WO2021112592A1 - Procédé de gestion de faisceau basé sur l'intelligence artificielle dans un système de communication sans fil, et appareil associé - Google Patents

Procédé de gestion de faisceau basé sur l'intelligence artificielle dans un système de communication sans fil, et appareil associé Download PDF

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WO2021112592A1
WO2021112592A1 PCT/KR2020/017558 KR2020017558W WO2021112592A1 WO 2021112592 A1 WO2021112592 A1 WO 2021112592A1 KR 2020017558 W KR2020017558 W KR 2020017558W WO 2021112592 A1 WO2021112592 A1 WO 2021112592A1
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terminal
csi
beam management
base station
information
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PCT/KR2020/017558
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English (en)
Korean (ko)
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박해욱
유준혁
박상천
박종현
성원진
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엘지전자 주식회사
서강대학교산학협력단
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Priority to KR1020227018990A priority Critical patent/KR20220103124A/ko
Publication of WO2021112592A1 publication Critical patent/WO2021112592A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0686—Hybrid systems, i.e. switching and simultaneous transmission
    • H04B7/0695—Hybrid systems, i.e. switching and simultaneous transmission using beam selection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/318—Received signal strength
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0408—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas using two or more beams, i.e. beam diversity
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0417—Feedback systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/0628—Diversity capabilities
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/0632—Channel quality parameters, e.g. channel quality indicator [CQI]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0636—Feedback format
    • H04B7/0639—Using selective indices, e.g. of a codebook, e.g. pre-distortion matrix index [PMI] or for beam selection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path

Definitions

  • the present specification relates to a wireless communication system, and more particularly, to a beam management method through artificial intelligence neural network learning and an apparatus supporting the same.
  • the mobile communication system has been developed to provide a voice service while ensuring user activity.
  • the mobile communication system has expanded its scope to not only voice but also data service.
  • an explosive increase in traffic causes a shortage of resources and users demand a higher-speed service, so a more advanced mobile communication system is required. .
  • next-generation mobile communication system requirements of the next-generation mobile communication system are largely to support explosive data traffic acceptance, a dramatic increase in the transmission rate per user, a significantly increased number of connected devices, very low end-to-end latency, and high energy efficiency.
  • Dual Connectivity Massive Multiple Input Multiple Output (MIMO), In-band Full Duplex, Non-Orthogonal Multiple Access (NOMA), Super Wideband
  • MIMO Massive Multiple Input Multiple Output
  • NOMA Non-Orthogonal Multiple Access
  • the present specification proposes an artificial intelligence-based beam management method in a wireless communication system.
  • the present specification proposes a method of defining beamforming vectors in consideration of mobility of a receiving device (eg, UE, vehicle), and forming a beam signature and a beambook.
  • a receiving device eg, UE, vehicle
  • forming a beam signature and a beambook e.g., a beambook
  • the present specification proposes a method of transmitting a reference signal based on a beam signature and a beambook, and performing artificial neural network learning using CSI (beam report) measured based on the reference signal.
  • the present specification proposes a method of updating a beam signature and a beambook based on a result derived through artificial neural network learning.
  • a base station to perform beam management in a wireless communication system
  • the method comprising: determining parameters related to beam management; transmitting, to a UE (USER EQUIPMENT, UE), a reference signal for beam management based on parameters related to the beam management; receiving, from the terminal, a beam related report calculated based on the reference signal; performing artificial neural network learning based on the beam-related report; and updating parameters related to the beam management based on the neural network learning result, wherein the parameters related to the beam management include i) location information of the terminal and ii) movement path information of the terminal, The location information of the terminal may be expressed as a zenith angle and an azimuth angle.
  • the determining of the parameters related to the beam management includes: determining candidates of a movement path of the terminal; and determining position candidates of the terminal in each candidate movement path.
  • beamforming vectors for position candidates of the terminal in each candidate movement path may constitute a beam signature.
  • sets of beam signatures of candidates of movement paths of the terminal may constitute a beambook.
  • a beam signature may be configured using only beamforming vectors for some candidates among the candidates of the movement path of the terminal.
  • the reference signal is a UE-specific reference signal, and a reference for each path based on a beamforming vector of the best quality among beamforming vectors constituting the beam signature.
  • a signal may be transmitted.
  • a plurality of reference signals may be transmitted for each path in consideration of the current position of the terminal and the position to be moved thereafter.
  • the method may further include receiving, from the terminal, capability information including a predefined movement path of the terminal.
  • the number of location candidates of the terminal in each candidate movement path may be predefined.
  • the beam-related report may include at least one of CQI, PMI, and L1-RSRP.
  • performing artificial neural network learning based on the beam-related report includes: setting at least one of the CQI, PMI, or L1-RSRP as input data, and the An artificial neural network model can be learned by setting the location information of the terminal and the movement path information of the terminal as output data.
  • the base station includes: one or more transceivers; one or more processors; and one or more memories storing instructions for operations executed by the one or more processors and coupled to the one or more processors, the operations comprising: determining parameters related to beam management; ; transmitting, to a UE (USER EQUIPMENT, UE), a reference signal for beam management based on parameters related to the beam management; receiving, from the terminal, a beam related report calculated based on the reference signal; performing artificial neural network learning based on the beam-related report; and updating parameters related to the beam management based on the neural network learning result, wherein the parameters related to the beam management include i) location information of the terminal and ii) movement path information of the terminal, The location information of the terminal may be expressed as a zenith angle and an azimuth angle.
