PCI and RSI conflict detection in a real LTE network using supervised learning

This paper tests two hypotheses regarding how well two distinct Long-Term Evolution (LTE) network problems can be detected through supervised techniques with near-real-time performance. The tested network problems are physical-cell-identity (PCI) conflicts and root-sequence-index (RSI) collisions. These were labeled through configured cell relations that verified these two conflicts. Furthermore, a real LTE network was used. The results obtained showed that both problems were best detected by using each key performance indicator (KPI) measurement as an individual feature. The highest average precisions obtained for PCI conflict detection were 31% and 26% for the 800 MHz and 1800 MHz frequency bands, respectively. The highest average precisions obtained for RSI collision detection were 61% and 60% for the 800 MHz and 1800 MHz frequency bands, respectively.