cs.AIFeb 26, 2026

Agentic AI for Intent-driven Optimization in Cell-free O-RAN

Authors: Mohammad Hossein Shokouhi, Vincent W. S. Wong

Organizations: Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, Canada

Read PDF
Ask AI
Cite

Copy citation

DOINot available for this paper

Abstract

Agentic artificial intelligence (AI) is emerging as a key enabler for autonomous radio access networks (RANs), where multiple large language model (LLM)-based agents reason and collaborate to achieve operator-defined intents. The open RAN (O-RAN) architecture enables the deployment and coordination of such agents. However, most existing works consider simple intents handled by independent agents, while complex intents that require coordination among agents remain unexplored. In this paper, we propose an agentic AI framework for intent translation and optimization in cell-free O-RAN. A supervisor agent translates the operator intents into an optimization objective and minimum rate requirements. Based on this information, a user weighting agent retrieves relevant prior experience from a memory module to determine the user priority weights for precoding. If the intent includes an energy-saving objective, then an open radio unit (O-RU) management agent will also be activated to determine the set of active O-RUs by using a deep reinforcement learning (DRL) algorithm. A monitoring agent measures and monitors the user data rates and coordinates with other agents to guarantee the minimum rate requirements are satisfied. To enhance scalability, we adopt a parameter-efficient fine-tuning (PEFT) method that enables the same underlying LLM to be used for different agents. Simulation results show that the proposed agentic AI framework reduces the number of active O-RUs by 41.93% when compared with three baseline schemes in energy-saving mode. Using the PEFT method, the proposed framework reduces the memory usage by 92% when compared with deploying separate LLM agents.

Figures & tables

Fig. 1: The considered system model. The operator intents are translated into objectives by the supervisor agent in the non-RT RIC. Near-RT agents determine the user priority weights and the set of active O-RUs. Agents in the near-RT RIC share an LLM with different QLoRA adapters.
Fig. 2: Block diagram of the proposed framework. The user weighting and O-RU management agents receive operator objectives from the supervisor agent, feedback from the monitoring agent, and prior knowledge from the memory module to determine the user weights and O-RU activations. Steps 4-6 are repeated until all the minimum rate requirements are satisfied.
Model Size3×3\times FP16 LLMs1×1\times FP16 LLM +3×3\times FP4 LLMs1×1\times FP4 LLM +
33 adapters33 adapters
7B45.7 GB15.3 GB11.4 GB3.8 GB
14B88.2 GB29.5 GB22.1 GB7.4 GB
Reduction in-67 %75 %92 %
Memory Usage
TABLE I: Comparison of memory usage for the three near-RT agents under different quantization and LoRA deployment settings
Fig. 3: The fraction of active O-RUs versus (a) the number of users (b) the total number of O-RUs
Intent TypeEnergy Saving (ES)Utility Maximization (UM)
OperatorEnter the energy-saving mode.Maximize the sum of log-rates.
Guarantee 50 Mbps for user 3.No minimum rate requirements.
Supervisor toObjective: ∑k∈Krk\sum_{k\in\mathcal{K}}r_{k}Objective: ∑k∈Klog⁡(rk)\sum_{k\in\mathcal{K}}\log(r_{k})
User WeightingConstraint: r3≥50 Mbpsr_{3}\geq 50\textrm{ Mbps}
Supervisor toObjective: Energy SavingObjective: Full Power
O-RU ManagementConstraint: r3≥50 Mbpsr_{3}\geq 50\textrm{ Mbps}
TABLE II: Two examples of operator intents and the corresponding supervisor messages to the near-RT agents
Fig. 4: The data rate of user 3 for different operator intents.
More in this topic

Wireless Communications

Wireless communications studies how information is sent over radio links and how networks manage signals, spectrum, and resources reliably.

See all 391 papers

Explore similar work

CardsList
Feb 15, 2026

Toward Autonomous O-RAN: A Multi-Scale Agentic AI Framework for Real-Time Network Control and Management

