Abstract:
An operation method of a terminal in a mobile communication system may comprise: determining whether prediction of a communication failure is needed while performing communication with a base station; in response to determining that prediction of the communication failure is required, performing a communication failure prediction procedure; and in response to predicted occurrence of the communication failure according to a result of performing the communication failure prediction procedure, performing a recovery procedure for the predicted communication failure.
Abstract:
The polarization beamforming communication apparatus of a base station estimates an azimuth, elevation, and polarization of each of terminals using a reference signal of a terminal received through a plurality of dual polarization antennas, determines a stream to be transmitted based on the azimuth, elevation, and polarization of the terminal, and sends the stream to be transmitted to the terminal through a polarization-matched beam formed in accordance with each of the plurality of dual polarization antennas using the azimuth, elevation, and polarization of the terminal.
Abstract:
A base station acquires an arrival time at which a signal that is transmitted from a plurality of terminals arrives at a plurality of Remote Radio Heads (RRH) that are installed within a cell, calculates an arrival delay time between the plurality of terminals and the plurality of RRHs using a predetermined reference time and an arrival time of the plurality of RRHs, selects RRHs to participate in receiving the uplink MU-MIMO among the plurality of RRHs using an arrival delay time between the plurality of terminals and the plurality of RRHs, and adjusts a transmitting time of the plurality of terminals using an arrival delay time between the plurality of terminals and RRHs to participate in receiving the uplink MU-MIMO.
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.