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

TL;DR

We address cooperative ISAC for LAE by jointly optimizing UAV trajectories, communication and sensing power, and dynamic time division in a slot-based framework. The core method combines alternating optimization with successive convex approximation, handling nonconvex rate and MI constraints and producing a KKT-point solution. The approach demonstrates clear gains over static or partially optimized baselines, highlighting the value of dynamic resource management under mobility and sensing requirements. This work delivers a practical framework for end-to-end efficiency in networked UAV ISAC systems, enabling adaptive beam scanning and trajectory-aware resource allocation with cumulative sensing guarantees.

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), and employ a time-division strategy in which beam scanning for sensing comes before data communication in each time slot. To maximize the sum communication rate while satisfying the total sensing mutual information (MI) requirement, we jointly optimize the UAV trajectories, communication and sensing power allocation, and the dynamic time-division ratio. The resulting non-convex optimization problem is efficiently solved using an alternating optimization (AO) framework. Simulation results demonstrate that our proposed joint design significantly outperforms benchmark schemes with static or partially optimized resources. The findings also reveal the critical importance of dynamic trajectory and resource management for effectively navigating the sensing-communication trade-off, especially under stringent power or sensing constraints.

Figures (8)

• Figure 1: System model.
• Figure 2: Time slot division.
• Figure 3: Optimized UAV Trajectories under Different Position Settings and thresholds of MI Constraints.
• Figure 4: The speed of the UAVs in each time slot.
• Figure 5: The communication rate of the UAV in each time slot.