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EP-4742551-A1 - DATA COLLECTION METHOD AND DEVICE FOR TRAINING BEAM PREDICTION MODEL IN WIRELESS COMMUNICATION SYSTEM

EP4742551A1EP 4742551 A1EP4742551 A1EP 4742551A1EP-4742551-A1

Abstract

The present disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. More specifically, a method performed by a terminal in a wireless communication system may comprise the steps of: receiving, from a base station, configuration information related to beam prediction-based beam management; on the basis of the configuration information, performing measurements related to the beam prediction-based beam management at a plurality of measurement time points, respectively; and transmitting, to the base station, a measurement report including results of the measurements performed at the plurality of measurement time points, respectively.

Inventors

  • LEE, Taeseop
  • KIM, SANGBUM
  • BAE, BEOMSIK

Assignees

  • Samsung Electronics Co., Ltd.

Dates

Publication Date
20260513
Application Date
20240626

Claims (15)

  1. A method performed by a terminal in a wireless communication system, the method comprising: receiving, from a base station, configuration information related to beam prediction-based beam management; based on the configuration information, performing measurements related to the beam prediction-based beam management at a plurality of measurement time points respectively; and transmitting, to the base station, a measurement report including results of the measurements performed at the plurality of measurement time points respectively.
  2. The method of claim 1, wherein the configuration information comprises at least one of information on a period at which the measurements are performed, information on a reporting interval of the measurement report, information for indicating the terminal to perform logging of the results of the measurements, information on a period at which the logging is performed, or information for indicating the terminal to report the results of the measurements as result values of layer 1 (L1) measurements.
  3. The method of claim 2, wherein the information on the reporting interval is configured as a value equal to or smaller than the period at which the measurements are performed.
  4. The method of claim 1, wherein each of the results of the measurements performed at the plurality of measurement time points respectively comprises a measurement result for all beams, and is used to train an artificial intelligence model for beam prediction.
  5. A method performed by a base station in a wireless communication system, the method comprising: transmitting, to a terminal, configuration information related to beam prediction-based beam management; and based on the configuration information, receiving, from the terminal, a measurement report including results of measurements related to the beam prediction-based beam management, the measurements being performed at a plurality of measurement time points respectively.
  6. The method of claim 5, wherein the configuration information comprises at least one of information on a period at which the measurements are performed, information on a reporting interval of the measurement report, information for indicating the terminal to perform logging of the results of the measurements, information on a period at which the logging is performed, or information for indicating the terminal to report the results of the measurements as result values of layer 1 (L1) measurements.
  7. The method of claim 6, wherein the information on the reporting interval is configured as a value equal to or smaller than the period at which the measurements are performed.
  8. The method of claim 5, wherein each of the results of the measurements performed at the plurality of measurement time points respectively comprises a measurement result for all beams, and is used to train an artificial intelligence model for beam prediction.
  9. A terminal in a wireless communication system, the terminal comprising: a transceiver; and a controller connected to the transceiver, wherein the controller is configured to: receive, from a base station, configuration information related to beam prediction-based beam management; based on the configuration information, perform measurements related to the beam prediction-based beam management at a plurality of measurement time points respectively; and transmit, to the base station, a measurement report including results of the measurements performed at the plurality of measurement time points respectively.
  10. The terminal of claim 9, wherein the configuration information comprises at least one of information on a period at which the measurements are performed, information on a reporting interval of the measurement report, information for indicating the terminal to perform logging of the results of the measurements, information on a period at which the logging is performed, or information for indicating the terminal to report the results of the measurements as result values of layer 1 (L1) measurements.
  11. The terminal of claim 10, wherein the information on the reporting interval is configured as a value equal to or smaller than the period at which the measurements are performed.
  12. The terminal of claim 9, wherein each of the results of the measurements performed at the plurality of measurement time points respectively comprises a measurement result for all beams, and are used to train an artificial intelligence model for beam prediction.
  13. A base station in a wireless communication system, the base station comprising: a transceiver; and a controller connected to the transceiver, wherein the controller is configured to: transmit, to a terminal, configuration information related to beam prediction-based beam management; and based on the configuration information, receive, from the terminal, a measurement report including results of measurements related to the beam prediction-based beam management, the measurements being performed at a plurality of measurement time points respectively.
  14. The base station of claim 13, wherein the configuration information comprises at least one of information on a period at which the measurements are performed, information on a reporting interval of the measurement report, information for indicating the terminal to perform logging of the results of the measurements, information on a period at which the logging is performed, or information for indicating the terminal to report the results of the measurements as result values of layer 1 (L1) measurements.
  15. The base station of claim 14, wherein the information on the reporting interval is configured as a value equal to or smaller than the period at which the measurements are performed.

