In this paper, we consider an energy-constrained unmanned aerial vehicle (UAV)-enabled mobile relay assisted secure communication system in the presence of a legitimate source-destination pair and multiple eavesdroppers with imperfect locations. The energy-constrained UAV employs the power splitting (PS) scheme to simultaneously receive information and harvest energy from the source, and then exploits the time switching (TS) protocol to perform information relaying. Furthermore, we consider a full-duplex destination node which can simultaneously receive confidential signals from the UAV and cooperatively transmit artificial noise (AN) signals to confuse malicious eavesdroppers. To further enhance the reliability and security of this system, we formulate a worst case secrecy rate maximization problem, which jointly optimizes the position of the UAV, the AN transmit power, as well as the PS and TS ratios. The formulated problem is non-convex and generally intractable. In order to circumvent the non-convexity, we decouple the original optimization problem into three subproblems; this facilitates the design of a suboptimal iterative algorithm. In each iteration, we propose a multi-dimensional search and numerical method to handle the subproblem. Numerical simulation results are provided to demonstrate the effectiveness and superior performance of the proposed joint design versus the conventional schemes in the literature.

This paper investigates a dual-unmanned aerial vehicle (UAV)-enabled secure communication system, in which, a UAV moves around to send confidential messages to a mobile user while another cooperative UAV transmits artificial noise signals to confuse malicious eavesdroppers. Both UAVs have energy constraints and the location information of eavesdroppers is imperfect. We consider a worst-case secrecy rate maximization problem of the mobile user over all time slots. This optimization problem is solved by jointly designing the three-dimensional (3D) trajectory of UAVs and the time allocation (recharging and service or jamming) under practical constraints including maximum UAV speed, UAV collision avoidance, UAV positioning error, and UAV energy harvesting. Specifically, we adopt a more practical UAV-ground channel model with both large-scale and small-scale fading components. Due to the non-convex feasible region constructed by the complicated constraints, directly finding the optimal solution of the original problem is intractable. To address this issue, we decouple the original optimization problem into three subproblems and develop an iterative algorithm to find its suboptimal solution by using the block coordinate descent technique. To solve each subproblem, certain advanced optimization tools, such as integer relaxation, S-procedure, and successive convex approximation techniques, are utilized. Numerical simulation results are provided to corroborate the theoretical derivations and to evaluate the performance of the proposed algorithm. Additionally, the numerical results assist to draw new insights on the 3D UAV trajectory by comparing the performance with conventional two-dimensional (2D) schemes.

This paper investigates an energy-constrained relay-assisted secure communication system in the presence of a legitimate source-destination pair and a passive eavesdropper with imperfect channel state information (CSI). The energy-constrained relay exploits a power splitting (PS) protocol to simultaneously harvest energy and decode information from the received signal from the source, and then forwards the confidential signal to the full-duplex (FD) destination while it simultaneously transmits artificial noise (AN) to confuse the passive eavesdropper. This robust beamforming design is developed by maximizing the secrecy rate under the constraints of energy harvesting (EH) requirement at the relay and AN transmit power at the destination. Specifically, we formulate the joint relay beamforming and PS ratio as well as the AN power optimization as a non-convex quadratically-constrained problem. In order to circumvent this non-convexity issue, we decompose the original problem into three subproblems which can be determined by the proposed semidefinite relaxation (SDR) and successive convex optimization methods. Furthermore, we derive closed-form expressions for the optimal solutions of each subproblem. Finally, the original non-convex problem can be solved through a convergence guaranteed iterative algorithm. Simulation results are provided to demonstrate the effectiveness and superior performance of the proposed robust design compared with the benchmark schemes.

This paper investigates a novel maritime wireless surveillance scenario, where a legitimate monitor vessel moves around to eavesdrop the suspicious communication from a suspicious unmanned aerial vehicle (UAV) to a suspicious vessel with the help of a cooperative UAV. Specifically, the cooperative UAV can adjust its jamming power and trajectory to exactly control the transmission rate of the suspicious link, thus improving the monitor vessel's surveillance performance. Furthermore, since the cooperative UAV cannot land or replenish energy on the sea surface, its jamming power allocation on the ocean should be carefully designed by the energy thresholds. Under such setup, we formulate a sum eavesdropping rate maximization problem, which jointly optimizes the jamming power and three-dimensional (3D) trajectory of the cooperative UAV, as well as the two-dimensional (2D) trajectory of the monitor vessel. To address this non-convex optimization problem, we decompose the design problem into three subproblems and propose an iterative algorithm to find its suboptimal solution. Numerical results show that the proposed jamming-assisted 3D joint design can significantly improve the eavesdropping rate and save the jamming power compared to the benchmark schemes.

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 under the constraints of total transmit power of secondary transmitter, and the interference leakage to the primary receiver, within which three different regularization schemes are developed. The key advantage of the proposed algorithm over conventional approaches is the capability to solve the power allocation problem with both perfect and imperfect channel state information. In a conventional setting, two completely different optimization frameworks have to be designed, namely the robust and non-robust designs. Furthermore, conventional algorithms are often based on iterative techniques, and hence, they require a considerable number of iterations, rendering them less suitable in future wireless networks where there are very stringent delay constraints. To meet the unprecedented requirements of future ultra-reliable low-latency networks, we propose an NN-based approach that can determine the power allocation in a CR network with significantly reduced computational time and complexity. As this trained NN only requires a small number of linear operations to yield the required power allocations, the approach can also be extended to different delay sensitive applications and services in future wireless networks. When evaluate the proposed method versus conventional approaches, using a suitable test set, the proposed approach can achieve more than 94% of the secrecy rate performance with less than 1% computation time and more than 93% satisfaction of interference leakage constraints. These results are obtained with significant reduction in computational time, which we believe that it is suitable for future real-time wireless applications.

The combination of time division multiple access (TDMA) and non-orthogonal multiple access (NOMA), referred to as hybrid TDMA-NOMA system, is considered as a potential solution to meet the unprecedented requirements for future wireless networks. While recent resource allocation techniques aiming to individually maximize either spectral efficiency (SE) or energy efficiency (EE), this paper considers an SE-EE trade-off based technique for a hybrid TDMA-NOMA system. This design offers an additional degree of freedom in resource allocation. The proposed design is formulated as a non-convex multi-objective optimization (MOO) problem. The MOO framework is reformulated as a single-objective optimization (SOO) problem by combining the multi-objectives through a weighted-sum objective function. With this, each of the original objectives is assigned with a weight factor to reflect its importance in the design. Then, the sequential convex approximation (SCA) and a second-order cone (SOC) approach are jointly utilized to deal with the non-convexity issues of the SOO problem. Simulation results reveal that the proposed trade-off based design strikes a good balance between the objective functions, while meeting the instantaneous requirements of the system.