Nokia Solutions and Networks Oy

Alcatel Lucent

Claudiu Mihailescu

Afef Feki

Veronique Capdevielle

Suresh Kalyanasundaram

Fahad Syed Muhammad

Deepak Kumar Nayak

Anand Bedekar

Rajeev Agrawal

Nidham Ben Rached

Jean-Michel Pugeat

Lorenzo Maggi

Hua Xu

Maunu Elias Holma

Alvaro Valcarce Rial

Shivanand Kadadi

Chandrashekhar Thejaswi

Mohammad Saad Khan

Tinhinane Ouadda

Wael Motamed

Frédéric Ratovelomanana

Véronique Capdevielle

Srilatha Ramachandran

Boris Kouassi

Nicolas Rots

Philipe Godin

Loïc Taton

There is herein disclosed a network node of a wireless communication system. The network node has means for selecting a first group of cells. Each cell of the first group of cells is a secondary cell, SCell, for carrier aggregation, CA, on a user equipment, UE, of the wireless communication system. The network node has means for obtaining a first set of labelled data for the first group of cells. The labelled data includes performance information regarding the first group of cells. The network node has means for training a machine learning model for the first group of cells using the first set of labelled data. The machine learning model is configured to be implemented on the network node of the wireless communication system for SCell selection. The network node has means for determining whether the machine learning model meets a performance criteria for at least one cell of the first group of cells.

Secondary cell selection

The invention relates to a method for configuring carrier aggregation for a current user equipment () located in a wireless communication network in a coverage area of a base station which is capable of emitting a plurality of carrier components with a plurality transmission beams per carrier component, the method comprising: retrieving quality metrics beams from a database () comprising quality metrics associated with a carrier component of the plurality of carrier components and with a user equipment of a plurality of user equipments; determining, using the quality metrics, one or more candidate carrier components to be configured or de-configured for the current user equipment as secondary carrier components; configuring or de-configuring said one or more carrier components.

Method and apparatus for carrier aggregation optimization

Disclosed is a method comprising obtaining a plurality of handover parameter values, using a first machine learning model to select a subset of handover parameter values from the plurality of handover parameter values, obtaining historical information of a plurality of terminal devices, determining a first set of optimal handover parameter values for the plurality of terminal devices from the subset of handover parameter values, tagging the first set of optimal handover parameter values with the historical information of the plurality of terminal devices to obtain a labelled dataset, and training a second machine learning model with the labelled dataset, wherein the trained second machine learning model is capable of predicting a second set of optimal handover parameter values for a first terminal device based on historical information of the first terminal device.

Machine learning based handover parameter optimization

Disclosed is a method comprising obtaining historical data from a plurality of access nodes comprised in a network, determining, for at least one of the plurality of access nodes, a region comprising a set of values for threshold pairs that comprise minimum and maximum threshold pairs, determining, from the region, one threshold pair, wherein the threshold pair defines pre-determined thresholds for determining if a cell is to be switched on or switched off, providing the threshold pair to the network for deployment, and collecting data regarding at least one key performance indicator from the plurality of access nodes.

Optimizing usage of power using switch off of cells

An apparatus for anomaly detection in a network, using an autoencoder including an encoder and a decoder. The apparatus includes a processor and a memory including computer program code, causing the apparatus to: providing the decoder with network configuration parameters used to obtain calculated network performance indicators, obtaining reconstructed network performance indicators from the decoder based on the network configuration parameters used to obtain the calculated network performance indicators, comparing the reconstructed network performance indicators with the calculated network performance indicators, detecting an anomaly when observing a deviation between the reconstructed network performance indicators and the calculated network performance indicators, providing the encoder with the calculated network performance indicators, obtaining estimated network configuration parameters from the encoder based on the calculated network performance indicators, detecting that the anomaly is related to the network configuration parameters.

Method and apparatus for anomaly detection in a network

Systems, methods, apparatuses, and computer program products for determining a grid-of-beams (GoB) are provided. One method may include collecting network data for training a neural network, train the neural network, using the collected data, to learn a non-discounted cumulative reward Q that evaluates a benefit of including a given beam into a grid-of-beams (GoB), iteratively applying the trained neural network to select at least one optimal beam to include in the grid-of-beams (GoB), and selecting one or more beams from the grid-of-beams (GoB) to transmit to a user equipment or to receive transmission from the user equipment.

Apparatus and method for designing a grid-of-beams using machine learning

In some embodiments, a computing system maintains, in a memory, information on one or more terminal devices, a dictionary of beams and information on a spatial grid. The computing system maps each terminal device to the spatial grid based on up-to-date results of radio measurements and calculates, for each spatial element, a load caused by the one or more terminal devices based on the mapping. The computing system evaluates, for each combination of a beam and a terminal device, a reference signal received power, RSRP, based on results of the radio measurements. The computing system calculates a first map of expected spatial distribution of traffic based on the calculated loads and beam-specific second maps of expected RSRP based on the values of the RSRP. Finally, the computing system causes performing beamforming optimization based on the first map and second maps.

Efficient data generation for beam pattern optimization

Growing community of inventors

Versailles, France

Claudiu Mihailescu