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

Abstract

With the rapid expansion of the Internet of Things (IoT) and heterogeneous wireless networks, Age of Information (AoI) has emerged as a critical metric for evaluating information freshness in real-time systems. AoI-oriented access optimization in heterogeneous multiple access networks is challenging because legacy access mechanisms, such as TDMA and ALOHA, may coexist over a shared channel, while conventional rule-based and learning-based methods often suffer from limited adaptability, slow convergence, and poor interpretability. In this paper, we propose Reflex-Core, an LLM-agent-based framework for AoI-oriented adaptive access in heterogeneous wireless networks. Reflex-Core adopts an “Observe-Reflect-Decide-Execute” closed-loop mechanism to refine transmission strategies through semantic feedback and historical memory. To provide an analytical foundation for reflection-guided strategy refinement, we derive a drift-plus-penalty design principle and construct a reflection-cycle-level reward target that jointly captures weighted AoI reduction and collision cost. This reward target guides reflection selection, reward model training, and PPO-based post-training. Based on Reflex-Core, we develop the Reflexive Multiple Access (RMA) protocol and a priority-aware RMA variant for differentiated freshness requirements. We further discuss an asynchronous edge-assisted implementation, where LLM-based reflection can be offloaded without blocking slot-level random access. Simulation results show that RMA reduces AoI by up to 14.9% compared with representative baselines and maintains robust performance in dynamic and priority-aware scenarios. Additional scalability and backbone-sensitivity experiments further confirm that Reflex-Core remains effective in a 20-node heterogeneous scenario with varied ALOHA loads and is robust when LongChat-7B-16k is replaced by Qwen2.5-7B-Instruct.