In this paper, we study the physical layer security in a two-tier heterogeneous wireless sensor networks (HWSN) based on simultaneous wireless information and power transfer (SWIPT) approach. Joint optimization problem of the transmit beamforming vector at the macrocell and femtocell, the artificial noise (AN) vector, and the power splitting ratio is modeled to maximize the minimal secrecy capacity of the wiretapped macrocell sensor nodes while considering the fairness among multiple M-SNs. In order to reduce the performance loss of the rank relaxation, we apply successive convex approximation (SCA) technique, first-order Taylor series expansion and sequential parametric convex approximation approach to transform the max-min program to a second order cone programming (SOCP) problem to iterate to a near-optimal solution. In addition, a novel SCA-based iterative algorithm is proposed while its convergence property is proved. The simulation shows that our SCA-based method outperforms the conventional methods.
In this paper, we study the physical layer security in a two-tier heterogeneous wireless sensor networks (HWSN) based on simultaneous wireless information and power transfer (SWIPT) approach. Joint optimization problem of the transmit beamforming vector at the macrocell and femtocell, the artificial noise (AN) vector, and the power splitting ratio is modeled to maximize the minimal secrecy capacity of the wiretapped macrocell sensor nodes while considering the fairness among multiple M-SNs. In order to reduce the performance loss of the rank relaxation, we apply successive convex approximation (SCA) technique, first-order Taylor series expansion and sequential parametric convex approximation approach to transform the max-min program to a second order cone programming (SOCP) problem to iterate to a near-optimal solution. In addition, a novel SCA-based iterative algorithm is proposed while its convergence property is proved. The simulation shows that our SCA-based method outperforms the conventional methods.
In this paper, we investigate the physical layer security in a two-tier heterogeneous wireless sensor network (HWSN) depending on simultaneous wireless information and power transfer (SWIPT) approach for multiuser multiple-input multiple-output wiretap channels with artificial noise (AN) transmission, where a more general system framework of HWSN only includes a macrocell and a femtocell. For the sake of implementing security enhancement and green communications, the joint optimization problem of the secure beamforming vector at the macrocell and femtocell, the AN vector, and the power splitting ratio is modeled to maximize the minimal secrecy capacity of the wiretapped macrocell sensor nodes (M-SNs) while considering the fairness among multiple M-SNs. To reduce the performance loss of the rank relaxation from the SDR technique while solving the non-convex max–min program, we apply successive convex approximation (SCA) technique, first-order Taylor series expansion and sequential parametric convex approximation (SPCA) approach to transform the max–min program to a second order cone programming (SOCP) problem to iterate to a near-optimal solution. In addition, we propose a novel SCA-SPCA-based iterative algorithm while its convergence property is proved. The simulation shows that our SCA-SPCA-based method outperforms the conventional methods.
In this paper, we investigate secure simultaneous wireless information and power transfer (SWIPT) in a two-tier downlink heterogeneous network (HetNet), wherein the ambient interference signals are exploited for both secure communications and wireless energy harvesting. We assume one macrocell base station (MBS) and several femtocell base stations (FBSs) simultaneously send information to their macrocell users (MUs) and femtocell users, respectively. Meanwhile, the FBSs also transfer energy to some energy receivers, who act as the potential eavesdroppers and are able to wiretap the confidential messages to one MU via the cross-tier interference links. Exploiting interference in the considered HetNet, we jointly optimize the beamforming vectors and artificial noise of the MBS and the FBSs to maximize the secrecy rate of the eavesdropped MU under the quality-of-service, energy harvesting, and transmit power constraints at relevant receivers/transmitters. In particular, we first investigate the ideal case with perfect eavesdropper's channel state information (ECSI) and the optimization problem turns out to be nonconvex. By using the tools of semidefinite relaxation (SDR) and one-dimensional line search, we successfully transfer the original problem into a more tractable two-stage problem to obtain the optimal solution. Furthermore, we extend our study to the imperfect ECSI case, where the worst-case based solution is obtained with the aid of SDR and successive convex approximation. Simulation results demonstrate the effectiveness of the proposed algorithms and also bring useful insights into the design of secure SWIPT in the presence of interference.
