Quick construction of data-driven models of the short-term behavior of wireless links

High-quality wireless link models can enable better simulations and reduce the development time for new algorithms and protocols. However, the models underlying current simulators are either based on too simple assumptions, so they are unrealistic, or are based on sophisticated machine learning techniques that require extensive training data from the target link, so they are more realistic but impractical. We consider the practical scenario where data collection time is limited (e.g. a few minutes) and cannot afford to deploy a testbed infrastructure with cabling, power and storage. We propose techniques that can construct an accurate machine learning model of the short-term behavior of a target wireless link given only limited training data for the latter, by adapting a reference model that was trained with abundant data. The parameters of the target model are a constrained transformation of the parameters of the reference model, thus the actual number of free parameters is much smaller, and can be reliably estimated with much less data. While estimating the target model from scratch requires 1 to 5 hours of target link data, we show our adaptation technique only requires under 3 minutes of data, for all packet reception rate regimes. We also show that we can construct adapted models for target links in different environments, packet sizes, interference conditions and radio technology (802.15.4 or 802.11b).