  • the one or more processors determine parameters related to beam management by the apparatus and, to a UE (USER EQUIPMENT, UE), transmits a reference signal for beam management based on parameters related to the beam management, and from the UE, a beam related report calculated based on the reference signal control to receive, perform artificial neural network learning based on the beam-related report, and update parameters related to the beam management based on the neural network learning result, wherein the parameters related to the beam management are i) of the terminal It includes location information and ii) movement path information of the terminal, and the location information of the terminal may be expressed as a zenith angle and an azimuth angle.
  • non-transitory computer-readable media storing one or more instructions according to an embodiment of the present specification, executable by one or more processors
  • the one or more commands determine parameters related to beam management, and transmit a reference signal for beam management to a UE (USER EQUIPMENT, UE) based on the parameters related to the beam management, the terminal to receive a beam-related report calculated based on the reference signal, perform artificial neural network learning based on the beam-related report, and update parameters related to the beam management based on the neural network learning result
  • the parameters related to the beam management include i) location information of the terminal and ii) movement path information of the terminal, and the location information of the terminal may be expressed as a zenith angle and an azimuth angle.
  • beamforming may be performed in consideration of mobility and position change of a receiving device (eg, UE, vehicle).
  • a receiving device eg, UE, vehicle.
  • artificial neural network learning may be performed using CSI (beam report) transmitted by a receiving device (eg, UE, vehicle), and a beamforming vector (ie, based on the learning result) Beam signature and Beambook) can be updated.
  • CSI beam report
  • a receiving device eg, UE, vehicle
  • Beamforming vector ie, based on the learning result
  • Beam signature and Beambook Beambook
  • beam generation and beam tracking with excellent performance may be possible within a limited feedback payload range.
  • FIG. 1 shows an example of the overall system structure of NR to which the method proposed in the present specification can be applied.
  • FIG. 2 illustrates a relationship between an uplink frame and a downlink frame in a wireless communication system to which the method proposed in the present specification can be applied.
  • FIG 3 shows an example of a frame structure in an NR system.
  • FIG. 4 shows an example of a resource grid supported by a wireless communication system to which the method proposed in the present specification can be applied.
  • 5 shows examples of an antenna port to which the method proposed in this specification can be applied and a resource grid for each numerology.
  • FIG. 6 illustrates physical channels and general signal transmission used in a 3GPP system.
  • FIG. 7 is a diagram illustrating an example of a downlink transmission/reception operation.
  • FIG. 8 is a diagram illustrating an example of an uplink transmission/reception operation.
  • FIG. 9 is a diagram illustrating an example of a DL BM procedure using a CSI-RS.
  • FIG. 10 is a diagram for describing a procedure for determining a reception beam in a downlink beam management procedure using a CSI-RS.
  • 11 is a flowchart illustrating an example of a transmission beam determination procedure of a base station.
  • FIG. 12 is a flowchart illustrating an example of a CSI-related procedure.
  • FIG. 13 shows an example of basic operations of a user terminal (UE) and a 5G network in a 5G communication system.
  • UE user terminal
  • FIG. 14 is a block diagram of an AI device according to an embodiment of the present invention.
  • 15 shows an example of signaling between a base station and a receiver to which the method proposed in this specification can be applied.
  • 16 is a diagram illustrating an artificial neural network according to an embodiment of the present specification.
  • 17 is an example of a vehicle route traveling through a six-lane crossroad in a city center for explaining the present embodiment.
  • FIG. 18 is a two-dimensional diagram illustrating the crossroad of FIG. 17 and shows an example of a moving path of a vehicle.
  • 19 is an example illustrating the zenith angle and the azimuth angle of the receiver directional vector at intervals of 1 m for 12 paths.
  • 21 is an example illustrating an evaluation of a signal-to-interference and noise ratio according to an increase in the number of users.
  • 22 is an example of evaluation results for SINR and sum data rate according to a change in distance between two arbitrary receivers.
  • FIG. 24 shows an example in which a plurality of base stations cooperatively perform beam signature transmission.
  • FIG. 25 shows an example of a beam management operation flowchart of a base station (BS) to which the methods proposed in the present specification can be applied.
  • 26 illustrates a communication system 1 applied to the present invention.
  • 29 shows another example of a wireless device to which the present invention is applied.
  • FIG. 30 illustrates a vehicle or an autonomous driving vehicle to which the present invention is applied.
  • FIG. 31 illustrates a vehicle to which the present invention is applied.
  • downlink means communication from a base station to a terminal
  • uplink means communication from a terminal to a base station
  • DL downlink
  • UL uplink
  • the transmitter may be a part of the base station
  • the receiver may be a part of the terminal
  • the transmitter may be a part of the terminal
  • the receiver may be a part of the base station.
  • the base station may be represented as a first communication device
  • the terminal may be represented as a second communication device.
  • a base station is a fixed station, a Node B, an evolved-NodeB (eNB), a Next Generation NodeB (gNB), a base transceiver system (BTS), an access point (AP), and a network (5G).
