# On Designing a Machine Learning Based Wireless Link Quality Classifier

arXiv: https://arxiv.org/abs/2007.02720 · HTML: https://mlsanity.com/paper/2007.02720/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [Wireless Link Quality Estimation Using LSTM Model](https://mlsanity.com/paper/2410.15357/) (2024)
- [Learning to Fairly Classify the Quality of Wireless Links](https://mlsanity.com/paper/2102.11655/) (2021)

## Abstract (arXiv)

Ensuring a reliable communication in wireless networks strictly depends on the effective estimation of the link quality, which is particularly challenging when propagation environment for radio signals significantly varies. In such environments, intelligent algorithms that can provide robust, resilient and adaptive links are being investigated to complement traditional algorithms in maintaining a reliable communication. In this respect, the data-driven link quality estimation (LQE) using machine learning (ML) algorithms is one of the most promising approaches. In this paper, we provide a quantitative evaluation of design decisions taken at each step involved in developing a ML based wireless LQE on a selected, publicly available dataset. Our study shows that, re-sampling to achieve training class balance and feature engineering have a larger impact on the final performance of the LQE than the selection of the ML method on the selected data.