Nokia Solutions and Networks Oy

Nokia Technologies Oy

Alcatel Lucent

Other

Inria

Inria Institut National De Recherche En Informatique Et En Automatique

Institut National De Recherche En Informatique Et En Automatique

Afef Feki

Piyush Gupta

Veronique Capdevielle

Fahad Syed Muhammad

Claudiu Mihailescu

Nidham Ben Rached

Suresh Kalyanasundaram

Lorenzo Maggi

Deepak Kumar Nayak

Ahmad Awada

Anand Bedekar

Oana-Elena Barbu

Rajeev Agrawal

Anna Pantelidou

István Zsolt Kovács

Pavan Koteshwar Srinath

Jean-Michel Pugeat

Taylan Sahin

Laurent Roullet

Francois Baccelli

Alberto Conte

Athul Prasad

Hua Xu

Daejung Yoon

Muhammad Majid Butt

Serge Papillon

Bessem Sayadi

Qi Liao

Maunu Elias Holma

Muhammad Ikram Ashraf

Chung Shue Chen

Alvaro Valcarce Rial

François Baccelli

Shivanand Kadadi

Alois Herzog

Luis Guilherme Uzeda Garcia

Eitan Altman

Majed Haddad

Laurent Thomas

Ilaria Malanchini

Christophe Grangeat

Mikko SÄILY

Chandrashekhar Thejaswi

Carine Balageas

Patrick Albert Krief

Sankaran Balasubramaniam

Ahmed Farhan Hanif

Frédéric Ratovelomanana

Anil Rao

Srilatha Ramachandran

Philippe Picquet

Véronique Capdevielle

Tinhinane Ouadda

Bertold Dickhaus

Wael Motamed

Taofik Saidi

Udaya Raju Chowta

Mohammad Saad Khan

Nicolas Rots

Boris Kouassi

Renaud Santoro

Siwar Kriaa

Santiago Agudelo Ramirez

Fuad Abinader

Melisa López

Arunkumar Halebid

Wolfgang Aichmann

Nidham Ben Rachec

MLaurent Thomas

MFrancois Baccelli

Daniel Andrews

A method may include receiving, by a first user device from a network node or a second user device, 1) a positioning measurement report including at least one positioning measurement measured by a reference device, and 2) reference positioning-related information to be used for testing and/or validating a machine learning model; determining estimated positioning-related information as outputs of the machine learning model based on at least a portion of the positioning measurement report as inputs to the machine learning model; determining a performance indication of the machine learning model based on the reference positioning-related information and the estimated positioning-related information, wherein the performance indication indicates a performance or accuracy of the machine learning model; and performing, by the first user device, an action based on the performance indication.

Machine learning model validation for UE positioning based on reference device information for wireless networks

Methods and apparatus are proposed for computing a power reduction policy to control radio emissions of a base station over a time window, with the objective to keep the time-averaged radio emissions over the time window below an authorized value set by regulations, with minimum impact on the performance of the overall system. The time window is subdivided into a plurality of time periods starting at a plurality of time steps, and the policy control comprises, at least at a first time step in the time window: generating a predicted average radio emissions over subsequent time periods in the time window, based on radio emissions historical data; finding couples of values of a power reduction factor and an average radio emissions that minimize a cost function for each time step in the time window, based on the predicted average radio emissions.

Method and apparatus for controlling radio emissions of a base station

A method for a physical random access channel (PRACH) attack detection includes detecting by a base station a plurality of preambles sent by devices through a PRACH; launching by the base station a random access (RA) procedure for at least one device for which a preamble has been detected; decoding by the base station at least one radio resource control (RRC) connection request received in the context of a launched RA procedure; determining by the base station a first metric and a second metric, wherein the first metric is the number of RRC connection requests successfully decoded over time, wherein the second metric is the number of preambles detected over the same time and for which a RA procedure has been launched; determining whether there is a suspicion of storm attack over the PRACH based on a result of a comparison of the first and second metrics.

Apparatus and method for detecting PRACH storm attacks

The present subject matter relates to an apparatus for handover of a user equipment into a target base station of communication system, the apparatus comprising means, the means being configured for: in response to selecting the target base station for handover of the user equipment to the target base station performing a first handover method by at least: comparing a current set of cell measurements of the user equipment with reference sets of cell measurements for determining whether the target base station is a fake base station; performing a handover of the user equipment based on a determination that the target base station is not a fake base station.