  • network AI system, RSU (road side unit), vehicle (vehicle), robot, drone (Unmanned Aerial Vehicle, UAV), AR (Augmented Reality) device, VR (Virtual Reality) device, etc. have.
  • the terminal may be fixed or have mobility, UE (User Equipment), MS (Mobile Station), UT (user terminal), MSS (Mobile Subscriber Station), SS (Subscriber Station), AMS (Advanced Mobile) Station), WT (Wireless terminal), MTC (Machine-Type Communication) device, M2M (Machine-to-Machine) device, D2D (Device-to-Device) device, vehicle, robot, AI module , drones (Unmanned Aerial Vehicle, UAV), AR (Augmented Reality) devices, VR (Virtual Reality) devices, and the like.
  • UAV Unmanned Aerial Vehicle
  • AR Augmented Reality
  • VR Virtual Reality
  • CDMA may be implemented with a radio technology such as Universal Terrestrial Radio Access (UTRA) or CDMA2000.
  • TDMA may be implemented with a radio technology such as Global System for Mobile communications (GSM)/General Packet Radio Service (GPRS)/Enhanced Data Rates for GSM Evolution (EDGE).
  • GSM Global System for Mobile communications
  • GPRS General Packet Radio Service
  • EDGE Enhanced Data Rates for GSM Evolution
  • OFDMA may be implemented with a radio technology such as IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, Evolved UTRA (E-UTRA), and the like.
  • UTRA is part of the Universal Mobile Telecommunications System (UMTS).
  • 3GPP 3rd Generation Partnership Project
  • Long Term Evolution is a part of Evolved UMTS (E-UMTS) using E-UTRA and LTE-A (Advanced)/LTE-A pro is an evolved version of 3GPP LTE.
  • 3GPP NR New Radio or New Radio Access Technology is an evolved version of 3GPP LTE/LTE-A/LTE-A pro.
  • LTE refers to technology after 3GPP TS 36.xxx Release 8.
  • LTE technology after 3GPP TS 36.xxx Release 10 is referred to as LTE-A
  • LTE technology after 3GPP TS 36.xxx Release 13 is referred to as LTE-A pro
  • 3GPP NR refers to technology after TS 38.xxx Release 15.
  • LTE/NR may be referred to as a 3GPP system.
  • "xxx" means standard document detail number.
  • LTE/NR may be collectively referred to as a 3GPP system.
  • RRC Radio Resource Control
  • RRC Radio Resource Control
  • next-generation communication As more and more communication devices require a larger communication capacity, the need for improved mobile broadband communication compared to the existing radio access technology is emerging.
  • massive MTC Machine Type Communications
  • massive MTC Machine Type Communications
  • Mmtc massive MTC
  • URLLC Ultra-Reliable and Low Latency Communication
  • the three main requirements areas for 5G are (1) Enhanced Mobile Broadband (eMBB) area, (2) Massive Machine Type Communication (mMTC) area and (3) Ultra-reliable and It includes an Ultra-reliable and Low Latency Communications (URLLC) area.
  • eMBB Enhanced Mobile Broadband
  • mMTC Massive Machine Type Communication
  • URLLC Ultra-reliable and Low Latency Communications
  • KPI key performance indicator
  • eMBB goes far beyond basic mobile internet access, covering rich interactive work, media and entertainment applications in the cloud or augmented reality.
  • Data is one of the key drivers of 5G, and for the first time in the 5G era, we may not see dedicated voice services.
  • voice is simply expected to be processed as an application using the data connection provided by the communication system.
  • the main causes for increased traffic volume are an increase in content size and an increase in the number of applications requiring high data rates.
  • Streaming services audio and video
  • interactive video and mobile Internet connections will become more widely used as more devices are connected to the Internet. Many of these applications require always-on connectivity to push real-time information and notifications to users.
  • Cloud storage and applications are rapidly increasing in mobile communication platforms, which can be applied to both work and entertainment.
  • cloud storage is a special use case that drives the growth of uplink data rates.
  • 5G is also used for remote work in the cloud, requiring much lower end-to-end latency to maintain a good user experience when tactile interfaces are used.
  • Entertainment For example, cloud gaming and video streaming are other key factors that increase the demand for mobile broadband capabilities. Entertainment is essential on smartphones and tablets anywhere, including in high-mobility environments such as trains, cars and airplanes.
  • Another use example is augmented reality for entertainment and information retrieval.
  • augmented reality requires very low latency and instantaneous amount of data.
  • URLLC includes new services that will transform industries through ultra-reliable/low-latency links available, such as remote control of critical infrastructure and self-driving vehicles. This level of reliability and latency is essential for smart grid control, industrial automation, robotics, and drone control and coordination.
  • 5G could complement fiber-to-the-home (FTTH) and cable-based broadband (or DOCSIS) as a means of delivering streams rated at hundreds of megabits per second to gigabits per second. This high speed is required to deliver TVs in resolutions of 4K and higher (6K, 8K and higher), as well as virtual and augmented reality.
  • Virtual Reality (VR) and Augmented Reality (AR) applications almost include immersive sporting events. Certain applications may require special network settings. For VR games, for example, game companies may need to integrate core servers with network operators' edge network servers to minimize latency.
  • Automotive is expected to be an important new driving force for 5G, with many use cases for mobile communication to vehicles. For example, entertainment for passengers requires simultaneous high capacity and high mobility mobile broadband. The reason is that future users will continue to expect high-quality connections regardless of their location and speed.
