Abstract: A method of monitoring and managing performance of an artificial neural network model for an air interface may comprise: receiving, by a network (NW) including a communication node performing a function of monitoring and managing performance of an artificial neural network model, a performance metric of the artificial neural network model from a user equipment (UE); and controlling, by the communication node, activation or deactivation of the artificial neural network model according to the performance metric, wherein the artificial neural network model is activated to improve a main performance metric of a mobile communication system including the communication node and the UE connected through an air interface.

Abstract: A method of a terminal may comprise: receiving a first message including state information of a base station from the base station; identifying preliminary inactive state information included in the first message when the state information of the base station indicates a preliminary inactive state; and performing a cell selection based on the preliminary inactive state information.

Abstract: A method of a terminal may comprise: receiving a reference signal from a base station; generating channel information based on the reference signal; generating wavelet-transformed channel information by applying wavelet transform to the channel information; generating compressed channel information by compressing the wavelet-transformed channel information; and transmitting the compressed channel information to the base station.

Abstract: A capacitor component includes a body having a first surface and a second surface opposing each other and including a multilayer structure in which a plurality of dielectric layers are stacked and first and second internal electrodes are alternately disposed with respective dielectric layers interposed therebetween and exposed to the first surface and the second surface, respectively, first and second metal layers covering the first surface and the second surface and connected to the first and second internal electrodes, respectively, first and second ceramic layers covering the first and second metal layers, and first and second external electrodes covering the first and second ceramic layers and connected to the first and second metal layers to be electrically connected to the first and second internal electrodes, respectively.

Abstract: An operation method of a first communication node may include: inputting first input data including first feedback information to a first encoder of a first artificial neural network corresponding to the first communication node; generating first latent data based on an encoding operation in the first encoder; generating a first feedback signal including the first latent data; and transmitting the first feedback signal to a second communication node, wherein the first latent data included in the first feedback signal is decoded into first restored data corresponding to the first input data in a second decoder of a second artificial neural network corresponding to the second communication node, and the first input data includes first common input data included in a common input data set previously shared between the first communication node and the second communication node.

Abstract: A method of a terminal may comprise: receiving a reference signal from a base station; generating a precoding vector based on the reference signal; generating a low-dimensional precoding vector by performing dimensionality reduction transformation on the precoding vector; quantizing the low-dimensional precoding vector; and transmitting the quantized low-dimensional precoding vector to the base station.

Abstract: An operation method of a first communication node may comprise: determining a latent space correction operation including a transformation operation for correcting latent data output from a first encoder of a first artificial neural network corresponding to the first communication node, based on information of a reference data set provided from a second communication node; encoding first input data including first feedback information through the first encoder; correcting first latent data output from the first encoder based on the determined latent space correction operation; and transmitting a first feedback signal including the corrected first latent data to the second communication node, wherein the corrected first latent data is decoded into first output data corresponding to the first input data in a second decoder of a second artificial neural network corresponding to the second communication node.

Abstract: A method of a terminal may comprise: receiving downlink reference signals from a base station; generating beam report information based on the downlink reference signals, and transmitting the beam report information to the base station; receiving, from the base station, beam prediction information based on the beam report information and valid time information on a valid time for the beam prediction information; and selecting a beam based on the beam prediction information and the valid time information, and using the selected beam.

Abstract: An operation method of a receiver may comprise: receiving reference signals from a transmitter in an entire use band; generating first channel information by performing channel estimation on each of the reference signals; generating second channel information by removing noises from the first channel information using a first artificial neural network; and generating third channel information for a grid of the entire use band based on the second channel information using a second artificial neural network.

Abstract: A method of a receiver in a communication system may comprise: transmitting, to a transmitter, artificial intelligence (AI) capability information of the receiver; receiving, from the transmitter, AI model information generated based on the AI capability information; selecting one or more AI models from among a plurality of AI models indicated by the AI model information; transmitting information of the one or more AI models to the transmitter; receiving, from the transmitter, channel state information (CSI) configuration information including information of at least one AI model among the one or more AI models; generating CSI feedback information based on the at least one AI model indicated by the CSI configuration information; and transmitting the CSI feedback information to the transmitter.

