Abstract: A method and network node for determining a Radio Frequency (RF) propagation model for a coverage area from an image view of the coverage area. The method selects a coverage area for a transmission point of a transmitter and obtains an image view of the selected coverage area. The method further recognizes, from a plurality of morphology types, a morphology type for the selected coverage area from the obtained image view using a machine learning model; and determines a RF propagation model for the selected coverage area based on the recognized morphology type.

Abstract: There is provided a computer-implemented method for processing data items for use in training a machine learning model to identify a relationship between the data items. The data items correspond to one or more features of a telecommunications network. For each feature of the one or more features, the corresponding data items are organised into a sequence according to time to obtain at least one sequence of data items. A single sequence of data items having the at least one sequence of data items is encoded to obtain an encoded sequence of data items. The single sequence of data items is encoded with information indicative of a position of data items in the single sequence of data items. The encoded sequence of data items is for use in training the machine learning model to identify the relationship between the data items.

Abstract: A method and network node for determining a Radio Frequency (RF) propagation model for a coverage area from an image view of the coverage area. The method selects a coverage area for a transmission point of a transmitter and obtains an image view of the selected coverage area. The method further recognizes, from a plurality of morphology types, a morphology type for the selected coverage area from the obtained image view using a machine learning model; and determines a RF propagation model for the selected coverage area based on the recognized morphology type.