  • Another use case in the automotive sector is augmented reality dashboards. It identifies objects in the dark and overlays information that tells the driver about the distance and movement of the object over what the driver is seeing through the front window.
  • wireless modules will enable communication between vehicles, information exchange between vehicles and supporting infrastructure, and information exchange between automobiles and other connected devices (eg, devices carried by pedestrians).
  • Safety systems can help drivers reduce the risk of accidents by guiding alternative courses of action to help them drive safer.
  • the next step will be remote-controlled or self-driven vehicles.
  • Smart cities and smart homes referred to as smart societies, will be embedded with high-density wireless sensor networks.
  • a distributed network of intelligent sensors will identify conditions for cost and energy-efficient maintenance of a city or house.
  • a similar setup can be performed for each household.
  • Temperature sensors, window and heating controllers, burglar alarms and appliances are all connected wirelessly. Many of these sensors are typically low data rates, low power and low cost. However, for example, real-time HD video may be required in certain types of devices for surveillance.
  • Smart grids use digital information and communication technologies to interconnect these sensors to collect information and act on it. This information can include supplier and consumer behavior, enabling smart grids to improve efficiency, reliability, economy, sustainability of production and distribution of fuels such as electricity in an automated manner.
  • the smart grid can also be viewed as another low-latency sensor network.
  • the health sector has many applications that can benefit from mobile communications.
  • the communication system may support telemedicine providing clinical care from a remote location. This can help reduce barriers to distance and improve access to consistently unavailable health care services in remote rural areas. It is also used to save lives in critical care and emergency situations.
  • a wireless sensor network based on mobile communication may provide remote monitoring and sensors for parameters such as heart rate and blood pressure.
  • Wireless and mobile communications are becoming increasingly important in industrial applications. Wiring is expensive to install and maintain. Thus, the possibility of replacing cables with reconfigurable wireless links is an attractive opportunity for many industries. However, achieving this requires that the wireless connection operate with cable-like delay, reliability and capacity, and that its management be simplified. Low latency and very low error probability are new requirements that need to be connected with 5G.
  • Logistics and freight tracking are important use cases for mobile communications that use location-based information systems to enable tracking of inventory and packages from anywhere.
  • Logistics and freight tracking use cases typically require low data rates but require wide range and reliable location information.
  • a new RAT system including NR uses an OFDM transmission scheme or a similar transmission scheme.
  • the new RAT system may follow OFDM parameters different from those of LTE.
  • the new RAT system may follow the existing LTE/LTE-A numerology as it is, but may have a larger system bandwidth (eg, 100 MHz).
  • one cell may support a plurality of numerologies. That is, terminals operating with different numerology may coexist in one cell.
  • Numerology corresponds to one subcarrier spacing in the frequency domain.
  • different numerology can be defined.
  • eLTE eNB An eLTE eNB is an evolution of an eNB that supports connectivity to EPC and NGC.
  • gNB A node that supports NR as well as connectivity with NGC.
  • New RAN Radio access networks that support NR or E-UTRA or interact with NGC.
  • a network slice is a network defined by an operator to provide an optimized solution for a specific market scenario that requires specific requirements with end-to-end coverage.
  • Network function is a logical node within a network infrastructure with well-defined external interfaces and well-defined functional behavior.
  • NG-C Control plane interface used for the NG2 reference point between the new RAN and NGC.
  • NG-U User plane interface used for the NG3 reference point between the new RAN and NGC.
  • Non-standalone NR A deployment configuration in which the gNB requires an LTE eNB as an anchor for control plane connection to EPC or an eLTE eNB as an anchor for control plane connection to NGC.
  • Non-Standalone E-UTRA Deployment configuration where eLTE eNB requires gNB as anchor for control plane connection to NGC.
  • User Plane Gateway The endpoint of the NG-U interface.
  • FIG. 1 shows an example of the overall system structure of NR to which the method proposed in the present specification can be applied.
  • NG-RAN consists of gNBs that provide NG-RA user plane (new AS sublayer/PDCP/RLC/MAC/PHY) and control plane (RRC) protocol termination for UE (User Equipment). do.
  • NG-RA user plane new AS sublayer/PDCP/RLC/MAC/PHY
  • RRC control plane
  • the gNBs are interconnected through an X n interface.
  • the gNB is also connected to the NGC through the NG interface.
  • the gNB is connected to an Access and Mobility Management Function (AMF) through an N2 interface and a User Plane Function (UPF) through an N3 interface.
  • AMF Access and Mobility Management Function
  • UPF User Plane Function
  • the numerology may be defined by subcarrier spacing and CP (Cyclic Prefix) overhead.
  • the plurality of subcarrier intervals is an integer N (or, ) can be derived by scaling.
  • the numerology used can be selected independently of the frequency band.
  • OFDM Orthogonal Frequency Division Multiplexing
  • a number of OFDM numerologies supported in the NR system may be defined as shown in Table 1.
  • NR supports multiple numerology (or subcarrier spacing (SCS)) to support various 5G services. For example, when SCS is 15kHz, it supports a wide area in traditional cellular bands, and when SCS is 30kHz/60kHz, dense-urban, lower latency and a wider carrier bandwidth, and when the SCS is 60 kHz or higher, a bandwidth greater than 24.25 GHz to overcome phase noise.