In this letter, secure beamforming designs are investigated in a multiple-input multiple-output secrecy channels with simultaneous wireless information and power transfer. In order to achieve fairness among different multiple energy harvesting receivers, the minimum harvested energy is maximized under the secrecy rate requirements. In particular, in order to reduce the computational complexity of the semidefinite programming problem, a successive convex approximation (SCA) iterative algorithm is proposed in the perfect channel state information (CSI) case to obtain a near-optimal rank-one solution. Moreover, the original problem is extended to the imperfect CSI case by incorporating a norm-bounded error model, where an SCA-based iterative algorithm is also proposed. Simulation results reveal that the SCA-based iterative algorithm achieves the same performance as the semidefinite relaxation method with reduced complexity.
In this paper, we investigate an artificial-noise-aided secure beamforming design for simultaneous wireless information and power transfer in a two-tier downlink heterogeneous cellular network, in which each energy receiver in a femtocell is seen as a potential eavesdropper to wiretap the confidential message intended for the information receiver. Our design objective is to maximize the secrecy rate at the information receiver, while satisfying the signal-to-interference-plus-noise ratio requirement of each macrouser and the energy harvesting and transmit power constraints. Both the scenarios of perfect and imperfect channel state information (CSI) are considered. With perfect CSI, the formulated optimization problem constitutes a difference of a convex function programming problem, which is hard to directly solve. To tackle this challenge, we transform it into a series of semidefinite programs by using successive convex approximation, and an iterative algorithm is proposed to arrive at a provably convergent solution. With imperfect CSI, we address robust secure beamforming relying on the worst-case design philosophy. To circumvent this predicament, we resort to the S-procedure to reformulate the robust quadratic matrix inequality (QMI) constraints and then obtain the linear matrix inequality representations for these QMIs. Numerical results are finally presented to demonstrate the performance of our proposed schemes in improving the secrecy rate of heterogeneous cellular networks.
In this paper, we pioneer the study of physical-layer security in heterogeneous networks (HetNets). We investigate secure communications in a two-tier downlink HetNet, which comprises one macrocell and several femtocells. Each cell has multiple users and an eavesdropper attempts to wiretap the intended macrocell user. First, we consider an orthogonal spectrum allocation strategy to eliminate co-channel interference, and propose the secrecy transmit beamforming only operating in the macrocell (STB-OM) as a partial solution for secure communication in HetNet. Next, we consider a secrecy-oriented non-orthogonal spectrum allocation strategy and propose two cooperative STBs which rely on the collaboration amongst the macrocell base station (MBS) and the adjacent femtocell base stations (FBSs). Our first cooperative STB is the STB sequentially operating in the macrocell and femtocells (STB-SMF), where the cooperative FBSs individually design their STB matrices and then feed their performance metrics to the MBS for guiding the STB in the macrocell. Aiming to improve the performance of STB-SMF, we further propose the STB jointly designed in the macrocell and femtocells (STB-JMF), where all cooperative FBSs feed channel state information to the MBS for designing the joint STB. Unlike conventional STBs conceived for broadcasting or interference channels, the three proposed STB schemes all entail relatively sophisticated optimizations due to QoS constraints of the legitimate users. To efficiently use these STB schemes, the original optimization problems are reformulated and convex optimization techniques, such as second-order cone programming and semidefinite programming, are invoked to obtain the optimal solutions. Numerical results demonstrate that the proposed STB schemes are highly effective in improving the secrecy rate performance of HetNet.
In this article, we study a robust beamforming design for multiuser multiple-input–multiple-output secrecy networks with simultaneous wireless information and power transfer (SWIPT). In this system, an access point, multiple Internet-of-Things (IoT) devices under the nonlinear energy harvesting (EH) model with a help of one cooperative jammer (CJ). We employ artificial noise (AN) generation to facilitate efficient wireless energy transfer and secure transmission. To achieve EH fairness, we aim to maximize the minimum harvested energy among users subject to secrecy rate constraint and total transmit power constraint in the presence of channel estimation errors. By incorporating a norm-bounded channel uncertainty model, the original robust problem is transformed into a two-layer optimization problem, where the inner layer problem is reformulated as semidefinite programming (SDP) and the outer layer problem is solved by a one-dimensional (1-D) line search algorithm. In addition, in order to reduce computational complexity, we propose an algorithm based on sequential parametric convex approximation (SPCA). Finally, simulation results show that the proposed SPCA method achieves the same performance as the two-layer algorithm with much lower complexity.