Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning

DOI

10.2991/978-94-6239-693-7_68How to use a DOI?

Keywords

Wi-Fi link quality; machine learning; Random Forest; Stacking Classifier; Voting Classifier; Decision Tree; exponential moving averages; low-complexity models; link quality prediction; wireless communication

Abstract

Stable communication and quality Wi-Fi systems, in particular, are among the main challenges posed by the variable nature of wireless communication environments. The ability to predict Wi-Fi link quality is of prime importance in making the network perform excellently, making the connectivity reliable, and preventing service disruptions. This paper reports on the potential of machine learning models to predict Wi-Fi link quality with high precision, while focusing on low-complexity models that can be deployed in resource-limited hardware environments. The assessment features several machine learning algorithms such as Random Forest, Stacking Classifier, Voting Classifier, and Decision Tree that use a linear combination of exponential moving averages to make predictions. The models are trained on a dataset created from real Wi-Fi link data, where the output is classified as one of three link quality levels: Very Good, Good, and Poor. The findings prove that the models are able to deliver predictions that are highly accurate, efficient, and even scalable in scenarios where the computational power is very limited.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - T. Thangarasan
AU  - B. Karthik
AU  - D. Kavya Sree
AU  - N. Manasa
AU  - Mehak Meeraj Shaik
PY  - 2026
DA  - 2026/06/16
TI  - Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 687
EP  - 698
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_68
DO  - 10.2991/978-94-6239-693-7_68
ID  - Thangarasan2026
ER  -