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International Conference on Communications

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

Weidong Mei, Zhi Chen, Jun Fang

Published May 1, 2017• 7 citations
Publisher

Abstract

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.

DOI
10.1109/icc.2017.7996665

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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.
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 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.
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.
This paper considers transmit optimization in multi-input multi-output (MIMO) wiretap channels, wherein we aim at maximizing the secrecy capacity or rate of an MIMO channel overheard by one or multiple eavesdroppers. Such optimization problems are nonconvex, and appear to be difficult especially in the multi-eavesdropper scenario. In this paper, we propose an alternating optimization (AO) approach to tackle these secrecy optimization problems. We first consider the secrecy capacity maximization (SCM) problem in the single eavesdropper scenario. An AO algorithm is derived through a judicious SCM reformulation. The algorithm conducts some kind of reweighting and water-filling in an alternating fashion, and thus is computationally efficient to implement. We also prove that the AO algorithm is guaranteed to converge to a Karush-Kuhn-Tucker (KKT) point of the SCM problem. Then, we turn our attention to the multiple eavesdropper scenario, where the artificial noise (AN)-aided secrecy rate maximization (SRM) problem is considered. Although the AN-aided SRM problem has a more complex problem structure than the previous SCM, we show that AO can be extended to deal with the former, wherein the problem is handled by solving convex problems in an alternating fashion. Again, the resulting AO method is proven to have KKT point convergence guarantee. For fast implementation, a custom-designed AO algorithm based on smoothing and projected gradient is also derived. The secrecy rate performance and computational efficiency of the proposed algorithms are demonstrated by simulations.
We consider the problem of secrecy rate maximization in a multi-input multi-output multi-eavesdropper (MIMOME) wiretap channel and present an exact solution. A general system model with a multi-antenna eavesdropper and a multi-antenna full-duplex receiver is considered. In particular, we perform joint beamforming and artificial noise optimization in an effort to maximize the achievable secrecy rate. The optimization is performed in the presence of artificial noise generated by both transmitter and legitimate receiver. The resulting optimization problem is non-convex and difficult to solve. We develop a minorization-maximization algorithm to solve the problem. It is iterative in nature and guaranteed to converge to a locally optimal solution. The results can therefore be used to benchmark existing methods. Numerical results are presented to demonstrate the efficacy of the proposed approach.
In this paper, we study the secrecy rate maximization (SRM) problem for a multiple-input-multiple-output (MIMO) secrecy channel, where a multi-antenna cooperative jammer is employed to improve secret communication in the presence of multiple multi-antenna eavesdroppers. The SRM problem are not jointly convex in terms of the transmit covariance matrices of the legitimate transmitter and the cooperative jammer. To solve the non-convexity issues, we propose an alternating optimization (AO) approach to design the transmit covariance matrix of the legitimate transmitter and the cooperative jammer. Simulation results validated the performance of the proposed algorithms.