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IEEE Communications Letters

AN-Aided Secrecy Precoding for SWIPT in Cognitive MIMO Broadcast Channels

Bing Fang, Zuping Qian, Zhong Wei, Wei Shao

Published Sep 1, 2015• 54 citations
Publisher

Abstract

In this letter, we study the secrecy precoding problem for simultaneous wireless information and power transfer (SWIPT) in a cognitive multiple-input multiple-output (MIMO) broadcast channel. We adopt an artificial noise (AN)-aided precoding scheme and formulate the problem as a secrecy rate maximization (SRM) problem, which is subject to both an interference power constraint imposed to protect the primary user (PU) and an energy harvesting constraint required by the secondary energy receiver (SER). Since the formulated SRM problem constitutes a difference convex (DC)-type programming problem, we solve it by employing a successive convex approximation (SCA) method. With the SCA method, the nonconvex part of the SRM problem can be locally linearized to its first-order Taylor expansion. Then, relying on solving a series of convexified optimization problems, an iterative precoding algorithm is developed. Numerical simulations are also provided to demonstrate the proposed algorithm. Results show that our algorithm can achieve a near-optimal performance with guaranteed convergence.

DOI
10.1109/lcomm.2015.2449315

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In this letter, we study the secrecy precoding problem for simultaneous wireless information and power transfer (SWIPT) in a cognitive multiple-input multiple-output (MIMO) broadcast channel. We adopt an artificial noise (AN)-aided precoding scheme and formulate the problem as a secrecy rate maximization (SRM) problem, which is subject to both an interference power constraint imposed to protect the primary user (PU) and an energy harvesting constraint required by the secondary energy receiver (SER). Since the formulated SRM problem constitutes a difference convex (DC)-type programming problem, we solve it by employing a successive convex approximation (SCA) method. With the SCA method, the nonconvex part of the SRM problem can be locally linearized to its first-order Taylor expansion. Then, relying on solving a series of convexified optimization problems, an iterative precoding algorithm is developed. Numerical simulations are also provided to demonstrate the proposed algorithm. Results show that our algorithm can achieve a near-optimal performance with guaranteed convergence.
In this paper, we study the secure transmission scheme in multiple-input multiple-output (MIMO) communication systems with simultaneous wireless information and power transfer (SWIPT), in which both power splitting (PS) receiver and artificial noise (AN) are taken into account. We aim to maximize the achievable secrecy rate at the desired receiver by jointly designing precoding matrix, AN covariance matrix and PS ratio at the desired receiver, under the total transmit power constraint and the minimum energy harvesting constraints at all the receivers. The formulated optimization problem is nonconvex and hard to tackle. Then, by transforming this problem into a canonical DC (difference of convex function) programming, we propose an iterative method to solve this problem, and analyze the convergence of the proposed algorithm. Simulation results are provided to show the efficiency of our proposed scheme.
In this letter, secure beamforming designs are investigated in a multiple-input multiple-output secrecy channels with simultaneous wireless information and power transfer. In order to achieve fairness among different multiple energy harvesting receivers, the minimum harvested energy is maximized under the secrecy rate requirements. In particular, in order to reduce the computational complexity of the semidefinite programming problem, a successive convex approximation (SCA) iterative algorithm is proposed in the perfect channel state information (CSI) case to obtain a near-optimal rank-one solution. Moreover, the original problem is extended to the imperfect CSI case by incorporating a norm-bounded error model, where an SCA-based iterative algorithm is also proposed. Simulation results reveal that the SCA-based iterative algorithm achieves the same performance as the semidefinite relaxation method with reduced complexity.
In this paper, we investigate the secure transmission in multiple-input multiple-output (MIMO) communication systems with simultaneous wireless information and power transfer (SWIPT), where the energy receivers (ERs) may eavesdrop the confidential information send to the desired receiver (DR). By taking into account both artificial noise (AN) and power splitting (PS), we aim to maximize the secrecy rate at the DR by jointly designing the precoding matrix, AN covariance matrix and PS ratio at the desired receiver, under the transmit power constraint and the energy harvesting (EH) constraints at all the receivers. To solve the formulated non-convex problem, we propose an alternating optimization (AO) algorithm through a judicious reformulation, wherein the problem is handled by solving a series of convex problems in an alternating method. Finally, numerical results are provided to validate our proposed algorithm.
In this work, we investigate a joint transmit beamforming and artificial noise (AN) covariance matrix design in a multiple-input multiple-output (MIMO) cognitive radio (CR) downlink network with simultaneous wireless information and power transfer (SWIPT), where the malicious energy receivers (ERs) may decode the desired information and hence can be treated as potential eavesdroppers (Eves). In order to improve the secure performance of the transmission, AN is embedded to the information-bearing signal, which acts as interference to the Eves and provides energy to all receivers. Specifically, this joint design is studied under a practical non-linear energy harvesting (EH) model, our aim is to maximize the secrecy rate at the SR subject to the transmit power budget, EH constraints and quality of service (QoS) requirement. The original problem is not convex and challenging to be solved. To circumvent its intractability, an equivalent reformulation of this secrecy rate maximization (SRM) problem is introduced, wherein the resulting problem is primal decomposable and thus can be handled by alternately solving two convex subproblems. Finally, numerical results are presented to verify the effectiveness of our proposed scheme.
In this paper, we consider a multiuser multiple-input multiple-output (MIMO) downlink communication system with simultaneous wireless information and power transfer (SWIPT). In particular, we focus on a realistic and efficient multi-receiver multi-eavesdropper MIMO SWIPT system, in which the channel state information (CSI) of each legitimate receiver and energy receiver (i.e., potential eavesdropper) is partially known to the transmitter. Based on this, we propose a robust artificial noise (AN)-aided secure transmission scheme for the system, where the channel uncertainties are modeled by the worst-case model. In the proposed scheme, we aim to maximize the worst-case achievable secrecy rate under the transmit power constraint and the energy harvesting (EH) constraint, by jointly optimizing the transmit precoding matrix and the AN covariance matrix. We utilize the S-Procedure and Taylor series approximation to transform the non-convex problem. Then, we apply the interior point method to tackle the transformed convex problem, obtaining the approximate optimal matrices and the corresponding maximum worst-case secrecy rate. Simulation results show that our proposed scheme achieves significant performance improvements in terms of convergence and the worst-case achievable secrecy rate.