We consider physical-layer security in a novel MISO cooperative overlay cognitive radio network (CRN) with a single eavesdropper. We aim to design an artificial noise (AN) aided secondary transmit strategy to maximize the joint achievable secrecy rate of both primary and secondary links, subject to a global secondary transmit power constraint and guaranteeing any transmission of secondary should at least not degrade the receive quality of primary network, under the assumption that global CSI is available. The resulting optimization problem is challenging to solve due to its non-convexity in general. A computationally efficient approximation methodology is proposed based on the semidefinite relaxation (SDR) technique and followed by a two-step alternating optimization algorithm for obtaining a local optimum for the corresponding SDR problem. This optimization algorithm consists of a one-dimensional line search and a non-convex optimization problem, which, however, through a novel reformulation, can be approximated as a convex semidefinite program (SDP). Analysis on the extension to multiple eavesdroppers scenario is also provided. Simulation results show that the proposed AN-aided joint secrecy rate maximization design (JSRMD) can significantly boost the secrecy performance over JSRMD without AN.
We consider physical-layer security in a novel MISO cooperative overlay cognitive radio network (CRN) with a single eavesdropper. We aim to design an artificial noise (AN) aided secondary transmit strategy to maximize the joint achievable secrecy rate of both primary and secondary links, subject to a global secondary transmit power constraint and guaranteeing any transmission of secondary should at least not degrade the receive quality of primary network, under the assumption that global CSI is available. The resulting optimization problem is challenging to solve due to its non-convexity in general. A computationally efficient approximation methodology is proposed based on the semidefinite relaxation (SDR) technique and followed by a two-step alternating optimization algorithm for obtaining a local optimum for the corresponding SDR problem. This optimization algorithm consists of a one-dimensional line search and a non-convex optimization problem, which, however, through a novel reformulation, can be approximated as a convex semidefinite program (SDP). Analysis on the extension to multiple eavesdroppers scenario is also provided. Simulation results show that the proposed AN-aided joint secrecy rate maximization design (JSRMD) can significantly boost the secrecy performance over JSRMD without AN.
Consider an MISO channel overheard by multiple eavesdroppers. Our goal is to design an artificial noise (AN)-aided transmit strategy, such that the achievable secrecy rate is maximized subject to the sum power constraint. AN-aided secure transmission has recently been found to be a promising approach for blocking eavesdropping attempts. In many existing studies, the confidential information transmit covariance and the AN covariance are not simultaneously optimized. In particular, for design convenience, it is common to prefix the AN covariance as a specific kind of spatially isotropic covariance. This paper considers joint optimization of the transmit and AN covariances for secrecy rate maximization (SRM), with a design flexibility that the AN can take any spatial pattern. Hence, the proposed design has potential in jamming the eavesdroppers more effectively, based upon the channel state information (CSI). We derive an optimization approach to the SRM problem through both analysis and convex conic optimization machinery. We show that the SRM problem can be recast as a single-variable optimization problem, and that resultant problem can be efficiently handled by solving a sequence of semidefinite programs. Our framework deals with a general setup of multiple multi-antenna eavesdroppers, and can cater for additional constraints arising from specific application scenarios, such as interference temperature constraints in interference networks. We also generalize the framework to an imperfect CSI case where a worst-case robust SRM formulation is considered. A suboptimal but safe solution to the outage-constrained robust SRM design is also investigated. Simulation results show that the proposed AN-aided SRM design yields significant secrecy rate gains over an optimal no-AN design and the isotropic AN design, especially when there are more eavesdroppers.
