Fair AI-STA for Legacy Wi-Fi: Enhancing Sensing and Power Management with Deep Q-Learning

TL;DR

This paper introduces an AI-driven Wi-Fi station that uses Deep Q-Learning to dynamically optimize sensitivity and power, improving fairness and QoS in legacy Wi-Fi networks.

Contribution

It presents a novel AI-STA framework utilizing Deep Q-Learning for adaptive sensitivity and power control to enhance fairness and QoS in Wi-Fi networks.

Findings

Outperforms traditional stations in fairness metrics.

Demonstrates potential for meeting QoS requirements.

Provides a robust AI-driven power and sensitivity optimization framework.

Abstract

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.