Curriculum-Guided Heterogeneous Multi-Agent Intelligence for Multi-UAV Cooperative ISAC

TL;DR

This paper introduces a multi-UAV cooperative ISAC system that leverages curriculum-guided policy optimization to enhance sensing and communication, demonstrating significant performance improvements over existing methods.

Contribution

It proposes a novel heterogeneous multi-UAV ISAC framework with a curriculum-based optimization algorithm for joint trajectory and beamforming design.

Findings

Achieves over 30% improvement in sensing performance.

Faster convergence and higher tracking accuracy.

Effective scalability for complex multi-UAV scenarios.

Abstract

Seamlessly unifying communication and sensing, sixth-generation (6G) networks are poised to transform into intelligent platforms with high spectral-energy efficiency and real-time environmental awareness. In the low-altitude economy, unmanned aerial vehicles (UAVs) enable air-ground integrated sensing and communication (ISAC) for applications such as logistics and inspection, yet most studies focus on single-UAV or homogeneous-agent designs. In contrast, this paper proposes a multi-UAV cooperative ISAC system that enables heterogeneous-agent collaboration between multiple UAVs and a ground base station (BS) for joint target sensing, tracking, and communication. The system is formulated as a posterior Cramer-Rao bound (PCRB) minimization problem under communication performance constraints, utilizing joint trajectory-beamforming optimization. To tackle the NP-hard nature of this problem, we design a curriculum-based heterogeneous-agent proximal policy optimization (C-HAPPO) algorithm, where curriculum learning guides progressive policy refinement and Kronecker/QR decomposition mitigates action dimensionality. Simulation results show that the proposed approach achieves more than a 30% improvement in sensing performance, faster convergence, and higher tracking accuracy than existing baselines, demonstrating its scalability and effectiveness for complex multi-UAV ISAC scenarios.