首页|Glasgow Caledonian University Researchers Discuss Findings in Machine Learning (Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling)
Glasgow Caledonian University Researchers Discuss Findings in Machine Learning (Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling)
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Research findings on artificial intelligence are discussed in a new report. According to news originating from Glasgow, United Kingdom, by NewsRx correspondents, research stated, "This article explores the convergence of artificial intelligence and its challenges for precise planning of LoRa networks. It examines machine learning algorithms in conjunction with empirically collected data to develop an effective propagation model for LoRaWAN." The news correspondents obtained a quote from the research from Glasgow Caledonian University: "We propose decoupling feature extraction and regression analysis, which facilitates training data requirements. In our comparative analysis, decision-tree-based gradient boosting achieved the lowest root-mean-squared error of 5.53 dBm. Another advantage of this model is its interpretability, which is exploited to qualitatively observe the governing propagation mechanisms. This approach provides a unique opportunity to practically understand the dependence of signal strength on other variables. The analysis revealed a 1.5 dBm sensitivity improvement as the LoR's spreading factor changed from 7 to 12. The impact of clutter was revealed to be highly non-linear, with high attenuations as clutter increased until a certain point, after which it became ineffective."
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