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IEEE Conference Proceedings

Secrecy rate region maximization for multi-user MIMO systems with artificial noise and service integration

Mei Weidong, Chen Zhi, Jun Fang

Published Jan 1, 2016• 0 citations
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Secrecy rate region maximization for multi-user MIMO systems with artificial noise and service integration

Weidong Mei, Zhi Chen, Jun Fang

This paper considers an artificial noise (AN)-aided transmit design for multi-user MIMO systems with integrated services. Specifically, two sorts of service messages are combined and serve 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. Our goal is to jointly design the input covariances for the multicast message, confidential message and AN, such that the achievable secrecy rate region is maximized subject to the sum power constraint. This problem is a vector optimization problem and inherently complex to solve. To find its Pareto boundary, the method of scalarization is used to transform it into an equivalent scalar optimization problem. Nonetheless, this scalar optimization problem is nonconvex and appears to be difficult especially in our considered scenario. Then we show that this problem can be handled by resorting to an alternating optimization (AO) algorithm. The merit of this AO algorithm lies in its provable convergence to one stationary point. Numerical results are finally presented to show the efficacy of our proposed method.

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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.

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This paper considers an optimal 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 receiver. The confidential message is kept perfectly secure from all the unauthorized receivers. Our goal is to jointly design the optimal input covariances for the multicast message, confidential message and AN, such that the achievable secrecy rate region is maximized subject to the sum power constraint. To this end, we exploit its hidden convexity and transform the original secrecy rate maximization (SRM) problem into a semidefinite programming. Furthermore, we prove that the optimal input covariance matrix of confidential message is always with rank one, which implies the optimality of beamforming. Numerical results are presented to show the efficacy of our proposed method.

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Sum secrecy rate maximization for multi-carrier MIMOME systems with full-duplex jamming

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This paper considers an artificial noise (AN)-aided transmit design for multi-user MIMO systems with integrated services. Specifically, two sorts of service messages are combined and serve 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. Our goal is to jointly design the input covariances for the multicast message, confidential message and AN, such that the achievable secrecy rate region is maximized subject to the sum power constraint. This problem is a vector optimization problem and inherently complex to solve. To find its Pareto boundary, the method of scalarization is used to transform it into an equivalent scalar optimization problem. Nonetheless, this scalar optimization problem is nonconvex and appears to be difficult especially in our considered scenario. Then we show that this problem can be handled by resorting to an alternating optimization (AO) algorithm. The merit of this AO algorithm lies in its provable convergence to one stationary point. Numerical results are finally presented to show the efficacy of our proposed method.
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.
This paper considers an optimal 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 receiver. The confidential message is kept perfectly secure from all the unauthorized receivers. Our goal is to jointly design the optimal input covariances for the multicast message, confidential message and AN, such that the achievable secrecy rate region is maximized subject to the sum power constraint. To this end, we exploit its hidden convexity and transform the original secrecy rate maximization (SRM) problem into a semidefinite programming. Furthermore, we prove that the optimal input covariance matrix of confidential message is always with rank one, which implies the optimality of beamforming. Numerical results are presented to show the efficacy of our proposed method.