Age of Information Minimization in UAV-Enabled Integrated Sensing and Communication Systems

• URL: http://arxiv.org/abs/2507.14299v1
• Date: Fri, 18 Jul 2025 18:17:09 GMT
• Title: Age of Information Minimization in UAV-Enabled Integrated Sensing and Communication Systems
• Authors: Yu Bai, Yifan Zhang, Boxuan Xie, Zheng Chang, Yanru Zhang, Riku Jantti, Zhu Han,
• Abstract summary: Unmanned aerial vehicles (UAVs) equipped with integrated sensing and communication (ISAC) capabilities are envisioned to play a pivotal role in future wireless networks.<n>We propose Age Information (AoI) system that simultaneously performs target sensing and multi-user communication.
• Score: 34.92822911897626
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unmanned aerial vehicles (UAVs) equipped with integrated sensing and communication (ISAC) capabilities are envisioned to play a pivotal role in future wireless networks due to their enhanced flexibility and efficiency. However, jointly optimizing UAV trajectory planning, multi-user communication, and target sensing under stringent resource constraints and time-critical conditions remains a significant challenge. To address this, we propose an Age of Information (AoI)-centric UAV-ISAC system that simultaneously performs target sensing and serves multiple ground users, emphasizing information freshness as the core performance metric. We formulate a long-term average AoI minimization problem that jointly optimizes the UAV's flight trajectory and beamforming. To tackle the high-dimensional, non-convexity of this problem, we develop a deep reinforcement learning (DRL)-based algorithm capable of providing real-time decisions on UAV movement and beamforming for both radar sensing and multi-user communication. Specifically, a Kalman filter is employed for accurate target state prediction, regularized zero-forcing is utilized to mitigate inter-user interference, and the Soft Actor-Critic algorithm is applied for training the DRL agent on continuous actions. The proposed framework adaptively balances the trade-offs between sensing accuracy and communication quality. Extensive simulation results demonstrate that our proposed method consistently achieves lower average AoI compared to baseline approaches.

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