Agent AI Zarządzający Siecią Open RAN Zorientowana na Użytkownika

Marcin Hoffmann

cs.NIOct 20, 2026

Agent AI Zarządzający Siecią Open RAN Zorientowana na Użytkownika

Authors: Marcin Hoffmann

Organizations: Politechnika Poznaska, Pozna · Rimedo Labs, Pozna

Abstract

Jednym z najważniejszych wyzwań związanych z implementacją sieci Open RAN o architekturze zorientowanej na użytkownika jest odpowiednie formowanie tzw. klastrów obsługujących. Problem ten może być efektywnie rozwiązany za pomocą zaproponowanego Agenta AI bazującego na modelu językowym. Wyniki symulacji pokazały, że zaproponowane rozwiązanie pozwala na ponad 2-krotne zwiększenie mediany rozkładu przepływności użytkowników względem standardowej architektury zorientowanej na sieć.

Figures & tables

Figure 1: Agenta AI zarządzający formowaniem klastrów obsługujących w sieci zorientowanej na użytkownika.

Parametr	Wartość
Model językowy	Qwen 3.5 32b
Częstotliwość nośna	3.6 GHz
Liczba bloków zasobów	69
Liczba O-CU/O-DU	1
Liczba jednostek radiowych O-RU	1 makro, 5 mikro
Wysokość anten - makro O-RU	45 m

Table 1: Parametry Symulacji

Figure 2: Mediana przepływności użytkowników w funkcji czasu. Dla Agenta AI uwzględniono średnią i odchylenie standardowe z 10 niezależnych przebiegów symulacyjnych.

Figure 3: Porównanie kwantyli rzędu 10, 50, i 90 z rozkładu przepływności użytkowników z całej symulacji.

Figure 4: AI agent for managing serving-cluster formation in a user-centric network.

Parameter	Value
Language model	Qwen 3.5 32b
Carrier frequency	3.6 GHz
Number of resource blocks	69
Number of O-CU/O-DU	1
Number of O-RU radio units	1 macro, 5 micro
Antenna height—macro O-RU	45 m

Table 2: Simulation parameters

Figure 5: Median user throughput versus time. For the AI agent, the mean and standard deviation over 10 independent simulation runs are shown.

Figure 6: Comparison of the 10th, 50th, and 90th percentiles of the user-throughput distribution over the entire simulation.

Open Radio Access Network

Open Radio Access Network (O-RAN) research studies disaggregated, programmable wireless networks and the intelligence that manages them.

See all 28 papers

Agentic AI for Intent-driven Optimization in Cell-free O-RAN
Feb 26, 2026Mohammad Hossein Shokouhi, Vincent W. S. WongWireless Communications Intent-Based Networking
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, Canada
Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligence
Feb 27, 2026Zhizhou He, Yang Luo, Xinkai Liu +4Network Slicing Wireless Communications
5G/6GIC, Institute for Communication Systems (ICS), University of Surrey, Guildford, UK · 6G Research Center, Khalifa University, Abu Dhabi, UAE
Toward Autonomous O-RAN: A Multi-Scale Agentic AI Framework for Real-Time Network Control and Management
Feb 15, 2026Hojjat Navidan, Mohammad Cheraghinia, Jaron Fontaine +5Multi-Agent Orchestration Language Model-Based Control
IDLab, Department of Information Technology at Ghent University - imec, Ghent, Belgium · Department of Electrical and Computer Engineering, Princeton University, USA

Feb 26, 2026J/K move · Enter open · S save

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

Mohammad Hossein Shokouhi, Vincent W. S. Wong

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

Wireless Communications Intent-Based Networking LLM Agent Orchestration Wireless Resource Allocation Open Radio Access Network Multi-Agent Coordination

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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.

Abstract

Open Radio Access Network

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