Abstract: A method of a first communication node may comprise: configuring at least one intelligent functionality among a plurality of intelligent functionalities based on functionality configuration information received from a second communication node; obtaining a training dataset corresponding to the at least one intelligent functionality based on an associated identifier (ID) received from the second communication node; training an intelligent model to perform the at least one intelligent functionality using the training dataset; and performing intelligent operations according to the at least one intelligent functionality using the trained intelligent model.

Abstract: A semiconductor memory device may include a mold structure that includes mold insulation films and gate electrodes alternately stacked on a first substrate, a channel structure that penetrates the mold structure and intersects the gate electrodes, a block separation region that extends in a first direction parallel to an upper surface of the first substrate and cuts the mold structure, a first dam region and a second dam region spaced apart from each other, that each having a closed loop in a plan view and each cutting the mold structure, pad insulation films in the first and second dam regions that are alternately stacked with the mold insulation films and include a material different from the mold insulation films, and a through via which penetrates through the first substrate, the mold insulation films, and the pad insulation films, in the first dam region but not in the second dam region.

Abstract: A method of a first training node may comprise: performing training on a first two-sided AI/ML model using a raw training data set collected for CSI feedback; generating a sequential training data set for sequential training on the first two-sided AI/ML model; performing pruning on the sequential training data set to obtain a reduced sequential training data set; and transmitting, to a second training node, two-sided AI/ML training data information including at least one of the reduced sequential training data set or sequential training data configuration information.

Abstract: Various embodiments for techniques for intelligent channel prediction and transmission of related information in a wireless communication system are disclosed. In one embodiment, a method for a first communication device to transmit channel state information (CSI) for a specific channel to a second communication device, may comprise predicting channel state at a plurality of time points by measuring the specific channel; generating CSI for transmitting a plurality of prediction information obtained through the prediction; and transmitting the CSI to the second communication device. At least some of the plurality of prediction information occupy different overheads in the CSI.

Abstract: Proposed is a wireless communication system and, in more detail, an apparatus and method for intelligent model and functionality management considering inference time in a wireless communication system. A method of operating user equipment (UE) for identifying and managing an intelligent functionalities or models in a wireless communication system, includes a process of receiving UE capability enquiry from a base station (BS), and a process of transmitting UE capability information to the base station, wherein the UE capability information includes inference time information, the base station identifies intelligent functionality and model that the user equipment can support, on the basis of the UE capability enquiry and the UE capability information, and performance of an intelligent model that the user equipment can perform is identified on the basis of the inference time information.

Abstract: A semiconductor memory device may include a mold structure that includes mold insulation films and gate electrodes alternately stacked on a first substrate, a channel structure that penetrates the mold structure and intersects the gate electrodes, a block separation region that extends in a first direction parallel to an upper surface of the first substrate and cuts the mold structure, a first dam region and a second dam region spaced apart from each other, that each having a closed loop in a plan view and each cutting the mold structure, pad insulation films in the first and second dam regions that are alternately stacked with the mold insulation films and include a material different from the mold insulation films, and a through via which penetrates through the first substrate, the mold insulation films, and the pad insulation films, in the first dam region but not in the second dam region.

Abstract: Disclosed are a thermoplastic resin composition and a molded product including the same, and based on 100 parts by weight of a base resin including (A) 70 wt % to 90 wt % of a polybutylene terephthalate resin and (B) 10 wt % to 30 wt % of an acrylate-based graft copolymer, the thermoplastic resin composition includes: (C) 1 part by weight to 5 parts by weight of an epoxy group-containing methacrylate-aromatic vinyl-unsaturated nitrile copolymer; (D) 15 parts by weight to 20 parts by weight of a carbon fiber; (E) 1 part by weight to 5 parts by weight of carbon nanotubes; and (F) 18 parts by weight to 20 parts by weight of aluminum diethyl phosphinate (ADEP).

Abstract: Proposed are an apparatus and a method for an intelligence beam management operation in a wireless communication system. An operation method of a user equipment (UE) in a wireless communication system includes receiving, from a base station (BS), configuration of a measurement set including beam entirety information of the base station used in beam measurement, receiving information on beam measurement from the base station, predicting the measurement set on the basis of the information on beam measurement, transmitting information on a recommended measurement set or a restricted measurement set to the base station, the information on the recommended measurement set and the restricted measurement set being in a bitmap form, and receiving a beam measurement result based on a modified measurement set from the base station.

Abstract: A method of identifying an artificial intelligence (AI)/machine learning (ML) functionality and model supported for mobile communication operated in a mobile communication system including a base station and one or more user equipments (UEs), the method comprising: delivering, from the base station, dataset identification information regarding at least one dataset to the UE; and reporting, by the UE, valid AI/ML-related UE capability for the at least one dataset corresponding to the dataset identification information to the base station.

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 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: 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 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 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 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: 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.