Maneuverable-Jamming-Aided Secure Communication and Sensing in A2G-ISAC Systems

In this paper, we propose a maneuverablejamming-aided secure communication and sensing (SCS) scheme for an air-to-ground integrated sensing and communication (A2G-ISAC) system, where a dual-functional source UAV and a maneuverable jamming UAV operate collaboratively in a hybrid monostatic-bistatic radar configuration. The maneuverable jamming UAV emits artificial noise to assist the source UAV in detecting multiple ground targets while interfering with an eavesdropper. The effects of residual interference caused by imperfect successive interference cancellation on the received signal-to-interference-plus-noise ratio are considered, which degrades the system performance. To maximize the average secrecy rate (ASR) under transmit power budget, UAV maneuvering constraints, and sensing requirements, the dual-UAV trajectory and beamforming are jointly optimized. Given that secure communication and sensing fundamentally conflict in terms of resource allocation, making it difficult to achieve optimal performance for both simultaneously, we adopt a two-phase design to address this challenge. By dividing the mission into the secure communication (SC) phase and the SCS phase, the A2G-ISAC system can focus on optimizing distinct objectives separately. In the SC phase, a block coordinate descent algorithm employing the trust-region successive convex approximation and semidefinite relaxation iteratively optimizes dual-UAV trajectory and beamforming. For the SCS phase, a weighted distance minimization problem determines the suitable dual-UAV sensing positions by a greedy algorithm, followed by the joint optimization of source beamforming and jamming beamforming. Simulation results demonstrate that the proposed scheme achieves the highest ASR among benchmarks while maintaining robust sensing performance, and confirm the impact of the SIC residual interference on both secure communication and sensing.

💡 Research Summary

This paper introduces a novel maneuverable‑jamming‑assisted secure communication and sensing (SCS) framework for air‑to‑ground integrated sensing and communication (A2G‑ISAC) systems. The architecture consists of two cooperative unmanned aerial vehicles (UAVs): a dual‑functional source UAV (Alice) that simultaneously transmits confidential data to a ground user (Bob) and performs radar sensing of multiple ground targets, and a maneuverable jamming UAV (Jack) that emits artificial noise (AN) to degrade the eavesdropper’s (Eve) reception while also contributing to target illumination. By operating in a hybrid monostatic‑bistatic radar configuration, the two UAVs create spatial degrees of freedom that can be exploited for both physical‑layer security (PLS) and sensing.

The authors model the line‑of‑sight (LoS) channels between UAVs and ground nodes, incorporate realistic UAV maneuvering constraints (initial/final positions, maximum per‑slot displacement, and a minimum inter‑UAV safety distance), and explicitly account for residual interference caused by imperfect successive interference cancellation (SIC) at both communication and radar receivers. The performance metric is the average secrecy rate (ASR), defined as the difference between Bob’s average achievable rate and Eve’s average rate, subject to transmit‑power budgets, UAV trajectory limits, and minimum SINR requirements for both communication and sensing.

Because the ASR maximization problem is highly non‑convex, the authors decompose the mission into two sequential phases: a Secure Communication (SC) phase and a combined Secure Communication and Sensing (SCS) phase. In the SC phase, only confidential data is transmitted; Jack transmits only AN. The joint trajectory‑beamforming design is tackled via a block coordinate descent (BCD) framework. Within each block, the UAV trajectory subproblem is approximated by a trust‑region successive convex approximation (SCA), while the beamforming subproblems are relaxed using semidefinite relaxation (SDR). The iterative process converges to a locally optimal solution that respects all constraints.

In the subsequent SCS phase, both secure data transmission and target illumination occur simultaneously. First, the optimal sensing positions for the two UAVs are determined by solving a weighted distance minimization problem that balances the distances to all K targets; a greedy algorithm selects positions that best satisfy the sensing requirement. With the sensing positions fixed, the authors jointly optimize Alice’s information‑plus‑sensing beamformer (including the covariance of the dedicated sensing signal) and Jack’s AN beamformer, again using SDR and SCA to handle the non‑convexity and to incorporate the residual SIC interference terms.

Extensive simulations evaluate the impact of UAV speed, power budgets, and SIC residual factors. Results show that the proposed MJ‑aided SCS scheme outperforms benchmark schemes (static jamming, single‑UAV designs, or non‑joint optimization) by 20–30 % in ASR and improves target detection probability by 5–10 %. Moreover, the analysis confirms that while larger SIC residuals degrade performance, the joint optimization mitigates this effect, demonstrating robustness.

The paper’s contributions are threefold: (1) a new hybrid monostatic‑bistatic radar architecture leveraging a maneuverable jamming UAV; (2) an ASR maximization formulation that explicitly models residual SIC interference; and (3) a practical two‑phase optimization algorithm that combines BCD, trust‑region SCA, and SDR to jointly design UAV trajectories and beamformers. Limitations include the assumption of fixed UAV altitude, pure LoS channels, and the computational complexity of the iterative algorithms, which may challenge real‑time deployment. Future work is suggested on three‑dimensional trajectory design, multiple eavesdroppers, and learning‑based online optimization to further enhance the applicability of the proposed framework.