Agentic AI for Intent-driven Optimization in Cell-free O-RAN
arXiv:2602.22539v1 Announce Type: new 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) manag
arXiv:2602.22539v1 Announce Type: new 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.
Executive Summary
This article proposes an agentic AI framework for intent-driven optimization in cell-free O-RAN. The framework consists of four agents: a supervisor agent, a user weighting agent, an O-RU management agent, and a monitoring agent. These agents collaborate to achieve operator-defined intents, such as energy-saving and minimum rate requirements. The framework utilizes a parameter-efficient fine-tuning method to enhance scalability. Simulation results demonstrate a significant reduction in active O-RUs and memory usage compared to baseline schemes. The proposed framework has the potential to improve the efficiency and autonomy of O-RANs, enabling more complex and dynamic optimization objectives.
Key Points
- ▸ The proposed framework utilizes multiple agents to achieve complex intents in cell-free O-RAN.
- ▸ The framework employs a parameter-efficient fine-tuning method to enhance scalability.
- ▸ Simulation results demonstrate significant reductions in active O-RUs and memory usage compared to baseline schemes.
Merits
Strength in Scalability
The parameter-efficient fine-tuning method enables the same underlying LLM to be used for different agents, reducing memory usage and enhancing scalability.
Improved Energy Efficiency
The framework's ability to reduce the number of active O-RUs by 41.93% when compared to baseline schemes has the potential to improve energy efficiency and reduce costs.
Demerits
Complexity and Interoperability
The framework's complexity may introduce interoperability issues and require significant integration efforts with existing O-RAN systems.
Limited Real-world Deployment
The framework's effectiveness in real-world scenarios may be limited by factors such as network topology, user behavior, and device heterogeneity.
Expert Commentary
While the proposed framework demonstrates significant promise, its effectiveness in real-world scenarios remains to be seen. Further research is needed to address the complexity and interoperability challenges introduced by the framework. Nevertheless, the framework's potential to improve the efficiency and autonomy of O-RANs makes it an exciting area of research. As the wireless industry continues to evolve, it is likely that frameworks like this will become increasingly important for achieving more complex and dynamic optimization objectives.
Recommendations
- ✓ Future research should focus on addressing the complexity and interoperability challenges introduced by the framework.
- ✓ The framework's effectiveness in real-world scenarios should be evaluated through large-scale simulations and experiments.
