用AI动态优化Wi-Fi信道，降低干扰和延迟。
MAC Revivo: Artificial Intelligence Paves the Way
- 用机器学习实时感知环境，智能调整接入策略。
- 实验显示干扰和延迟显著下降，保障确定性延迟。
- 适合高密度物联网场景，提升未来Wi-Fi可靠性。
物联网（IoT）设备广泛采用Wi-Fi和/或蓝牙功能，导致工业、科学与医疗（ISM）频段严重干扰和拥塞。传统Wi-Fi介质访问控制（MAC）设计难以应对日益复杂的无线环境，且难以保证服务质量（QoS）。本文探索将先进人工智能（AI）方法融入Wi-Fi MAC协议设计。提出AI-MAC，一种利用机器学习算法动态适应网络变化、优化信道接入、缓解干扰并确保确定性延迟的创新方案。通过智能预测与管理干扰，AI-MAC旨在为下一代Wi-Fi网络提供稳健解决方案，实现无缝连接与增强的QoS。实验结果表明，AI-MAC显著降低了干扰和延迟，为日益拥挤的ISM频段中的可靠高效无线通信开辟了新路径。
原文摘要 · Abstract (English)
The vast adoption of Wi-Fi and/or Bluetooth capabilities in Internet of Things (IoT) devices, along with the rapid growth of deployed smart devices, has caused significant interference and congestion in the industrial, scientific, and medical (ISM) bands. Traditional Wi-Fi Medium Access Control (MAC) design faces significant challenges in managing increasingly complex wireless environments while ensuring network Quality of Service (QoS) performance. This paper explores the potential integration of advanced Artificial Intelligence (AI) methods into the design of Wi-Fi MAC protocols. We propose AI-MAC, an innovative approach that employs machine learning algorithms to dynamically adapt to changing network conditions, optimize channel access, mitigate interference, and ensure deterministic latency. By intelligently predicting and managing interference, AI-MAC aims to provide a robust solution for next generation of Wi-Fi networks, enabling seamless connectivity and enhanced QoS. Our experimental results demonstrate that AI-MAC significantly reduces both interference and latency, paving the way for more reliable and efficient wireless communications in the increasingly crowded ISM band.
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