Weidong Mei

Improving Physical Layer Security Using UAV-Enabled Mobile Relaying

Mobile relaying has aroused great interest in wireless communications recently, thanks to the rapid development and evolvement of unmanned aerial vehicles. This letter establishes the utility of mobile relaying in facilitating secure wireless communications. In particular, we consider transmit optimization in a four-node (source, destination, buffer-aided mobile relay, and eavesdropper) channel setup, wherein we aim at maximizing the secrecy rate of this system. However, the secrecy rate maximization problem is nonconvex and intractable to solve. To circumvent the nonconvexity, we exploit the difference-of-concave (DC) program to develop an iterative algorithm, which is proven to have Karush–Kuhn–Tucker point convergence guarantee. The algorithm conducts a water-filling-based solution in each DC iteration, and thus is computationally efficient to implement. In addition, for a given special case, we show that each DC iteration could yield a closed-form solution, which further reduces the computational complexity. Simulation results demonstrate that the proposed mobile relaying scheme could significantly outperform the static relaying scheme in terms of secrecy enhancement.

Artificial Noise Aided Energy Efficiency Optimization in MIMOME System With SWIPT

This letter studies energy efficient optimization problem for a general multiple-input multiple-output multiple-eavesdropper system, in which the legitimate receiver exploits a power splitting (PS) scheme for decoding information and harvesting energy simultaneously. In order to cripple the eavesdroppers’ reception and provide energy for the legitimate receiver to harvest, the transmitter also emits artificial noise (AN) in the transmission. Our interest in this letter lies in the joint design of the transmit covariance matrices and PS ratio, such that the secrecy energy efficiency (SEE) is maximized. The SEE maximization problem is a nonconvex fractional problem. To handle it, we resort to the fractional programming to transform its objective function into a more tractable subtractive form. Furthermore, we show that the resultant problem is primal decomposable by recasting it as an equivalent form amenable to alternating optimization. Numerical results indicate that incorporating AN and PS is beneficial to the SEE enhancement.

Robust artificial-noise aided transmit design for multi-user MISO systems with integrated services

This paper considers an optimal artificial noise (AN)-aided transmit design for multi-user MISO systems in the eyes of service integration. Specifically, two sorts of services are combined and served simultaneously: one multicast message intended for all receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from all the unauthorized receivers. This paper considers a general case of imperfect channel state information (CSI), aiming at a joint and robust design of the input covariances for the multicast message, confidential message and AN, such that the worst-case secrecy rate region is maximized subject to the sum power constraint. To this end, we reveal its hidden convexity and transform the original worst-case robust secrecy rate maximization (SRM) problem into a sequence of semidefinite programming. Numerical results are presented to show the efficacy of our proposed method.

On Artificial-Noise-Aided Transmit Design for Multiuser MISO Systems With Integrated Services

This paper considers an optimal artificial noise (AN)-aided transmit design for multi-user MISO systems in the eyes of service integration. Specifically, two sorts of services are combined and served simultaneously: one multicast message intended for all receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from all the unauthorized receivers. This paper considers a general case of imperfect channel state information (CSI), aiming at a joint and robust design of the input covariances for the multicast message, confidential message and AN, such that the worst-case secrecy rate region is maximized subject to the sum power constraint. To this end, we reveal its hidden convexity and transform the original worst-case robust secrecy rate maximization (SRM) problem into a sequence of semidefinite programming. Numerical results are presented to show the efficacy of our proposed method.

Robust Sum Secrecy Rate Optimization for MISO Systems with Device-to-Device Communication

This paper considers a cellular multiple-input single- output (MISO) system overheard by multiple eavesdroppers, in the presence of one pair of device- to-device (D2D) nodes working as an underlay to the cellular network. A novel eavesdropping scenario is studied in this paper, where the eavesdroppers intend to simultaneously overhear the cellular communication as well as the D2D communication. Assuming imperfect channel state information (CSI) at the transmitter, our goal is to design the input covariance matrix of confidential message such that the worst-case sum secrecy rate is maximized, while satisfying the quality of service (QoS) requirement in the D2D communication. Although this worst-case sum secrecy rate maximization (SSRM) problem is non-convex, we show that it can be handled by solving a sequence of semidefinite programming (SDP) problems. Moreover, we give complexity analysis of our proposed optimization method and prove that transmit beamforming is an optimal strategy for the confidential message transmission. Numerical results are presented to verify the efficacy of our proposed method.

