Abstract: A computer-implemented method for analyzing issues in a cellular network is provided. The cellular network includes a plurality of cells, including source cells and neighbor cells. The method includes building a network graph representing features of the cells. The method includes identifying, using the network graph, sub-graphs for each source cell indicating a network issue. The method includes, for each network issue of the source cell for each sub-graph, ranking each feature. The method includes, for each source cell, training, using a feature set identified, a first machine learning (ML) model. The method includes training a second ML model, using patterns identified by each cluster, to classify an unidentified pattern of one or more neighbor cells contribution to the source cell network issue and identify root cause information for the issue.

Abstract: Embodiments include computer-implemented methods of forecasting performance issues associated with cells in a wireless network. Such methods include for each of a plurality of cells in the wireless network, obtaining first time-series data representing a plurality of key performance indicators (KPIs) at a first plurality of past time points and applying a multi-layer forecasting model to the first time-series data to determine second time-series data representing the plurality of KPIs for each of the plurality of cells at a second plurality of future time points. Such methods include, based on the second time-series data, determining respective probabilities of occurrence for a respective plurality of different performance issues, associated with the plurality of cells, during one or more future time periods that include the second plurality of future time points. Other embodiments include network optimization functions associated with the wireless network, which are configured to perform such methods.

Abstract: The invention refers to method, performed by a network optimization function associated with a wireless network (100), of forecasting performance issues associated with cells (106, 111, 116) in the wireless network, the method comprising: for each of a plurality of cells in the wireless network, obtaining (1210) first time-series data representing a plurality of key performance indicators, KPIs, at a first plurality of past time points; applying (1220) a multi-layer forecasting model to the first time-series data to determine second time-series data representing the plurality of KPIs for each of the plurality of cells at a second plurality of future time points; and based on the second time-series data, determining (1240) respective probabilities of occurrence for a respective plurality of different performance issues, associated with the plurality of cells, during one or more future time periods that include the second plurality of future time points; the method further refers to a corresponding network opti

Abstract: A computer-implemented method for analyzing issues in a cellular network is provided. The cellular network includes a plurality of cells, including source cells and neighbor cells. The method includes building a network graph representing features of the cells. The method includes identifying, using the network graph, sub-graphs for each source cell indicating a network issue. The method includes, for each network issue of the source cell for each sub-graph, ranking each feature. The method includes, for each source cell, training, using a feature set identified, a first machine learning (ML) model. The method includes training a second ML model, using patterns identified by each cluster, to classify an unidentified pattern of one or more neighbor cells contribution to the source cell network issue and identify root cause information for the issue.

Abstract: The invention refers to method, performed by a network optimization function associated with a wireless network (100), of forecasting performance issues associated with cells (106, 111, 116) in the wireless network, the method comprising: for each of a plurality of cells in the wireless network, obtaining (1210) first time-series data representing a plurality of key performance indicators, KPIs, at a first plurality of past time points; applying (1220) a multi-layer forecasting model to the first time-series data to determine second time-series data representing the plurality of KPIs for each of the plurality of cells at a second plurality of future time points; and based on the second time-series data, determining (1240) respective probabilities of occurrence for a respective plurality of different performance issues, associated with the plurality of cells, during one or more future time periods that include the second plurality of future time points; the method further refers to a corresponding network opti