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Resource Allocation for Secure Dual-UAV-Assisted ISAC System

This paper proposes an efficient iterative algorithm based on block coordinate descent and successive convex approximation to maximize the average secrecy rate in a dual-UAV-assisted secure ISAC system by jointly optimizing user scheduling, time allocation, transmit power, and UAV trajectories under various physical and performance constraints.

Original authors: Hongjiang Lei, Jianshuo Geng, Ki-Hong Park, Jia Ye, Liang Yang, Xiaqing Miao, Gaofeng Pan

Published 2026-08-26

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CC BY 4.0

Original authors: Hongjiang Lei, Jianshuo Geng, Ki-Hong Park, Jia Ye, Liang Yang, Xiaqing Miao, Gaofeng Pan

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

In the invisible airwaves that cradle our modern world, a quiet battle is constantly being waged between those who wish to send a message and those who wish to steal it. For decades, the technology of communication and the technology of sensing—like radar that detects objects—have operated as separate tools, each using its own slice of the radio spectrum. However, a new approach called integrated sensing and communication is beginning to merge these two functions, allowing a single device to talk and listen at the same time, much like a person who can speak to a friend while simultaneously watching for a threat in the crowd. This efficiency is vital as our networks become crowded, but it introduces a new vulnerability: the very signals that help a system see its surroundings can also reveal its secrets to an eavesdropper lurking nearby. To protect these shared signals, researchers are turning to the sky, using unmanned aerial vehicles, or drones, not just as flying cell towers, but as agile guardians that can move, sense, and jam intruders in real time.

A team of researchers has recently explored how to make this aerial defense system as effective as possible. They focused on a setup involving two drones working in tandem: one acts as a base station, flying high to talk to people on the ground and simultaneously sending out radar pulses to locate a potential spy; the other is a friendly jammer, tasked with blasting noise to confuse that spy. The challenge they faced was not just getting the drones to fly, but figuring out exactly how they should move, how much power to use, and when to switch between talking and listening, all while dealing with the fact that the spy's exact location might not be known perfectly. The researchers treated the spy's location as a fuzzy area rather than a single point, acknowledging that in the real world, sensors are rarely perfect. Their goal was to maximize the amount of secret information that could be safely delivered to the ground users without the spy intercepting it, all while ensuring the drones did not run out of battery before their mission was complete.

To solve this complex puzzle, the team developed a step-by-step computer algorithm that acts like a master planner. Instead of trying to calculate every possible flight path and power setting at once—a task that would be too difficult for any computer—they broke the problem down into smaller, manageable pieces. The algorithm first decides which ground user the base station should talk to at any given moment, then determines how to split the time between sensing the spy and sending data. Next, it calculates the precise power levels and the specific shape of the signal beams the jammer should use. Finally, it plots the flight paths for both drones, ensuring they move smoothly and efficiently. The system repeats this cycle, refining the plan over and over until it finds the best possible combination of movements and settings. In their simulations, the researchers tested this method in two different environments: one where the ground users were clustered on one side of the flight path and the spy on the other, and another where the users were scattered around the spy. In both cases, the algorithm successfully guided the drones to move closer to the users to improve communication while simultaneously positioning the jammer drone to hover near the spy and disrupt its ability to listen.

The results of these simulations showed that this coordinated approach significantly outperforms older methods where drones might follow a fixed path or use simpler jamming techniques. The study found that by allowing the jammer drone to carry multiple antennas, the system became much better at blocking the spy, as the extra antennas helped focus the jamming noise more precisely. The researchers also discovered that the system is sensitive to how accurately the spy's location is known; when the uncertainty about the spy's position was larger, the overall security performance dropped slightly, which is an expected trade-off in such a dynamic environment. Furthermore, the simulations revealed a delicate balance between the need to sense the spy and the need to communicate with users. If the system demands a very high level of sensing accuracy, it must dedicate more time and energy to scanning, which inevitably reduces the time available for sending secret messages. Conversely, if the goal is purely to maximize data speed, the sensing might become too weak to reliably track the intruder.

Ultimately, the work demonstrates that a dual-drone system, when guided by a smart optimization algorithm, can create a robust shield for wireless communications. The proposed method successfully navigates the constraints of battery life, speed limits, and imperfect information to keep the airwaves secure. While the findings are currently based on computer simulations rather than physical flight tests, they provide a strong blueprint for future networks. The researchers suggest that the next step would be to test these concepts with multiple spies and more complex weather conditions, and perhaps even integrate smart surfaces on the ground to help reflect signals. For now, the study confirms that by letting drones move intelligently and work together, we can build communication networks that are not only faster and more efficient but also far more secure against the invisible threats that lurk in the spectrum.

Technical Summary: Resource Allocation for Secure Dual-UAV-Assisted ISAC System

Problem Formulation
This work investigates the secrecy performance of a dual-Uncrewed Aerial Vehicle (UAV)-assisted Integrated Sensing and Communication (ISAC) system. The system comprises a Base Station (BS) UAV and a friendly Jammer UAV operating in a Time-Division ISAC (TD-ISAC) framework. The BS UAV performs two primary functions: communicating with multiple ground users and transmitting radar signals to detect a ground eavesdropper. Crucially, the eavesdropper's location is not perfectly known; it is modeled as an uncertainty region derived from preliminary coarse estimation. The detected location information is forwarded to the Jammer UAV, which utilizes a Uniform Linear Array (ULA) to transmit Artificial Noise (AN) and suppress the eavesdropper.

