Hierarchical Online Optimization for IRS-enabled MEC in Vehicular Networks
Research Paper#Vehicular Networks, MEC, IRS, Optimization, Deep Reinforcement Learning🔬 Research|Analyzed: Jan 3, 2026 06:28•
Published: Dec 31, 2025 06:14
•1 min read
•ArXivAnalysis
This paper addresses the critical challenges of task completion delay and energy consumption in vehicular networks by leveraging IRS-enabled MEC. The proposed Hierarchical Online Optimization Approach (HOOA) offers a novel solution by integrating a Stackelberg game framework with a generative diffusion model-enhanced DRL algorithm. The results demonstrate significant improvements over existing methods, highlighting the potential of this approach for optimizing resource allocation and enhancing performance in dynamic vehicular environments.
Key Takeaways
- •Proposes a novel architecture for IRS-enabled low-altitude MEC in vehicular networks.
- •Formulates a multi-objective optimization problem to minimize task completion delay and energy consumption.
- •Introduces a Hierarchical Online Optimization Approach (HOOA) based on a Stackelberg game.
- •Employs a generative diffusion model-enhanced DRL algorithm for efficient problem solving.
- •Demonstrates significant performance improvements over existing methods in simulations.
Reference / Citation
View Original"The proposed HOOA achieves significant improvements, which reduces average task completion delay by 2.5% and average energy consumption by 3.1% compared with the best-performing benchmark approach and state-of-the-art DRL algorithm, respectively."