Open Radio Access Networks (O-RAN) promise flexible 6G network access through disaggregated, software-driven components and open interfaces, but this programmability also increases operational complexity. Multiple control loops coexist across the service management layer and RAN Intelligent Controller (RIC), while independently developed control applications can interact in unintended ways. In parallel, recent advances in generative Artificial Intelligence (AI) are enabling a shift from isolated AI models toward agentic AI systems that can interpret goals, coordinate multiple models and control functions, and adapt their behavior over time. This article proposes a multi-scale agentic AI framework for O-RAN that organizes RAN intelligence as a coordinated hierarchy across the Non-Real-Time (Non-RT), Near-Real-Time (Near-RT), and Real-Time (RT) control loops: (i) A Large Language Model (LLM) agent in the Non-RT RIC translates operator intent into policies and governs model lifecycles. (ii) Small Language Model (SLM) agents in the Near-RT RIC execute low-latency optimization and can activate, tune, or disable existing control applications; and (iii) Wireless Physical-layer Foundation Model (WPFM) agents near the distributed unit provide fast inference close to the air interface. We describe how these agents cooperate through standardized O-RAN interfaces and telemetry. Using a proof-of-concept implementation built on open-source models, software, and datasets, we demonstrate the proposed agentic approach in two representative scenarios: robust operation under non-stationary conditions and intent-driven slice resource control.

Hojjat Navidan, Mohammad Cheraghinia, Jaron Fontaine +5

IDLab, Department of Information Technology at Ghent University - imec, Ghent, Belgium · Department of Electrical and Computer Engineering, Princeton University, USA

Multi-Agent OrchestrationLanguage Model-Based ControlNetwork SlicingWireless CommunicationsOpen Radio Access NetworkIntent-Based Networking+1
AskPDF
Feb 27, 2026

Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligence

Open RAN (O-RAN) exposes rich control and telemetry interfaces across the Non-RT RIC, Near-RT RIC, and distributed units, but also makes it harder to operate multi-tenant, multi-objective RANs in a safe and auditable manner. In parallel, agentic AI systems with explicit planning, tool use, memory, and self-management offer a natural way to structure long-lived control loops. This article surveys how such agentic controllers can be brought into O-RAN: we review the O-RAN architecture, contrast agentic controllers with conventional ML/RL xApps, and organise the task landscape around three clusters: network slice life-cycle, radio resource management (RRM) closed loops, and cross-cutting security, privacy, and compliance. We then introduce a small set of agentic primitives (Plan-Act-Observe-Reflect, skills as tool use, memory and evidence, and self-management gates) and show, in a multi-cell O-RAN simulation, how they improve slice life-cycle and RRM performance compared to conventional baselines and ablations that remove individual primitives. Security, privacy, and compliance are discussed as architectural constraints and open challenges for standards-aligned deployments. This framework achieves an average 8.83% reduction in resource usage across three classic network slices.

Zhizhou He, Yang Luo, Xinkai Liu +4

5G/6GIC, Institute for Communication Systems (ICS), University of Surrey, Guildford, UK · 6G Research Center, Khalifa University, Abu Dhabi, UAE

Network SlicingWireless CommunicationsOpen Radio Access NetworkWireless Resource AllocationAgentic AISelf-Evolving Agents
AskPDF
Jun 20, 2026

From RAN Control to Agentic Intelligence: Architecture and Vision for Energy Efficient AI-RAN

Future 6G networks will rely on highly distributed, AI-native Radio Access Networks (RANs), where communication and AI workloads share a common infrastructure. This evolution, combined with increasing deployment density and continuous AI processing, is expected to significantly increase RAN energy consumption. While Open RAN (O-RAN) introduces a programmable and modular control framework through the RAN Intelligent Controller (RIC) and Service Management and Orchestration (SMO), current approaches remain largely policy-driven, limiting adaptive energy-aware coordination across multiple applications. In parallel, AI-RAN promotes the convergence of AI and RAN infrastructures through AI-for-RAN, AI-on-RAN, and AI-and-RAN paradigms, yet efficient mechanisms to jointly orchestrate performance, latency, and energy remain an open challenge. This article proposes an agentic AI-native RAN architecture that bridges O-RAN's structured control with AI-RAN's unified vision. Leveraging semantic intent abstraction and Large Language Model (LLM)-driven coordination, the framework enables adaptive orchestration, conflict resolution, and energy-aware multi-objective optimization across heterogeneous workloads. Through representative AI-for-RAN and AI-on-RAN use cases, we show how such coordination can improve resource efficiency and reduce operational energy consumption, paving the way toward sustainable 6G networks.

Sabrine Aroua, Alexis I. Aravanis, Ilias Chatzistefanidis +7

Ericsson Research, France · Laboratory of Signals and Systems (L2S), Universite Paris-Saclay, CentraleSupelec, CNRS, France · BubbleRAN, France +1

LLM Agent OrchestrationWireless CommunicationsOpen Radio Access Network
AskPDF