  • SCS subcarrier spacing
  • the NR frequency band is defined as a frequency range of two types (FR1, FR2).
  • FR1 and FR2 may be configured as shown in Table 2 below.
  • FR2 may mean a millimeter wave (mmW).
  • Downlink and uplink transmission It is composed of a radio frame having a section of .
  • each radio frame is It consists of 10 subframes having a period of .
  • FIG. 2 illustrates a relationship between an uplink frame and a downlink frame in a wireless communication system to which the method proposed in the present specification can be applied.
  • the transmission of uplink frame number i from the UE is higher than the start of the corresponding downlink frame in the corresponding UE. have to start earlier.
  • the slots are in a subframe are numbered in increasing order of are numbered in increasing order of one slot is consists of consecutive OFDM symbols of is determined according to the used numerology and slot configuration.
  • slot in subframe The start of the OFDM symbol in the same subframe chronologically aligned with the beginning of
  • Table 3 shows the number of OFDM symbols per slot in a normal CP ( ), the number of slots per radio frame ( ), the number of slots per subframe ( ), and Table 4 shows the number of OFDM symbols per slot, the number of slots per radio frame, and the number of slots per subframe in the extended CP.
  • 3 shows an example of a frame structure in an NR system. 3 is only for convenience of description, and does not limit the scope of the present invention.
  • a mini-slot may consist of 2, 4, or 7 symbols, and may consist of more or fewer symbols.
  • an antenna port (antenna port), a resource grid (resource grid), a resource element (resource element), a resource block (resource block), a carrier part (carrier part), etc. can be considered.
  • an antenna port is defined such that a channel on which a symbol on an antenna port is carried can be inferred from a channel on which another symbol on the same antenna port is carried.
  • the two antenna ports are QC/QCL (quasi co-located or QC/QCL). quasi co-location).
  • the wide range characteristic includes at least one of delay spread, Doppler spread, frequency shift, average received power, and received timing.
  • FIG. 4 shows an example of a resource grid supported by a wireless communication system to which the method proposed in the present specification can be applied.
  • the resource grid is displayed in the frequency domain. It is composed of subcarriers, and one subframe is Although the OFDM symbol is described as an example, it is not limited thereto.
  • a transmitted signal is one or more resource grids composed of subcarriers; and It is described by the OFDM symbols of From here, to be. remind denotes the maximum transmission bandwidth, which may vary between uplink and downlink as well as numerologies.
  • 5 shows examples of an antenna port to which the method proposed in this specification can be applied and a resource grid for each numerology.
  • resource element each element of the resource grid for the antenna port p is referred to as a resource element (resource element), index pair is uniquely identified by From here, is an index in the frequency domain, denotes a position of a symbol within a subframe.
  • index pair this is used From here, to be.
  • Numerology and a resource element for antenna port p. is a complex value corresponds to In cases where there is no risk of confusion, or if a particular antenna port or numerology is not specified, the indices p and can be dropped, so that the complex value is or this can be
  • the physical resource block (physical resource block) on the frequency domain It is defined as contiguous subcarriers.
  • Point A serves as a common reference point of the resource block grid and may be obtained as follows.
  • - offsetToPointA for PCell downlink represents the frequency offset between point A and the lowest subcarrier of the lowest resource block overlapping the SS/PBCH block used by the UE for initial cell selection, 15 kHz subcarrier spacing for FR1 and It is expressed in resource block units assuming 60 kHz subcarrier spacing for FR2;
  • - absoluteFrequencyPointA indicates the frequency-position of point A expressed as in ARFCN (absolute radio-frequency channel number).
  • Common resource blocks set the subcarrier interval It is numbered from 0 upwards in the frequency domain for .
  • Subcarrier spacing setting The center of subcarrier 0 of common resource block 0 for 'point A' coincides with 'point A'.
  • Common resource block number (number) in the frequency domain and subcarrier spacing A resource element (k,l) for ? may be given as in Equation 1 below.
  • the NR system can support up to 400 MHz per one component carrier (CC). If the terminal operating in such a wideband CC always operates with the RF for the entire CC turned on, the terminal battery consumption may increase.
  • different numerology e.g., sub-carrier spacing
  • the base station may instruct the terminal to operate only in a partial bandwidth rather than the entire bandwidth of the wideband CC, and the partial bandwidth is defined as a bandwidth part (BWP) for convenience.
  • BWP may consist of continuous resource blocks (RBs) on the frequency axis, and may correspond to one numerology (e.g., sub-carrier spacing, CP length, slot/mini-slot duration).
  • the base station may set a plurality of BWPs even within one CC configured for the terminal. For example, in the PDCCH monitoring slot, a BWP occupying a relatively small frequency region may be configured, and a PDSCH indicated by the PDCCH may be scheduled on a larger BWP.
  • some UEs may be configured as a different BWP for load balancing.
  • a part of the spectrum may be excluded from the entire bandwidth, and both BWPs may be configured in the same slot.
  • the base station may configure at least one DL/UL BWP to the terminal associated with the wideband CC, and at least one DL/UL BWP among DL/UL BWP(s) configured at a specific time (L1) signaling or MAC CE or RRC signaling), switching to another configured DL/UL BWP can be instructed (by L1 signaling, MAC CE or RRC signaling, etc.), or when a timer value expires based on a timer, the determined DL/UL BWP It can also be switched to UL BWP.
