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IEEE Transactions on Wireless Communications

Optimal Geometric Solutions to UAV-Enabled Covert Communications in Line-of-Sight Scenarios

Hangmei Rao, Sa Xiao, Shihao Yan, Jianquan Wang, Wanbin Tang

Published Jun 30, 2022• 37 citations
Publisher

Abstract

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.

DOI
10.1109/twc.2022.3185492

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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 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.
Secure communication technology has attracted more and more attention to improve the security of the Internet of Things (IoT). In this article, we discuss the maximum secrecy capacity problem for a mobile unmanned aerial vehicle (UAV) assisted secure communication in IoT, where the UAV relays the covert information from the transmitter (Alice) to the legal receiver (Bob) without detection by an eavesdropper (Eve) though sharing the spectrum with the ground device-to-device (D2D) communications. Our design aims to maximize the sum secrecy capacity Bob received by optimizing the transmit power and the UAV’s trajectory jointly while guaranteeing that the normal communication behavior between ground D2D pairs is not affected. Therefore, we propose a jointly optimizing the transmit power and UAV trajectory algorithm (JOTUTA) based on the block coordinate descent (BCD) method. Specifically, we first transform the non-convex problem of transmit power based on a fixed UAV’s trajectory into a convex problem by D.C. (difference of two convex functions) programming. Secondly, we solve the problem of UAV’s trajectory optimization with given transmit power by the successive convex approximation (SCA) method. Experiments results show the effectiveness of JOTUTA and the advantages of improving the secrecy capacity are verified compared with existing work.
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 investigates an average secrecy rate (ASR) maximization problem for an unmanned aerial vehicle (UAV) enabled wireless communication system, wherein a UAV is employed to deliver confidential information to a ground destination in the presence of a terrestrial passive eavesdropper. By employing an artificial noise (AN) injection based secure two-phase transmission protocol, we aim at jointly optimizing the UAV’s trajectory, network transmission power, and AN power allocation over a given time horizon to enhance the ASR performance. Specifically, we divide the original non-convex problem into four subproblems, and propose a successive convex approximation based efficient iterative algorithm to solve it suboptimally with guaranteed convergence. Simulation results demonstrate significant security advantages of our designed scheme over other known benchmarks, particularly for stringent flight durations.
This paper investigates an average secrecy rate (ASR) maximization problem for an unmanned aerial vehicle (UAV) enabled wireless communication system, wherein a UAV is employed to deliver confidential information to a ground destination in the presence of a terrestrial passive eavesdropper. By employing an artificial noise (AN) injection based secure two-phase transmission protocol, we aim at jointly optimizing the UAV’s trajectory, network transmission power, and AN power allocation over a given time horizon to enhance the ASR performance. Specifically, we divide the original non-convex problem into four subproblems, and propose a successive convex approximation based efficient iterative algorithm to solve it suboptimally with guaranteed convergence. Simulation results demonstrate significant security advantages of our designed scheme over other known benchmarks, particularly for stringent flight durations.