Automatic Feature Selection Technique for Next Generation Self-Organizing Networks

作者：Palacios David^*; de la Bandera Isabel; Gomez Andrades Ana; Flores Lydia; Barco Raquel

来源：IEEE Communications Letters, 2018, 22(6): 1272-1275.

DOI：10.1109/LCOMM.2018.2825392

• Self-organizing networks
• dimensionality reduction
• feature selection
• performance indicator
• fault diagnosis
• feature engineering
• root cause analysis

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.

• 出版日期2018-6