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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.18526 |
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| _version_ | 1866915508333314048 |
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| author | Zeng, Han Wang, Haibo Fan, Luhao Zhu, Bingcheng You, Xiaohu Zhang, Zaichen |
| author_facet | Zeng, Han Wang, Haibo Fan, Luhao Zhu, Bingcheng You, Xiaohu Zhang, Zaichen |
| contents | The vision of 6G communication demands autonomous and resilient networking in environments without fixed infrastructure. Yet most multi-agent reinforcement learning (MARL) approaches focus on isolated stages - exploration, relay formation, or access - under static deployments and centralized control, limiting adaptability. We propose the AI Agent Access (A\^3) Network, a unified, embodied intelligence-driven framework that transforms multi-agent networking into a dynamic, decentralized, and end-to-end system. Unlike prior schemes, the A\^3 Network integrates exploration, target user access, and backhaul maintenance within a single learning process, while supporting on-demand agent addition during runtime. Its decentralized policies ensure that even a single agent can operate independently with limited observations, while coordinated agents achieve scalable, communication-optimized coverage. By embedding link-level communication metrics into actor-critic learning, the A\^3 Network couples topology formation with robust decision-making. Numerical simulations demonstrate that the A\^3 Network not only balances exploration and communication efficiency but also delivers system-level adaptability absent in existing MARL frameworks, offering a new paradigm for 6G multi-agent networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18526 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI Agent Access (A\^3) Network: An Embodied, Communication-Aware Multi-Agent Framework for 6G Coverage Zeng, Han Wang, Haibo Fan, Luhao Zhu, Bingcheng You, Xiaohu Zhang, Zaichen Systems and Control The vision of 6G communication demands autonomous and resilient networking in environments without fixed infrastructure. Yet most multi-agent reinforcement learning (MARL) approaches focus on isolated stages - exploration, relay formation, or access - under static deployments and centralized control, limiting adaptability. We propose the AI Agent Access (A\^3) Network, a unified, embodied intelligence-driven framework that transforms multi-agent networking into a dynamic, decentralized, and end-to-end system. Unlike prior schemes, the A\^3 Network integrates exploration, target user access, and backhaul maintenance within a single learning process, while supporting on-demand agent addition during runtime. Its decentralized policies ensure that even a single agent can operate independently with limited observations, while coordinated agents achieve scalable, communication-optimized coverage. By embedding link-level communication metrics into actor-critic learning, the A\^3 Network couples topology formation with robust decision-making. Numerical simulations demonstrate that the A\^3 Network not only balances exploration and communication efficiency but also delivers system-level adaptability absent in existing MARL frameworks, offering a new paradigm for 6G multi-agent networks. |
| title | AI Agent Access (A\^3) Network: An Embodied, Communication-Aware Multi-Agent Framework for 6G Coverage |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.18526 |