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This paper investigates the application of intelligent reflecting surface (IRS) in an underlay cognitive radio network (CRN), where a multi-antenna cognitive base station (CBS) utilizes spectrum assigned to the primary user (PU) to communicate with a secondary user (SU) via IRS in the presence of multiple coordinated eavesdroppers. To achieve the trade-off between the secrecy rate (SR) and energy consumption, we propose a secrecy energy efficiency (SEE) maximization scheme by jointly design the transmit beamforming at CBS and the reflect beamforming at IRS under the SR constraint of SU, the transmit power constraint of CBS, the limited interference temperature of PU and the unit modulus constraint of IRS. The problem is challenging to solve due to the coupled optimization variables and unit modulus constraint, for which an iterative alternating optimization algorithm is proposed. As for optimizing the reflect beamforming, we introduce an auxiliary variable and convert the original non-convex problem into a semi-definite programming with rank-1 constraint, and then propose an iterative penalty function based algorithm to implement the optimal reflect beamforming. As for optimizing the transmit beamforming, we convert the original problem into an equivalent subtractive form, which is further transformed into a convex function by employing the difference of two-convex functions method. Furthermore, we provide a second-order-cone-programming approximation approach to reduce the computational complexity. The effectiveness and superiority of our proposed algorithm are verified in the simulation results.
Cognitive radio networks (CRNs) and millimeter wave (mmWave) communications are two major technologies to enhance the spectrum efficiency (SE). Considering that the SE improvement in the CRNs is limited due to the interference temperature imposed on
In this paper, we investigate different secrecy energy efficiency (SEE) optimization problems in a multiple-input single-output underlay cognitive radio (CR) network in the presence of an energy harvesting receiver. In particular, these energy effici
This paper investigates a machine learning-based power allocation design for secure transmission in a cognitive radio (CR) network. In particular, a neural network (NN)-based approach is proposed to maximize the secrecy rate of the secondary receiver
In this paper we investigate cooperative secure communications in a four-node cognitive radio network where the secondary receiver is treated as a potential eavesdropper with respect to the primary transmission. The secondary user is allowed to trans
Energy-efficient design and secure communications are of crucial importance in wireless communication networks. However, the energy efficiency achieved by using physical layer security can be limited by the channel conditions. In order to tackle this