Telefonaktiebolaget Lm Ericsson (publ)

Ricardo Da Silva Souza

Klaus Raizer

Amadeu Do Nascimento Junior

Jean Paulo Martins

Henrik Rydén

Reza Moosavi

Joel Berglund

Peter Vaderna

Abdulrahman Alabbasi

Alexandros Palaios

Anders Landström

Alberto Hata

Xueying Hou

Bo Xu

Agnieszka Ziembla

Embodiments herein relate to method performed by a radio network node for handling communication in a wireless communication network. The radio network node dynamically applies different muting patterns for muting an active component of one or more antenna branches of the radio network node and/or of one or more antenna panels of the radio network node, wherein the different muting patterns are associated with one or more types of physical channels used for communicating and/or a communication parameter related to one or more UEs. The radio network node performs a communication using the dynamically applied different muting patterns.

Radio network node, and method performed therein

Methods and apparatus for implementing reinforcement learning are provided. A method in a client node that instructs actions in an environment in accordance with a policy includes identifying one or more critical states of the environment for which a current policy provides unreliable actions. The method further includes initiating transmission to a server of a retraining request, the retraining request having information relating to the one or more critical states. The method further includes receiving a new policy from the server, wherein the new policy is generated by the server using reinforcement learning based on the information relating to the one or more critical states, and instructing actions in the environment in accordance with the new policy.

Machine-learning models and apparatus

A computer-implemented method for acquiring new data samples and for maintaining a set of data samples in a database, wherein the set of data samples are configured to form input to a function associated with a predictive performance, the method including obtaining at least one relevance metric (M), where the relevance metric is indicative of an increase in the predictive performance of the function when using a data sample as input together with the set of data samples compared to when not using the data sample, obtaining a relevance criterion (C), where the relevance criterion identifies relevant data samples in a set of data samples based on the at least one relevance metric, signaling the at least one relevance metric (M) and the relevance criterion (C) to a data collecting network node.

Methods for processing data samples in communication networks

According to embodiments herein e.g. a method performed by an orchestration agent unit for handling a service in a communications network is provided. The orchestration agent unit evaluate a number of intent types of the service based on a performance of one or more performance metrics related to intent of respective intent type when performed in the communications network including a plurality of orchestration agent units. The orchestration agent unit selects an intent type of the service based on the evaluation; and sends to at least one of the plurality of orchestration agent units, an indication indicating the selected intent type of the service.

Orchestration agent unit and method therein in a communications network

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Indaiatuba, Brazil

Ricardo Da Silva Souza