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

2025-07-18

AI Summary

To address the multi-objective co-optimization challenge in resource-constrained and time-sensitive unmanned aerial vehicle (UAV)-based integrated sensing and communication (ISAC) systems, this paper proposes a joint trajectory planning and beamforming framework centered on Age of Information (AoI) minimization. The method innovatively integrates Soft Actor-Critic deep reinforcement learning, Kalman filter–driven target motion prediction, and regularized zero-forcing interference suppression to enable dynamic trade-offs between sensing accuracy and communication quality. It supports real-time, coordinated decision-making for multi-user communications and dynamic target sensing. Simulation results demonstrate that the proposed approach reduces average AoI by 23.6% compared to baseline schemes, significantly enhancing system timeliness and information freshness.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Minimize Age of Information in UAV-ISAC systems
• Optimize UAV trajectory and beamforming jointly
• Balance sensing accuracy and communication quality

Innovation

• DRL-based algorithm optimizes UAV trajectory and beamforming
• Kalman filter enhances target state prediction accuracy
• Soft Actor-Critic trains agent for continuous action decisions