Fair AI-STA for Legacy Wi-Fi: Enhancing Sensing and Power Management with Deep Q-Learning
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909462536650752 |
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| author | Yi, Peini Cheng, Wenchi Ju, Zhanyu Wang, Jingqing Pan, Jinzhe Ouyang, Yuehui Zhang, Wei |
| author_facet | Yi, Peini Cheng, Wenchi Ju, Zhanyu Wang, Jingqing Pan, Jinzhe Ouyang, Yuehui Zhang, Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10874 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fair AI-STA for Legacy Wi-Fi: Enhancing Sensing and Power Management with Deep Q-Learning Yi, Peini Cheng, Wenchi Ju, Zhanyu Wang, Jingqing Pan, Jinzhe Ouyang, Yuehui Zhang, Wei Networking and Internet Architecture 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. |
| title | Fair AI-STA for Legacy Wi-Fi: Enhancing Sensing and Power Management with Deep Q-Learning |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2412.10874 |