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Sensors

Optimization Method for Secrecy Capacity of UAV Relaying Based on Dynamic Adjustment of Power Allocation Factor

Yunqi Hao, Youyang Xiang, Qilong Du, Xianglu Li, Chen Ding, Dong Hou, Jie Tian

Published Jan 15, 2026• 0 citations
Publisher

Abstract

The broadcast nature of wireless channels introduces significant security vulnerabilities in information transmission, particularly when the eavesdropper is close to the legitimate destination. In such scenarios, the eavesdropping channel often exhibits high spatial correlation with, or even superior quality to, the legitimate channel. This makes it challenging for traditional power optimization methods to effectively suppress the eavesdropping rate. To address this challenge, this paper proposes an optimization method for the secrecy capacity of unmanned aerial vehicle (UAV) relaying based on the dynamic adjustment of the power allocation factor. By injecting artificial noise (AN) during signal forwarding and combining it with real-time channel state information, the power allocation factor can be dynamically adjusted to achieve precise jamming of the eavesdropping link. We consider a four-node communication model consisting of a source, a UAV, a legitimate destination, and a passive eavesdropper, and formulate a joint optimization problem to maximize the secrecy rate. Due to the non-convexity of the original problem, we introduce relaxation variables and apply successive convex approximation (SCA) to reformulate it into an equivalent convex optimization problem. An analytical solution for the power allocation factor is derived using the water-filling (WF) algorithm. Furthermore, an alternating iterative optimization algorithm with AN assistance is proposed to achieve global optimization of the system parameters. Simulation results demonstrate that, compared to traditional power optimization schemes, the proposed algorithm substantially suppresses the eavesdropping channel capacity while enhancing transmission efficiency, thereby significantly improving both secrecy performance and overall communication reliability.

DOI
10.3390/s26020592

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The broadcast nature of wireless channels introduces significant security vulnerabilities in information transmission, particularly when the eavesdropper is close to the legitimate destination. In such scenarios, the eavesdropping channel often exhibits high spatial correlation with, or even superior quality to, the legitimate channel. This makes it challenging for traditional power optimization methods to effectively suppress the eavesdropping rate. To address this challenge, this paper proposes an optimization method for the secrecy capacity of unmanned aerial vehicle (UAV) relaying based on the dynamic adjustment of the power allocation factor. By injecting artificial noise (AN) during signal forwarding and combining it with real-time channel state information, the power allocation factor can be dynamically adjusted to achieve precise jamming of the eavesdropping link. We consider a four-node communication model consisting of a source, a UAV, a legitimate destination, and a passive eavesdropper, and formulate a joint optimization problem to maximize the secrecy rate. Due to the non-convexity of the original problem, we introduce relaxation variables and apply successive convex approximation (SCA) to reformulate it into an equivalent convex optimization problem. An analytical solution for the power allocation factor is derived using the water-filling (WF) algorithm. Furthermore, an alternating iterative optimization algorithm with AN assistance is proposed to achieve global optimization of the system parameters. Simulation results demonstrate that, compared to traditional power optimization schemes, the proposed algorithm substantially suppresses the eavesdropping channel capacity while enhancing transmission efficiency, thereby significantly improving both secrecy performance and overall communication reliability.
The broadcast nature of wireless channels introduces significant security risks during information transmission. When the eavesdropper is in close proximity to the legitimate receiver, their channel state information (CSI) may be highly correlated or even superior to that of the legitimate channel, rendering traditional power optimization methods ineffective in suppressing eavesdropping rates. To address this issue, this paper proposes an artificial noise (AN)-assisted unmanned aerial vehicle (UAV) relay system. By leveraging the UAV relay to inject artificial noise while transmitting signals and dynamically adjusting the power allocation factor based on real-time CSI and the UAV's position, precise interference control over the eavesdropping channel is achieved. This study establishes a fournode communication system comprising a source, relay, legitimate receiver, and eavesdropper, formulating a joint optimization problem aimed at maximizing secrecy capacity. Given the nonconvex nature of the optimization problem, this paper employs slack variables and the successive convex approximation (SCA) method to equivalently transform the original problem into a convex problem. A closed-form analytical solution for the power allocation factor is derived using the staircase water-filling (WF) algorithm. Furthermore, an alternating iterative optimization algorithm based on artificial noise assistance is proposed to achieve global optimization of system parameters. Simulation results demonstrate that, compared to traditional power optimization schemes, the proposed algorithm reduces the eavesdropping capacity and improves the information transmission efficiency, significantly enhancing both system security and transmission performance.
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.
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.
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 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.