The problems of energy harvesting in wireless com- munication systems have recently drawn much attention. In this paper, we focus on the investigation of energy harvesting maximization (EHM) in the important secrecy multi-input multi- output (MIMO) systems where little research has been done due to their complexity. Particularly, this paper studies the resource allocation strategies in MIMO wiretap channels, wherein we attempt to maximize the harvested energy by one or multiple multi-antenna energy receivers (ERs) (potential eavesdropper) while guaranteeing the secure communication for the multi- antenna information receiver (IR). Two types of IR, with and without the capability to cancel the interference from energy signals, are taken into account. In the scenario of single energy receiver (ER), we consider the joint design of the transmis- sion information and energy covariances for EHM. Both of the optimization problems for the two types of IR are non- convex, and appear to be difficult. To circumvent them, the combination of first order Taylor approximation and sequential convex optimization approach is proposed. Then, we extend our attention to the scenario with multiple ERs, where the artificial noise (AN) aided weighted sum-energy harvesting maximization (WS-EHM) problem is considered. Other than the approaches adopted in solving the EHM problems, an algorithm conducts in an alternating fashion is proposed to handle this problem. In particular, we first perform a judicious transformation of the WS- EHM problem. Then, a block Gauss-Seidel (GS) algorithm based on logarithmic barrier method and gradient projection (GP) is derived to obtain the optimal solution of the reformulation by solving convex problems alternately. Furthermore, the resulting block GS method is proven to converge to a Karush-Kuhn-Tucker (KKT) point of the original WS-EHM problem...
The problems of energy harvesting in wireless com- munication systems have recently drawn much attention. In this paper, we focus on the investigation of energy harvesting maximization (EHM) in the important secrecy multi-input multi- output (MIMO) systems where little research has been done due to their complexity. Particularly, this paper studies the resource allocation strategies in MIMO wiretap channels, wherein we attempt to maximize the harvested energy by one or multiple multi-antenna energy receivers (ERs) (potential eavesdropper) while guaranteeing the secure communication for the multi- antenna information receiver (IR). Two types of IR, with and without the capability to cancel the interference from energy signals, are taken into account. In the scenario of single energy receiver (ER), we consider the joint design of the transmis- sion information and energy covariances for EHM. Both of the optimization problems for the two types of IR are non- convex, and appear to be difficult. To circumvent them, the combination of first order Taylor approximation and sequential convex optimization approach is proposed. Then, we extend our attention to the scenario with multiple ERs, where the artificial noise (AN) aided weighted sum-energy harvesting maximization (WS-EHM) problem is considered. Other than the approaches adopted in solving the EHM problems, an algorithm conducts in an alternating fashion is proposed to handle this problem. In particular, we first perform a judicious transformation of the WS- EHM problem. Then, a block Gauss-Seidel (GS) algorithm based on logarithmic barrier method and gradient projection (GP) is derived to obtain the optimal solution of the reformulation by solving convex problems alternately. Furthermore, the resulting block GS method is proven to converge to a Karush-Kuhn-Tucker (KKT) point of the original WS-EHM problem...
Consider a multiple-input multiple-output (MIMO) beamforming system, where a multi-antenna base station transmits information and energy simultaneously to a multi-antenna information receiver (IR) and a number of multi-antenna energy receivers (ERs). The ERs are assumed to possess dual functionality such that they can also decode information. This gives rise to the physical layer security issue of providing confidential information to the IR while ensuring efficient energy harvesting for the ERs. Hence, we employ an artificial noise (AN)-aided secrecy approach at the transmitter, where the AN improves the security by interfering with the information decoding at the ERs while, at the same time, providing them with wireless energy to harvest. In this paper, we address the problem of jointly optimizing the beamforming matrix for the IR and the covariance matrix of the AN such that the secrecy rate is maximized subject to energy harvesting constraints and a total power constraint. The corresponding optimization problem is a difference of convex functions (DC) programming problem, which is generally non-convex. Nevertheless, we propose an alternating optimization (AO) strategy to tackle this problem for the cases of a square and a non-square IR channel. The performance of the proposed algorithms is demonstrated by simulation results.
