Cooperative ISAC for LAE: Joint Trajectory Planning, Power allocation, and Dynamic Time Division

TL;DR

This paper introduces a cooperative ISAC framework for multi-UAV systems, optimizing trajectories, power, and time division to enhance communication and sensing performance in aerial-ground networks.

Contribution

It presents a joint optimization approach for UAV trajectories, power, and time division that outperforms static and non-cooperative schemes in integrated sensing and communication.

Findings

Joint design significantly improves communication rates.

Dynamic resource management enhances sensing-communication trade-off.

Proposed algorithm converges reliably to a finite objective value.

Abstract

To enhance the performance of aerial-ground networks, this paper proposes an integrated sensing and communication (ISAC) framework for multi-UAV systems. In our model, ground base stations (BSs) cooperatively serve multiple unmanned aerial vehicles (UAVs), employing a dynamic time-division strategy where beam scanning for sensing precedes data communication in each time slot. To maximize the sum communication rate while satisfying a mission-level cumulative radar mutual information (MI) requirement, we jointly optimize the UAV trajectories, communication and sensing power allocation, and the time-division ratio. The resulting highly coupled non-convex optimization problem is efficiently solved using an alternating optimization (AO) and successive convex approximation (SCA) framework, which yields a non-decreasing objective sequence and convergence to a finite objective value under the adopted surrogate-based iterative procedure. Extensive simulation results demonstrate that our proposed joint design significantly outperforms benchmark schemes with static trajectories, partially optimized resources, or non-cooperative single-BS transmission. Furthermore, a comprehensive sensitivity analysis reveals the distinct mechanisms by which sensing thresholds and the number of UAVs influence resource allocation and spatial organization, highlighting the critical importance of dynamic, multi-dimensional resource management for effectively navigating the sensing-communication trade-off in low-altitude economies.