Unmanned aerial vehicles (UAVs) equipped with data transmission and sensing facilities are gaining more popularity in different applications due to its miniaturization and mobility. In this paper, a UAV-based overlay cognitive radio (CR) network is investigated in which the UAV is used as a secondary user (SU). This paper proposes an efficient energy management solution to improve the performance of the UAV. When SUs opportunistically utilize the licensed spectrum of the primary network, spectrum sensing is needed to determine whether to transmit data or not, so the sensing time and secondary transmission power should be jointly optimized. We formulate this non-convex optimization problem subject to multiple constraints, which seeks to investigate on the effect of the sensing time and transmission power on the performance of the system. The problem is difficult to tackle, then we propose an algorithm applying the techniques of alternating optimization and dichotomy method. In addition, we compare the proposed algorithm with the particle swarm optimization (PSO) algorithm to verify its performance. Numerical results show that our proposed algorithm outperforms the PSO algorithm and significantly enhances the energy efficiency of the UAV-based CR system.

This paper considers a multiple-input multiple-output (MIMO) secure simultaneous wireless information and power transfer (SWIPT) system aided by the intelligent reflecting surface (IRS), where the transmitter (T), the information receiver (IR) and energy receiver (ER) are all equipped with multiple antennas. Assuming that the ER may be potential eavesdropper (Eve), we aim to maximize the secrecy rate via jointly designing the precoding matrix, the artificial noise (AN) covariance, and the phase shift matrix, subject to the harvested energy and unit modulus reflect coefficient (RC) constraints. The formulated secrecy rate maximization (SRM) problem is non-convex with multiple coupled variables. To tackle it, an inexact block coordinate descent (IBCD) method is proposed, where the unit modulus constraint is handled by the penalty majorization-minimization (MM) and the complex circle manifold (CCM) methods. Finally, simulation results validate the effectiveness of IRS in enhancing the security.

Unmanned Aerial Vehicle (UAV) aided communication has the potential to provide on-demand wireless services and improve the outdoor link throughput. Applications for UAVs are rapidly growing with the development of Internet of Things. Because of limited battery energy, the UAVs need time-limited spectrum access to complete data transmission. Hence there are two challenges for the UAV-based communication: 1) Spectrum-efficient design; 2) Energy-efficient design. In this paper, we investigate the optimization of spectrum efficiency (SE) and energy efficiency (EE) for cognitive UAV network based on location information. Because of high mobility, the cognitive radio (CR) based UAVs operate on different frequency bands that vary with time and space. Thus, one spectrum band that is available in one region may not be necessarily available in another region. Based on location information of the primary transmitter and the UAV, we propose a hybrid mode in which the sensing performance and UAV’s transmit power can be adjusted simultaneously to satisfy the outage constraint of the primary user. The multi-objective optimization theory is used to solve the tradeoff between SE and EE. The UAV’s transmit power, sensing time and sensing threshold are optimized jointly to solve the tradeoff problem. To further improve the SE and EE performance, we propose a multi-frame combined sensing scheme, in which multiple frames are bundled together. Simulation results are provided to show the SE-EE tradeoff design, to validate the effectiveness of the proposed hybrid mode, and to show the advantages of the multi-frame combined sensing scheme in EE performance.

The deployment of unmanned aerial vehicle (UAV)-enabled cognitive radio system in the area covered by primary network is studied in this letter. The UAV shares the spectrum with primary user (PU) and aims to maximize the transmitted bits with limited battery capacity. When the UAV flies towards the primary transmitter, the sensing performance and throughput of the UAV system will be improved. However, the energy consumption increases linearly with the flying distance. The goal of this letter is to find the optimal position of the UAV and optimize the sensing performance as well as the power allocation such that the transmitted bits is maximized while the PU is sufficiently protected. An iterative optimization algorithm is proposed to obtain the system parameters. Simulation results show the efficiency of the proposed approach in terms of transmitted bits as compared to other schemes.

In this paper, we investigate a robust transmit design in underlay energy harvesting (EH) cognitive radio networks (CRNs), where the malicious energy receivers (ERs) can be treated as potential eavesdroppers (Eves) due to their ability of wiretapping the desired information. Considering a general case of statistical Eves’ channel state information (CSI), to transmit signals in a secret and energy-efficient manner, an outage-constrained secrecy energy efficiency (OC-SEE) optimization problem is formulated to design transmit beamforming vectors. The original problem is non-convex and challenging to solve. To make it more tractable, we first rewrite this problem by semi-definite relaxation (SDR), and Bernstein-type inequality (BTI) approach is resorted to conservatively approximate the probabilistic constraints. Then, based on the fractional programming theory, an equivalent parametric reformulation is proposed to convert the original problem into a subtractive form. Furthermore, by introducing auxiliary variables, with the aid of Sphere Bounding method, the tractable solution of this OC-SEE maximization problem can be obtained through solving a two-stage optimization problem. Finally, numerical results are provided to validate the efficacy of the proposed design.

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 semi-definite 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 some existing schemes.