公开(公告)号：US20250056211A1

公开(公告)日：2025-02-13

申请号：US18798910

申请日：2024-08-09

Applicant: Nokia Technologies Oy

Inventor： Amaanat ALI ,  Jerediah FEVOLD ,  Sari Kaarina NIELSEN ,  Mahmood Reza ALIZADEH ASHRAFI ,  István Zsolt KOVÁCS

IPC: H04W8/24 ,  H04W88/06

Abstract: Example embodiments of the present disclosure relate to methods, devices, apparatuses and computer readable storage medium for capability information transmission. In a method, a first apparatus receives, from a second apparatus, a capability enquiry comprising Machine Learning (ML) capabilities with one or more applicable conditions and an indication requesting the first apparatus to indicate support of at least one activation condition for the applicable conditions. The first apparatus transmits, to the second apparatus, capability information comprising the applicable conditions and the at least one activation condition for the applicable conditions. The at least one activation condition indicates whether a corresponding applicable condition is currently supported or not supported.

公开(公告)号：US20250056251A1

公开(公告)日：2025-02-13

申请号：US18785480

申请日：2024-07-26

Applicant: Nokia Technologies Oy

Inventor： Sakira HASSAN ,  Endrit DOSTI ,  Jerediah FEVOLD ,  István Zsolt KOVÁCS

IPC: H04W24/02 ,  H04W24/10

Abstract: Triggering of artificial intelligence/machine learning training in a network data analytics function is provided. A method for triggering artificial intelligence/machine learning training in a network data analytics function may include obtaining at least one machine learning model for training or retraining based on measurement data of a network. The method may also include determining that data collection is required prior to training or retraining the at least one machine learning model, and receiving one or more measurement reports that includes at least one dataset from the data collection. The method may further include determining whether additional assisted information from one or more network devices is required for training or retraining. The at least one machine learning model may be trained or retrained based on all collected datasets, which includes the at least one dataset from the data collection.

公开(公告)号：US20250056330A1

公开(公告)日：2025-02-13

申请号：US18795600

申请日：2024-08-06

Applicant: Nokia Technologies Oy

Inventor： Diomidis MICHALOPOULOS ,  Prajwal KESHAVAMURTHY ,  Taylan SAHIN ,  Jerediah FEVOLD

IPC: H04W28/24 ,  H04W48/16

Abstract: A network element receives, from a network node of a wireless communications system, information indicative of an association between a plurality of implementations for a network element of the wireless communications system to perform a respective task and required allocations of resources for the network element to perform the respective task in accordance with one or more respective implementations of the plurality. The network element selects a first implementation of the plurality of implementations to perform the respective task based on available resources for the network element to perform the respective task relative to the required allocation of resources to perform the respective task in accordance with the first implementation. The network element performs the respective task in accordance with the first implementation. The network element causes transmission, to the network node, of information indicative of the first implementation selected to perform the respective task.

公开(公告)号：US20250056477A1

公开(公告)日：2025-02-13

申请号：US18792760

申请日：2024-08-02

Applicant: Nokia Technologies Oy

Inventor： Muhammad Ikram ASHRAF ,  Ganesh VENKATRAMAN ,  Afef FEKI ,  Jerediah FEVOLD

IPC: H04W64/00 ,  H04B17/318 ,  H04W24/08

Abstract: Example embodiments of the present disclosure relate to methods, devices, apparatuses and computer readable storage medium for a model monitoring for positioning, especially for assisted Artificial Intelligence/Machine Learning (AI/ML) positioning without measured ground truth (GT). The method comprises: determining, based on three or more transmission reception points, TRPs, at least one reference location associated with a positioning performance monitoring of a second apparatus; generating assistance data for monitoring a positioning performance at the second apparatus at least comprising at least one of: respective positioning measurement data of at least one positioning measurement type in the at least one reference location; respective monitoring metric associated with the at least one positioning measurement type in the at least one reference location; or the at least one reference location; and transmitting the assistance data to a second apparatus.

公开(公告)号：US20250056289A1

公开(公告)日：2025-02-13

申请号：US18790220

申请日：2024-07-31

Applicant: Nokia Technologies Oy

Inventor： Taylan SAHIN ,  Jerediah FEVOLD ,  Endrit DOSTI ,  Muhammad Ikram ASHRAF

IPC: H04W24/10 ,  H04L5/00 ,  H04W64/00

Abstract: Method, apparatuses, and computer program products provide means for establishing a mechanism through which a network can configure user equipment to determine how to process an indication of region of the user equipment location when establishing a network connection. An example method includes: receiving, from a visited network, a first indication of a region of an apparatus location, where the apparatus includes a User Equipment (UE); and determining, based on a second indication in the apparatus, whether the apparatus shall ignore the first indication for network selection, where the second indication is received from a home network of the apparatus via a container transparent to the visited network. The region of the apparatus location may include a country of the apparatus location. The container may include a Steering of Roaming transparent container. The container may be delivered via a rejection message.

公开(公告)号：US20250053872A1

公开(公告)日：2025-02-13

申请号：US18797512

申请日：2024-08-08

Applicant: Nokia Technologies Oy

Inventor： Jerediah FEVOLD ,  Endrit DOSTI ,  Muhammad Ikram ASHRAF

IPC: G06N20/00

Abstract: A network element evaluates performance of a first AI/ML model being used by a UE. The network element sends, based on the evaluation, configuration to a network entity involved in performing model retuning of the first AI/ML model to aid in the model retuning. The network element monitors and evaluates performance of a second AI/ML model that is a retuned version of the first AI/ML model. The first and second AI/ML models are from a same lineage of AI/ML models. The network element stores, in response to the evaluation of the second AI/ML model, the second AI/ML model for use by other UE(s). A UE receives the configuration, and performs operation(s) to aid in the performing retuning. The retuning creates a second AI/ML model that is a retuned version of the first AI/ML model. The UE switches from the first AI/ML model to the second AI/ML model.