Joint Optimization of Trajectory Control, Resource Allocation, and Task Offloading for Multi-UAV-Assisted IoV

TL;DR

This paper presents a hierarchical optimization framework for multi-UAV-assisted IoV systems, combining trajectory planning, resource scheduling with DRL and LLMs, and task offloading to improve delay and energy efficiency.

Contribution

It introduces a novel hybrid resource scheduling paradigm integrating DRL and LLMs, along with a decoupled optimization framework for UAV trajectory and task offloading.

Findings

Significant improvement in task success rate over traditional methods

Enhanced system efficiency and reduced delay in dense urban environments

Effective decoupling of DRL training from LLM interventions

Abstract

This paper investigates a multi-Unmanned Aerial Vehicle (UAV) joint base station-assisted Internet of Vehicles (IoV) task offloading system in dense urban environments. To minimize system delay and energy consumption under strict coupling constraints, the complex non-convex optimization problem is decoupled into a hierarchical execution framework. First, a sequential distributed optimization algorithm based on Second-Order Cone Programming (SOCP) is proposed to optimize the 3D flight trajectory of each UAV, ensuring adaptive network coverage. Second, a novel hybrid resource scheduling paradigm synergizing Deep Reinforcement Learning (DRL) and Large Language Models (LLMs) is developed. Within this framework, the DRL agent dictates the initial resource allocation, while the LLM acts as a semantic macro-scheduler to rectify long-tail allocation imbalances for failed and surplus tasks. Crucially, a reward decoupling mechanism is introduced to isolate DRL training from external LLM interventions, thereby ensuring policy convergence. Finally, the task offloading ratios are precisely determined via Linear Programming (LP) within an alternating optimization loop. Simulation results demonstrate that the proposed method significantly outperforms traditional multi-agent reinforcement learning baselines in terms of task success rate and system efficiency.