2026-06-02T00:40:34Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/325212026-02-03T12:10:50Zcom_10630_2254col_10630_37959

00925njm 22002777a 4500 dc Luo Chen, Hao Qiang author Segura, David author Baena-González, José Carlos author Khatib, Emil Jatib author Barco-Moreno, Raquel author 2024 The high demand for better services in cellular networks is the motivation behind the evolution of said network. Currently, the Open Radio Access Network (O-RAN) paradigm was proposed to provide more intelligent management of the user radio access, improving the quality of services, by applying Artificial Intelligence (IA); and Machine Learning (ML) algorithms. Despite their high potential, ML is highly dependent on the integrity of applied data, especially in the training stage. To avoid any data alteration, in this work an algorithm for anomaly detection in network metrics is proposed. This approach is based on a state machine to determine the network behaviour and Otsu thresholding. The algorithm performance is evaluated on data obtained from a 5G microcell. https://hdl.handle.net/10630/32521 Procesado de imágenes Detection of anomalous samples based on automatic thresholds.