Abstract: The present disclosure relates to system(s) and method(s) for building an ARIMA based Time Series prediction/forecast model for Key Performance Indicators (KPIs) and Performance Management (PM) counters in a telecommunication network. The system receives historical data, for a predefined period, associated with a prediction/forecast model. The system further pre-processes the historical data in order to evaluate statistical characteristics of stationarity of the historical data. Based on the evaluation, the system stationarizes the data first by backfilling anomalies and missing data and then applying techniques associated with differencing, moving averages and auto-correlation. The system further builds the Time Series based prediction/forecast model using the data using ACF and PACF correlation functions.

Abstract: A method of optimizing traffic steering (TS) radio resource management (RRM) decisions for handover of individual user equipment (UE) in Open Radio Access Network (O-RAN) includes: providing an O-RAN-compliant near real time RAN intelligent controller (near-RT RIC) configured to interact with O-RAN nodes; and utilizing an artificial intelligence (AO-based TS application xApp in the near-RT RIC to optimize TS handover control and maximize UE throughput utility. The TS xApp is configured utilizing a virtualized and simulated environment for O-RAN, which virtualized and simulated environment for O-RAN is provided by ns-O-RAN platform. The optimization problem to be solved is formulated as a Markov Decision Process (MDP), and a solution to the optimization problem is derived by using at least one reinforcement learning (RL) technique.

Abstract: The present disclosure relates to system(s) and method(s) for predicting a Key Performance Indicator (KPI) in a telecommunication network is illustrated. The system is configured to monitor a set of counters and a Key Performance Indicator corresponding to a telecommunication network. The set of counters and the Key Performance Indicator (KPI) are monitored for a predefined time interval to gather sample data. The system is configured to analyze the sample data using a data analysis technique in order to identify a subset of counters, from the set of counters, influencing the KPI and a correlation coefficient associated with each counter from the subset of counters, wherein the correlation coefficient associated with each counter is identified after normalizing the subset of counters. The system is configured to apply regression on the subset of counters and the KPI in order to build a correlation equation between the subset of counters and the KPI.

Abstract: The present disclosure relates to system(s) and method(s) for building an ARIMA based Time Series prediction/forecast model for Key Performance Indicators (KPIs) and Performance Management (PM) counters in a telecommunication network. The system receives historical data, for a predefined period, associated with a prediction/forecast model. The system further pre-processes the historical data in order to evaluate statistical characteristics of stationarity of the historical data. Based on the evaluation, the system stationarizes the data first by backfilling anomalies and missing data and then applying techniques associated with differencing, moving averages and auto-correlation. The system further builds the Time Series based prediction/forecast model using the data using ACF and PACF correlation functions.

Abstract: Disclosed is a system for profiling one or more nodes based on a hybrid Key Performance Indicator (KPI). Initially, a flag indicating an issue with a KPI is received. A set of Performance Management (PM) counters undergoing periodic changes in performance beyond a predefined threshold by using machine learning based statistical techniques is identified. The set of PM counters may comprise a set event based of PM counters and a set of protocol based PM counters. A hybrid KPI is created based on combination of the set event based of PM counters and the set of protocol based PM counters. One or more nodes are profiled by comparing the hybrid KPI associated to the node with hybrid KPI corresponding to each of the one or more nodes.

Abstract: Disclosed is a system for profiling one or more nodes based on a hybrid Key Performance Indicator (KPI). Initially, a flag indicating an issue with a KPI is received. A set of Performance Management (PM) counters undergoing periodic changes in performance beyond a predefined threshold by using machine learning based statistical techniques is identified. The set of PM counters may comprise a set event based of PM counters and a set of protocol based PM counters. A hybrid KPI is created based on combination of the set event based of PM counters and the set of protocol based PM counters. One or more nodes are profiled by comparing the hybrid KPI associated to the node with hybrid KPI corresponding to each of the one or more nodes.

Abstract: Disclosed is a system for profiling one or more nodes based on a hybrid Key Performance Indicator (KPI). Initially, a flag indicating an issue with a KPI is received. A set of Configuration Management (CM) may be changed or identified by SME. Deviation in magnitude of each CM parameters from a predefined CM magnitude is computed to determine a changed CM parameter with deviation magnitude higher than deviation magnitude of remaining CM parameters. A set of Performance Management (PM) counters is identified by comparing magnitude of each PM with a predefined threshold value or using machine learning or statistical techniques. A hybrid KPI is created based on combination of the changed CM parameters and a subset of PM counters. One or more nodes are profiled by comparing the hybrid KPI associated to the node with hybrid KPI corresponding to each of the one or more nodes.

Abstract: Disclosed is a system for profiling one or more nodes based on a Key Performance Indicator (KPI) associated to a node. Initially, a receiving module receives a flag indicating an issue with the KPI of a node present in a network of nodes. An identification module identifies a set of Performance Management (PM) counters influencing the KPI using machine learning based statistical method of correlation. A determination module determines a subset of PM counters, from the set of PM counters, by comparing each PM counter with a corresponding predefined threshold limit. A normalization module normalizes the subset of PM counters by computing a variance of the subset of PM counters. A profile module profiles one or more nodes, present in the network of nodes, by comparing the variance associated to the node with variance corresponding to each of the one or more nodes.

Abstract: The present disclosure relates to system(s) and method(s) for predicting a Key Performance Indicator (KPI) in a telecommunication network is illustrated. The system is configured to monitor a set of counters and a Key Performance Indicator corresponding to a telecommunication network. The set of counters and the Key Performance Indicator (KPI) are monitored for a predefined time interval to gather sample data. The system is configured to analyze the sample data using a data analysis technique in order to identify a subset of counters, from the set of counters, influencing the KPI and a correlation coefficient associated with each counter from the subset of counters, wherein the correlation coefficient associated with each counter is identified after normalizing the subset of counters. The system is configured to apply regression on the subset of counters and the KPI in order to build a correlation equation between the subset of counters and the KPI.