved via the novel line-dimensional-like search method. Moreover, we characterize the rank profile of SDP relaxed solution for these two approaches. Specifically, the optimal solution is proved to be rank-one. Numerical results are provided to verify the performance of the proposed algorithms, where the LDI-based scheme outperforms the BTI-based scheme in terms of energy efficiency.
This paper consider a multiple-input multiple- output (MIMO) downlink system with simultaneous wireless information and power transfer (SWIPT). In this system, we provide secure communication by considering all non-linear energy receivers (ERs) as the potential eavesdroppers. We aim to design an optimal covariance matrix of the transmit signal based on the proposed outage-constrained secrecy rate maximization (OC-SRM) problem, where the proposed system keeps the probability of the secrecy rate outage and the harvested energy outage caused by uncertainties regarding the channel state information (CSI) below the given thresholds. The proposed maximization problem is non-convex and unlikely to be efficiently computable. To surmount the non-convexity of the proposed OC-SRM problem, we resort several transformations and approximations. In the partial channel uncertainty (PCU) model, we introduce auxiliary variables to transfer the Shannon capacity expressions into easy handled forms based on the concept of Fenchel conjugate arguments. As a compromise, we employ three safe approximation approaches to find the upper bounds of the probabilistic constraints. Moveover, an alternating optimization (AO) algorithm is proposed to solve the reformulated problem. The performance of the proposed design is efficiently validated by numerical results in our paper.
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.
This paper considers simultaneous wireless information apower transfer (SWIPT) in a multiple-input multiple-output (MIMO) wiretapnd power transfer (SWIPT) in a multiple-input multiple-output (MIMO) wiretap channel with energy harvesting receivers. The main objective is to keep the probability of the legitimate user's achievable secrecy rate outage as well as the energy receivers' harvested energy outage as caused by CSI uncertainties below given thresholds. This probabilistic-constrained secrecy rate maximization problem presents a significant analytical and computational challenge since any closed-form for the probabilistic constraints with log-det functions is intractable. In this paper, we address this challenging issue using codeviation inequalitiesnvex restrictions. In particular, we derive decomposition-based large deviation inequalities to transform the probabilistic constraints into second-order cone (SOC) constraints which are easier to handle. Then we show that a robust safe solution can be obtained through solving two convex sub-problems in an alternating fashion.
In this paper, we study the multiple-input multiple-output (MIMO) wiretap channel performing simultaneous wireless information and power transfer (SWIPT), where there are a multiple-antenna base station (BS), a multiple-antenna information decoding (ID) user, and a multiple-antenna energy harvesting (EH) user. The messages intended to the ID user need to be kept confidential to the EH user. The channels between BS and users are modeled as fading channels with different spatial correlations and line-of-sight (LoS) components. We assume that only statistical channel state information (CSI) is available at the BS. Our objective is to design the optimal transmit covariance matrix at the BS for maximizing the ergodic secrecy rate subject to the BS power constraint and the harvested energy requirement for the EH user. To this end, we present an approximation for the ergodic secrecy rate by using large dimensional random matrix theory and the method of Taylor series expansion. Based on this, an iterative algorithm is proposed to find the transmit covariance matrix. Simulation results are provided to verify the accuracy of the approximation and the performance of the design.