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

TL;DR

This paper introduces Reflex-Core, an LLM-agent-based framework that optimizes Age of Information in heterogeneous wireless networks through a novel closed-loop mechanism, leading to significant reductions in AoI and faster convergence.

Contribution

The paper presents a new LLM-agent-based framework with a closed-loop Observe-Reflect-Decide-Execute mechanism for AoI optimization in heterogeneous networks, including the design of RMA protocols.

Findings

RMA protocol reduces average AoI by up to 14.9% compared to baselines.

Priority-based RMA improves convergence rate by approximately 20%.

Framework demonstrates effective autonomous access control in diverse network scenarios.

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%.