Linear Combination of Exponential Moving Averages for Wireless Channel Prediction

Abstract

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.

preprint, 6 pages

References in corpus (4)

• Seamless Link-level Redundancy to Improve Reliability of Industrial Wi-Fi Networks
• A neural approach to synchronization in wireless networks with heterogeneous sources of noise
• SDMAC: A Software-Defined MAC for Wi-Fi to Ease Implementation of Soft Real-time Applications
• Predicting Wireless Channel Quality by means of Moving Averages and Regression Models

Cited by in corpus (4)

• Mixing Neural Networks and Exponential Moving Averages for Predicting Wireless Links Behavior
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