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

TL;DR

This paper evaluates the effectiveness of moving averages in estimating Wi-Fi link quality, highlighting their advantages and limitations as a baseline for future AI-based spectrum management techniques.

Contribution

It provides a systematic analysis of simple moving average methods for real-time Wi-Fi link quality estimation, serving as a baseline for AI-driven improvements.

Findings

Moving averages offer a quick, simple estimation of link quality.

They have limitations in capturing rapid spectrum variability.

Results serve as a benchmark for future AI-based approaches.

Abstract

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.