Abstract: The present disclosure discloses beamforming methods and apparatuses. According to an exemplary embodiment of the present disclosure, an antenna message generation deep neural network (DNN) and a beam characteristic generation DNN of a bipartite graph neural network (BGNN) having terminals as vertices on one side and APs as vertices on another side may be constructed.

Abstract: A method of an NES controller comprises: receiving a first NES intent instructing additional energy saving; determining a first cell to be deactivated based on the first NES intent; obtaining a first UE list of all UEs served by the first cell; determining whether all UEs included in the first UE list are capable of handover; in response to determining that all UEs included in the first UE list are capable of handover, transmitting a handover instruction command to a first base station managing the first cell, so that all UEs included in the first UE list are handed over to individual target cells; and in response to receipt of a handover complete message for all UEs included in the first UE list from the first base station, transmitting deactivation instruction information of the first cell to the first base station.

Abstract: An operation method of a CP operating in an ultra-dense cooperative transmission network may include: obtaining a large-scale propagation gain (LPG) matrix representing channel gains between M access points (APs) and K user terminals, M being a natural number greater than or equal to 1, and K being a natural number greater than or equal to 1; obtaining information on a required power consumption; and transmitting, to at least part of the M APs, a control message indicating the at least part of the M APs to perform a control operation for reducing power consumption of the ultra-dense cooperative transmission network based on the required power consumption and the LPG matrix.

Abstract: The present disclosure provides a method for pilot allocation using the Hungarian algorithm in a cell-free massive MIMO system. The method includes obtaining a first matrix by computing a large-scale channel gain between each of a plurality of UEs and each of a plurality of APs included in the cell-free massive MIMO system; assigning each of the plurality of UEs to a first group or a second group by using the first matrix; allocating a pilot to each UE in the second group; obtaining a second matrix by computing the reusability of pilots between each UE in the first group and each UE in the second group; and allocating a pilot to each UE in the first group by applying the Hungarian algorithm to the second matrix.

Abstract: A method for power allocation in a dAP may comprise: when a change cycle of a transmit power determination vector arrives, generating an uplink message including long-term CSI, the uplink message being normalized such that the long-term local CSI becomes a value within a preconfigured limit range; transmitting the uplink message to a central processing unit through a fronthaul; receiving a downlink message vector for power allocation from the central processing unit through the fronthaul; generating decentralized determination information using the downlink message vector; and extracting a transmit power determination vector based on the decentralized determination information.

Abstract: The present invention relates to a system and a method for mobile cross-authentication comprising: generating an online authentication code (Ocode) and a mobile authentication code (Mcode) from an authentication server device when performing online authentication, providing the online authentication code (Ocode) and the mobile authentication code (Mcode) to a computer terminal device and a mobile terminal of the user respectively, receiving and verifying the online authentication code and the mobile authentication code received by the computer terminal device and the mobile terminal to the authentication server device through the mobile terminal and the computer terminal device respectively.

Abstract: The present disclosure discloses beamforming methods and apparatuses. According to an exemplary embodiment of the present disclosure, an antenna message generation deep neural network (DNN) and a beam characteristic generation DNN of a bipartite graph neural network (BGNN) having terminals as vertices on one side and APs as vertices on another side may be constructed.

Abstract: An operation method performed by a terminal in a communication system may include: receiving, from a TRP, a first discovery signal including an identifier of a first transmission beam by using a first reception beam; measuring a first received signal strength of the first discovery signal; receiving, from the TRP, a second discovery signal including an identifier of a second transmission beam by using a second reception beam; measuring a second received signal strength of the second discovery signal; transmitting, to the TRP, first beam pair information including the identifier of the first transmission beam and an identifier of the first reception and the first received signal strength information; and transmitting, to the TRP, second beam pair information including the identifier of the second transmission beam and an identifier of the second reception beam and the second received signal strength information.

Abstract: An operation method of a CP may comprise: requesting a status report from each of terminals and receiving the status report; determining switching from basic transmission mode to cell-free massive MIMO transmission mode for at least part of the terminals based on the status reports; instructing the at least part of the terminals and at least one AN to perform cell-free massive MIMO transmission for the at least part of the terminals to configure cell-free massive MIMO transmission mode; determining analog beam(s) and/or digital precoder(s) to be applied by the at least one AN to the at least part of the terminals based on channel qualities between the at least part of the terminals and the at least one AN; and allowing the at least one AN to perform cell-free massive MIMO transmission to the at least part of the terminals using the analog beam(s) and/or digital precoder(s).

Abstract: Disclosed is a beamforming method using a deep neural network. The deep neural network may include an input layer, L hidden layers, and an output layer, and the beamforming method may include: obtaining channel information h between a base station and K terminals and a transmit power limit value P of the base station, and inputting h and P into the input layer; and performing beamforming on signals to be transmitted to the K terminals using beamforming vectors derived using the output layer and at least one activation function, wherein the base station transmits the signals to the K terminals using M transmit antennas. Here, the output layer may be configured in a direct beamforming learning (DBL) scheme, a feature learning (FL) scheme, or a simplified feature learning (SFL) scheme.

Abstract: An operation method of a CP operating in an ultra-dense cooperative transmission network may include: obtaining a large-scale propagation gain (LPG) matrix representing channel gains between M access points (APs) and K user terminals, M being a natural number greater than or equal to 1, and K being a natural number greater than or equal to 1; obtaining information on a required power consumption; and transmitting, to at least part of the M APs, a control message indicating the at least part of the M APs to perform a control operation for reducing power consumption of the ultra-dense cooperative transmission network based on the required power consumption and the LPG matrix.

