This paper aims to investigate the benefit of using intelligent reflecting surface (IRS) in multi-user multiple-input single-output (MU-MISO) systems, in the presence of eavesdroppers. We maximize the

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In this letter, we investigate the outage-constrained robust secure design in a multiple-input single-output (MISO) energy harvesting (EH) cognitive radio network (CRN), where the malicious energy receivers (ERs) may wiretap the desired information and hence can be treated as potential eavesdroppers (Eves). In particular, considering a non-linear energy harvesting (EH) model, our objective is to design the transmit covariance matrix to maximize the secrecy energy efficiency (SEE) under the given outage probability and transmit power constraints, while satisfying the EH and quality-of-service (QoS) requirements. To tackle the original non-convex problem, we resort to semidefinite relaxation (SDR) and Bernstein-type inequality (BTI)based approximations to reformulate it into a tractable form. Then, the original problem becomes decomposable and can be efficiently solved by handling a two-stage optimization problem. At last, numerical results are provided to demonstrate the effectiveness and superior performance of the proposed design in comparisons with the existing schemes. Index Terms-Cognitive radio network (CRN), energy harvesting, secrecy energy efficiency (SEE), outage probability.

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 efficient designs are developed with different assumptions of channels state information (CSI) at the transmitter, namely perfect CSI, statistical CSI and imperfect CSI with bounded channel uncertainties. In particular, the overarching objective here is to design a beamforming technique maximizing the SEE while satisfying all relevant constraints linked to interference and harvested energy between transmitters and receivers. We show that the original problems are non-convex and their solutions are intractable. By using a number of techniques, such as non-linear fractional programming and difference of concave (DC) functions, we reformulate the original problems so as to render them tractable. We then combine these techniques with the Dinkelbach's algorithm to derive iterative algorithms to determine relevant beamforming vectors which lead to the SEE maximization. In doing this, we investigate the robust design with ellipsoidal bounded channel uncertainties, by mapping the original problem into a sequence of semidefinite programs This paper has been presented in part at the IEEE Wireless Communications and Signal Processing (WCSP), October 11-13, 2017 [1]. M. Zhang is with the ). Z. Ding is with the School of Electrical and Electronic Engineering, The University of Manchester, Manchester, UK (email: zhiguo.ding@manchester.ac.uk). O. A. Dobre is with the employing the semidefinite relaxation, non-linear fractional programming and S-procedure. Furthermore, we show that the maximum SEE can be achieved through a search algorithm in the single dimensional space. Numerical results, when compared with those obtained with existing techniques in the literature, show the effectiveness of the proposed designs for SEE maximization.

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Unmanned aerial vehicle (UAV)-enabled communication has emerged as an irreplaceable technology in military, disaster relief and emergency scenarios. This correspondence investigates the average throughput in a UAV-enabled cognitive radio network, where the UAV is regarded as a dedicated secondary user to enhance the network coverage and spectral efficiency. Based on the probabilistic line-of-sight channel, we exploit the joint design of UAV trajectory and resource allocation to maximize the average throughput under the constraints of cochannel interference and completion time. The original problem is a mixed integer non-convex problem which is generally NPhard. We first decompose the primal problem into a bilevel programming problem, and then propose an efficient highquality algorithm based on the particle swarm optimization approach. The optimized trajectory reveals the trade-off between throughput and co-channel interference. Numerical results verify the superiority of the proposed algorithm as compared to other benchmark schemes.