Abstract: A method of determining a service used by a communications network node during a given time period. The method comprises selecting the period of interest from a larger period comprising a plurality of sub-periods, and then determining at least one service in use using a service determination model. This model uses several inputs, including upstream and downstream traffic volumes during the period of interest, as well as traffic volumes for each sub-period preceding the period of interest. The service determination model is a trained model that uses machine learning techniques to predict the sequences of services used. This model is trained to determine at least one target service used during a specific training period. Using this method, it is possible to determine the services used by a communication network node during a given period.

Abstract: A method for generating a temporal sequence of output values at an output rate equal to the input rate multiplied by an upsampling factor K from a first temporal sequence of input values of a traffic-related counter at the input rate is disclosed. The input values are in a first range. The temporal sequence of input values is preprocessed to generate a sequence of scaled values the output rate. The scaled values are in a second range derived from the first range by a non-linear function. A trained iterative denoising process is applied to the sequence of scaled values and a noise signal at the output rate to generate a sequence of denoised upsampled values at the output rate in the second range.

Abstract: Disclosed herein is an apparatus for providing one or more performance characteristics of a physical layer for Discrete Multi-Tone, DMT, based communication. The apparatus comprises means for receiving one or more first inputs, each first input indicative of a parameter associated with the physical layer. The apparatus comprises means for receiving one or more second inputs, each second input indicative of a performance characteristic of the physical layer, to be output. The apparatus comprises means for selecting, based on the one or more second inputs, one or more processing modules of a digital twin of the physical layer carrier data. The apparatus comprises means for determining, using the selected one or more processing modules and the one or more first inputs, the one or more performance characteristics. The apparatus comprises means for providing the one or more performance characteristics as output. A method and computer program are also disclosed.

Abstract: An apparatus, method and computer program is described as obtaining forwarded traffic volume data and discarded traffic volume data of a point-to-multipoint communication network in each of a plurality of discrete time periods over an observation period; generating calibrated forwarded and discarded traffic volume data, wherein calibration data is dependent on a calibration level; filtering, by applying signal processing techniques, multiple instances of said calibrated forwarded and discarded traffic volume data; identifying first sample periods within said observation period; and determining impact scores for the forwarded and discarded traffic volume data for each of said one or more first sample periods.

Abstract: An apparatus, method and computer program is described as obtaining forwarded traffic volume data and discarded traffic volume data of a point-to-multipoint communication network in each of a plurality of discrete time periods over an observation period; generating calibrated forwarded and discarded traffic volume data, wherein calibration data is dependent on a calibration level; filtering, by applying signal processing techniques, multiple instances of said calibrated forwarded and discarded traffic volume data; identifying first sample periods within said observation period; and determining impact scores for the forwarded and discarded traffic volume data for each of said one or more first sample periods.

Abstract: An apparatus, method and computer program provide for obtaining channel response data including a channel frequency response of a channel over a frequency spectrum, wherein the channel frequency response is generated in response to a transmission over the channel or a simulation thereof; and generating an indication of channel impairments in response to applying the channel response data to a transformer-based machine-learning (ML) model trained to predict a channel impairment estimate.

Abstract: An apparatus, method and computer program is described comprising: obtaining channel response data comprising a first channel frequency response of a channel over a first frequency spectrum, where the first channel frequency response is generated in response to a transmission over the channel or a simulation thereof; and generating an estimate of a second channel frequency response of the channel over a second frequency spectrum in response to applying the channel response data to a machine-learning model, where the second frequency spectrum is different to the first frequency spectrum.

Abstract: Embodiments of the present disclosure relate to an apparatus including means configured to perform: obtaining a measured channel frequency response, and a measured noise power spectral density for a line of a wired network susceptible to an impairment; deriving, in case of an indication of an impairment present on the line, from the measured channel frequency response and the measured noise power spectral density, a first theoretical noise representation for the line with the impairment and a second theoretical noise representation for the line without the impairment; and determining information indicative of a location of the impairment in the line, by processing the measured noise power spectral density, the first theoretical noise representation, and the second theoretical noise representation with a neural network.

Abstract: Embodiments relate to an apparatus for monitoring a telecommunication network including a plurality of telecommunication channels. The apparatus is configured to obtain operational data specifying the functioning of respective telecommunication channels for respective groups of telecommunication channels. The apparatus is configured to determine a difference between a number of impacted telecommunication channels in said group and an expected number of impacted telecommunication channels in said group, based on said operational data. The apparatus is configured to determine differences.