This study considers a cellular multiple‐input single‐output system overheard by multiple eavesdroppers, in the presence of multiple pairs of single‐antenna device‐to‐device (D2D) nodes working as an underlay. All D2D nodes are permitted to access the cellular channel for their own communications. A novel eavesdropping scenario, termed as simultaneous eavesdropping, is studied, where the eavesdroppers intend to simultaneously overhear the cellular communication as well as D2D communications. Assuming imperfect channel state information (CSI) at the transmitter, the authors goal is to design the robust transmit covariance of confidential message and artificial noise, such that the worst‐case sum secrecy rate is maximised. This worst‐case sum secrecy rate maximisation problem is challenging to solve due to its non‐convexity and semi‐infiniteness incurred by the imperfect CSI. As a compromise, the authors develop a tractable approximation to its objective secrecy rate function for deriving a computationally efficient lower bound. Then a successive convex approximation (SCA) algorithm is proposed to solve the approximated problem in an iterative manner with provable convergence guarantee. Then it is proved that the SCA algorithm always yields a beam‐forming solution to the confidential message transmission. Numerical results illustrate that the proposed scheme outperforms the traditional one.
This study considers a cellular multiple‐input single‐output system overheard by multiple eavesdroppers, in the presence of multiple pairs of single‐antenna device‐to‐device (D2D) nodes working as an underlay. All D2D nodes are permitted to access the cellular channel for their own communications. A novel eavesdropping scenario, termed as simultaneous eavesdropping, is studied, where the eavesdroppers intend to simultaneously overhear the cellular communication as well as D2D communications. Assuming imperfect channel state information (CSI) at the transmitter, the authors goal is to design the robust transmit covariance of confidential message and artificial noise, such that the worst‐case sum secrecy rate is maximised. This worst‐case sum secrecy rate maximisation problem is challenging to solve due to its non‐convexity and semi‐infiniteness incurred by the imperfect CSI. As a compromise, the authors develop a tractable approximation to its objective secrecy rate function for deriving a computationally efficient lower bound. Then a successive convex approximation (SCA) algorithm is proposed to solve the approximated problem in an iterative manner with provable convergence guarantee. Then it is proved that the SCA algorithm always yields a beam‐forming solution to the confidential message transmission. Numerical results illustrate that the proposed scheme outperforms the traditional one.
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.
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.
Consider a cellular multiple-input single-output (MISO) channel, in the presence of multiple eavesdroppers (Eves) and one pair of single-antenna device-to-device (D2D) nodes working as an underlay. A novel eavesdropping scenario, termed as selective eavesdropping, is studied in this paper, where Eves arbitrarily select one target from the cellular receiver and the D2D receiver to overhear, but their selection is unknown to any other nodes. Since Eves' two sorts of selection would lead to two different secrecy rates, we define the achievable secrecy rate as the smaller one of the two rates. With imperfect channel state information on all links, our interest lies in the robust design of transmit covariances at the cellular transmitter, such that the worst-case achievable secrecy rate is maximized. This worst-case secrecy rate maximization problem is nonconvex by nature. To deal with it, we develop a convex approximation to seek a computationally efficient lower bound. In particular, the solution can be efficiently computed by successively solving a sequence of convex optimization problems. Then it is proved that the obtained lower bound is attainable by simply utilizing single-stream beamforming. Numerical results are finally presented to demonstrate the efficacy of our proposed methods.
In the present study, the authors investigate robust secrecy rate optimisation problems for a multiple‐input‐single‐output secrecy channel with multiple device‐to‐device (D2D) communications. The D2D communication nodes access this secrecy channel by sharing the same spectrum, and help to improve the secrecy communications by confusing the eavesdroppers. In return, the legitimate transmitter ensures that the D2D communication nodes achieve their required rates. In addition, it is assumed that the legitimate transmitter has imperfect channel state information of different nodes. For this secrecy network, the authors solve two robust secrecy rate optimisation problems: (a) robust power minimisation problem, subject to the probability based secrecy rate and the D2D transmission rate constraints; (b) robust secrecy rate maximisation problem with the transmit power, the probabilistic based secrecy rate and the D2D transmission rate constraints. Owing to the non‐convexity of robust beamforming design based on two statistical channel uncertainty models, the authors present two conservative approximation approaches based on ‘Bernstein‐type’ inequality and ‘S‐Procedure’ to solve these robust optimisation problems. Simulation results are provided to validate the performance of these two conservative approximation methods, where it is shown that ‘Bernstein‐type’ inequality based approach outperforms the ‘S‐Procedure’ approach in terms of achievable secrecy rates.
The probabilistic robust beamforming with the help of artificial noise (AN) is investigated in this study, where a multiple‐input‐single‐output wiretap scenario with K single‐antenna eavesdroppers is considered. Based on a more practical assumption that the transmitter derives imperfect legitimate user's channel state information (LCSI) and statistical eavesdroppers' channel state information (ECSI), secrecy rate maximisation (SRM) under a total transmit power constraint and K probabilistic secrecy outage constraints are studied. To deal with it, the K probabilistic constraints are firstly approximated to the deterministic worst‐case ones by resorting to sphere bounding. Secondly, by introducing a slack variable the approximated worst‐case SRM can be further recast as a series of semi‐definite programmings. In this way, the jointly optimised robust beamforming vector and the AN covariance are determined in a tractable convex fashion. Moreover, the authors can prove that the derived AN‐aided secure robust beamforming actually is the optimal solution to the approximated worst‐case SRM problem. Numerical results reveal that the proposed AN‐aided probabilistic secure robust beamforming could achieve outage secrecy rate improvement in comparison with the existing optimal worst‐case robust beamforming without AN, optimal outage constrained robust beamforming without AN, the non‐robust isotropic AN transmission, and the non‐robust AN‐aided beamforming scheme.
This paper studies secure wireless transmission from a multi-antenna transmitter to a single-antenna intended receiver overheard by multiple eavesdroppers with considering the imperfect channel state information (CSI) of wiretap channel.To enhance security of communication link, the artificial noise (AN) is generated at transmitter.We first design the robust joint optimal beamforming of secret signal and AN to minimize transmit power with constraints of security quality of service (QoS), i.e., minimum allowable signal-to-interference-and-noise ratio (SINR) at receiver and maximum tolerable SINR at eavesdroppers.The formulated design problem is shown to be nonconvex and we transfer it into linear matrix inequalities (LMIs).The semidefinite relaxation (SDR) technique is used and the approximated method is proved to solve the original problem exactly.To verify the robustness and tightness of proposed beamforming, we also provide a method to calculate the worst-case SINR at eavesdroppers for a designed transmit scheme using semidefinite programming (SDP).Additionally, the secrecy rate maximization is explored for fixed total transmit power.To tackle the nonconvexity of original formulation, we develop an iterative approach employing sequential parametric convex approximation (SPCA).The simulation results illustrate that the proposed robust transmit schemes can effectively improve the transmit performance.