Simulation results demonstrate that the proposed method significantly reduces the time-averaged CRB by over 10%, compared with the ISAC system without UAV assistance, and also achieves a higher sensing accuracy than both the fixed-UAV-trajectory and the maximum-ratio-transmission-based beamforming benchmarks.
Abstract
In this paper, we study an unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) system, where a UAV enhances the sensing capability of a base station (BS) towards a target while ensuring reliable communication towards a downlink user. This architecture is practically attractive for future wireless networks due to the UAV's controllable mobility and adaptive sensing coverage in wireless environments. The sensing performance is characterized by the average Cram\'er-Rao bound (CRB), which quantifies the minimum variance of the unbiased angle-of-arrival estimation. To enhance the sensing performance, the UAV trajectory and beamforming parameters are jointly optimized under power and mobility constraints, while satisfying communication requirements to the downlink user. To address the resulting non-convex problem, we employ null-space projection for beamforming design and adopt deep reinforcement learning for the trajectory optimization over a discrete-time scale. In each time slot, beamforming is optimized based on the channel state information to improve CRB performance while mitigating interference between the BS and the communication user. Simulation results demonstrate that the proposed method significantly reduces the time-averaged CRB by over 10%, compared with the ISAC system without UAV assistance, and also achieves a higher sensing accuracy than both the fixed-UAV-trajectory and the maximum-ratio-transmission-based beamforming benchmarks.
An unmanned aerial vehicle (UAV)-enabled ISAC system employing rate-splitting multiple access (RSMA) and a joint beamforming and trajectory optimization framework is investigated and results demonstrate that the proposed algorithm significantly improves the achievable system downlink rate.
Shunxuan Wang, Qi Zhu· Italian National Conference...· 0 citations
The rapid proliferation of Uncrewed Aerial Vehicles (UAVs) introduces significant challenges to low-altitude airspace security, particularly from unauthorized intrusions. To address these vulnerabilities, Integrated Sensing and Communication (ISAC) has emerged as a key enabler for anti-UAV systems. However, existing studies focusing on cellular networks with fixed base stations are ill-suited for the continuous movement of target UAVs, thus failing to meet the dual demands of flexible sensing and reliable positioning. To address this, we propose an ISAC-enabled anti-UAV scheme solely based on cooperative UAVs. Specifically, we first derive the optimal transmit power under the constraint of space-air transmission outage probability tolerance. Subsequently, we deduce the sensing Fisher information matrix and Cramér-Rao Bound (CRB) by incorporating the position uncertainty of the target UAV. Then, we formulate a long-term CRB minimization problem to enhance cooperative sensing performance. To tackle this NP-hard problem, we design a robust optimization algorithm that jointly optimizes transmit-receive beamforming, association scheduling, and UAV trajectory, by transforming the structurally complex CRB matrix into a set of semi-definite constraints, and resolving the inherent position uncertainty. Numerical results demonstrate that our proposed algorithm outperforms representative algorithms in terms of sensing accuracy and robustness.
Xiaojie Wang, Lingfei Li, Zhaolong Ning et al.· IEEE Transactions on Wireles...· 1 citation
This work investigates the secrecy performance of a dual-uncrewed aerial vehicle (UAV)-assisted secure ISAC system, and maximizes the average secrecy rate by optimizing user scheduling strategies, time allocation, transmit power, and UAV trajectories.
Hongjiang Lei, Jianshuo Geng, Ki-Hong Park et al.· 1 citation
Integrated sensing and communication (ISAC) technology, when deployed on unmanned aerial vehicles (UAVs), enables aerial base stations to simultaneously provide wireless connectivity to ground users and perform environmental sensing through echo signal analysis. However, the broadcast nature of wireless transmission, combined with the line-of-sight (LoS) propagation characteristics of UAVs, increases the risk of passive eavesdropping on transmitted signals during ISAC missions. This paper investigates the joint trajectory design and power allocation (JTDPA) problem for UAV-enabled ISAC systems in environments with multiple mobile ground users and potential eavesdroppers. The proposed approach formulates the optimization problem as a constrained Markov decision process (CMDP), aiming to balance communication rate, secrecy rate, and energy consumption. To address the limitations of existing secure trajectory designs, such as unnecessary energy expenditure and overly conservative avoidance actions, we propose a two-stage (TS) strategy that incorporates the safe twin delayed deep deterministic policy gradient (Safe-TD3) algorithm, referred to as TS-SafeTD3. In the first stage (sensing stage), the UAV navigates toward a user-centric location without communication to enhance initial coverage efficiency, while satisfying the minimum-distance safety constraints with respect to potential eavesdroppers.In the second stage (ISAC stage), Safe-TD3 is employed to jointly optimize both trajectory and power allocation under the same safety constraints to maximize the weighted secrecy rate. Simulation results indicate that the proposed algorithm improves the weighted secrecy rate and energy efficiency under various operational conditions, while maintaining a low violation probability of the safety constraints.
Yu-Jia Chen, Hai-Yan Huang, Ting-Wei Chen et al.· IEEE Transactions on Network...· 0 citations
Unmanned Aerial Vehicles (UAVs) are promising relay platforms due to their flexible deployment and high probability of line-of-sight (LoS) connectivity. This paper compares three deep reinforcement learning (DRL) algorithms-Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Recurrent PPO with LSTM memory-for joint UAV trajectory and energy optimization in UAV based relay systems. The problem formulated is a non-convex optimization problem that minimizes UAV propulsion energy while satisfying Quality of Service (QoS) and mobility constraints under realistic 3GPP channel conditions. Simulation results show that all methods achieve over 99% QoS satisfaction. SAC exhibits the fastest convergence, whereas the proposed Recurrent PPO achieves the lowest energy consumption (44.72 kJ), reducing energy usage by 5.1% compared with PPO. These results highlight the trade-off between convergence speed and energy efficiency in DRL-based UAV relay optimization.
Aniket Subbanwar, Ojas Joshi, Amit Agarwal· International Conference on...· 0 citations
In this paper, we investigate a movable antenna (MA)-assisted uncrewed aerial vehicle (UAV) swarm communication system. Unlike conventional fixed-position antenna (FPA) systems, each UAV is equipped with an MA array distributed on two hemispherical surfaces at the head and tail, significantly expanding the spatial degrees of freedom (DoFs) in three-dimensional (3-D) seamless coverage. A far-field line-of-sight (LoS) channel model is adopted to characterize the UAV-to-UAV (U2U) communication links, incorporating both antenna positioning and radiation patterns. We formulate an achievable sum rate maximization problem by jointly optimizing the antenna position vectors (APVs) and transmit/receive beamforming vectors, subject to constraints on maximum transmit power, limited antenna moving region, and minimum inter-antenna spacing. To tackle this non-convex and highly coupled problem, we propose a two-loop iterative optimization algorithm that effectively combines the Spider Wasp Optimizer (SWO) for APV optimization and alternative optimization (AO) for beamforming design. Extensive simulation results demonstrate that the proposed MA-assisted scheme outperforms traditional FPA systems and other benchmark algorithms under various settings. The performance gains are attributed to the efficient optimization of antenna positions within the hemispherical moving region for interference suppression and coverage enhancement.
Fansheng Song, Lipeng Zhu, Xiangyu Pi et al.· IEEE Transactions on Communi...· 0 citations
