Quality of Monitoring for Cellular Networks

5G networks and beyond introduce a larger number of Network Elements (NEs) functions than former cellular generations. The increase in NEs will, thus, result significantly increasing the Management-Plane (M-Plane) data collected from NEs. Therefore, conventional centralized Management Systems (NMSs) will face fundamental challenges processing M-Plane data. In this paper, we present concept Quality Monitoring (QoM) as solution, which is able to reduce already at First, QoM aggregates raw into Key Performance Indicators (KPIs). To these KPIs, applies data-driven algorithm define information loss limits for classes specific each KPI time series. Then, compressing utilizing lossy-compression method, derivative Piece-Wise Constant Approximation (PWCA) algorithm. evaluate performance use live LTE network calculate four while has different statistical characteristics. We also three named Exact , xmlns:xlink="http://www.w3.org/1999/xlink">Optimized xmlns:xlink="http://www.w3.org/1999/xlink">Sharp . For all class higher compression rate highest rate. Assuming that, example, produce 280 MB containing that needs be transferred operations center; KPIs represent contents data, solution transfer over network. As result, achieves an estimated 95% gain transfer.