This work employs an unmanned aerial vehicle (UAV) as a jammer to aid a covert communication from a transmitter Alice to a receiver Bob, where the LAV transmits artificial noise (AN) with random power to deliberately create interference to a warden Willie. To maximize the system performance, we formulate an optimization problem to jointly design the UAV’s trajectory and Alice’s transmit power. The formulated optimization problem is non-convex that is difficult to tackle directly. To this end, this work, for the first time, develops a geometric (GM) method to solve the optimization problem. Our examination shows that the GM method can significantly outperforms a benchmark method in terms of achieving a higher average covert rate and the complexity of the GM method is lower than that of the benchmark method.
This work employs an unmanned aerial vehicle (UAV) as a jammer to aid a covert communication from a transmitter Alice to a receiver Bob, where the LAV transmits artificial noise (AN) with random power to deliberately create interference to a warden Willie. To maximize the system performance, we formulate an optimization problem to jointly design the UAV’s trajectory and Alice’s transmit power. The formulated optimization problem is non-convex that is difficult to tackle directly. To this end, this work, for the first time, develops a geometric (GM) method to solve the optimization problem. Our examination shows that the GM method can significantly outperforms a benchmark method in terms of achieving a higher average covert rate and the complexity of the GM method is lower than that of the benchmark method.
This work employs an unmanned aerial vehicle (UAV) as a jammer to aid a covert communication from a transmitter Alice to a receiver Bob, where the UAV transmits artificial noise (AN) with random power to deliberately create interference to a warden Willie. In the considered system, the UAV's trajectory is critical to the covert communication performance, since the AN transmitted by the UAV also generates interference to Bob. To maximize the system performance, we formulate an optimization problem to jointly design the UAV's trajectory and Alice's transmit power. The formulated optimization problem is non-convex and is normally solved by a conventional iterative (CI) method, which requires multiple approximations based on Taylor expansions and an initialization on the UAV's trajectory. In order to eliminate these requirements, this work, for the first time, develops a geometric (GM) method to solve the optimization problem. By analyzing the covertness constraint, the GM method decouples the joint optimization into optimizing the UAV's trajectory and Alice's transmit power separately. Our examination shows that the GM method can significantly outperform the CI method in terms of achieving a higher average covert rate and the complexity of the GM method is lower than that of the CI method.
This work employs an unmanned aerial vehicle (UAV) as a jammer to aid a covert communication from a transmitter Alice to a receiver Bob, where the UAV transmits artificial noise (AN) with random power to deliberately create interference to a warden Willie. In the considered system, the UAV’s trajectory is critical to the covert communication performance, since the AN transmitted by the UAV also generates interference to Bob. To maximize the system performance, we formulate an optimization problem to jointly design the UAV’s trajectory and Alice’s transmit power. The formulated optimization problem is non-convex and is normally solved by a conventional iterative (CI) method, which requires multiple approximations based on Taylor expansions and an initialization on the UAV’s trajectory. In order to eliminate these requirements, this work, for the first time, develops a geometric (GM) method to solve the optimization problem. By analyzing the covertness constraint, the GM method decouples the joint optimization into optimizing the UAV’s trajectory and Alice’s transmit power separately. Our examination shows that the GM method can significantly outperform the CI method in terms of achieving a higher average covert rate and the complexity of the GM method is lower than that of the CI method.
This letter presents a new cooperative jamming approach to secure the unmanned aerial vehicle (UAV) communication by leveraging jamming from other nearby UAVs to defend against the eavesdropping. In particular, we consider a two-UAV scenario when one UAV transmitter delivers the confidential information to a ground node (GN), and the other UAV jammer cooperatively sends the artificial noise to confuse the ground eavesdropper for protecting the confidentiality of the data transmission. By exploiting the fully controllable mobility, the two UAVs can adaptively adjust their locations over time (a.k.a. trajectories) to facilitate the secure communication and cooperative jamming. We assume that the two UAVs perfectly know the GN's location and partially know the eavesdropper's location a priori. Under this setup, we maximize the average secrecy rate from the UAV transmitter to the GN over one particular time period, by optimizing the UAVs' trajectories, jointly with their communicating/jamming power allocations. Although the formulated problem is non-convex, we propose an efficient solution by applying the techniques of alternating optimization and successive convex approximation.
Unmanned aerial vehicle (UAV) aided augmented reality (AR) has developed rapidly in recent years and has become a promising technology in disaster rescue, transportation, agriculture, and environmental monitoring. However, the information leakage is challenging the usage of UAV-aided AR systems with wireless communications. In this article, a dual UAVs assisted covert communication system (CCS) is proposed, where one UAV transmits the covert message to ground receivers and a cooperative UAV performs as a jammer to interfere with the malicious eavesdropper. However, flying UAVs result in fast channel fading between the UAVs and ground nodes, which reduces the covert rate of the CCSs. To maximize the average covert rate, we formulated a non-convex optimization problem under the constraint that the detection error probability (DEP) of the monitor is minimum. Furthermore, the problem is decomposed into three subproblems and transformed into convex, and these subproblems are solved alternately by designing an iterative algorithm. Simulation results reveal that the average covert rate performance of the proposed optimization algorithm can respectively achieve 16% and 40% gains than those without covert constraint and without the cooperative UAV used as a jammer.
This paper proposes a mobile unmanned aerial vehicle (UAV) jamming scheme under the probabilistic line-ofsight channel model (PLCM) to improve the secrecy of ground wiretap channels, in which a friendly UAV is deployed to cooperatively transmit jamming signals to confuse the suspicious eavesdropper. Our goal is to maximize the average (expected) secrecy rate by jointly optimizing the source transmit power, UAV jamming power and trajectory for a given flight time. Since the expected secrecy rate is highly complicated with respect to the UAV trajectory, we derive a more tractable lower bound for it. Nevertheless, the resulting optimization problem remains a non-convex problem, which is difficult to solve optimally. Therefore, we propose an efficient iterative algorithm to obtain a suboptimal solution to it by applying the block coordinate descent (BCD) and successive convex approximation (SCA) techniques. Simulation results show that the joint power and trajectory optimization scheme under the PLCM significantly outperforms various benchmark schemes.
This paper investigates the jointly transmit power and unmanned aerial vehicle (UAV)/jammer deployment optimization problem for UAV enabled jammer-assisted secure communication systems, where a jammer is exploited to assist transmitting data from a source node to a legitimate destination node in the presence of multiple eavesdroppers. To increase the secrecy energy efficiency, which is defined as the achievable secrecy rate per energy consumption unit, we formulate a secrecy energy efficiency maximization problem by jointly optimizing the transmit power and UAV/jammer deployment. The resulting optimization problem is shown to be a non-convex and fractional optimization problem, which is challenging to solve. As such, we decompose the original problem into two sub-problems, and then, an efficient iterative algorithm is proposed by leveraging the extensive search method and Dinkelbach method in combination with successive convex approximation (SCA) techniques. Numerical simulation results show that the proposed scheme significantly improves the secrecy energy efficiency of the system.