Abstract: Apparatuses and methods in a communication system are disclosed. In a network element, an encoder module obtains 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 performs (800) feature reduction providing at its output a set of activations. A clustering module performs (802) batch normalisation and an amplitude limitation to the output of the encoder module to obtain normalised activations. A clustering control module calculates a projection of the normalised activations and determines (804) a clustering loss. A decoder module calculates (806) a reconstruction loss. The network element backpropagates the reconstruction loss and the clustering loss through the modules.

Abstract: According to an aspect, there is provided a local diagnosis system comprising means for performing the following. The local diagnosis system detects one or more anomaly events associated with a communications network. Each anomaly event defines an anomaly pattern describing a data point in a performance indicator space. Then, the local diagnosis system updates one or more local cluster models to incorporate the one or more anomaly patterns within complexity constraints. Each of the one or more local cluster models corresponds to a different diagnosis label defining a diagnosis. In response to failing according to one or more per-defined criteria to incorporate, in the updating, the one or more anomaly patterns to the one or more local cluster models, the local diagnosis system forwards at least the one or more local cluster models and one or more associated diagnosis labels to a central diagnosis system for further diagnosis.

Abstract: Network management apparatus and methods are described. A network management apparatus comprises network data receiving means for receiving network data that is representative of the current condition of a communications network, the network data comprising a plurality of values indicative of the performance of network elements; network data transformation means for transforming the received network data into a network state vector that is indicative of a current state of the network; and network state prediction means for predicting a future network state vector of the network from the current network state vector, the network state prediction means comprising a self-learning prediction module having a memory for storing at least one internal state.

Abstract: Systems, methods, apparatuses, and computer program products for deploying an unmanned vehicle base station (BS) are provided. One method may include receiving an indication from at least one device of its capability to switch to a temporary cell of an unmanned vehicle base station (BS), and determining, based on knowledge of locations on a planned path of the unmanned vehicle base station (BS), which of the at least one device is located within coverage of one of the locations on the planned path. The method may then include configuring discontinuous reception cycles or Power-Saving Mode (PSM) of the at least one device so that the at least one device is awake when the unmanned vehicle base station (BS) establishes the temporary cell.

Abstract: Network management apparatus and methods are described. A network management apparatus comprises network data receiving means for receiving network data that is representative of the current condition of a communications network, the network data comprising a plurality of values indicative of the performance of network elements; network data transformation means for transforming the received network data into a network state vector that is indicative of a current state of the network; and network state prediction means for predicting a future network state vector of the network from the current network state vector, the network state prediction means comprising a self-learning prediction module having a memory for storing at least one internal state.

Abstract: According to an aspect, there is provided a local diagnosis system comprising means for performing the following. The local diagnosis system detects one or more anomaly events associated with a communications network. Each anomaly event defines an anomaly pattern describing a data point in a performance indicator space. Then, the local diagnosis system updates one or more local cluster models to incorporate the one or more anomaly patterns within complexity constraints. Each of the one or more local cluster models corresponds to a different diagnosis label defining a diagnosis. In response to failing according to one or more per-defined criteria to incorporate, in the updating, the one or more anomaly patterns to the one or more local cluster models, the local diagnosis system forwards at least the one or more local cluster models and one or more associated diagnosis labels to a central diagnosis system for further diagnosis.

Abstract: An EMA method of enabling CNM in communication networks comprises, for a given time instant t, extracting (S601) features from an n-dimensional input vector Xt containing at least one of continuous valued environmental parameters, network configuration values and key performance indicator values, and forming a d-dimensional feature vector Yt from the extracted features, quantizing (S602) the formed feature vector Yt by selecting, for the extracted vector Yt, a single quantum corresponding to an internal state of k internal states of an internal state-space model, mapping (S603), for each dimension Sm of an m-dimensional output vector St, an output state bin of a number of output state bins present for dimension Sm to the selected internal state, and, for each cognitive function off cognitive functions, selecting (S604) a subset out of the output vector St, each of the subsets having a dimension equal to or smaller than m and containing feature values required by the cognitive function, the f selected subsets

Abstract: Systems, methods, apparatuses, and computer program products for deploying an unmanned vehicle base station (BS) are provided. One method may include receiving an indication from at least one device of its capability to switch to a temporary cell of an unmanned vehicle base station (BS), and determining, based on knowledge of locations on a planned path of the unmanned vehicle base station (BS), which of the at least one device is located within coverage of one of the locations on the planned path. The method may then include configuring discontinuous reception cycles or Power-Saving Mode (PSM) of the at least one device so that the at least one device is awake when the unmanned vehicle base station (BS) establishes the temporary cell.

Abstract: Systems, methods, apparatuses, and computer program products for mobility management in NR based non-terrestrial networks are provided. One method includes signaling geographical definitions of Geo-Areas (GAs) and/or an update to GA map to at least one user equipment (UE) in a network, and receiving a tracking area update (TAU) and/or radio access network (RAN)-based notification area update (RNAU) when the at least one UE detects a change in the at least one UE's GA location. The method may also include registering the change in the at least one UE's GA location, and configuring at least one of radio access network (RAN)-based notification area (RNA) or tracking area (TA) based on the registered change in the at least one UE's GA location.