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

Mohammad Hossein Shokouhi, Vincent W. S. Wong

TL;DR

An agentic AI framework for intent translation and optimization in cell-free O-RAN is proposed and a parameter-efficient fine-tuning (PEFT) method that enables the same underlying LLM to be used for different agents to enhance scalability is adopted.

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.

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

TL;DR

An agentic AI framework for intent translation and optimization in cell-free O-RAN is proposed and a parameter-efficient fine-tuning (PEFT) method that enables the same underlying LLM to be used for different agents to enhance scalability is adopted.

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.
Paper Structure (5 sections, 15 equations, 4 figures, 2 tables, 1 algorithm)

This paper contains 5 sections, 15 equations, 4 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Agentic AI Framework for Cell-free O-RAN
  4. Performance Evaluation
  5. Conclusion

Figures (4)

  • Figure 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.
  • Figure 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.
  • Figure 3: The fraction of active O-RUs versus (a) the number of users (b) the total number of O-RUs
  • Figure 4: The data rate of user 3 for different operator intents.