Nokia Technologies Oy

Teemu Mikael Veijalainen

Jani Matti Johannes Moilanen

Lauri Ilari Kuru

Kalle Petteri Kela

Tero Henttonen

Wolfgang Zirwas

Ahmad Awada

Amaanat Ali

Mikko Johannes Honkala

Panagiotis Spapis

István Zsolt Kovács

Leo Mikko Johannes Kärkkäinen

Janne Tapio Ali-Tolppa

Jani Moilanen

Stefano Paris

Lorenzo Maggi

Halit Murat Gürsu

Hans Thomas Höhne

István Kovács

Luis Guilherme Uzeda Garcia

Muhammad Majid Butt

Marco Centenaro

Qiyang Zhao

Sina Khatibi

Petteri Kela

Melisa López

An example method, apparatus, and computer-readable storage medium are provided for exploration procedures for network optimization. In one example implementation, the method may include generating, by a first network element, exploration data, the exploration data being generated by the first network element for evaluating performance at a second network element; transmitting, by the first network element, the exploration data to the second network element; and receiving, by the first network element, exploration data feedback from the second network element, the exploration data feedback received from the second network element based on processing of the exploration data by the second network element. In another example implementation, the method may include receiving, by a second network element, exploration data from a first network element; generating, by the second network element, exploration data feedback, the exploration data feedback generated in response to and based on the exploration data received from the first network element; and transmitting, by the second network element, the exploration data feedback to the first network element.

Exploration data for network optimization

Systems, methods, apparatuses, and computer program products for error probability feedback are provided. One method may include transmitting, to at least one user equipment, a configuration for protocol data unit error probability calculation and reporting. The method may also include receiving, from the at least one user equipment, feedback related to the protocol data unit error probability.

Protocol data unit (PDU) error probability feedback

Systems, methods, apparatuses, and computer program products for beam selection using data radio bearer specific machine learning are provided. For example, a method can include providing one or more inputs regarding a plurality of beams to a machine learning model. The method can also include obtaining at least one output value regarding the plurality of beams from the machine learning model. The machine learning model can be a data radio bearer specific machine learning model, a data radio bearer group specific machine learning model, or a model trained to output selectively data radio bearer specific values or data radio bearer group specific values.

Machine learning in beam selection

A technique includes receiving, from one or more sensors, sensor data samples; receiving radio network information data samples associated with a radio network; determining, based on an association of one or more received sensor data samples with one or more of the received radio network information data samples, a first set of one or more associated sensor and radio network information data samples; developing a model that is trained based on at least a portion of the first set the associated sensor and radio network information data samples that are relevant to performance of the radio network; and improving performance of the radio network based on at least the model.

Radio-network self-optimization based on data from radio network and spatiotemporal sensors

A technique may include receiving, from one or more sensors, sensor data samples; receiving radio network information data samples associated with a radio network; determining one or more associated sensor and radio network information data samples based on an association of one or more received sensor data samples with one or more of the received radio network information data samples; selecting at least some of the one or more associated sensor and radio network information data samples that are relevant to performance of the radio network; and forwarding the selected associated sensor and radio network information data samples for subsequent use.

Associating and storing data from radio network and spatiotemporal sensors

Systems, methods, apparatuses, and computer program products for real-time uplink (UL) power control are provided. One method may include estimating, by a network node, a power allocation function for determining power allocation across a plurality of legs that transmit simultaneously, the power allocation function being based on radio channel statistics and network performance metrics. The method may then include signaling, to a user equipment, the power allocation function and the parameters used to compute an output of the power allocation function.

Uplink power adjustment for packet data convergence protocol (PDCP) duplication

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Helsinki, Finland

Teemu Mikael Veijalainen