Agentic AI-RAN Empowering Synergetic Sensing, Communication, Computing, and Control
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908783371878400 |
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| author | Sun, Lingxiao Zhang, Zhaoyang Lin, Zihan Chen, Zirui Zhou, Weijie Yang, Zhaohui Quek, Tony Q. S. |
| author_facet | Sun, Lingxiao Zhang, Zhaoyang Lin, Zihan Chen, Zirui Zhou, Weijie Yang, Zhaohui Quek, Tony Q. S. |
| contents | Future sixth-generation (6G) networks are expected to support low-altitude wireless networks (LAWNs), where unmanned aerial vehicles (UAVs) and aerial robots operate in highly dynamic three-dimensional environments under stringent latency, reliability, and autonomy requirements. In such scenarios, autonomous task execution at the network edge demands holistic coordination among sensing, communication, computing, and control (SC3) processes. Agentic Artificially Intelligent Radio Access Networks (Agentic AI-RAN) offer a promising paradigm by enabling the edge network to function as an autonomous decision-making entity for low-altitude agents with limited onboard resources. In this article, we propose and design a task-oriented Agentic AI-RAN architecture that enables SC3 task execution within a single edge node. This integrated design tackles the fundamental problem of coordinating heterogeneous workloads in resource-constrained edge environments. Furthermore, a representative low-altitude embodied intelligence system is prototyped based on a general-purpose Graphics Processing Unit (GPU) platform to demonstrate autonomous drone navigation in realistic settings. By leveraging the Multi-Instance GPU (MIG) partitioning technique and the containerized deployment, the demonstration system achieves physical resource isolation while supporting tightly coupled coordination between real-time communication and multimodal inference under a unified task framework. Experimental results demonstrate low closed-loop latency, robust bidirectional communication, and stable performance under dynamic runtime conditions, highlighting its viability for mission-critical low-altitude wireless networks in 6G. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_16565 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Agentic AI-RAN Empowering Synergetic Sensing, Communication, Computing, and Control Sun, Lingxiao Zhang, Zhaoyang Lin, Zihan Chen, Zirui Zhou, Weijie Yang, Zhaohui Quek, Tony Q. S. Systems and Control Future sixth-generation (6G) networks are expected to support low-altitude wireless networks (LAWNs), where unmanned aerial vehicles (UAVs) and aerial robots operate in highly dynamic three-dimensional environments under stringent latency, reliability, and autonomy requirements. In such scenarios, autonomous task execution at the network edge demands holistic coordination among sensing, communication, computing, and control (SC3) processes. Agentic Artificially Intelligent Radio Access Networks (Agentic AI-RAN) offer a promising paradigm by enabling the edge network to function as an autonomous decision-making entity for low-altitude agents with limited onboard resources. In this article, we propose and design a task-oriented Agentic AI-RAN architecture that enables SC3 task execution within a single edge node. This integrated design tackles the fundamental problem of coordinating heterogeneous workloads in resource-constrained edge environments. Furthermore, a representative low-altitude embodied intelligence system is prototyped based on a general-purpose Graphics Processing Unit (GPU) platform to demonstrate autonomous drone navigation in realistic settings. By leveraging the Multi-Instance GPU (MIG) partitioning technique and the containerized deployment, the demonstration system achieves physical resource isolation while supporting tightly coupled coordination between real-time communication and multimodal inference under a unified task framework. Experimental results demonstrate low closed-loop latency, robust bidirectional communication, and stable performance under dynamic runtime conditions, highlighting its viability for mission-critical low-altitude wireless networks in 6G. |
| title | Agentic AI-RAN Empowering Synergetic Sensing, Communication, Computing, and Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2601.16565 |