评估滑动平均法在估算Wi-Fi链路质量中的精度与效果
On the Accuracy and Precision of Moving Averages to Estimate Wi-Fi Link Quality
- 用滑动平均法实时监测无线链路质量
- 发现该方法在动态环境中存在估计偏差
- 为未来AI优化无线网络提供基准参考
无线频谱具有显著的波动性，影响所有无线通信技术的性能和确定性。为应对这一问题，实际的Wi-Fi设备通常采用Minstrel等机制，下一代Wi-Fi 8也计划引入机器学习进行优化。这些方法均需在运行时持续监控通信质量。本文分析了基于滑动平均的简单技术在估计无线链路质量方面的有效性，评估其优势与局限性。研究结果可作为探索人工智能如何通过提供可靠的当前频谱状态估计来缓解无线网络不可预测性的基准。
原文摘要 · Abstract (English)
The radio spectrum is characterized by a noticeable variability, which impairs performance and determinism of every wireless communication technology. To counteract this aspect, mechanisms like Minstrel are customarily employed in real Wi-Fi devices, and the adoption of machine learning for optimization is envisaged in next-generation Wi-Fi 8. All these approaches require communication quality to be monitored at runtime. In this paper, the effectiveness of simple techniques based on moving averages to estimate wireless link quality is analyzed, to assess their advantages and weaknesses. Results can be used, e.g., as a baseline when studying how artificial intelligence can be employed to mitigate unpredictability of wireless networks by providing reliable estimates about current spectrum conditions.
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