Priority-Aware Resource Allocation in AoI-Oriented UL-OFDMA Wi-Fi Networks Based on Multiagent Reinforcement Learning
Pengxue Liu, Dalong Zhang, Fasong Wang, Shijie Shi, Yanbin Zhang, Yitong Li · IEEE Internet of Things Journal · 2025
The proliferation of time-sensitive Internet of Things (IoT) applications has significantly increased the demand for real-time communication in uplink orthogonal frequency division multiple access (UL-OFDMA) Wi-Fi networks. Despite extensive studies, how to meet the heterogeneous age of information (AoI) requirements across different stations (STAs) in Wi-Fi networks remains an open question. To tackle this issue, we propose a multi-agent reinforcement learning (MARL) resource allocation algorithm based on independent hybrid proximal policy optimization (IHPPO), aiming to minimize the AoI and power consumption for each STA while guaranteeing the heterogeneous AoI requirements among STAs within a resource-constrained environment. Specifically, the proposed strategy utilizes HPPO to directly optimize the original hybrid action space by combining discrete resource unit (RU) selection and continuous transmit power adjustment. Extensive simulations demonstrate the superior performance of the IHPPO mechanism in terms of convergence performance and the trade-off between AoI and power consumption, relative to the decomposed multi-agent deep deterministic policy gradient (DE-MADDPG) algorithm, the fully decentralized MADDPG (FD-MADDPG) algorithm, and the random method in different access scenarios.