Abstract: A method and system for predicting a state of a cell in a radio access network are described herein. The method includes obtaining information of the cell, determining one or more sets of conditions based on the information and predicting that the cell will enter a sleeping state when at least one set of the one or more sets of conditions is fulfilled. The method further includes outputting an action to prevent the cell from entering the sleeping state based on the probability and a number of wireless devices currently connected to the cell.

Abstract: Methods include training, using a generative adversarial network, a generator model using data noise that includes data corresponding to real problems of a telecommunication site, generating a generated problem that has not occurred at the telecommunication site and that has a non-zero probability of occurring at the site in the future, providing the generated problem to a virtual agent that is configured to generate a solution action to resolve the generated problem, evaluating the solution action relative to the generated problem to determine a performance value corresponding to the solution action, and responsive to the performance value being higher than other performance values corresponding to other solution actions for the generated problem, generating a generic problem model that corresponds to the generated problem and that is associated with the solution action.

Abstract: A method and system for predicting a state of a cell (11) in a radio access network are described herein. The method comprises obtaining (S210) information of the cell (11), determining (S220) one or more sets of conditions based on the information and predicting (S230) that the cell (11) will enter a sleeping state when at least one set of the one or more sets of conditions is fulfilled. The method further comprises outputting (S240) an action to prevent the cell (11) from entering the sleeping state based on the probability and a number of wireless devices currently connected to the cell (11).

Abstract: Methods include training, using a generative adversarial network, a generator model using data noise that includes data corresponding to real problems of a telecommunication site, generating a generated problem that has not occurred at the telecommunication site and that has a non-zero probability of occurring at the site in the future, providing the generated problem to a virtual agent that is configured to generate a solution action to resolve the generated problem, evaluating the solution action relative to the generated problem to determine a performance value corresponding to the solution action, and responsive to the performance value being higher than other performance values corresponding to other solution actions for the generated problem, generating a generic problem model that corresponds to the generated problem and that is associated with the solution action.

Abstract: The disclosure provides methods, apparatus and machine-readable mediums for the detection of sleeping cells in a cellular network. A method of detecting a sleeping cell in a cellular communication network comprises: monitoring power consumption of a radio transmission point of the cellular communication network; providing the power consumption of the radio transmission point as an input to a classification model, developed using a machine-learning algorithm; and obtaining an output from the classification model, the output classifying the radio transmission point as serving one of a sleeping cell and a non-sleeping cell.