Energy-Efficient 3D Trajectory Optimization and Resource Allocation for UAV-Enabled ISAC Systems.

Area of Science:

• Electrical Engineering
• Computer Science
• Robotics

Background:

• Unmanned aerial vehicles (UAVs) offer flexible, autonomous aerial platforms for sensing and communication.
• Limited onboard energy restricts UAV operational duration and efficiency.
• Integrated Sensing and Communication (ISAC) systems combine radar sensing and data transmission on a single platform.

Purpose of the Study:

• To maximize Sensing Energy Efficiency (SEE) in a UAV-assisted ISAC system.
• To jointly optimize UAV 3D trajectory, task scheduling, and power allocation.
• To address the energy bottleneck for sustained UAV operations in remote and time-critical scenarios.

Main Methods:

• Formulation of a non-convex fractional programming problem to maximize SEE.
• Development of an iterative algorithm combining the Dinkelbach method and Block Coordinate Descent (BCD).
• Joint optimization of UAV trajectory, task scheduling, and power allocation under kinematic and coverage constraints.

Main Results:

• The proposed iterative algorithm effectively solves the complex optimization problem.
• Achieved superior trade-offs between sensing performance and energy consumption compared to existing methods.
• Demonstrated the viability of optimizing multiple parameters for enhanced energy efficiency.

Conclusions:

• The proposed optimization strategy significantly improves SEE for UAV-assisted ISAC systems.
• Joint optimization of trajectory, scheduling, and power is crucial for energy-efficient UAV operations.
• This research provides a framework for designing sustainable and efficient aerial sensing and communication platforms.