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arXiv · 2509.18526

AI Agent Access (A\^3) Network: An Embodied, Communication-Aware Multi-Agent Framework for 6G Coverage

Han Zeng·Haibo Wang·Luhao Fan·Bingcheng Zhu·Xiaohu You·Zaichen Zhang
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Abstract

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.

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Han Zeng, Haibo Wang, Luhao Fan, Bingcheng Zhu, Xiaohu You, Zaichen Zhang. 2025-09-23. AI Agent Access (A\^3) Network: An Embodied, Communication-Aware Multi-Agent Framework for 6G Coverage. https://arxiv.org/abs/2509.18526

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