An LLM-Agent-Based Framework for Age of Information Optimization in Heterogeneous Random Access Networks

2026-01-26

AI Summary

This work addresses the challenge of optimizing Age of Information (AoI) in heterogeneous wireless networks, where existing random access strategies suffer from overly idealized models, slow convergence, and poor generalization. To overcome these limitations, the authors propose Reflex-Core, a novel framework that introduces large language model (LLM) agents into AoI optimization for the first time. Reflex-Core establishes a closed-loop control mechanism—comprising observation, reflection, decision-making, and execution—and integrates supervised fine-tuning (SFT) with proximal policy optimization (PPO) to enable intelligent, AoI-aware random access. The resulting Reflex-based Multiple Access (RMA) protocol and its priority-aware variant significantly enhance system timeliness and adaptability. Experimental results demonstrate that RMA reduces average AoI by up to 14.9%, while the priority-enhanced version accelerates convergence by approximately 20%.

Technology Category

• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

With the rapid expansion of the Internet of Things (IoT) and heterogeneous wireless networks, the Age of Information (AoI) has emerged as a critical metric for evaluating the performance of real-time and personalized systems. While AoI-based random access is essential for next-generation applications such as the low-altitude economy and indoor service robots, existing strategies, ranging from rule-based protocols to learning-based methods, face critical challenges, including idealized model assumptions, slow convergence, and poor generalization. In this article, we propose Reflex-Core, a novel Large Language Model (LLM) agent-based framework for AoI-driven random access in heterogeneous networks. By devising an"Observe-Reflect-Decide-Execute"closed-loop mechanism, this framework integrates Supervised Fine-Tuning (SFT) and Proximal Policy Optimization (PPO) to enable optimal, autonomous access control. Based on the Reflex-Core framework, we develop a Reflexive Multiple Access (RMA) protocol and a priority-based RMA variant for intelligent access control under different heterogeneous network settings. Experimental results demonstrate that in the investigated scenarios, the RMA protocol achieves up to a 14.9% reduction in average AoI compared with existing baselines, while the priority-based version improves the convergence rate by approximately 20%.

Problem

• Age of Information
• Heterogeneous Networks
• Random Access
• IoT
• Real-time Systems

Innovation

• Large Language Model (LLM)
• Age of Information (AoI)
• Reflex-Core
• Reinforcement Learning
• Heterogeneous Random Access