Predicting Loss of Communication Between Radio Enabled Devices Using Deep Recurrent Neural Networks
This thesis investigates the effectiveness of applying recurrent neural networks (RNN) to detect communication errors between radio devices, also known as supervision violations, on imbalanced data. The task is to classify whether a supervision violation is to occur within seven days. The available data is in the form of radio packets, which are being re-sampled and pre-processed such that they ca