Handover in presence of false base stations

A method and apparatus () for beam failure management for a user equipment (UE) () are described. A beam failure instance counter (BFI_counter) is determined, that is indicative of a number of consecutive beam failure instances occurred at the UE (), at a time instance. A location of the UE () is determined at the time instance. A beam failure probability factor is determined, based at least on the location of the UE () at the time instance and the BFI_counter. The beam failure probability factor is indicative of a probability of occurrence of a beam failure at the location of the UE (), after a plurality of further time instances. Further, the beam failure probability factor is compared with a beam failure threshold probability (beamFailureThresholdProb). Thereafter, a beam failure is declared if the beam failure probability factor is higher than the beamFailureThresholdProb.

Method and system for beam failure management

A deep transfer reinforcement learning (DTRL) method based on transfer learning within a deep reinforcement learning (DRL) framework is provided to accelerate the GoB optimization decisions when experiencing environment changes in the same source radio network agent or when being applied from a source radio network agent to a target radio network agent. The transferability of the knowledge embedded in a pre-trained neural network model as a Q-approximator is exploited, and a mechanism to transfer parameters from a source agent to a target agent is provided, where the transferability criterion is based on the similarity measure between the source and target domain.

Apparatus, method and computer program for accelerating grid-of-beams optimization with transfer learning

An apparatus, method and computer program are disclosed. The apparatus may include circuitry configured for receiving from a target device, at a first time instance, a set of first measurement data associated with each of a plurality of base stations and determining a first position of the target device based on the received first sets of measurement data. The circuitry may also receive from the target device, at each of one or more subsequent time instances, a second set of measurement data associated with one, or each of a smaller number, of the base stations and determining, at each of the one or more subsequent time instances, a respective position of the target device based on the position determined at a previous time instance and the second set of measurement data.

Position determination

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

An apparatus, method and computer program is described including: obtaining first channel measurement data for a primary component carrier used for communications between a device and a network node of a mobile communication system; providing the first channel measurement data as an input to an algorithm to obtain a first embedding output; comparing the first embedding output with embedding outputs of a plurality of reference channel measurement data input to said algorithm to identify a closest reference channel measurement data to the first channel measurement data; identifying a set of reference channel information associated with the identified closest reference channel measurement data, wherein the identified set of reference channel information includes a carrier aggregation policy defining one or more secondary communication channels; and setting a carrier aggregation policy for the primary component carrier in accordance with the carrier aggregation policy.

Apparatuses, methods and computer program using channel measurement data for a primary component carrier

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 solution for managing a radio map based on radio fingerprinting measurements. A method comprises: acquiring a radio map of an area, the radio map based on radio frequency measurements performed between at least one access node of a wireless network and terminal devices within the area; detecting at least one gap in the radio map and determining geographical coordinates of the at least one gap; causing transmission of a message comprising at least one information element requesting for additional measurements and comprising the geographical coordinates of the at least one gap; in response to the message, receiving at least one radio frequency measurement report related to at least one terminal device and comprising radio frequency measurement data and at least one measurement location where the radio frequency measurement data has been measured; and updating the radio map on the basis of the radio frequency measurement data.

Radio map improvements

Devices, methods and computer programs for determining a radio transmission power threshold are disclosed. At least some of the example embodiments described herein may allow improving resource usage by serving a maximum amount of traffic and not wasting resources, while also ensuring the possibility of serving a minimum traffic level.

Determining a radio transmission power threshold, and related devices, methods and computer programs

Apparatuses, methods and computer programs are described including, receiving user equipment downlink power measurements from one or more base stations of a communication system, wherein the one or more base stations are configured to provide a plurality of user equipment downlink power measurements for user equipment in communication with a respective base station of the one or more base stations; determining inter-beam interference for multiple directive beams of the or each base station based on the user equipment download power measurements; and assigning one or more beams to one or more clusters such that, for all beams within a cluster, inter-beam interference is below a threshold, wherein beams within a cluster share time and/or frequency resources.

Resource scheduling in communication systems using beam-specific power control

An apparatus comprises means for performing, at a CU of a master node: receiving an individual PDU from one or more incoming PDUs, wherein each PDU is to be transmitted to a UE via one amongst respective paths towards at least one DU of the master node and at least one secondary node, the UE being in DC or MC with the master node and the at least one secondary node; collecting a respective data set for each of the paths; receiving, from a ML model inputting each data set, a respective estimated delay of transmission for each of the paths; selecting, from each of the paths, a path based on the estimated delay of transmission; and transmitting the PDU to the UE via the selected path.

Apparatus, method and computer program for routing data in a dual or multi-connectivity configuration

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

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Sceaux, France

Afef Feki