  • the activated DL/UL BWP is defined as the active DL/UL BWP.
  • the configuration for DL/UL BWP may not be received.
  • the DL/UL BWP assumed by the terminal is the initial active DL It is defined as /UL BWP.
  • a specific field indicating BWP eg, BWP indicator field
  • DCI eg, DCI format 1_1
  • the value of the corresponding field is for DL reception for the UE (in advance)
  • the terminal receiving the DCI may be configured to receive DL data in a specific DL BWP indicated by the corresponding field.
  • a specific field indicating BWP eg, BWP indicator field
  • DCI eg, DCI format 0_1
  • the terminal receiving the DCI may be configured to transmit UL data in a specific UL BWP indicated by the corresponding field.
  • a terminal receives information through a downlink (DL) from a base station, and the terminal transmits information through an uplink (UL) to the base station.
  • Information transmitted and received between the base station and the terminal includes data and various control information, and various physical channels exist according to the type/use of the information they transmit and receive.
  • the terminal When the terminal is powered on or newly enters a cell, the terminal performs an initial cell search operation, such as synchronizing with the base station (S601). To this end, the terminal receives a primary synchronization signal (PSS) and a secondary synchronization signal (SSS) from the base station, synchronizes with the base station, and obtains information such as a cell ID. Thereafter, the terminal may receive a physical broadcast channel (PBCH) from the base station to obtain intra-cell broadcast information. Meanwhile, the UE may receive a downlink reference signal (DL RS) in the initial cell search step to check the downlink channel state.
  • PSS primary synchronization signal
  • SSS secondary synchronization signal
  • PBCH physical broadcast channel
  • DL RS downlink reference signal
  • the UE After completing the initial cell search, the UE acquires more specific system information by receiving a Physical Downlink Control Channel (PDCCH) and a Physical Downlink Control Channel (PDSCH) according to information carried on the PDCCH. It can be done (S602).
  • PDCCH Physical Downlink Control Channel
  • PDSCH Physical Downlink Control Channel
  • the terminal may perform a random access procedure (RACH) with respect to the base station (S603 to S606).
  • RACH random access procedure
  • the UE transmits a specific sequence as a preamble through a Physical Random Access Channel (PRACH) (S603 and S605), and a response message to the preamble through the PDCCH and the corresponding PDSCH ((Random Access (RAR)) Response) message)
  • PRACH Physical Random Access Channel
  • RAR Random Access
  • a contention resolution procedure may be additionally performed (S606).
  • the UE After performing the procedure as described above, the UE performs PDCCH/PDSCH reception (S607) and Physical Uplink Shared Channel (PUSCH)/Physical Uplink Control Channel (Physical Uplink) as a general uplink/downlink signal transmission procedure.
  • Control Channel (PUCCH) transmission (S608) may be performed.
  • the UE may receive downlink control information (DCI) through the PDCCH.
  • DCI downlink control information
  • the DCI includes control information such as resource allocation information for the terminal, and different formats may be applied according to the purpose of use.
  • DCI format 0_0 and DCI format 0_1 are used for scheduling PUSCH in one cell
  • DCI format 1_0 and DCI format 1_1 are used for scheduling PDSCH in one cell.
  • Information included in DCI format 0_0 is CRC scrambled and transmitted by C-RNTI, CS-RNTI, or MCS-C-RNTI.
  • DCI format 0_1 is used to reserve a PUSCH in one cell.
  • Information included in DCI format 0_1 is CRC scrambled and transmitted by C-RNTI or CS-RNTI or SP-CSI-RNTI or MCS-C-RNTI.
  • DCI format 1_0 is used for scheduling PDSCH in one DL cell.
  • DCI format 1_0 Information included in DCI format 1_0 is CRC scrambled and transmitted by C-RNTI or CS-RNTI or MCS-C-RNTI.
  • DCI format 1_1 is used for scheduling PDSCH in one cell.
  • Information included in DCI format 1_1 is CRC scrambled and transmitted by C-RNTI, CS-RNTI, or MCS-C-RNTI.
  • DCI format 2_1 is used to inform PRB(s) and OFDM symbol(s) that the UE may assume that transmission is not intended.
  • Information such as preemption indication 1, preemption indication 2, ..., preemption indication N included in DCI format 2_1 is CRC scrambled and transmitted by the INT-RNTI.
  • control information transmitted by the terminal to the base station through the uplink or received by the terminal from the base station includes a downlink/uplink ACK/NACK signal, a channel quality indicator (CQI), a precoding matrix index (PMI), and a rank indicator (RI). ) and the like.
  • the UE may transmit the above-described control information such as CQI/PMI/RI through PUSCH and/or PUCCH.
  • FIG. 7 is a diagram illustrating an example of a downlink transmission/reception operation.
  • the base station schedules downlink transmission such as frequency/time resources, transport layer, downlink precoder, MCS, and the like (S701).
  • the base station may determine a beam for PDSCH transmission to the terminal through the beam management operations described above.
  • the terminal receives downlink control information (DCI: Downlink Control Information) for downlink scheduling (ie, including scheduling information of the PDSCH) on the PDCCH from the base station (S702).
