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

TL;DR

This paper proposes an energy-efficient system for UAVs that jointly optimizes trajectory, task scheduling, and power to improve sensing performance.

Contribution

The novel approach jointly optimizes 3D trajectory, task scheduling, and power allocation to maximize sensing energy efficiency in UAV-enabled ISAC systems.

Findings

The proposed algorithm achieves a better trade-off between sensing performance and energy consumption.

Simulation results validate the effectiveness of the iterative optimization method.

Abstract

Owing to their high flexibility, autonomous operation, and rapid deployment capability, unmanned aerial vehicles (UAVs) serve as effective aerial platforms for sensing and communication in remote and time-critical scenarios. However, their limited onboard energy budget poses a significant bottleneck for sustained operations. This paper investigates an energy-efficient UAV-assisted integrated sensing and communication (ISAC) system, aiming to maximize the sensing energy efficiency (SEE), defined as the ratio of the total radar estimation rate to the total energy consumption. Unlike prior works focused solely on rate maximization or fairness, our design jointly optimizes the UAV’s 3D trajectory, task scheduling, and power allocation under kinematic and coverage constraints to maximize the SEE. To solve the formulated non-convex fractional programming problem, we propose an efficient iterative algorithm based on the Dinkelbach method and block coordinate descent (BCD). Simulation results demonstrate that the proposed scheme achieves a superior trade-off between sensing performance and energy consumption.

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