Abstract: Disclosed are techniques for channel information transfer in a communication system. A method of a terminal may comprise: receiving, from a base station, first signal configuration information including transmission resource information of a first signal indicated for training; receiving, from the base station, at least one first signal according to the transmission resource information; and training an interference cancellation artificial neural network based on the at least one first signal.

Abstract: A method of a base station may comprise: receiving, from each of terminals belonging to the base station, a capability information report message related to an artificial neural network; generating artificial neural network configuration information based on capability of the base station and the capability information report message received from each of the terminals; and transmitting the generated artificial neural network configuration information to the terminals belonging to the base station.

Abstract: An operation method performed by a mobile base station in a communication system may comprise: obtaining altitude information; configuring altitude indicator information indicating whether the obtained altitude information is higher or lower than an altitude reference in cooperation with a drone management device based on the obtained altitude information; and transmitting the configured altitude indicator information to a terminal.

Abstract: An operation method of a terminal in a communication system may include: receiving, from a base station, a first reference signal through a use channel; generating subchannel state information for each of a plurality of subchannels of the use channel based on the first reference signal; compressing the subchannel state information using a first neural network encoder; forming a subchannel group including at least one subchannel; generating subchannel group state information from the compressed subchannel state information of the at least one subchannel belonging to the subchannel group by using a second neural network encoder; and transmitting the subchannel group state information to the base station.

Abstract: The present disclosure relates to a method and apparatus for constructing a network adaptable to consecutive/complex domains. An apparatus for constructing a domain adaptive network according to an embodiment of the present disclosure includes a memory configured to store data; and a processor configured to control the memory, wherein the processor is configured to determine a weight to be applied to one or more neural networks based on input data, construct a final neural network by applying the weight to the one or more neural networks, and output result data of the input data using the final neural network, wherein the one or more neural networks are trained using data for each prototype domain.

Abstract: An operation method of a terminal in a communication system may comprise: predicting traffic using a traffic prediction function configured according to traffic prediction configuration information; transmitting a buffer prediction status report including a traffic prediction result to a base station; receiving a first physical uplink shared channel (PUSCH) resource allocation information generated based on the buffer prediction status report; and transmitting data to the base station using first resources allocated by the first PUSCH resource allocation information.

Abstract: Provided is an infrastructure and a vehicle for communicating with the same. The infrastructure recognizes a first area and a second area, and when a proportion of the second area of the image information is greater than or equal to a reference proportion, controls first and second adjusters to change a photographing area of a camera. The vehicle recognizes a hidden area, when a proportion of the hidden area is greater than or equal to a reference proportion, transmits an adjustment command of the first camera to the infrastructure, and when the proportion of the hidden area is less than the reference proportion. The vehicle matches an image of the hidden area to second image information of the second camera to generate a top view image.

Abstract: A learning method of distributed artificial intelligence is disclosed. The method is comprising: updating, by the local device, parameters of a corresponding local model by training the local model based on a corresponding local dataset; generating, by the local device, compressed gradient information by compressing gradients of the parameter of the local model; transmitting, by the local device, the compressed gradient information to a parameter server; reconstructing, by a parameter server, gradients of the parameters of the local model based on the compressed gradient information received from the local device; and updating, by the parameter server, parameters of a global model based on the reconstructed gradients of the parameters of the local model.

Abstract: 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.

Abstract: An operation method of a terminal may comprise: receiving, from a serving base station, measurement configuration information and measurement prediction configuration information; measuring signal strengths of the serving base station and a neighboring base station according to the measurement configuration information; predicting signal strengths of the serving base station and the neighboring base station based on a result of measuring the signal strengths of the serving base station and the neighboring base station, according to the measurement prediction configuration information; and transmitting, to the serving base station, a report message including the predicted signal strengths of the serving base station and the neighboring base station according to the measurement prediction configuration information.