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

arXiv: https://arxiv.org/abs/2605.04436 · HTML: https://mlsanity.com/paper/2605.04436/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 61 references that are in the map.
- First posted on arXiv 6 May 2026.

## Builds on (most cited first, 20 of 20)

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- [Mobile Edge Computing: A Survey on Architecture and Computation Offloading](https://mlsanity.com/paper/1702.05309/) (2017)
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- [Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Control](https://mlsanity.com/paper/2408.00214/) (2024)
- [RadioDiff-k2: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Construction](https://mlsanity.com/paper/2504.15623/) (2025)
- [A Swarming Approach to Optimize the One-hop Delay in Smart Driving Inter-platoon Communications](https://mlsanity.com/paper/1807.07301/) (2018)
- [Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization](https://mlsanity.com/paper/2007.15105/) (2020)
- [Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing](https://mlsanity.com/paper/2202.12028/) (2022)
- [Multi-objective Optimization for Multi-UAV-assisted Mobile Edge Computing](https://mlsanity.com/paper/2404.15292/) (2024)
- [DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV](https://mlsanity.com/paper/2408.09194/) (2024)
- [Efficient 3-D Placement of an Aerial Base Station in Next Generation Cellular Networks](https://mlsanity.com/paper/1603.00300/) (2016)
- [UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization](https://mlsanity.com/paper/1812.02658/) (2018)
- [DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV](https://mlsanity.com/paper/2411.13104/) (2024)
- [Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading](https://mlsanity.com/paper/2010.09094/) (2020)
- [Enhanced Velocity-Adaptive Scheme: Joint Fair Access and Age of Information Optimization in Vehicular Networks](https://mlsanity.com/paper/2507.18328/) (2025)
- [Interpretable and Secure Trajectory Optimization for UAV-Assisted Communication](https://mlsanity.com/paper/2307.02002/) (2023)
- [Large Language Model-Based Task Offloading and Resource Allocation for Digital Twin Edge Computing Networks](https://mlsanity.com/paper/2507.19050/) (2025)
- [Velocity-Adaptive Access Scheme for Semantic-Aware Vehicular Networks: Joint Fairness and AoI Optimization](https://mlsanity.com/paper/2512.01571/) (2025)

## Abstract (arXiv)

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.