Nokia Technologies Oy

Nokia Solutions and Networks Oy

Nokia Solutions and Networks Gmbh & Co. Kg

Hmd Global Oy

Cinzia Sartori

Devaki Chandramouli

Mika Kasslin

Klaus Ingemann Pedersen

Malgorzata Tomala

Anna Pantelidou

Laurent Thiebaut

Subramanya Chandrashekar

Christian Mannweiler

Ahmad Awada

Peter Rost

Tero Henttonen

Ingo Viering

Daniela Laselva

Anja Jerichow

Hans Thomas Hoehne

Woonhee Hwang

Seppo Ilmari Vesterinen

Olli Alanen

Saurabh Khare

Amaanat Ali

Andreas Maeder

Matti Einari Laitila

Anatoly Andrianov

Juergen Michel

Rainer Liebhart

Konstantinos Samdanis

Krzysztof Kordybach

Bernhard Wegmann

Richard Waldhauser

Edward Grinshpun

Andreas Lobinger

Sameerkumar V Sharma

Janne Petteri Tervonen

Haitao Tang

Olaf Pollakowski

Hakon Helmers

Clemens Suerbaum

Janne Tapio Ali-Tolppa

Kaisu Iisakkila

Rainer Stademann

Yannick Lair

Peter Schneider

Shubhranshu Singh

Thomas Höhne

Jari Pekka Mustajärvi

Dirk Rose

Janne Ali-Tolppa

Paolo Zanier

Dario Bega

Axel Klein

Fabio Giust

Jari MUSTAJÄRVI

There is provided a network apparatus that is caused to receive as part of a handover procedure for handover of a terminal to the network apparatus, metadata about at least one machine learning model accessible for execution and/or training by the terminal, determining whether or not the terminal should execute and/or train the at least one machine learning model after the terminal is handed over to the network apparatus; and signal the result of the determining to the terminal.

Communication system for machine learning metadata

As an aspect, there is provided an apparatus, caused at least to: receive, by a user device from an access node, a measurement configuration for training of a machine learning operations control model, the measurement configuration comprising an indication of at least one parameter to be measured and at least one acceptance condition for measurement values; obtain at least one measurement value; compare the at least one measurement value to the at least one acceptance condition for defining usability of the at least one measurement value for the training, and when the at least one measurement value is usable for the training, carry out the training of the operations control model, and when the at least one measurement value is not usable for the training, transmit the at least one measurement value to the access node.

Machine learning assisted operations control

There is provided an apparatus comprising means for: configuring a measurement collection for one or more pre-determined optimization problems at a first data producer; transmitting a first measurement request to the first data producer, wherein the first measurement request is indicative of requested measurement(s) for the one or more pre-determined optimization problems, time sampling indication and granularity; receiving a measurement response comprising first measurement results corresponding to the first measurement request; and providing the first measurement results to one or more optimization algorithms for solving the one or more pre-determined optimization problems.

Apparatus for radio access network data collection

An apparatus, method and computer program product for: interfacing with at least a first radio access network function and a second radio access network function, sending a first message to the first radio access network function and a second message to the second radio access network function, the first message and the second message comprising a request to report information based on at least one criterion, receiving the reported information from the first radio access network function and the second radio access network function, and grouping the reported information and providing the grouped information to at least one radio access network optimization algorithm.

Providing information

Devices, methods and computer programs for a packet data unit session for machine learning exploration for wireless communication network optimization are disclosed. A client device detects a trigger condition being satisfied for machine learning exploration for wireless communication network optimization. In response, the client device initiates establishment of a packet data unit (PDU) session for the machine learning exploration. The PDU session for the machine learning exploration is associated with a machine learning exploration indicator that indicates that the PDU session is reserved for machine learning exploration.

Packet data unit session for machine learning exploration for wireless communication network optimization

A technique, comprising: creating at a management plane of a communication network a register of services and/or a register of producers of services in the communication network; and based on at least one of the registers, providing to a consumer information about one or more producers providing services associated with one or more service attributes indicated by the consumer and/or information about one or more services associated with one or more service attributes indicated by the consumer.

