Telefonaktiebolaget Lm Ericsson (publ)

Henrik RydÉn

Iana Siomina

Pradeepa Ramachandra

Mårten Sundberg

Yutao Sui

Sara Modarres Razavi

Reza Moosavi

David Sandberg

Joel Berglund

Vijaya Yajnanarayana

Pontus Arvidson

Abdulrahman Alabbasi

Alexandros Palaios

László Hévizi

Sakib Bin Redhwan

Heunchul Lee

Hamed Farhadi

Johannes Nygren

Agnes Rensfelt

Roman Zhohov

Methods and nodes in a communications network for using a machine learning model that has been trained using training data from a first radio environment, in a second radio environment, wherein the machine learning model takes a first set of features as input and outputs a label based on a first labelling scheme, the first labelling scheme having been determined for the first radio environment. A method comprises obtaining () a dataset in the second radio environment comprising a plurality of data points comprising values of the first set of features, labelling () each data point in the dataset using the first labelling scheme, and determining () from the labelled dataset whether the first labelling scheme is suitable for use in the second radio environment. Responsive to determining that the first labelling scheme is unsuitable for use in the second radio environment, the method further comprises determining () a second labelling scheme for the machine learning model, using an unsupervised labelling method.

Dynamic labeling for machine learning models for use in dynamic radio environments of a communications network

Systems and methods related to traffic flow prediction in a wireless network are disclosed. In one embodiment, a computer-implemented method comprises collecting training data comprising Internet Protocol (IP) addresses extracted from packets for traffic flows in a wireless network and one or more actual traffic type related parameters for each of the traffic flows. The method further comprises training heavy-hitter IP address encodings based on the extracted IP addresses and encoding the extracted IP addresses using the trained heavy-hitter IP address encodings. The method further comprises training a traffic type predictor of a traffic flow predictor based on the encoded IP addresses and the one or more actual traffic type related parameters for each of the traffic flows, where the traffic type predictor is a learning model that maps encoded IP addresses to one or more predicted traffic type related parameters.

Traffic flow prediction in a wireless network using heavy-hitter encoding and machine learning

A computer implemented method for managing radio resources used by UEs in a cellular communication network. The communication network includes a plurality of radio resources, each radio resource defining a dimension of a multidimensional radio space over which the communication network extends. The method is performed by a management node and includes determining that a UE fulfils a location condition with respect to a region of the communication network, and providing to the UE a distributed resource management model for the region of the communication network, the distributed resource management model having a radio coverage model including defining radio characteristics of radio zones in the region. The distributed resource management model is operable for use, by the UE, to identify a radio resource action for execution by the UE. Also disclosed are a method performed by a UE, a management node, a UE and a computer readable storage medium.

Method and apparatus for managing radio resources in a communication network

Growing community of inventors

Stockholm, Sweden

Henrik RydÉn