Sum secrecy rate optimization for MIMOME wiretap channel with artificial noise and D2D underlay communication

This paper considers a cellular multiple-input multiple-output multiple-eavesdropper (MIMOME) channel, with a pair of single-antenna device-to-device (D2D) nodes working as an underlay. A novel eavesdropping scenario is studied in this paper, where the eavesdroppers intend to simultaneously overhear the cellular communication and the D2D communication. Our goal is to jointly optimize the covariance of confidential message and artificial noise, as well as the transmit power at the D2D transmitter, such that the sum secrecy rate is maximized, while satisfying the quality of service constraint on the D2D communication. This sum secrecy rate maximization (SSRM) problem is non-convex by nature. To handle it, an equivalent reformulation of this SSRM problem is introduced, wherein the resulting problem becomes primal decomposable and thus can be iteratively solved using an alternating optimization (AO) algorithm. Also, we prove that the AO algorithm is bound to converge to a stationary point of the primal SSRM problem. Furthermore, we extend the SSRM problem to a more general case with multiple pairs of D2D nodes. Again, the resulting problem is shown to be solvable by the AO algorithm, with provable convergence to the stationary point. Finally, numerical results are presented to verify the efficacy of our proposed method.

Biobjective transmitter optimization for service integration in MIMO Gaussian broadcast channel

This paper considers a two-receiver multiple-input multiple-output (MIMO) Gaussian broadcast channel model with integrated services. Specifically, two sorts of service messages are combined and served simultaneously: one multicast message intended for both receivers and one confidential message intended for only one receiver and kept perfectly secure from the other receiver. Our goal is to jointly design the transmit covariances of the multicast message and confidential message, such that the secrecy capacity region is maximized. This maximization problem is a biobjective optimization problem, but can be converted into a general scalar optimization problem via our proposed method of scalarization. Nonetheless, the equivalent scalar problem is nonconvex by nature. To circumvent the nonconvex issue, a provably convergent difference-of-concave (DC) approach is introduced to solve it in an iterative fashion. In view of the high computational complexity of the DC approach, a power splitting method is also devised for fast implementation of service integration. The security performance and computational efficiency of our proposed algorithms are finally demonstrated by numerical results.

Energy Efficiency Region for Gaussian MISO Channels With Integrated Services

This letter studies energy efficient optimization problem for a two-receiver multiple-input single-output channel with two integrated service messages: one multicast message intended for both receivers and one confidential message intended only for one receiver and required to be perfectly secure from the other receiver. We aim to characterize the energy efficiency (EE) tradeoff between the two services, and more specifically, to find the Pareto boundary of the EE region. Such an EE region maximization problem is a nonconvex vector maximization problem, and we propose a method of scalarization to reformulate it into a scalar optimization problem. Despite the nonconvexity of the scalar problem, we show that it can be iteratively solved by combining fractional programming and difference-of-concave programming methods. Further, we prove that our obtained EE performance could always be achieved by simply using the single-stream beamforming for confidential message transmission. Finally, numerical results demonstrate the efficacy of our proposed methods in characterizing the EE tradeoff.

Energy-efficient optimization for MISO Gaussian broadcast channel with integrated services

This paper considers an energy-efficient transmit design in a three-node MISO wiretap channel in the eyes of service integration. Specifically, we combine two sorts of services, and serve them simultaneously: one multicast message intended for both receivers and one confidential message intended for only one authorized receiver. The confidential message must be kept perfectly secure from the unauthorized receiver. Our goal is to jointly design the input covariance matrices of the multicast message and confidential message such that the secrecy energy efficiency (SEE) is maximized, subject to the multicast rate, secrecy rate and total transmit power constraints. Due to the nonconvexity of this problem, an equivalent parametric reformulation, based on the fractional programming theory, is proposed to recast this problem as a sequence of semidefinite programs. By this means, the maximum SEE can be found via a root search algorithm. Moreover, we also give an approach to constructing a rank-one optimal covariance matrix of the confidential message from our proposed algorithm, which implies the feasibility of transmit beamforming to achieve the maximum SEE. Numerical results are finally presented to verify the efficacy of our proposed method.

Artificial-noise aided transmit design for outage constrained service integration

This paper considers an artificial noise (AN)-aided transmit design for multi-user MISO systems in the eyes of service integration. Specifically, we combine two sorts of services, and serve them simultaneously: one multicast message intended for all receivers and one confidential message intended for only one authorized receiver. The confidential message is kept perfectly secure from all the unauthorized receivers. Assuming imperfect channel state information (CSI) of unauthorized receivers at the transmitter, our goal is to jointly design the input covariances of the multicast message, confidential message and AN such that the outage secrecy rate is maximized for a given outage probability, while keeping the outage probability of multicast message for each user below a certain threshold. Due to the intrinsical complexity of this problem, a safe and convex albeit suboptimal reformulation, based on two advanced convex restriction approaches, is applied to generate a tractable approximation for this problem. By this means, a computationally efficient lower bound on the outage secrecy rate can be determined. We also prove the feasibility of beamforming to achieve the obtained secrecy rate. Numerical results are presented to verify the efficacy of our proposed method.