2026-06-03T00:22:26Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/302262026-02-03T11:10:23Zcom_10630_2254col_10630_37953

Automatic Feature Selection Technique for Next Generation Self-Organizing Networks Palacios, David De la Bandera Cascales, Isabel Gómez-Andrades, Ana Flores, Lydia Barco-Moreno, Raquel Sistemas de comunicaciones inalámbricos Despite self-organizing networks (SONs) pursue the automation of management tasks in current cellular networks, the selection of the most useful performance indicators (PIs), used as inputs for SON functions, is still performed by network experts. In this letter, a novel supervised technique for the automatic selection of PIs for self-healing functions is proposed, relying on the dissimilarity of their statistical behavior under different network states. Results using data from a live network show that the proposed method outperforms an expert’s selection, allowing the volume and complexity of both network databases and SON functions to be reduced without an expert’s intervention. 2024-02-09T07:38:03Z 2024-02-09T07:38:03Z 2024-02-09T07:38:03Z 2018 journal article D. Palacios, I. de-la-Bandera, A. Gómez-Andrades, L. Flores and R. Barco, "Automatic Feature Selection Technique for Next Generation Self-Organizing Networks," in IEEE Communications Letters, vol. 22, no. 6, pp. 1272-1275, June 2018. https://hdl.handle.net/10630/30226 10.1109/LCOMM.2018.2825392 eng open access IEEE