Abstract: Embodiments described herein relate to methods and apparatuses for training and using a graph neural network, GNN.

Abstract: A method comprises transmitting a transmit signal via a transmit radio chain and over-the-air from an antenna system and generating a predicted PIM signal for the transmit signal using a non-linear machine learning model by transforming the transmit signal to a signal feature representation composed of delay-aligned discrete-time samples and at least one discrete-time phase offset. The non-linear machine learning model uses a first neural network column for the delay-aligned discrete-time samples and a second neural network column for the at least one discrete-time phase offset. The predicted PIM signal is obtained as output from the non-linear machine learning model when the signal feature representation is fed as input to the non-linear machine learning model. The method comprises receiving a receive signal over-the-air at the antenna system and via a receive radio chain and removing PIM from the receive signal by subtracting the predicted PIM signal from the receive signal.

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: 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.