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 paper considers a basic MIMO information-energy broadcast system, where a multi-antenna transmitter transmits information and energy simultaneously to a multi-antenna information receiver and a dual-functional multi-antenna energy receiver which is also capable of decoding information. Due to the open nature of wireless medium and the dual purpose of information and energy transmission, secure information transmission while ensuring efficient energy harvesting is a critical issue for such a broadcast system. Providing that physical layer security techniques are adopted for secure transmission, we study beamforming design to maximize the achievable secrecy rate subject to a total power constraint and an energy harvesting constraint. First, based on semidefinite relaxation, we propose global optimal solutions to the secrecy rate maximization (SRM) problem in the single-stream case and a specific full-stream case. Then, we propose inexact block coordinate descent (IBCD) algorithm to tackle the SRM problem of general case with arbitrary number of streams. We prove that the IBCD algorithm can monotonically converge to a Karush-Kuhn-Tucker (KKT) solution to the SRM problem. Furthermore, we extend the IBCD algorithm to the joint beamforming and artificial noise design problem. Finally, simulations are performed to validate the effectiveness of the proposed beamforming algorithms.
Consider a wireless scenario in which a multi-antenna transmitter wants to send a confidential message to a single-antenna information receiver (IR) while transferring wireless energy to a number of multi-antenna energy receivers (ERs). In order to keep the ERs from retrieving the confidential message, an artificial noise (AN)-aided physical-layer secrecy approach is employed at the transmitter. The AN has dual purpose: First, it can interfere with the ERs' information receptions and thus help improve security. Secondly, it provides wireless energy for the ERs to harvest. Assuming imperfect channel state information at the transmitter, we jointly optimize the co-variances of confidential information and AN such that the secrecy rate at the IR is maximized, while each ER receives a prescribed amount of wireless energy. Although this secrecy-rate maximization problem is non-convex, we show that it can be handled by solving a sequence of convex optimization problems. Numerical results are provided to demonstrate the efficacy of the proposed design.
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 modern era, wireless networks continue to expand worldwide and play a pronounced role in daily life. However, wireless communication has become vulnerable to intelligent invaders in the network. Hence, the security aspects of wireless communication have been the center of modern-era research. Physical Layer Security (PLS) schemes have been proven to be very effective against potential eavesdroppers thanks to their low-complexity nature compared to encryption techniques. Therefore, this thesis is dedicated to the physical layer security of multiple-input multiple-output (MIMO) systems. More specifically, we provide efficient optimization techniques to maximize the achievable secrecy rate of MIMO Wiretap channels (WTC). First, we present novel numerical approaches to finding the achievable secrecy rate of the MIMO WTC subject to multiple power constraints. There is a challenge in deriving computationally efficient solutions to the secrecy rate maximization problem because the secrecy rate is expressed as a difference of convex functions (DC) of the transmit covariance matrix. However, to deal with it, we provided local optimization methods, such as the accelerated DC algorithm (ADCA) and a numerical method named partial best response algorithm (PBRA) based on the concave-convex equivalent reformulation of the secrecy capacity problem. Along with it, we provide a convex reformulation for degraded channels. Interestingly, our experiments have shown that the local optimization methods achieve global optimality for the secrecy rate maximization problem under power constraints. In this regard, we provide analytical proof with Karush-Kuhn-Tucker (KKT) conditions to validate our observation. Motivated by our finding, we propose an accelerated gradient projection algorithm with adaptive momentum parameters (AGPnc) that directly solves the secrecy rate maximization problem rather than the equivalent convex-concave form. We then extend the scope of our research of physical layer security enhancement by considering the Intelligent reflecting surface (IRS)-aided MIMO WTC system, as the IRS has achieved enormous attention recently for enhancement in spectral efficiency. In this context, we provided a numerical solution for the problem of maximizing the achievable secrecy rate of the considered system by jointly optimizing the input covariance matrix and the IRS phase shifts. The proposed is an iterative method based on block successive maximization (BSM) where each iteration is done in closed form. We then extend our research further toward maximizing the secrecy energy efficiency (SEE) of the (IRS)-aided MIMO WTC system. In relation to the SEE maximization problem, we provide a low-complexity based local optimization method. The challenge of this considered problem is the coupling between design variables in both the objective and the constraint. The main idea of the proposed method is to apply a penalty dual decomposition based alternating gradient projection (PDDAPG) method to obtain an efficient solution. Thanks to the low complexity nature of proposed methods, these methods can scale favourably with large-scale wireless systems, which has been an inherent issue of the existing studies. In this way, the contributions of this thesis will significantly impact future research in the field of physical layer security, especially toward the future generation of wireless communication.