# Linear Combination of Exponential Moving Averages for Wireless Channel Prediction

arXiv: https://arxiv.org/abs/2312.07945 · HTML: https://mlsanity.com/paper/2312.07945/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning](https://mlsanity.com/paper/2109.04786/) (2021)
- [Seamless Link-level Redundancy to Improve Reliability of Industrial Wi-Fi Networks](https://mlsanity.com/paper/2211.03575/) (2022)
- [SDMAC: A Software-Defined MAC for Wi-Fi to Ease Implementation of Soft Real-time Applications](https://mlsanity.com/paper/2211.06074/) (2022)
- [Predicting Wireless Channel Quality by means of Moving Averages and Regression Models](https://mlsanity.com/paper/2306.08634/) (2023)
- [A neural approach to synchronization in wireless networks with heterogeneous sources of noise](https://mlsanity.com/paper/2212.03327/) (2022)

## Built on by (most cited first, 6 of 6)

- [Improving Wi-Fi Network Performance Prediction with Deep Learning Models](https://mlsanity.com/paper/2507.11168/) (2025)
- [Mixing Neural Networks and Exponential Moving Averages for Predicting Wireless Links Behavior](https://mlsanity.com/paper/2411.11185/) (2024)
- [On the Accuracy and Precision of Moving Averages to Estimate Wi-Fi Link Quality](https://mlsanity.com/paper/2411.12265/) (2024)
- [Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning](https://mlsanity.com/paper/2509.18933/) (2025)
- [On the Prediction of Wi-Fi Performance through Deep Learning](https://mlsanity.com/paper/2512.00211/) (2025)
- [Integration of LSTM Networks in Random Forest Algorithms for Stock Market Trading Predictions](https://mlsanity.com/paper/2512.02036/) (2025)

## Abstract (arXiv)

The ability to predict the behavior of a wireless channel in terms of the frame delivery ratio is quite valuable, and permits, e.g., to optimize the operating parameters of a wireless network at runtime, or to proactively react to the degradation of the channel quality, in order to meet the stringent requirements about dependability and end-to-end latency that typically characterize industrial applications. In this work, prediction models based on the exponential moving average (EMA) are investigated in depth, which are proven to outperform other simple statistical methods and whose performance is nearly as good as artificial neural networks, but with dramatically lower computational requirements. Regarding the innovation and motivation of this work, a new model that we called EMA linear combination (ELC), is introduced, explained, and evaluated experimentally. Its prediction accuracy, tested on some databases acquired from a real setup based on Wi-Fi devices, showed that ELC brings tangible improvements over EMA in any experimental conditions, the only drawback being a slight increase in computational complexity.