Task allocation and joint resource optimization for multi-UAV integrated sensing and communication based on the MDP-PPO algorithm

Abstract

This paper investigates the task assignment and joint resource optimization problem for integrated sensing and communication (ISAC) in multi-UAV systems. To support the cooperative execution of detection, tracking, and communication tasks, a unified optimization framework is formulated by jointly considering task assignment, power allocation, and bandwidth allocation. Specifically, the UAV set, task set, and task-specific performance models are first established, and the system state is characterized by task priority, remaining power, remaining bandwidth, and task completion status. Then, task assignment constraints, UAV power constraints, bandwidth constraints, and task-type constraints are incorporated into the optimization problem. By introducing task-priority weights and a task-balancing factor, the objective is formulated as the maximization of the overall joint performance of the system. Since the considered problem involves discrete task assignment variables, continuous resource allocation variables, and nonconvex coupled constraints, it is difficult to solve efficiently using conventional optimization methods. To address this issue, the problem is modeled as a Markov decision process (MDP), and a proximal policy optimization (PPO)-based solution framework is developed to jointly determine UAV selection, power allocation, and bandwidth allocation actions. Simulation results demonstrate that, compared with P-DQN, SAC, and PADDPG, the proposed framework achieves superior joint performance, thereby verifying its effectiveness for multi-UAV ISAC joint optimization.