Description

[Technical Field] The disclosure relates to a data collection method and device for training an artificial intelligence (AI) and machine learning (ML) model for predicting beam information between a base station and a terminal in a mobile communication system. [Background Art] 5G mobile communication technologies define broad frequency bands such that high transmission rates and new services are possible, and can be implemented not only in "Sub 6GHz" bands such as 3.5GHz, but also in "Above 6GHz" bands referred to as mmWave including 28GHz and 39GHz. In addition, it has been considered to implement 6G mobile communication technologies (referred to as Beyond 5G systems) in terahertz (THz) bands (for example, 95GHz to 3THz bands) in order to accomplish transmission rates fifty times faster than 5G mobile communication technologies and ultra-low latencies one-tenth of 5G mobile communication technologies. At the beginning of the development of 5G mobile communication technologies, in order to support services and to satisfy performance requirements in connection with enhanced Mobile BroadBand (eMBB), Ultra Reliable Low Latency Communications (URLLC), and massive Machine-Type Communications (mMTC), there has been ongoing standardization regarding beamforming and massive MIMO for mitigating radio-wave path loss and increasing radio-wave transmission distances in mmWave, supporting numerologies (for example, operating multiple subcarrier spacings) for efficiently utilizing mmWave resources and dynamic operation of slot formats, initial access technologies for supporting multi-beam transmission and broadbands, definition and operation of BWP (BandWidth Part), new channel coding methods such as a LDPC (Low Density Parity Check) code for large amount of data transmission and a polar code for highly reliable transmission of control information, L2 pre-processing, and network slicing for providing a dedicated network specialized to a specific service. Currently, there are ongoing discussions regarding improvement and performance enhancement of initial 5G mobile communication technologies in view of services to be supported by 5G mobile communication technologies, and there has been physical layer standardization regarding technologies such as V2X (Vehicle-to-everything) for aiding driving determination by autonomous vehicles based on information regarding positions and states of vehicles transmitted by the vehicles and for enhancing user convenience, NR-U (New Radio Unlicensed) aimed at system operations conforming to various regulation-related requirements in unlicensed bands, NR UE Power Saving, Non-Terrestrial Network (NTN) which is UE-satellite direct communication for providing coverage in an area in which communication with terrestrial networks is unavailable, and positioning. Moreover, there has been ongoing standardization in air interface architecture/protocol regarding technologies such as Industrial Internet of Things (IIoT) for supporting new services through interworking and convergence with other industries, IAB (Integrated Access and Backhaul) for providing a node for network service area expansion by supporting a wireless backhaul link and an access link in an integrated manner, mobility enhancement including conditional handover and DAPS (Dual Active Protocol Stack) handover, and two-step random access for simplifying random access procedures (2-step RACH for NR). There also has been ongoing standardization in system architecture/service regarding a 5G baseline architecture (for example, service based architecture or service based interface) for combining Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) technologies, and Mobile Edge Computing (MEC) for receiving services based on UE positions. As 5G mobile communication systems are commercialized, connected devices that have been exponentially increasing will be connected to communication networks, and it is accordingly expected that enhanced functions and performances of 5G mobile communication systems and integrated operations of connected devices will be necessary. To this end, new research is scheduled in connection with eXtended Reality (XR) for efficiently supporting AR (Augmented Reality), VR (Virtual Reality), MR (Mixed Reality) and the like, 5G performance improvement and complexity reduction by utilizing Artificial Intelligence (AI) and Machine Learning (ML), AI service support, metaverse service support, and drone communication. Furthermore, such development of 5G mobile communication systems will serve as a basis for developing not only new waveforms for providing coverage in terahertz bands of 6G mobile communication technologies, multi-antenna transmission technologies such as Full Dimensional MIMO (FD-MIMO), array antennas and large-scale antennas, metamaterial-based lenses and antennas for improving coverage of terahertz band signals, high-dimensional space multiplexing technology using OAM (Orbital Angular