公开(公告)号：US20240155553A1

公开(公告)日：2024-05-09

申请号：US18549414

申请日：2021-03-09

Applicant: Nokia Technologies Oy

Inventor： Samad ALI ,  Sofonias HAILU ,  Satya Krishna JOSHI ,  Rauli Jarkko Kullervo JÄRVELÄ

IPC: H04W64/00 ,  H04B7/06 ,  H04B17/318 ,  H04B17/391

CPC classification number: H04W64/006 ,  H04B7/06956 ,  H04B17/328 ,  H04B17/3913

Abstract: Various techniques are provided for receiving, by a base station (BS) from a user equipment (UE), a communication including a feature vector, storing, by the BS, a dataset including one or more feature vectors associated with the UE, communicating, by the BS to a network device, the dataset associated with the UE, receiving, by the BS from the network device, a machine learning (ML) model, the ML model being trained, using the dataset, to detect UE orientation, and communicating, by the BS to the UE, the trained ML model.

公开(公告)号：US20240223135A1

公开(公告)日：2024-07-04

申请号：US18556768

申请日：2022-04-25

Applicant: Nokia Technologies Oy

Inventor： Samad ALI ,  Oskari TERVO ,  Esa Tapani TIIROLA ,  Kari Pekka PAJUKOSKI

IPC: H03F1/32 ,  H04B1/04

CPC classification number: H03F1/3247 ,  H04B1/0475 ,  H03F2200/451

Abstract: Disclosed is a method comprising selecting (401), by a base station, a power amplifier distortion model from a set of power amplifier distortion models, wherein the power amplifier distortion model comprises a pre-trained machine learning model configured to compensate power amplifier distortion. The method further comprises receiving (402), by the base station, one or more uplink data transmissions from a terminal device, and compensating (403), by the base station, at least a part of the power amplifier distortion from the one or more uplink data transmissions based at least partly on the power amplifier distortion model.

公开(公告)号：US20240028961A1

公开(公告)日：2024-01-25

申请号：US18266004

申请日：2021-01-25

Applicant: NOKIA TECHNOLOGIES OY

Inventor： Samad ALI ,  Anna PANTELIDOU ,  Rauli Jarkko Kullervo JÄRVELÄ

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: There are provided measures for enablement of federated machine learning for terminals to improve their machine learning capabilities. Such measures exemplarily comprise, at a terminal, receiving a configuration indicative of an instruction to participate in federated learning of a global machine learning model, the configuration including timing information related to said federated learning, and performing, based on said configuration, a machine learning process based on undertaken network performance related measurements, wherein said timing information includes a time limit with respect to a local machine learning model resulting from said machine learning process, and said time limit is a specification of a moment in time by when said local machine learning model is to be completed or a specification of a moment in time by when transmission of said local machine learning model is to be completed, and wherein said configuration is a minimization of drive tests configuration.