This article studies the reliable and secure communications for a wireless-powered non-orthogonal multiple access (NOMA) system in the presence of multiple eavesdroppers. A joint artificial noise and power allocation (JAP) scheme is proposed to ensure the reliability and security for the system. Based on the JAP scheme, analytical closed-form expressions for connection outage probability (COP), secrecy outage probability (SOP) and effective secrecy throughput (EST) are derived, respectively, to evaluate the performance of the security-required user. Also, the asymptotic SOP and EST are analyzed to gain further insights. The numerical results demonstrate the correctness of the theoretic analysis and indicate that the proposed JAP scheme can achieve a better performance than the benchmark schemes.

The flexibility and controllable mobility of unmanned aerial vehicles (UAVs) render them easier to become aerial platforms carrying out integrated sensing and communication (ISAC) functionality, and the cooperation among multiple UAVs is a promising way to achieve simultaneous multi-static radar sensing and coordinated multiple point (CoMP) transmission, leading to an enhanced ISAC service. However, due to the intrinsically limited resources that UAVs can utilize, it is challenging to achieve performance improvement for dual purposes. Toward this end, in this paper, an orthogonal frequency division multiple access (OFDMA) UAV-enabled ISAC system is investigated, and a joint trajectory planning and resource allocation problem is formulated to minimize the Cramér-Rao lower bounds (CRLB) for target location estimation while guaranteeing the communication quality-of-service (QoS) constraints. The formulated problem is non-convex and difficult to solve in general, and we first decompose the original problem into three sub-problems and then propose the corresponding algorithms to obtain the optimal solutions efficiently. The extensive simulations demonstrate the convergence of the proposed algorithm and the performance improvement on the localization with different communication requirements compared to conventional techniques.

An excellent routing protocol is important for wireless sensor network (WSN) construction and efficient data transmission. With the continuous expansion of the application scenarios and scopes of the Internet of Things, the existing WSN routing protocols are no longer suitable for the complex network structure and the huge demands of communications. Aiming at these issues of existing routing protocols, such as short network lifetime caused by high energy consumption and uneven distribution of surviving nodes, this article proposes an energy-saving clustering protocol based on adaptive Voronoi dividing, named energy-saving clustering by Voronoi adaptive dividing (ESCVAD) protocol. The innovation of ESCVAD protocol lies in the adaptive clustering algorithm based on Voronoi dividing and cluster head election optimization algorithm based on distance and energy comprehensive weighting. The advantage of proposed algorithms is effectively to balance the energy consumption between cluster head nodes and cluster member nodes. The simulation results show that, compared with the traditional routing protocols, such as low energy adaptive clustering hierarchy (LEACH) protocol and stable energy protocol (SEP), the proposed ESCVAD protocol can effectively reduce the clustering frequency and cluster head electing frequency, so as to reduce signaling interaction frequency, finally result in the energy consumption down and the network lifetime up. Among the six protocols for comparation, ESCVAD has the best network lifetime and energy efficiency.

Conventional multi-antenna technology with fixed position antenna (FPA) array cannot make full use of the spatial degree of freedom offered by wireless channels. This paper proposes a novel covert communication system empowered by movable-antenna (MA) array, in which a transmitter equipped with a linear MA array sends covert information to a legitimate user while being monitored by multiple single and FPA wardens. With the aim of maximizing the covert rate, we jointly design the transmit beamforming and antenna position considering the constraints of covertness, transmitter’s power as well as antenna position. Despite the non-convexity of the optimization problem, we solve two subproblems in an iterative manner by means of the block coordinate descent algorithm. Numerical results indicate that the proposed MA array scheme can provide superior covertness performance in comparison with the conventional FPA array especially when a small number of antennas is adopted at the transmitter.

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.

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.