Obtaining machine learning (ML) models for secondary method of orientation detection in user equipment (UE)
AssigneeNokia Technologies Oy
InventorsSamad Ali, Sofonias Hailu, Satya Krishna Joshi, Rauli Jarkko Kullervo Järvelä
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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.
Claims17
An apparatus comprising:
- at least one processor;
- and
- at least one memory including computer program code;
- the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to:
- receive, from a user equipment (UE), a communication including a feature vector;
- store a dataset including one or more vectors associated with the UE;
- communicate, to a network device, the dataset associated with the UE;
- receive, from the network device, a machine learning (ML) model, the ML model being trained, using the dataset, to detect UE orientation;
- and
- communicate, to the UE, the trained ML model,
- wherein the apparatus is further caused to:
- receive, from the UE, a communication indicating a sensor failure at the UE;
- and
- receive, from the UE, a communication including a request for the trained ML model, wherein the receiving of the trained ML model from the network device is in response to a request for the trained ML model based on the communication of the request for the trained model from the UE.
An apparatus comprising:
- at least one processor;
- and
- at least one memory including computer program code;
- the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to:
- generate a feature vector based on measurements performed by the apparatus;
- communicate, to a base station (BS), a message including the feature vector;
- and
- receive, from the BS, a machine learning (ML) model, the ML model being trained, using the feature vector, to detect an orientation of the apparatus,
- further causing the apparatus to:
- determine at least one sensor failure, the sensor being associated with determining orientation of the apparatus;
- communicate, to the BS, a message indicating the sensor failure at the apparatus;
- and
- communicate, to the BS, a message including a request for the trained ML model.
A method comprising
- generating, by a user equipment (UE), a feature vector based on UE measurements;
- communicating, by the UE to a base station (BS), a message including the feature vector;
- and
- receiving, by the UE from the BS, a machine learning (ML) model, the ML model being trained, using the feature vector, to detect UE orientation,
- further comprising:
- determining, by the UE, at least one sensor failure, the sensor being associated with determining UE orientation;
- communicating, by the UE to the BS, a message indicating the sensor failure at the UE;
- and
- communicating, by the UE to the BS, a message including a request for the trained ML model.
Dependencies are inferred from the claim text.