Abstract: The disclosure relates to a 5G or pre-5G communication system for supporting higher data transmission rates than 4G communication systems such as LTE systems. According to an embodiment, a method performed by a base station supporting beam training using a beamforming codebook may comprise storing a plurality of codebooks corresponding to a plurality of different beamwidths, receiving a beam adjustment request from a user equipment (UE), determining, among the plurality of codebook, a codebook including beam codebooks for beam coverages including a beamwidth corresponding to a channel state of the UE, in response to receiving the beam adjustment request, and transmitting, to the UE, signals that are beamformed based on the codebook through a plurality of transmission beams using digital beamforming matrixes of the codebook.

Abstract: A method of a base station in a mobile communication system is provided, which includes receiving position information of at least one UE; determining an initial position of a UAV based on the position information; transmitting, to the UAV, control information related to the initial position and association information between the at least one UE and the UAV; receiving, from the UAV, first feature information related to a communication state between the at least one UE and the UAV; and transmitting control information related to a movement position of the UAV based on an output of a reinforced learning network to which the first feature information is input.

Abstract: In a 5G communication system or a 6G communication system for supporting higher data rates beyond a 4G communication system such as long term evolution (LTE), a method of a first terminal in a wireless communication system is disclosed and may include performing channel measurement, based on one or more first reference signals received from a base station; identifying channel distribution information between the first terminal and the base station, based on the measured channel; selecting one or more representative channel vectors (RCVs), based on the identified channel distribution information; generating one or more constellations corresponding to the selected one or more RCVs; transmitting constellation set information including the generated one or more constellations to the base station; and performing communication with the base station, based on the generated one or more constellations.

Abstract: The disclosure relates to a 5G or pre-5G communication system for supporting higher data transmission rates than 4G communication systems such as LTE systems. According to an embodiment, a method performed by a base station supporting beam training using a beamforming codebook may comprise storing a plurality of codebooks corresponding to a plurality of different beamwidths, receiving a beam adjustment request from a user equipment (UE), determining, among the plurality of codebook, a codebook including beam codebooks for beam coverages including a beamwidth corresponding to a channel state of the UE, in response to receiving the beam adjustment request, and transmitting, to the UE, signals that are beamformed based on the codebook through a plurality of transmission beams using digital beamforming matrixes of the codebook.

Abstract: The disclosure relates to a pre-5th-generation (5G) or 5G communication system to be provided for supporting higher data rates beyond 4th-generation (4G) communication system such as long term evolution (LTE). The method includes identifying whether a channel forecast value is valid, the channel forecast value being acquired based on applying a learning algorithm to an input comprising channel values estimated based on first reference signals (RSs) received from a base station, transmitting, to the base station, an RS request message for requesting an update of the learning algorithm, based on the determining that the channel forecast value is invalid, receiving, from the base station, second RSs of an RS density determined based on the RS request message, generating an updated learning algorithm based on channel values estimated from the second RSs, and transmitting channel forecast information acquired based on the updated learning algorithm to the base station.

Abstract: A method of a base station in a mobile communication system is provided, which includes receiving position information of at least one UE; determining an initial position of a UAV based on the position information; transmitting, to the UAV, control information related to the initial position and association information between the at least one UE and the UAV; receiving, from the UAV, first feature information related to a communication state between the at least one UE and the UAV; and transmitting control information related to a movement position of the UAV based on an output of a reinforced learning network to which the first feature information is input.

Abstract: In a 5G communication system or a 6G communication system for supporting higher data rates beyond a 4G communication system such as long term evolution (LTE), a method of a first terminal in a wireless communication system is disclosed and may include performing channel measurement, based on one or more first reference signals received from a base station; identifying channel distribution information between the first terminal and the base station, based on the measured channel; selecting one or more representative channel vectors (RCVs), based on the identified channel distribution information; generating one or more constellations corresponding to the selected one or more RCVs; transmitting constellation set information including the generated one or more constellations to the base station; and performing communication with the base station, based on the generated one or more constellations.

Abstract: The disclosure relates to a pre-5th-generation (5G) or 5G communication system to be provided for supporting higher data rates beyond 4th-generation (4G) communication system such as long term evolution (LTE). The method includes identifying whether a channel forecast value is valid, the channel forecast value being acquired based on applying a learning algorithm to an input comprising channel values estimated based on first reference signals (RSs) received from a base station, transmitting, to the base station, an RS request message for requesting an update of the learning algorithm, based on the determining that the channel forecast value is invalid, receiving, from the base station, second RSs of an RS density determined based on the RS request message, generating an updated learning algorithm based on channel values estimated from the second RSs, and transmitting channel forecast information acquired based on the updated learning algorithm to the base station.