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International Conference on Intelligent Control and Information Processing

UAV-Aided Secure Communication With Deployment Optimization and Cooperative Jamming

Hongbing Li, Juan Wu, Lei Luo, Jiang Xiong

Published Dec 3, 2021• 1 citation
Publisher

Abstract

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.

DOI
10.1109/icicip53388.2021.9642178

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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.
The rapid development of Uncrewed Aerial Vehicle (UAV)-enabled wireless networks has greatly enhanced communication flexibility and connectivity in challenging and security-sensitive scenarios. However, achieving high communication security and energy efficiency remains difficult, especially when the locations of potential eavesdroppers are uncertain and time-varying. Existing studies on UAV-enabled secure communications have advanced Secrecy Energy Efficiency (SEE)-oriented design from multiple perspectives, but the joint integration of cooperative dual-UAV design, cooperative jamming, bounded eavesdropper-location uncertainty, and robust SEE maximization remains insufficiently explored. To address this issue, we investigate a cooperative UAV-enabled secure communication system in which a source UAV transmits confidential data to a legitimate user with the assistance of a jammer UAV. We formulate a robust SEE maximization problem under bounded eavesdropper-location uncertainty and develop an efficient solution framework based on alternating optimization, Successive Convex Approximation (SCA), the S-procedure, and Dinkelbach’s method. Simulation results demonstrate that the proposed method achieves superior SEE and stronger robustness against eavesdropper-location uncertainty compared with benchmark schemes.
Cognitive unmanned aerial vehicle (UAV) communication systems combine benefits of both the cognitive radio and UAV, which improves the spectral efficiency and communication coverage area. However, high-quality air-ground channel links maybe more vulnerable to potential eavesdropping or jamming attacks. Thus, this paper proposes a secure transmission approach assisted by deploying a cooperative UAV to transmit artificial noise to jam an active eavesdropper, in order to maximize the system secrecy rate under the quality of service (QoS) requirement of a primary device. Specifically, we jointly optimize the flight trajectory and transmission power of the cooperative jammer to maximize the system's secrecy rate under strict constraints. To achieve this, we convert the non-convex problem into an approximately convex problem using the block coordinate descent algorithm and successive convex approximation method. Simulation results show that compared to existing algorithms, the proposed algorithm in this study can significantly improve the system's secrecy rate.
The broadcast nature of air-to-ground line-of-sight (LoS) wireless channel imposes a great challenge in secure unmanned aerial vehicle (UAV) communications. To address this issue, this paper investigates UAV-ground communications from the physical-layer security perspective. Specifically, the investigated scenario includes a UAV serving as the base station (BS) that transmits confidential signals to a legitimate ground user, and there are multiple eavesdroppers on the ground with unknown position information. To further enhance the secrecy performance of the UAV-ground communications, an idle UAV can be employed to serve as a friendly jammer, which can transmit jamming signals to confuse the eavesdroppers. In our proposed strategy, the flying trajectory and the transmit power for both the UAVs are jointly optimized by maximizing the worst-case secrecy rate (WCSR) of the system. Considering the intractability of the formulated non-convex problem, we further provide a block coordinate descent-based iterative optimization method. Simulations verify that our proposed algorithm can significantly improve the average WCSR in comparison with the existing works.
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 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.
This paper investigates a security-aware design for unmanned aerial vehicle (UAV)-assisted communication networks in adversarial environments with multiple aerial eavesdroppers (AEs) whose locations are imperfectly known. To enhance physical layer security (PLS), multiple cooperative aerial jammers (AJs) transmit artificial noise (AN), aiming to suppress the interception capability of AEs. Imperfect channel state information (CSI) is modeled via bounded small-scale fading errors, and location uncertainty of AEs is characterized by bounded spherical regions, enabling a deterministic worst-case secrecy rate formulation. Based on these uncertainty models, a worst-case robust secrecy energy efficiency (SEE) metric is defined as the ratio between the aggregate worst-case secrecy rate and the total energy consumption. Given the non-convex nature of this problem, a tractable solution framework is developed based on Dinkelbach’s method, block coordinate descent (BCD), and successive convex approximation (SCA). Simulation results reveal the benefits of cooperative multi-AJ operation in enhancing secrecy robustness against CSI errors and location uncertainty of AEs while maintaining energy efficiency.