Agentic AI-RAN: Framework, Mechanism, and Applications

Abstract

The rapid development of artificial intelligence (AI) has led to the emergence of agent technology driven by large language models (LLMs), which are regarded as valid solutions to numerous cross-field challenges owing to their tremendous intelligence and autonomy. When the agents are applied to the radio access network (RAN), a new paradigm, agentic AI-RAN, rises in response, which integrates communication, sensing, computation, and control through the advanced decision-making capabilities and self-sufficiency of agents. Due to different design purposes, agentic AI-RAN has two unique features compared to conventional AI-empowered RAN. First, an agent in agentic AI-RAN does not only process network data passively as a learning model but also perceives, reasons, plans, and acts upon the radio environment autonomously, which fundamentally reshapes the way RAN functions are orchestrated. Second, an agent in agentic AI-RAN must jointly handle multi-dimensional resources including communication, sensing, computation, and control rather than optimizing along a single dimension as done in standard AI-RAN systems, since the agentic capability emerges only from the co-deployment of these heterogeneous functionalities. Due to these unique features, in this paper, we present an overview on the fundamentals and key challenges in the design of agentic AI-RAN. We first introduce the concept of agentic AI-RAN and provide its basic framework. Then, we focus on agentic AI-RAN techniques in two areas: the co-deployment mechanism of agents over the RAN infrastructure and the agentic interaction with the radio environment. For each area, we identify the main problems and challenges. Then, we will provide a comprehensive treatment of these problems. Two use cases, one based on Py5chesim and another one based on Bubble RAN by NVIDIA, are demonstrated as well. Finally, we point out future research in the field of agentic AI-RAN. In a nutshell, this article provides a holistic set of guidelines on how to design agentic AI-RAN systems over real-world wireless communication networks, aiming to stimulate more innovative research on this emerging paradigm.