RT Conference Proceedings
T1 A Semi-Supervised Location-Aware Anomaly Detection Method for Ultra-Dense Indoor Scenarios.
A1 Villegas Carrasco, Javier
A1 Fortes-Rodríguez, Sergio
A1 Barco-Moreno, Raquel
K1 Telecomunicaciones
AB Over the past few years, indoor cellular deploymentshave been on the rise. These scenarios are characterized by theiruser density and fast-changing conditions, thus, being prone tofailures. Moreover, the steady development of indoor and outdoorpositioning techniques is expected to provide a reliable sourceof information. Thus, the availability of user location is beingconsidered to be a key enabler to improve the resilience andperformance of automatic failure management and optimizationtechniques. Taking this into consideration, the present workproposes a semi-supervised location-aware anomaly detectionmethod for the management of failures such as cell outages andinterference problems.
YR 2023
FD 2023
LK https://hdl.handle.net/10630/27055
UL https://hdl.handle.net/10630/27055
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026