Hierarchical Online Optimization Approach for IRS-enabled Low-altitude MEC in Vehicular Networks
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908741185568768 |
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| author | Wang, Yixian Sun, Geng Sun, Zemin Wang, Jiacheng Zhao, Changyuan Tian, Daxin Niyato, Dusit Mao, Shiwen |
| author_facet | Wang, Yixian Sun, Geng Sun, Zemin Wang, Jiacheng Zhao, Changyuan Tian, Daxin Niyato, Dusit Mao, Shiwen |
| contents | In this paper, we propose an intelligent reflecting surface (IRS)-enabled low-altitude multi-access edge computing (MEC) architecture, where an aerial MEC server cooperates with a terrestrial MEC server to provide computing services, while hybrid IRSs (i.e., building-installed and UAV-carried IRSs) are deployed to enhance the air-ground connectivity under blockage. Based on this architecture, we formulate a multi-objective optimization problem (MOOP) to minimize the task completion delay and energy consumption by jointly optimizing task offloading, UAV trajectory control, IRS phase-shift configuration, and computation resource allocation. The considered problem is NP-hard, and thus we propose a hierarchical online optimization approach (HOOA) to efficiently solve the problem. Specifically, we reformulate the MOOP as a Stackelberg game, where MEC servers collectively act as the leader to determine the system-level decisions, while the vehicles act as followers to make individual decisions. At the follower level, we present a many-to-one matching mechanism to generate feasible discrete decisions. At the leader level, we propose a generative diffusion model-enhanced twin delayed deep deterministic policy gradient (GDMTD3) algorithm integrated with a Karush-Kuhn-Tucker (KKT)-based method, which is a deep reinforcement learning (DRL)-based approach, to determine the continuous decisions. Simulation results demonstrate that 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. Moreover, the proposed HOOA exhibits superior convergence stability while maintaining strong robustness and scalability in dynamic environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_24659 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical Online Optimization Approach for IRS-enabled Low-altitude MEC in Vehicular Networks Wang, Yixian Sun, Geng Sun, Zemin Wang, Jiacheng Zhao, Changyuan Tian, Daxin Niyato, Dusit Mao, Shiwen Networking and Internet Architecture In this paper, we propose an intelligent reflecting surface (IRS)-enabled low-altitude multi-access edge computing (MEC) architecture, where an aerial MEC server cooperates with a terrestrial MEC server to provide computing services, while hybrid IRSs (i.e., building-installed and UAV-carried IRSs) are deployed to enhance the air-ground connectivity under blockage. Based on this architecture, we formulate a multi-objective optimization problem (MOOP) to minimize the task completion delay and energy consumption by jointly optimizing task offloading, UAV trajectory control, IRS phase-shift configuration, and computation resource allocation. The considered problem is NP-hard, and thus we propose a hierarchical online optimization approach (HOOA) to efficiently solve the problem. Specifically, we reformulate the MOOP as a Stackelberg game, where MEC servers collectively act as the leader to determine the system-level decisions, while the vehicles act as followers to make individual decisions. At the follower level, we present a many-to-one matching mechanism to generate feasible discrete decisions. At the leader level, we propose a generative diffusion model-enhanced twin delayed deep deterministic policy gradient (GDMTD3) algorithm integrated with a Karush-Kuhn-Tucker (KKT)-based method, which is a deep reinforcement learning (DRL)-based approach, to determine the continuous decisions. Simulation results demonstrate that 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. Moreover, the proposed HOOA exhibits superior convergence stability while maintaining strong robustness and scalability in dynamic environments. |
| title | Hierarchical Online Optimization Approach for IRS-enabled Low-altitude MEC in Vehicular Networks |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2512.24659 |