This paper addresses physical layer security in unmanned aerial vehicle (UAV)-assisted communication. We propose a secure beamforming optimization algorithm based on an Improved Artificial Bee Colony (I-ABC) framework to solve the non-convex problem of maximizing the system’s secrecy rate. The algorithm concurrently designs the information-bearing signal beam and the artificial noise (AN) beam, ensuring quality of service (QoS) for the legitimate user while degrading the eavesdropper’s channel. A key innovation is the I-ABC’s channelaware dynamic perturbation strategy, which effectively explores the non-convex solution space to avoid local optima and ensure rapid convergence. Simulation results demonstrate that our algorithm exhibits rapid convergence and significantly enhances the secrecy rate compared to schemes without AN, confirming its efficacy for secure transmission.
This paper addresses physical layer security in unmanned aerial vehicle (UAV)-assisted communication. We propose a secure beamforming optimization algorithm based on an Improved Artificial Bee Colony (I-ABC) framework to solve the non-convex problem of maximizing the system’s secrecy rate. The algorithm concurrently designs the information-bearing signal beam and the artificial noise (AN) beam, ensuring quality of service (QoS) for the legitimate user while degrading the eavesdropper’s channel. A key innovation is the I-ABC’s channelaware dynamic perturbation strategy, which effectively explores the non-convex solution space to avoid local optima and ensure rapid convergence. Simulation results demonstrate that our algorithm exhibits rapid convergence and significantly enhances the secrecy rate compared to schemes without AN, confirming its efficacy for secure transmission.
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.
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.
Unmanned Aerial Vehicles (UAVs) have found compelling applications in intelligent logistics, search and rescue as well as in air-borne Base Station (BS). However, their communications are prone to both channel errors and eavesdropping. Hence, we investigate the max-min secrecy fairness of UAV-aided cellular networks, in which Cooperative Rate-Splitting (CRS) aided downlink transmissions are employed by each multi-antenna UAV Base Station (UAV-BS) to safeguard the downlink of a two-user Multi-Input Single-Output (MISO) system against an external multi-antenna Eavesdropper (Eve). Realistically, only Imperfect Channel State Information (ICSI) is assumed to be available at the transmitter. Additionally, we consider a realistic total power constraint and guarantee the specific Quality of Service (QoS) requirements of the legitimate users. To handle the worst-case channel uncertainty of the legitimate users and an external Eve, we conceive a robust secure resource allocation algorithm, which maximizes the minimum worst-case secrecy rate of the legitimate users. Based on the CRS principle, the transmitter splits and encodes the messages of legitimate users into common as well as private streams and the user having stronger CSI is asked to help the cell-edge user by opportunistically forwarding its decoded common message. In contrast to the existing schemes adopted in the literature for ensuring secure transmission of the first cooperative phase only, in our proposed solution the common message has a twin-fold mission. Explicitly, apart from serving as the desired message, it also acts as Artificial Noise (AN) for drowning out Eve without consuming extra power. This is in stark contrast to the conventional AN designs. In the second phase, the pure AN is directed towards the Eve, deploying a robust Maximum Ratio Transmitter (MRT) beamformer at the UAV-BS. To solve the resultant non-convex optimization problem we resort to the Sequential Parametric Convex Approximation (SPCA) method together with a bespoke initialization algorithm to avoid any failure due to infeasibility. Our simulation results confirm that the proposed secure transmission scheme outperforms the existing cooperative benchmarkers.
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.