Abstract: A computing device (110) generates, from a network graph representing a plurality of network elements in a wireless communication network (100) and based on a plurality of network performance metrics, a model of the wireless communication network (100) that groups the network elements having similar radio environments (170) together. The computing device (110) also generates a network configuration recommendation (370) for at least one of the network elements based on the model.

Abstract: A method of controlling uplink power levels of radio cells in a wireless communications network is disclosed. The method comprises grouping sets of neighboring cells into a plurality of cell clusters, identifying at least one first cell cluster comprising at least one cell being subjected to interference and having a signal quality exceeding a signal quality threshold value, identifying at least one second cell cluster comprising at least one cell being subjected to interference and having a signal quality below the signal quality threshold value; creating a model based on the identified first cell cluster to map a nominal uplink power level to an estimated signal quality value thereof, and adjusting a nominal uplink power level of the at least one cell of the second cell cluster by applying the nominal uplink power level derived from the created model to attain a corresponding estimated signal quality value therein.

Abstract: Embodiments described herein relate to methods and apparatus for determining cell upgrades, for example whether to upgrade a Long Term Evolution (LTE) cell to Massive Multiple Input Multiple Output (M-MIMO) technology. There is provided a method for determining a cell upgrade metric. The method receives samples having user session data from user equipment communicating within a cell. Training data is obtained from the samples by applying a filtering rule and an autoencoder trained using the samples. The encoder and code layers of the trained autoencoder are combined with a classifier network to generate a combined classification network. The combined classification network is trained using the training data. The trained combined classifier network is used to classify user session data within the same or a different cell and using a plurality of the classifications to determine the cell upgrade metric for the cell.

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 method is provided for identifying operating conditions of a system. Input data relating to operation of the system is applied to a multi-class model for classification, where the multi-class model is configured for classifying the data into one of a plurality of predefined classes, and each class corresponds to a respective operating condition of the system. A confidence level of the classification by the multi-class model is determined. If the confidence level is below a threshold confidence level, the input data is applied to a plurality of binary models, where each binary model is configured for determining whether the data is or is not in a respective one of the predefined classes. If the plurality of binary models determine that the data is not in any of the respective predefined classes, the data can be taken into consideration when updating the multi-class model.

Abstract: A method 300 is disclosed for generating and training a model to detect interference conditions at a cell in a wireless cellular network and to classify the impact of detected interference conditions on performance of the wireless cellular network in the cell. The method comprises, for each of a plurality of cells in the wireless cellular network (360), obtaining data representing received signal power at a base station serving the cell over a period of time (310) and obtaining data representing a plurality of performance metrics for the cell over the time period (330). The method further comprises obtaining classifications of the received signal power data into one of a plurality of cell interference conditions (320) and the performance metric data into one of a plurality of cell impact classes (340).