Telefonaktiebolaget Lm Ericsson (publ)

Amadeu Do Nascimento Junior

Klaus Raizer

Ricardo Da Silva Souza

Alberto Hata

Jean Paulo Martins

Rafia Inam

Aneta Vulgarakis Feljan

Yifei Jin

Pedro Henrique Gomes Da Silva

Allan Vidal

André Luis Ogando Paraense

Ricardo Ribeiro Gudwin

Mateus Augusto Silva Santos

Andrii Berezovskyi

A Cognitive Manager (CM) controls a RAN. The CM obtains from the RAN a set of local needs identifying a radio communication performance criterion that is to be satisfied. A set of neighbor needs are obtained from a neighbor CM. An event message is received from the RAN containing a radio metric that does not presently satisfy at least one of the set of local needs. A set of present neighbor improvement action drives being performed or will be performed to satisfy the set of neighbor needs, are obtained from the neighbor CM. A combination of the set of local needs, the set of the neighbor needs, the set of present neighbor improvement action drives, and the radio metric, is used to determine a local improvement action drive that is to be performed by the RAN. An adjustment report identifying the local improvement action drive is provided to the RAN.

Dynamic collaboration among cognitive agents managing radio access networks

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

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

A method for placing a virtual network function (VNF) in a datacenter system based on historical data. The method includes receiving, by a placement service from a client device, a VNF placement request that includes an identifier of the VNF, wherein the VNF placement request requests placement of an instantiation of the VNF with a node in a set of candidate nodes of the datacenter system; calculating, by the placement service, a set of similarity values between the set of candidate nodes of the datacenter system and the historical data, wherein the historical data represents previous placements of the VNF in the datacenter system, including performance indicators associated with these placements; and determining, by the placement service, a placement node from the set of candidate nodes for placing the instantiation of the VNF based on the set of similarity values.

Virtual network function placement in a cloud environment based on historical placement decisions and corresponding performance indicators

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Indaiatuba, Brazil

Amadeu Do Nascimento Junior