A DDPG-LSTM Framework for Optimizing UAV-Enabled Integrated Sensing and Communication

초록

Highlights What are the main findings? A novel integrated sensing and communication (ISAC)-enabled unmanned aerial vehicle (UAV) architecture is proposed, enabling a single UAV to jointly perform uplink communication and radar sensing. A long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG)-based deep reinforcement learning (DRL) framework is developed to optimize UAV trajectory and uplink power control. The proposed approach effectively adapts to dynamic target movements, enhancing both sensing accuracy and communication reliability. What is the implication of the main finding? The framework enables energy-efficient UAV operation by minimizing movement energy consumption while maintaining sensing performance. The framework enables UAVs to autonomously and efficiently balance sensing and communication tasks in dynamic environments. The solution supports real-time adaptation to target mobility, making it suitable for practical ISAC-UAV applications such as surveillance and smart city monitoring.Abstract This paper proposes a novel dual-functional radar-communication (DFRC) framework that integrates unmanned aerial vehicle (UAV) communications into an integrated sensing and communication (ISAC) system, termed the ISAC-UAV architecture. In this system, the UAV's mobility is leveraged to simultaneously serve multiple single-antenna uplink users (UEs) and perform radar-based sensing tasks. A key challenge stems from the target position uncertainty due to movement, which impairs matched filtering and beamforming, thereby degrading both uplink reception and sensing performance. Moreover, UAV energy consumption associated with mobility must be considered to ensure energy-efficient operation. We aim to jointly maximize radar sensing accuracy and minimize UAV movement energy over multiple time steps, while maintaining reliable uplink communications. To address this multi-objective optimization, we propose a deep reinforcement learning (DRL) framework based on a long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) network. By leveraging historical target trajectory data, the model improves prediction of target positions, enhancing sensing accuracy. The proposed DRL-based approach enables joint optimization of UAV trajectory and uplink power control over time. Extensive simulations validate that our method significantly improves communication quality and sensing performance, while ensuring energy-efficient UAV operation. Comparative results further confirm the model's adaptability and robustness in dynamic environments, outperforming existing UAV trajectory planning and resource allocation benchmarks.

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