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: Embodiments relate to an apparatus comprising means configured for: obtaining echo response data representative of the echo response of a communication line, wherein the echo response data specifies the echo response based on two dimensions and includes first dimension data and second dimension data, determining at least one property of the communication line based on processing the echo response data with a neural network, wherein the neural network comprises at least: a first convolutional branch for processing the first dimension data, a second convolutional branch for processing the second dimension data, a dense part for processing the outputs of the first and second convolutional branches.

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: An apparatus, method and computer program is described comprising: combining first features extracted from an echo signal using a convolutional encoder of a convolutional encoder-decoder having first weights, wherein the echo signal is obtained in response to a transmission over a channel or a simulation thereof; and using a convolutional decoder of the convolutional encoder-decoder to generate an estimate of a frequency response of the channel based on the echo signal, wherein the convolutional decoder has second weights.

Abstract: Embodiments relate to an apparatus comprising means configured for: obtaining echo response data representative of the echo response of a communication line, wherein the echo response data specifies the echo response based on two dimensions and includes first dimension data and second dimension data, determining at least one property of the communication line based on processing the echo response data with a neural network, wherein the neural network comprises at least: a first convolutional branch for processing the first dimension data, a second convolutional branch for processing the second dimension data, a dense part for processing the outputs of the first and second convolutional branches.

Abstract: The method includes obtaining a loop length of a terminal section of the communication line; determining whether there is an impairment in the communication line; if no impairment, classifying the communication line into the first group; if impairment is determined, perform obtaining a location of the impairment in the communication line; determining whether the impairment is located in the terminal section of the communication line based on the loop length; if so, classifying the communication line into a second group; else, classifying the communication line into the first group; wherein, in the first group, it is qualified for an new broadband communication service, and if in the second group, it is not qualified, and wherein, the new broadband communication service is to be deployed over the terminal section of the communication line.

Abstract: The method includes obtaining a loop length of a terminal section of the communication line; determining whether there is an impairment in the communication line; if no impairment, classifying the communication line into the first group; if impairment is determined, perform obtaining a location of the impairment in the communication line; determining whether the impairment is located in the terminal section of the communication line based on the loop length; if so, classifying the communication line into a second group; else, classifying the communication line into the first group; wherein, in the first group, it is qualified for an new broadband communication service, and if in the second group, it is not qualified, and wherein, the new broadband communication service is to be deployed over the terminal section of the communication line.

Abstract: Embodiments relate to a method and an apparatus for predicting the bitrate of a repaired communication channel. The method may include, generating a dataset specifying, for a plurality of communication channels, a channel frequency response of a communication channel affected by an impairment, and a channel frequency response of the communication channel non-affected by the impairment, training, based on the dataset, a machine learning model configured to predict a channel frequency response of the repaired communication channel based on the channel frequency response of the communication channel affected by an impairment.

Abstract: Embodiments relate to a method and an apparatus for predicting the bitrate of a repaired communication channel. The method may comprise: generating a dataset specifying, for a plurality of communication channels: a channel frequency response of a communication channel affected by an impairment, and a channel frequency response of the communication channel non-affected by said impairment, training, based on the dataset, a machine learning model configured for predicting, based on the channel frequency response of an impaired communication channel, the channel frequency response of the repaired communication channel.

Abstract: Embodiments relate to a method for detecting a broken binder in an access network including a plurality of telecommunication lines grouped in respective binders, executed by a detection device, including identifying a plurality of telecommunication lines for which a transmission interruption has been detected within a given time period obtaining line data associated with the respective identified telecommunication lines, for respective pairs of identified telecommunication lines, determining a virtual distance between the two telecommunication lines, in function of the associated line data, determining at least one cluster of telecommunication lines in function of the virtual distances, and detecting a broken binder in function of the determined at least one cluster.

Abstract: Embodiments relate to a method for detecting a broken binder in an access network including a plurality of telecommunication lines grouped in respective binders, executed by a detection device, including identifying a plurality of telecommunication lines for which a transmission interruption has been detected within a given time period obtaining line data associated with the respective identified telecommunication lines, for respective pairs of identified telecommunication lines, determining a virtual distance between the two telecommunication lines, in function of the associated line data, determining at least one cluster of telecommunication lines in function of the virtual distances, and detecting a broken binder in function of the determined at least one cluster.