Abstract: An operation method of a CP may include: collecting, from terminals or ANs, information on AN(s) to which each of the terminals is connectable, and determining a set of active AN(s) based on the information; adjusting connections between active AN(s) and the terminals based on cooperative transmission constraints; calculating first energy efficiency according to the active AN(s), and calculating second energy efficiency in a state in which at least one AN of active AN(s) is deactivated or activated; and maintaining the set of active AN(s) when second energy efficiency is not improved over first energy efficiency, and updating the set of active AN(s) by excluding or further including the at least one AN when second energy efficiency is improved over first energy efficiency, and performing iteratively from the calculating first energy efficiency and second energy efficiency.

Abstract: A method for calibration, in a serial fronthaul in which a calibration device and L (L is a natural number equal to or greater than 1) access node(s) (AN(s)) are serially connected, may include: transmitting, by the calibration device, to at least part of the L AN(s), a calibration command message indicating calibration of transmission paths; determining, by the calibration device and the at least part of the L AN(s), time delay values and phase characteristic values of the transmission paths of the at least part of the L AN(s); and transmitting, by the calibration device, to the at least part of the L AN(s), a calibration adjustment message indicating calibration of the transmission paths based on the time delay values and the phase characteristic values of the transmission paths of the at least part of the L AN(s).

Abstract: Provided is an apparatus and method for wideband short-range wireless communication using a directional antenna in a millimeter wave band, and the method for wideband short-range wireless communication according to an embodiment may determine a first time interval and a second time interval for a cooperated data frame transfer based on a packet transmission time at each transmission from a source node to a destination node, transmit a frame to a relay node through an antenna pattern directed towards the relay node at a start point of the first time interval, and transmit the frame to the destination node through an antenna pattern directed towards the destination node after a predetermined period of time from a start point of the second time interval.

Abstract: An operation method performed by a terminal in a communication system may include: receiving, from a TRP, a first discovery signal including an identifier of a first transmission beam by using a first reception beam; measuring a first received signal strength of the first discovery signal; receiving, from the TRP, a second discovery signal including an identifier of a second transmission beam by using a second reception beam; measuring a second received signal strength of the second discovery signal; transmitting, to the TRP, first beam pair information including the identifier of the first transmission beam and an identifier of the first reception and the first received signal strength information; and transmitting, to the TRP, second beam pair information including the identifier of the second transmission beam and an identifier of the second reception beam and the second received signal strength information.

Abstract: A method for calibration, in a serial fronthaul in which a calibration device and L (L is a natural number equal to or greater than 1) access node(s) (AN(s)) are serially connected, may include: transmitting, by the calibration device, to at least part of the L AN(s), a calibration command message indicating calibration of transmission paths; determining, by the calibration device and the at least part of the L AN(s), time delay values and phase characteristic values of the transmission paths of the at least part of the L AN(s); and transmitting, by the calibration device, to the at least part of the L AN(s), a calibration adjustment message indicating calibration of the transmission paths based on the time delay values and the phase characteristic values of the transmission paths of the at least part of the L AN(s).

Abstract: An operation method of a first AN for a hybrid beamforming-based cooperative transmission in a C-RAN includes obtaining a spatial channel covariance with terminals subject to a service; selecting a set of terminals to be serviced from among the terminals subject to the service; determining a radio frequency (RF) precoding matrix by which interference between the terminals to be serviced is minimized; transferring information on the determined RF precoding matrix to a centralized processor (CP); and generating an effective channel by configuring an RF precoder based on the determined RF precoding matrix.

Abstract: An operation method of a CP may include: collecting, from terminals or ANs, information on AN(s) to which each of the terminals is connectable, and determining a set of active AN(s) based on the information; adjusting connections between active AN(s) and the terminals based on cooperative transmission constraints; calculating first energy efficiency according to the active AN(s), and calculating second energy efficiency in a state in which at least one AN of active AN(s) is deactivated or activated; and maintaining the set of active AN(s) when second energy efficiency is not improved over first energy efficiency, and updating the set of active AN(s) by excluding or further including the at least one AN when second energy efficiency is improved over first energy efficiency, and performing iteratively from the calculating first energy efficiency and second energy efficiency.

Abstract: An operation method of a CP may comprise: requesting a status report from each of terminals and receiving the status report; determining switching from basic transmission mode to cell-free massive MIMO transmission mode for at least part of the terminals based on the status reports; instructing the at least part of the terminals and at least one AN to perform cell-free massive MIMO transmission for the at least part of the terminals to configure cell-free massive MIMO transmission mode; determining analog beam(s) and/or digital precoder(s) to be applied by the at least one AN to the at least part of the terminals based on channel qualities between the at least part of the terminals and the at least one AN; and allowing the at least one AN to perform cell-free massive MIMO transmission to the at least part of the terminals using the analog beam(s) and/or digital precoder(s).

Abstract: Disclosed is a beamforming method using a deep neural network. The deep neural network may include an input layer, L hidden layers, and an output layer, and the beamforming method may include: obtaining channel information h between a base station and K terminals and a transmit power limit value P of the base station, and inputting h and P into the input layer; and performing beamforming on signals to be transmitted to the K terminals using beamforming vectors derived using the output layer and at least one activation function, wherein the base station transmits the signals to the K terminals using M transmit antennas. Here, the output layer may be configured in a direct beamforming learning (DBL) scheme, a feature learning (FL) scheme, or a simplified feature learning (SFL) scheme.