Abstract: A method and server for generating a question for personality and aptitude tests using a question and answer network based on a language model are disclosed. The server according to the present disclosure includes a communication unit configured to communicate with a terminal, a database configured to store tester information, a memory configured to store artificial intelligence model data for a generative artificial intelligence model, and a processor configured to generate personality and aptitude question information suitable for characteristics of a person from personal behavior characteristic information and existing question information by using the generative artificial intelligence model.

Abstract: An energy-independent water electrolysis fuel cell water vehicle system is suggested. The energy-independent water electrolysis fuel cell water vehicle system suggested in the present invention comprises: a water electrolysis processing unit for performing a water electrolysis process by using supplied water while power is not being received from the outside; a gas control unit for adjusting the pressure of a hydrogen gas produced through the water electrolysis process, storing the hydrogen gas in a hydrogen storage by using a gas compression method, and then supplying the hydrogen gas; a fuel cell for generating electrical energy on the basis of the supplied hydrogen gas; and a power control unit for supplying the generated electrical energy as a driving power for the energy-independent water electrolysis fuel cell water vehicle system.

Abstract: A method of a terminal may comprise: receiving, from a serving cell, a first message including measurement report configuration information; in response to transmission of a measurement report message being triggered based on the first message, generating a measurement report message; and in response to a measurement report message transmission condition configured by the first message being satisfied, transmitting the measurement report message to the serving cell.

Abstract: A method of identifying AI/ML functionalities/models supported for mobile communication operated in mobile communication systems including a base station and one or more terminals may comprise: identifying, by at least one of the base station or the one or more terminals, information related to the AI/ML functionalities supportable by the one or more terminals; and identifying, by at least one of the base station or the one or more terminals, AI/ML model information supportable by the one or more terminals.

Abstract: A method of a terminal may comprise: receiving a reference signal from a base station; generating a first latent vector based on the reference signal by using an artificial neural network, the first latent vector being common CSI for M resource regions, and M being a positive integer; generating a second latent vector based on the reference signal by using the artificial neural network, the second latent vector being per-resource region CSI for each of the M resource regions; and transmitting structured CSI including the first latent vector and the second latent vectors to the base station.

Abstract: Disclosed are a method and an apparatus for improving mobility management performance by reducing unnecessary handovers in a communication system. a method of a terminal may comprise: receiving cell prediction configuration information; generating measurement results by performing measurements on a serving cell and neighbor cell(s) based on signal strength measurement configuration information; and generating a result of predicting a best cell by performing best cell prediction based on the measurement results.

Abstract: A link shared through a chat room in a chat application is displayed. When a selection of the link is received, a content execution result corresponding to the link through an in-app browser of the chat application or an external browser distinct from the chat application is displayed. A notification for a new chat message is then displayed while the content execution result is being displayed through the in-app browser or the external browser. When a selection for the notification of the new chat message is received, the display of the content execution result is switched to a display of the chat room in which the new chat message was received. A content box, which enables access to the content execution result, is displayed on the switched display of the chat room.

Abstract: Disclosed are CSI transmission method and apparatus. A method of a terminal may comprise: receiving, from a base station, information on a measurement period for at least one channel state information-reference signal (CSI-RS) and a reporting period for transmitting at least one CSI; receiving the at least one CSI-RS during the measurement period; generating at least one measured CSI based on the at least one CSI-RS; generating at least one predicted CSI through a CSI prediction model based on the at least one CSI-RS and the at least one CSI; and transmitting the at least one measured CSI and the at least one predicted CSI to the base station.

Abstract: A method of a terminal may comprise: receiving augmentation assistance information from a base station; generating augmented channel data by applying the augmentation assistance information to original channel data; and training an artificial neural network using the augmented channel data.

Abstract: A method of a terminal may comprise: receiving a channel state information (CSI) request message from a base station, the CSI request message including first indication information indicating one of quantization configuration information of a first artificial intelligence (AI) model configured in the terminal to encode and transmit CSI; quantizing generated CSI feedback information based on the first indication information; and transmitting a CSI report message including the quantized CSI feedback information to the base station.

Abstract: A method of a communication node may comprise: transmitting required network configurations for applying each of artificial neural network models to a network node; and transmitting a status report of the first model including a model identifier field and a model information field for each of the artificial neural network models to the network node to activate at least one artificial neural network model among the artificial neural network models, wherein each of the required network configurations includes a configuration identifier and network configuration information.

Abstract: A method of a terminal may comprise: transmitting channel state prediction-related information to a base station; receiving a channel prediction request signal for a first time instance from the base station; receiving auxiliary reference signal(s) (RS(s)) and a first RS from the base station based on the channel state prediction-related information; and transmitting channel prediction information for the first time instance to the base station, wherein the channel state prediction-related information includes information indicating that channel state information prediction of the terminal is possible, and the channel prediction request signal includes configuration information of the auxiliary RS(s) and configuration information of the first RS.

Abstract: A method of a terminal may comprise: receiving, from a CU of a base station, configuration information of one or more candidate cells and configuration information for measurement; performing L1 measurement on the candidate cells based on the configuration information for measurement; acquiring uplink synchronization for the candidate cells by performing an uplink synchronization management procedure for the candidate cells based on the configuration information of the candidate cells; reporting a result of the L1 measurement to a DU of the base station; receiving, from the DU of the base station, information on a target cell, being the target cell to switch among the candidate cells, based on the result of the L1 measurement; and applying the uplink synchronization acquired through the uplink synchronization management procedure to the target cell.

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: 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.