Trajectory Design for UAV-Based Low-Altitude Wireless Networks in Unknown Environments: A Digital Twin-Assisted TD3 Approach

2025-10-28

AI Summary

Deploying low-altitude wireless networks (LAWN) in unknown environments faces challenges including uncertain terrain topology, ground network failure, and difficulty in achieving secure and efficient UAV trajectory planning. Method: This paper proposes a digital twin–driven collaborative UAV trajectory optimization framework. It constructs a dynamic virtual environment via integrated sensing-and-communication signals to enable real-time cyber-physical synchronization; designs a digital twin co-training and online deployment mechanism that continuously updates the model with real-time sensory data; and jointly optimizes user scheduling and continuous trajectories using simulated annealing initialization combined with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Contribution/Results: Simulations demonstrate that the proposed method significantly reduces task completion time, accelerates convergence, enhances flight safety, and outperforms multiple baseline approaches in comprehensive performance.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Unmanned aerial vehicles (UAVs) are emerging as key enablers for low-altitude wireless network (LAWN), particularly when terrestrial networks are unavailable. In such scenarios, the environmental topology is typically unknown; hence, designing efficient and safe UAV trajectories is essential yet challenging. To address this, we propose a digital twin (DT)-assisted training and deployment framework. In this framework, the UAV transmits integrated sensing and communication signals to provide communication services to ground users, while simultaneously collecting echoes that are uploaded to the DT server to progressively construct virtual environments (VEs). These VEs accelerate model training and are continuously updated with real-time UAV sensing data during deployment, supporting decision-making and enhancing flight safety. Based on this framework, we further develop a trajectory design scheme that integrates simulated annealing for efficient user scheduling with the twin-delayed deep deterministic policy gradient algorithm for continuous trajectory design, aiming to minimize mission completion time while ensuring obstacle avoidance. Simulation results demonstrate that the proposed approach achieves faster convergence, higher flight safety, and shorter mission completion time compared with baseline methods, providing a robust and efficient solution for LAWN deployment in unknown environments.

Problem

• Designing safe UAV trajectories in unknown environments
• Constructing virtual environments using digital twin technology
• Minimizing mission time while ensuring obstacle avoidance

Innovation

• Digital twin-assisted framework for UAV training
• Integrated sensing and communication signals for mapping
• Simulated annealing with TD3 for trajectory optimization