  • DCI Downlink Control Information
  • DCI format 1_0 or 1_1 may be used for downlink scheduling, and in particular, DCI format 1_1 includes the following information: DCI format identifier (Identifier for DCI formats), bandwidth part indicator (Bandwidth part indicator), frequency domain Resource allocation (Frequency domain resource assignment), time domain resource assignment (Time domain resource assignment), PRB bundling size indicator (PRB bundling size indicator), rate matching indicator (Rate matching indicator), ZP CSI-RS trigger (ZP CSI-RS) trigger), antenna port(s) (Antenna port(s)), transmission configuration indication (TCI), SRS request, DMRS (Demodulation Reference Signal) sequence initialization (DMRS sequence initialization)
  • the number of DMRS ports can be scheduled, and also SU (Single-user) / MU (Multi-user) transmission Scheduling is possible.
  • the TCI field consists of 3 bits, and the QCL for the DMRS is dynamically indicated by indicating a maximum of 8 TCI states according to the TCI field value.
  • the terminal receives downlink data from the base station on the PDSCH (S703).
  • the UE detects a PDCCH including DCI format 1_0 or 1_1, it decodes the PDSCH according to an indication by the corresponding DCI.
  • the UE when the UE receives a PDSCH scheduled by DCI format 1_1, the UE may set a DMRS configuration type by a higher layer parameter 'dmrs-Type', and the DMRS type is used to receive the PDSCH.
  • the terminal may set the maximum number of front-loaded DMRS symbols for the PDSCH by the upper layer parameter 'maxLength'.
  • DMRS configuration type 1 when a single codeword is scheduled for the terminal and an antenna port mapped with an index of ⁇ 2, 9, 10, 11 or 30 ⁇ is specified, or the terminal schedules two codewords , the UE assumes that all remaining orthogonal antenna ports are not associated with PDSCH transmission to another UE. Or, in the case of DMRS configuration type 2, if a single codeword is scheduled for the terminal and an antenna port mapped with an index of ⁇ 2, 10 or 23 ⁇ is specified, or if the terminal is scheduled with two codewords, the terminal It is assumed that the remaining orthogonal antenna ports are not associated with PDSCH transmission to another terminal.
  • the precoding granularity P' is a consecutive resource block in the frequency domain.
  • P' may correspond to one of ⁇ 2, 4, broadband ⁇ . If P' is determined to be wideband, the UE does not expect to be scheduled with non-contiguous PRBs, and the UE may assume that the same precoding is applied to the allocated resource.
  • P' is determined as any one of ⁇ 2, 4 ⁇ , a precoding resource block group (PRG) is divided into P' consecutive PRBs. The actual number of consecutive PRBs in each PRG may be one or more. The UE may assume that the same precoding is applied to consecutive downlink PRBs in the PRG.
  • PRG precoding resource block group
  • the UE In order for the UE to determine a modulation order, a target code rate, and a transport block size in the PDSCH, the UE first reads the 5-bit MCS field in the DCI, the modulation order and the target code determine the rate. Then, the redundancy version field in the DCI is read, and the redundancy version is determined. Then, the UE determines the transport block size by using the number of layers and the total number of allocated PRBs before rate matching.
  • a transport block may be composed of one or more code block groups (CBGs), and one CBG may be composed of one or more code blocks (CBs).
  • CBGs code block groups
  • CBs code blocks
  • ACK/NACK transmission and retransmission in CB/CBG units may also be possible.
  • the UE may receive information on CB/CBG from the base station through DCI (e.g. DCI format 0_1, DCI format 1_1, etc.).
  • the UE may receive information on a data transmission unit (e.g. TB / CB / CBG) from the base station.
  • the base station schedules uplink transmission such as frequency/time resources, transport layer, uplink precoder, MCS, and the like (S801).
  • the base station may determine the beam for the UE to transmit the PUSCH through the beam management operations described above.
  • the terminal receives the DCI for uplink scheduling (ie, including scheduling information of the PUSCH) from the base station on the PDCCH (S802).
  • DCI format 0_0 or 0_1 may be used for uplink scheduling, and in particular, DCI format 0_1 includes the following information: DCI format identifier (Identifier for DCI formats), UL/SUL (Supplementary uplink) indicator (UL/ SUL indicator), bandwidth part indicator (Bandwidth part indicator), frequency domain resource assignment (Frequency domain resource assignment), time domain resource assignment (Time domain resource assignment), frequency hopping flag (Frequency hopping flag), modulation and coding scheme (MCS) : Modulation and coding scheme), SRS resource indicator (SRI: SRS resource indicator), precoding information and number of layers (Precoding information and number of layers), antenna port(s) (Antenna port(s)), SRS request (SRS) request), DMRS sequence initialization, UL-SCH (Uplink Shared Channel) indicator (UL-SCH indicator)
  • SRS resources configured in the SRS resource set associated with the higher layer parameter 'usage' may be indicated by the SRS resource indicator field.
  • 'spatialRelationInfo' may be set for each SRS resource, and the value may be one of ⁇ CRI, SSB, SRI ⁇ .
  • the terminal transmits uplink data to the base station on the PUSCH (S803).
  • the UE detects a PDCCH including DCI format 0_0 or 0_1, it transmits a corresponding PUSCH according to an indication by the corresponding DCI.
