# On the Accuracy and Precision of Moving Averages to Estimate Wi-Fi Link Quality

arXiv: https://arxiv.org/abs/2411.12265 · HTML: https://mlsanity.com/paper/2411.12265/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning](https://mlsanity.com/paper/2109.04786/) (2021)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [What Will Wi-Fi 8 Be? A Primer on IEEE 802.11bn Ultra High Reliability](https://mlsanity.com/paper/2303.10442/) (2023)
- [DeepWiFi: Cognitive WiFi with Deep Learning](https://mlsanity.com/paper/1910.13315/) (2019)
- [Linear Combination of Exponential Moving Averages for Wireless Channel Prediction](https://mlsanity.com/paper/2312.07945/) (2023)
- [SDMAC: A Software-Defined MAC for Wi-Fi to Ease Implementation of Soft Real-time Applications](https://mlsanity.com/paper/2211.06074/) (2022)

## Abstract (arXiv)

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.