Nokia Technologies Oy

Nokia Solutions and Networks Oy

Janne Ali-Tolppa

Anna Pantelidou

Tejas Subramanya

Ahmad Awada

Srinivasan Selvaganapathy

Jedrzej Stanczak

Amaanat Ali

Malgorzata Tomala

Christian Rom

Ömer Bulakci

Henning Sanneck

Cinzia Sartori

Stephen Mwanje

Borislava Gajic

Muhammad Naseer-Ul-Islam

Lars Christoph Schmelz

Alperen Gundogan

Rakash SivaSiva Ganesan

Márton Kajó

Laurent Ciavaglia

Method comprising: receiving a trust level requirement for a service; translating the trust level requirement into a requirement for at least one of a fairness, an explainability, and a robustness of a calculation performed by an artificial intelligence pipeline related to the service; providing the requirement for the at least one of the fairness, the explainability, and the robustness to a trust manager of the artificial intelligence pipeline.

Framework for trustworthiness

Systems, methods, apparatuses, and computer program products for survival time dependent flexible handover execution are provided. One method may include providing, by a network node, at least one handover execution condition to at least one user equipment. The at least one handover execution condition may include one or more delta values with respect to one or more actual handover execution conditions to allow for early and/or late execution of a handover by the at least one user equipment. The method may also include indicating, to the at least one user equipment, a transfer interval and/or a survival time information for which the at least one handover execution condition is taken into account.

Survival time dependent flexible handover execution

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

A method including: receiving, by a multi-panel user equipment from a serving network entity, information indicative of predicted transmissions of reference signals by a serving network entity or target network entity; and applying a panel prioritization strategy for the multi-panel user equipment based on the received information indicative of predicted transmissions of reference signals.

Panel prioritization for multi panel user equipment

There are provided measures for network objectives management. Such measures for enabling network objectives management in radio access networks exemplarily comprise receiving a key performance indicator optimization input indicative of a key performance indicator and an optimization direction of said key performance indicator, deriving at least one key performance indicator entry based on said key performance indicator optimization input, said at least one key performance indicator entry comprising at least said optimization direction of said key performance indicator and prioritized target values for said key performance indicator, storing said at least one key performance indicator entry, deciding, for a cognitive function, a target value for said key performance indicator of said prioritized target values for said key performance indicator expected to be achieved by said cognitive function, and providing said decided target value for said key performance indicator and said optimization direction of said key performance indicator to said cognitive function.

Network objectives management

There are provided measures for trust related management of artificial intelligence or machine learning pipelines. Such measures exemplarily include, at a first network entity managing artificial intelligence or machine learning trustworthiness in a network, transmitting a first artificial intelligence or machine learning trustworthiness related message towards a second network entity managing artificial intelligence or machine learning trustworthiness in an artificial intelligence or machine learning pipeline in the network, and receiving a second artificial intelligence or machine learning trustworthiness related message from the second network entity, where the first artificial intelligence or machine learning trustworthiness related message includes at least one criterion related to an artificial intelligence or machine learning trustworthiness aspect.

Trust related management of artificial intelligence or machine learning pipelines

Growing community of inventors

Pirkkala, Finland

Janne Ali-Tolppa