Method and apparatus for use in communication networks having control and management planes

An example method, apparatus, and computer-readable storage medium are provided for radio resource control (RRC) procedures for machine learning (ML). In an example implementation, the method may include receiving, by a user equipment (UE), machine learning (ML) configuration from a network node; collecting, by the user equipment (UE), machine learning (ML) data based at least on the machine learning (ML) configuration received from the network node, the machine learning (ML) data being collected from one or more layers of the user equipment (UE) in a coordinated manner; and transmitting, by the user equipment (UE), the collected machine learning (ML) data to the network node. In another example implementation, the method may include transmitting, by a network node, machine learning (ML) configuration to a user equipment (UE); and receiving, by the network node, machine learning (ML) data from the user equipment (UE), the machine learning (ML) data received in response to the machine learning (ML) configuration transmitted to the user equipment (UE).

Radio resource control procedures for machine learning

Methods, apparatuses and computer program products are provided to provide access control to a hosted virtual network in a communications system. An example method, apparatus and computer program product is configured to transmit a network identifier associated with a hosted virtual network to one or more allowed user equipment in a coverage area via a radio access network in the coverage area. The network identifier may include a Public Land Mobile Network identifier and a centrally or locally managed network identifier. The network identifier is configured to be stored on the one or more user equipment. The method, apparatus and computer program product is further configured to bar access to the hosted virtual network for one or more user equipment that are not allowed user equipment.

Method and apparatus for controlling access to hosted virtual network using network identifiers

An apparatus for retrieving at least one of an access network or core network assigned user equipment identifier (NGAP ID) from outside of the access or core network using IP addressing information. The apparatus configured to receive IP addressing information of the user equipment; determine a subscriber permanent user identity for the user equipment from the IP addressing information; generate and transmit a request for the at least one of the access network or core network assigned user equipment identifier, the request including the subscriber permanent identity; and receive the at least one of the access network or core network assigned user equipment identifier.

Retrieving a core network or access network assigned user equipment identifier

If a first condition for a handover of an analytics calculation for a user equipment by an analytics function is met, the analytics function requests, of at least one other analytics function of the communication network, preparation of the handover of the analytics calculation. If a second condition for the handover of the analytics calculation is met, the analytics function confirms the handover to one of the at least one other analytics function, the analytics calculation for the user equipment at the analytics function being deemed complete.

Network data analytics function predictive handover

This document discloses a solution for addressing network slices in a radio interface between a station and an access node. According to an aspect, the station is granted access to a network slice and, when transmitting a data frame of the network slice, the network slice is identified in the data frame by a medium access control address of the data frame.

Network slicing in radio interface

Authentication in a public land mobile network, PLMN, having tenant slices is performed by a network element that has: a memory comprising program code; a communication circuitry for communication with entities in the PLMN; and a processing circuitry configured to execute the program code and according to the program code to cause: detecting a registration request from a mobile communication device, MCDt; detecting whether the registration request requests access to a network slice with one-tier authentication with the network slice, and: if yes, causing beginning of authenticating the MCDt with the network slice independently of any authentication between the MCDt and the PLMN.

Authentication in public land mobile networks comprising tenant slices

The method includes receiving first video data and network performance information from at least one first agent node associated with at least one first camera, determining event detection information based on the first video data and the network performance information, determining a slice configuration for at least one network slice based upon the first video data, network performance information and the event detection information, and controlling an operation of the communication network by instantiating the at least one network slice based on the slice configuration information.

Method and system for automated dynamic network slice deployment using artificial intelligence

Improving user experience during handover. Transmitting by a source base station to a target base station a handover request message including a range of QoS profiles, receiving by the source bases station from the target base station a handover request acknowledge message indicating the radio resources or QoS profile selected from the range of QoS profiles, transmitting by the source base station station an identified resource gap between the selected QoS profile of the target base station and the currently used QoS profile in the source base station to an experience management entity.

Flexible quality of service for inter-base station handovers within wireless network

In accordance with an example embodiment of the present invention, a method comprising: receiving, by a device of a first communication network, a list of single network slice selection assistance information (S-NSSAI), wherein the list of S-NSSAI comprises characteristics of each network slice supported by a second communication network; selecting, by the device of the first communication network, at least one network slice of the first communication network based at least on the characteristics; associating, by the device of the first communication network, the selected at least one network slice of the first communication network with at least one network slice of the second communication network; and indicating the association to a device of the second communication network.

Automatic association of peer network slices for enabling cross-operator inter-slice handover

Growing community of inventors

Pullach, Germany

Cinzia Sartori