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.

翻译：随着Wi-Fi网络日益复杂以及802.11协议的迭代演进，载波侦听多路访问/冲突避免（CSMA/CA）协议在实现传统与现代Wi-Fi设备间的公平信道接入和高效资源分配方面面临重大挑战。为应对这些挑战，我们提出一种配备深度Q学习（DQN）模块的人工智能驱动站点（AI-STA），该模块能动态调整其接收灵敏度阈值与发射功率。AI-STA算法旨在最大化资源分配的公平性，同时确保满足多样化的服务质量（QoS）要求。通过在Wi-Fi网络中进行离散事件仿真评估AI-STA的性能，结果表明其在公平性和QoS指标上均优于传统站点。尽管AI-STA未展现出极其卓越的性能，但通过引入更多MAC参数，其在满足QoS与公平性要求方面具有显著潜力。所提出的人工智能驱动灵敏度与功率算法为在传统Wi-Fi网络中优化AI-STA设备的灵敏度与功率控制提供了一个稳健的框架。