A Channel-Quality Classification Analysis for IoT Communication based on Machine Learning
Alan Torres-Alvarado, Luis Alberto Morales-Rosales, Ignacio Algredo‐Badillo · 2021
The Internet of Things (IoT) involves authentication processes that can be prompt to errors due to noise and radiation if they are implemented in hardware, as is the case of Cognitive Radios (CRs). Since noise is related to Channel Quality. In this article is presented an analysis of different ML algorithms for low and high Channel Quality classification. This can serve for future architectures to sense, know and understand the environment and react in accordance with the user necessities. The results showed that the most suitable ML algorithm (from the selected set) is Random Forest, since it achieved the highest score with a 95.54%.