Abstract: An electronic device includes at least one antenna, and a channel estimation and equalization module for processing a reception signal received through the at least one antenna. The channel estimation and equalization module may identify the received signal and a reference signal related to the received signal. The channel estimation and equalization module may also, via deep learning based on the received signal and the reference signal: extract features of the received signal and the reference signal, estimate a channel of the received signal, based on the extracted features, and restore a signal corresponding to the received signal.

Abstract: Provided is an electronic device including a memory storing a state inference model, and at least one instruction; a transceiver; and at least one processor configured to execute the at least one instruction to: obtain, via the transceiver, first state information of each of a plurality of devices at a first time point, obtain second state information of each of the plurality of devices at a second time point that is a preset time interval after the first time point, by inputting the first state information to the state inference model, and determine an inference distribution ratio of the artificial neural network of each of the plurality of devices, based on the second state information of each of the plurality of devices, where the electronic device is determined among the plurality of devices, based on network states of the plurality of devices.

Abstract: Provided is a learning assistance method using an electronic device. The method may include receiving lecture material, generating first feature vectors corresponding to pages included in the lecture material using a first neural network trained to distinguish pages included in a document, receiving an input image including a lecture scene based on the lecture material, generating a second feature vector corresponding to the lecture scene using a second neural network, and determining output page information indicating a page corresponding to the lecture scene among the pages included in the lecture material based on a similarity between the second feature vector and the first feature vectors.

Abstract: According to an embodiment, an electronic device comprises at least one processor and a memory, wherein the memory stores instructions that, when executed, cause the at least one processor to control the electronic device to: obtain data to be classified, obtain a feature vector from the data by performing convolution on the data and a plurality of filters using a classification model stored in the memory, identify outputs corresponding to subfeatures using a split layer including the subfeatures resulting from splitting the feature vector, and output a class corresponding to the data based on the outputs.

Abstract: Disclosed are an electronic device and a method for controlling an electronic device. Specifically, the present disclosure relates to: an electronic device configured to input an acquired image into a trained artificial intelligence model, acquire information about the image from a plurality of classifiers which are included in the artificial intelligence model and correspond to a plurality of layers classified according to higher and lower concepts of an object included in the image, train the artificial intelligence model on the basis of the information about the acquired image, and perform image recognition by using the trained artificial intelligence model; and a method for controlling an electronic device.

Abstract: According to an embodiment, an electronic device comprises at least one processor and a memory, wherein the memory stores instructions that, when executed, cause the at least one processor to control the electronic device to: obtain data to be classified, obtain a feature vector from the data by performing convolution on the data and a plurality of filters using a classification model stored in the memory, identify outputs corresponding to subfeatures using a split layer including the subfeatures resulting from splitting the feature vector, and output a class corresponding to the data based on the outputs.