The core optimization problem aims to maximize the Average Secrecy Rate (ASR) of the system. This is achieved by jointly optimizing:

User scheduling strategies.
Time allocation ratios (dividing slots for sensing, information transmission to the jammer, and user communication).
Transmit power of the BS UAV.
Transmit beamforming vectors of the Jammer UAV.
Flight trajectories of both UAVs.

The optimization is subject to practical constraints, including maximum UAV velocity and acceleration, initial and final position requirements, transmit power budgets, propulsion energy consumption limits, minimum communication Quality of Service (QoS) for users, and minimum sensing performance thresholds. The problem is formulated as a non-convex, nonlinear mixed-integer programming problem due to the coupling of variables, binary scheduling constraints, and the non-concave nature of the secrecy rate function involving the $[\cdot]^+$ operator and worst-case eavesdropper location analysis.

Methodology
To address the non-convexity and complexity of the joint optimization problem, the authors propose an efficient iterative algorithm based on the Block Coordinate Descent (BCD) technique. The original problem is decomposed into six sequential subproblems:

User Scheduling: The binary scheduling variables are relaxed to continuous values and optimized as a linear programming problem.
Time Allocation: Optimized as a convex problem given other variables.
Transmit Power: The non-convex power constraints and objective are handled using Successive Convex Approximation (SCA), where non-convex terms are approximated by their first-order Taylor expansions.
Beamforming: The transmit beamforming vector is optimized using Semi-Definite Relaxation (SDR) to handle the rank-one constraint, converting the problem into a semi-definite programming (SDP) form.
BS UAV Trajectory: The trajectory is optimized by introducing slack variables and applying SCA to convexify the non-convex distance and rate constraints related to the sensing and communication links.
Jammer UAV Trajectory: Similar to the BS UAV, the trajectory is optimized using slack variables and SCA, specifically addressing the nonlinearity introduced by the steering vector of the jammer's antenna array.

The algorithm iterates through these subproblems until convergence, defined by a threshold on the change in the objective value.

Key Contributions
The paper distinguishes itself from existing literature through the following contributions:

Holistic Secure Framework: Unlike prior works that often assume perfect Channel State Information (CSI) or ignore propulsion energy constraints, this work proposes a practical framework considering imperfect eavesdropper location knowledge, limited propulsion energy, and realistic sensing thresholds.
Dual-UAV Cooperation: The study explicitly models a cooperative scenario where a BS UAV detects an eavesdropper and relays this information to a dedicated Jammer UAV, which then performs active jamming. This contrasts with single-UAV systems or those relying solely on passive security measures.
Joint Optimization: The work jointly optimizes user scheduling, time allocation, transmit power, beamforming, and dual UAV trajectories. This comprehensive approach addresses the trade-offs between communication fairness, sensing accuracy, and security in the presence of location uncertainty.
Robustness to Uncertainty: By adopting a worst-case approach over a sampled set of potential eavesdropper locations within the uncertainty region, the proposed scheme ensures secrecy performance even when the eavesdropper's exact position is unknown.

Numerical Results
Simulation results validate the proposed algorithm's convergence and effectiveness. Key findings include:

Trajectory Adaptation: The BS UAV optimizes its path to approach scheduled users closely to enhance communication rates, while the Jammer UAV proactively approaches the eavesdropper's uncertainty region to maximize jamming efficacy.
Performance Gains: The proposed scheme significantly outperforms benchmark algorithms, including a fixed-trajectory approach and a single-antenna jammer scenario.
Parameter Sensitivity:
- Increasing the number of transmit antennas ( $N_t$ ) at the Jammer improves the ASR by degrading the eavesdropper's link quality.
- A smaller uncertainty region ( $\Delta$ ) leads to higher ASR, confirming the benefit of more accurate location estimation.
- There is a clear trade-off between communication and sensing: stricter sensing thresholds ( $R_{sen}^{min}$ ) reduce the ASR as more resources are diverted to sensing tasks.
Convergence: The iterative algorithm demonstrates rapid convergence in various scenarios, including different user distributions and flight paths.

Significance
The paper claims that its primary significance lies in bridging the gap between theoretical ISAC security models and practical deployment constraints. By integrating sensing, communication, and jamming under realistic conditions—specifically imperfect location knowledge and energy limitations—the work provides a robust framework for securing next-generation wireless networks. The proposed closed-loop framework, which unifies sensing, jamming, and communication, offers a viable solution for maintaining secure transmissions in dynamic UAV-assisted environments where eavesdropper locations are uncertain. The authors note that while the current work focuses on a single eavesdropper and Line-of-Sight (LoS) channels, it lays the groundwork for future research involving multiple mobile eavesdroppers and more complex channel models.

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