Disclosed is an electronic device including a processor and memory, the processor being configured to perform frequency interpolation on a channel estimation at all resource elements (REs) located where a demodulation reference signal is transmitted, perform time interpolation on a frequency domain interpolated channel obtained from the frequency interpolation, and calculate an enhanced channel estimation based on channel estimates at REs in a frequency domain and REs in a time domain, the channel estimates being output from the time interpolation.

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.

In a first method, a wireless device estimates a delay profile of a channel impulse response, CIR, for a channel between a network node and the wireless device, compresses the delay profile using a compression function, and transmits the compressed delay profile. The compression function includes a first function and a quantizer. The first function is configured to receive input data and reduce a dimension of the input data. In a second method, a network node receives a compressed delay profile of CIR for a channel between a network node and a wireless device, decompresses the compressed delay profile using a decompression function, and estimates a position of the wireless device based on at least the decompressed delay profile. The decompression function includes a first function which is configured to receive input data and provide output data in a higher dimensional space than the input data.

A learning-based system and method for interference whitening method. In some embodiments, the method includes receiving a signal; extracting a first set of features from the signal; making a first selection, by a first neural network, based on the first set of features; and selecting a first covariance matrix, from a plurality of covariance matrices, based on the first selection.

This application provides communication methods and communications apparatuses. In an example method, a first communications apparatus determines whether a first channel learning model is applicable, where the first channel learning model is used to determine first channel information based on target channel information, and a data amount of the first channel information is less than a data amount of the target channel information. The first communications apparatus sends a first message in response to determining that determining that the first channel learning model is not applicable, where the first message is used to indicate that the first channel learning model is not applicable. According to the example method, the first communications apparatus can determine applicability of the first channel learning model without assistance of a second communications apparatus.

A method of a base station may comprise: determining one of machine learning (ML) models for receiving channel information for a channel to communicate with a terminal based on capability information of the terminal; providing configuration information of the determined ML model to the terminal; updating the determined ML model through online training with the terminal; and receiving channel information using the updated ML model from the terminal when communicating with the terminal.

A monitoring system and monitoring method for detecting a spectral anomaly in a cellular wireless network, in particular a 5G private uRLLC network, wherein an RF receiver monitors the cellular wireless network spectrum and derives spectrum and/or physical measurement values of the spectrum of the cellular wireless network, and a processing unit of the monitoring system executes a spectral anomaly neural network trained by a machine learning algorithm in a training system, wherein the processing unit obtains the spectrum and/or the physical measurement values of the spectrum and processes it to detect a spectral anomaly information. Further, a training system and training method for training a spectral anomaly neural network, wherein the training system/method is used in a cellular wireless network, in particular a 5G private uRLLC network, and an RF receiver of the training system monitors the cellular wireless network spectrum and derives spectrum and/or physical measurement values of the spectrum of the cellular wireless network, and a processor of the training system executes a machine learning algorithm to train the spectral anomaly neural network based upon the derived spectrum and/or physical measurement values of the spectrum of the cellular wireless network.

A method of wireless communication, executed by a user equipment (UE), receives, from a base station, a broadcast or multicast message including a known payload, as well as a configuration for the known payload. The method also trains an artificial neural network with the known payload. A method of wireless communication, executed by a base station, configures a known payload for multiple UEs and signals, to the UEs, an indication of which physical channel will include the known payload, as well as time/frequency resources of the known payload. The method also broadcasts or multicasts the known payload to facilitate neural network training at the UEs.

Disclosed is a method for estimating a channel of a terminal by a base station in a wireless communication system supporting multiple antennas, the method comprising the steps of: estimating a moving speed of the terminal on the basis of a first channel value acquired at a current time point and a second channel value acquired at a previous time point; determining, on the basis of the estimated moving speed, a complexity degree corresponding to the number of channel values for multiple time points including the current time point; and estimating a channel of the terminal at a next time point on the basis of the determined complexity degree.

An operation method of a terminal using a channel estimation artificial intelligence (AI) model may comprise: receiving, from a base station, information on the channel estimation AI model; performing first channel estimation using the channel estimation AI model by receiving a first signal A from a base station; and receiving, from the base station, data based on the estimated channel.