Nokia Technologies Oy

Brenda Vilas Boas

Wolfgang Zirwas

Zexian Li

Oana-Elena Barbu

Athul Prasad

Amir Mehdi Ahmadian Tehrani

Embodiments of the present disclosure disclose devices, methods and apparatuses for channel prediction. A terminal device obtains model configuration information. The model configuration information indicates a first prediction model for channel prediction. The terminal device receives overfitting configuration information from a network device. The overfitting configuration information indicates configuration on reference signals for overfitting by the first prediction model. The terminal device reports channel information predicted based on the first prediction model and the overfitting configuration information.

Devices, methods and apparatuses for channel prediction

A method for performing multiple-input and multiple-output channel estimation includes: generating, using a first machine-learning model, an initial set of estimated channel information from a first input set of channel information, wherein the first input set of channel information corresponds to a first plurality of radio-frequency chains, and wherein the estimated set of channel information corresponds to the first plurality of radio-frequency chains and a second plurality of radio-frequency chains; generating, using a second machine-learning model, a set of estimated channel phases from the initial set of estimated channel information and a second input set of channel information, wherein the second set of input channel information corresponds to the second plurality of radio-frequency chains; and combining the initial set of estimated channel information and the set of estimated channel phases to generate an enhanced set of estimated channel information.

Channel estimation using machine learning

Method, comprising receiving a terminal location information or a location-like information from a terminal; selecting one or more first pairs of prior channel information among one or more stored first pairs of prior channel information based on the terminal location information or the location-like information, respectively; inputting the terminal location information or the location-like information, respectively, and the selected one or more first pairs of prior channel information into a trained interpolation neural network to obtain a first estimation of a channel between the terminal and a base station as an output from the interpolation neural network; providing the weights of the trained neural network to the terminal; wherein each of the one or more first pairs of prior channel information comprises a location information related to a respective prior channel and a first representation of the respective prior channel.

Growing community of inventors

Munich, Germany

Brenda Vilas Boas