Deep Learning for Anomaly Detection in Microwave Links : Challenges and Impact on Weather Classification

Sammanfattning: Artificial intelligence is receiving a great deal of attention in various fields of science and engineering due to its promising applications. In today’s society, weather classification models with high accuracy are of utmost importance. An alternative to using conventional weather radars is to use measured attenuation data in microwave links as the input to deep learning-based weather classification models. Detecting anomalies in the measured attenuation data is of great importance as the output of a classification model cannot be trusted if the input to the classification model contains anomalies. Designing an accurate classification model poses some challenges due to the absence of predefined features to discriminate among the various weather conditions, and due to specific domain requirements in terms of execution time and detection sensitivity. In this thesis we investigate the relationship between anomalies in signal attenuation data, which is the input to a weather classification model, and the model’s misclassifications. To this end, we propose and evaluate two deep learning models based on long short-term memory networks (LSTM) and convolutional neural networks (CNN) for anomaly detection in a weather classification problem. We evaluate the feasibility and possible generalizations of the proposed methodology in an industrial case study at Ericsson AB, Sweden. The results show that both proposed methods can detect anomalies that correlate with misclassifications made by the weather classifier. Although the LSTM performed better than the CNN with regards to top performance on one link and average performance across all 5 tested links, the CNN performance is shown to be more consistent.