Network state modelling

A method for a network element, comprising:

• obtaining by an encoder module as an input network data that is representative of the current condition of the communications network, the network data comprising a plurality of values indicative of the performance of network elements and perform feature reduction providing at its output a set of activations;
• performing in a clustering module batch normalisation and an amplitude limitation to the output of the encoder module to obtain normalised activations;
• obtaining by a clustering control module as an input a sparsity constraint, calculating a projection of the normalised activations by utilising a mask controlled by the sparsity constraint and determining a clustering loss controlling the clustering module by calculating distance between the normalised activations and the projection;
• forming by a decoder module from the normalised activations reconstructed network data and determine a reconstruction loss;
• and
• backpropagating, by the network element, the reconstruction loss and the clustering loss through the modules to train the modules by gradually reducing the value of the sparsity constraint.

A computer program comprising instructions for causing an apparatus to perform at least the following:

• obtaining by an encoder module as an input network data that is representative of the current condition of the communications network, the network data comprising a plurality of values indicative of the performance of network elements and perform feature reduction providing at its output a set of activations;
• performing in a clustering module batch normalisation and an amplitude limitation to the output of the encoder module to obtain normalised activations;
• obtaining by a clustering control module as an input a sparsity constraint, calculating a projection of the normalised activations by utilising a mask controlled by the sparsity constraint and determining a clustering loss controlling the clustering module by calculating distance between the normalised activations and the projection;
• forming by a decoder module from the normalised activations reconstructed network data and determine a reconstruction loss;
• and
• backpropagating the reconstruction loss and the clustering loss through the modules to train the modules by gradually reducing the value of the sparsity constraint