RT Conference Proceedings
T1 Detection of anomalous samples based on automatic thresholds.
A1 Luo Chen, Hao Qiang
A1 Segura, David
A1 Baena-González, José Carlos
A1 Khatib, Emil Jatib
A1 Barco-Moreno, Raquel
K1 Procesado de imágenes
AB 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.
YR 2024
FD 2024
LK https://hdl.handle.net/10630/32521
UL https://hdl.handle.net/10630/32521
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026