With the increasing complexity of Wi-Fi networks and the iterative evolution of 802.11 protocols, the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol faces significant challenges in achieving fair channel access and efficient resource allocation between legacy and modern Wi-Fi devices. To address these challenges, we propose an AI-driven Station (AI-STA) equipped with a Deep Q-Learning (DQN) module that dynamically adjusts its receive sensitivity threshold and transmit power. The AI-STA algorithm aims to maximize fairness in resource allocation while ensuring diverse Quality of Service (QoS) requirements are met. The performance of the AI-STA is evaluated through discrete event simulations in a Wi-Fi network, demonstrating that it outperforms traditional stations in fairness and QoS metrics. Although the AI-STA does not exhibit exceptionally superior performance, it holds significant potential for meeting QoS and fairness requirements with the inclusion of additional MAC parameters. The proposed AI-driven Sensitivity and Power algorithm offers a robust framework for optimizing sensitivity and power control in AI-STA devices within legacy Wi-Fi networks.
@article{arxiv.2412.10874,
title = {Fair AI-STA for Legacy Wi-Fi: Enhancing Sensing and Power Management with Deep Q-Learning},
author = {Peini Yi and Wenchi Cheng and Zhanyu Ju and Jingqing Wang and Jinzhe Pan and Yuehui Ouyang and Wei Zhang},
journal= {arXiv preprint arXiv:2412.10874},
year = {2025}
}