In this paper, we address the physical-layer security issue of a secondary user (SU) in a spectrum-sharing cognitive radio network (CRN) from an information-theoretic perspective. Specially, we consider a secure multiple-input single-output (MISO) cognitive radio channel, where a multi-antenna SU transmitter (SU-Tx) sends confidential information to a legitimate SU receiver (SU-Rx) in the presence of an eavesdropper and on the licensed band of a primary user (PU). The secrecy capacity of the channel is characterized, which is a quasiconvex optimization problem of finding the capacity-achieving transmit covariance matrix under the joint transmit power and interference power constraints. Two numerical approaches are proposed to derive the optimal transmit covariance matrix. The first approach recasts the original quasiconvex problem into a single convex semidefinite program (SDP) by exploring its inherent convexity; while the second one explores the relationship between the secure CRN and the conventional CRN and transforms the original problem into a sequence of optimization problems associated with the conventional CRN, which helps to prove that beamforming is the optimal strategy for the secure MISO CR channel. In addition, to reduce the computational complexity, three suboptimal schemes are presented, namely, scaled secret beamforming (SSB), projected secret beamforming (PSB) and projected cognitive beamforming (PCB). Lastly, computer simulation results show that the three suboptimal schemes can approach the secrecy capacity well under certain conditions.
In this paper, we address the issue of optimal transmitter design to achieve physical layer security for a multiple-input single-output (MISO) cognitive radio network (CRN), in which a secondary user transmitter (SU-Tx) sends confidential information to a SU receiver (SU-Rx) on the same frequency band with a primary user (PU) in the presence of an eavesdropper receiver (ED-Rx). It is assumed that all the channel state information (CSI) of the secondary, primary and eavesdropper channels is not perfectly known at the SU-Tx. The optimal transmitter design, under the restriction of Gaussian signaling without preprocessing of information, involves a nonconvex semiinfinite optimization problem which maximizes the rate of the secondary link while avoiding harmful interference to the PU and keeping the eavesdropper totally ignorant of the messages sent regardless of the uncertainties in the CSI. We propose two approaches to solve this challenging optimization problem. The first one relates the original problem to a sequence of semiinfinite capacity-achieving transmitter design problems in an auxiliary CRN without any eavesdropper, which can then be solved through transformations and using convex semidefinite programs (SDPs). The second approach explores the hidden convexity of the problem and hence transforms it into a single SDP, which significantly reduces the computational complexity. Furthermore, a few heuristic beamforming solutions for the ease of implementation are also introduced. Finally, simulation results are presented to evaluate the performance of the proposed optimal and suboptimal solutions.
To solve the transmission problemof physical layer security(PLS) in relay cognitive radio network (CRN), this paper investigates the PLS transmission scheme of multiple relays CRN, which contains two pairs of secondary nodes without direct link each other.Compared with the model that consists ofa pair of secondary node users and a single relay in terms of theoretical analysis and optimization, the proposed scheme is more complicated.To maximizethe joint secret rate of the secondary users,power allocation of the twosecondarysource transmission node withbeamforming andartificial noise assist (PBNAS) is obtained.Furthermore, exploiting the semidefinite programming theory (SDR) and the successive convex approximation (SCA) method, the original complex nonconvex problem is transformed into a convex optimization problem, and the iterative procedure is summarized.Finally, the security transmission rate versus different number relays andrelays total power is simulated.The simulation results show that the security performance of the PBNAS scheme is the best.
In the Cognitive Radio Network (CRN) with multiple multi-antenna eavesdroppers, to make the system security rate maximum, secure communication over the physical layer that is subjected to the interference power constraints at the Primary Users (PU) is provided by designing the transmit covariance optimization of Secondary User Transmitter (SU-Tx).When?the?Channel State?Information (CSI) is known, the properties of the matrices and Charnes-Cooper transformation are used, the non-convex function is converted to a Semi-Definite Programming (SDP) to get the optimization scheme of SU-Tx. Simulation results show that compared with the existing sub-optimal transmission designs, the proposed method improves the secrecy rate and has more advantages on the complexity.
This paper studies cooperative secure transmission in wireless relay networks, where a source sends a confidential message to a legitimate destination through a set of amplify-and-forward (AF) relays in the presence of multiple eavesdroppers. To prevent the eavesdroppers from retrieving the message, we assume that the relays employ a joint cooperative beamforming (CB) and artificial noise (AN) strategy. Under both the total and individual relay power constraints, we propose an optimal joint CB-AN design based on secrecy rate maximization (SRM). We treat the SRM problem by a combination of two-level optimization and semidefinite relaxation (SDR). For the latter, we prove that SDR always has a rank-one solution for the SRM problem - which identifies that SDR is optimal. Simulation results are presented to show the efficacy of the proposed joint CB-AN design.