  • PUSCH transmission two transmission schemes are supported: codebook-based transmission and non-codebook-based transmission.
  • codebook-based transmission when the upper layer parameter 'txConfig' is set to 'codebook', the terminal is set to codebook-based transmission. On the other hand, when the upper layer parameter 'txConfig' is set to 'nonCodebook', the terminal is configured for non-codebook based transmission. If the upper layer parameter 'txConfig' is not set, the UE does not expect to be scheduled by DCI format 0_1. If the PUSCH is scheduled by DCI format 0_0, PUSCH transmission is based on a single antenna port. In the case of codebook-based transmission, the PUSCH may be scheduled in DCI format 0_0, DCI format 0_1, or semi-statically.
  • the UE transmits the PUSCH based on SRI, TPMI (Transmit Precoding Matrix Indicator) and transmission rank from DCI, as given by the SRS resource indicator field and the Precoding information and number of layers field.
  • SRI SRI
  • TPMI Transmit Precoding Matrix Indicator
  • the TPMI is used to indicate a precoder to be applied across the antenna port, and corresponds to the SRS resource selected by the SRI when multiple SRS resources are configured.
  • the TPMI is used to indicate a precoder to be applied across an antenna port, and corresponds to the single SRS resource.
  • a transmission precoder is selected from the uplink codebook having the same number of antenna ports as the upper layer parameter 'nrofSRS-Ports'.
  • the terminal is configured with at least one SRS resource.
  • the SRI indicated in slot n is associated with the most recent transmission of the SRS resource identified by the SRI, where the SRS resource precedes the PDCCH carrying the SRI (ie, slot n).
  • the PUSCH may be scheduled in DCI format 0_0, DCI format 0_1, or semi-statically.
  • the UE may determine the PUSCH precoder and transmission rank based on the wideband SRI, where the SRI is given by the SRS resource indicator in the DCI or by the higher layer parameter 'srs-ResourceIndicator' is given.
  • the UE uses one or multiple SRS resources for SRS transmission, where the number of SRS resources may be configured for simultaneous transmission within the same RB based on UE capabilities. Only one SRS port is configured for each SRS resource. Only one SRS resource may be set as the upper layer parameter 'usage' set to 'nonCodebook'.
  • the maximum number of SRS resources that can be configured for non-codebook-based uplink transmission is 4.
  • the SRI indicated in slot n is associated with the most recent transmission of the SRS resource identified by the SRI, where the SRS transmission precedes the PDCCH carrying the SRI (ie, slot n).
  • the BM procedure is a set of base station (eg gNB, TRP, etc.) and/or terminal (eg UE) beams that can be used for downlink (DL) and uplink (uplink, UL) transmission/reception.
  • base station eg gNB, TRP, etc.
  • terminal eg UE
  • L1 layer 1
  • L2 layer 2
  • - Beam measurement an operation in which a base station or a UE measures characteristics of a received beamforming signal.
  • Beam determination an operation of the base station or UE to select its own transmit beam (Tx beam) / receive beam (Rx beam).
  • Beam sweeping an operation of covering a spatial area using a transmit and/or receive beam for a predetermined time interval in a predetermined manner.
  • Beam report an operation in which the UE reports information of a beam-formed signal based on beam measurement.
  • the BM procedure can be divided into (1) a DL BM procedure using a synchronization signal (SS)/physical broadcast channel (PBCH) block or CSI-RS, and (2) a UL BM procedure using a sounding reference signal (SRS).
  • each BM procedure may include Tx beam sweeping for determining a Tx beam and Rx beam sweeping for determining an Rx beam.
  • DL BM Procedure Downlink Beam Management Procedure
  • the downlink beam management procedure includes (1) the base station transmitting a beamforming DL RS (eg, CSI-RS or SS block (SSB)) and (2) the UE transmitting a beam report. may include steps.
  • a beamforming DL RS eg, CSI-RS or SS block (SSB)
  • SSB SS block
  • the beam reporting may include a preferred DL RS ID (identifier) (s) and L1-RSRP corresponding thereto.
  • DL RS ID may be an SSB resource indicator (SSBRI) or a CSI-RS resource indicator (CRI).
  • SSBRI SSB resource indicator
  • CRI CSI-RS resource indicator
  • the SSB beam and the CSI-RS beam may be used for beam measurement.
  • the measurement metric is L1-RSRP for each resource/block.
  • SSB may be used for coarse beam measurement, and CSI-RS may be used for fine beam measurement.
  • SSB can be used for both Tx beam sweeping and Rx beam sweeping. Rx beam sweeping using SSB may be performed while the UE changes the Rx beam for the same SSBRI across multiple SSB bursts.
  • one SS burst includes one or more SSBs
  • one SS burst set includes one or more SSB bursts.
  • a configuration for a beam report using SSB is performed during CSI/beam configuration in an RRC connected state (or RRC connected mode).
  • the terminal receives from the base station a CSI-ResourceConfig IE including a CSI-SSB-ResourceSetList including SSB resources used for BM.
  • the BM configuration using the SSB is not defined separately, and the SSB is set as a CSI-RS resource.
  • the csi-SSB-ResourceSetList parameter indicates a list of SSB resources used for beam management and reporting in one resource set.
  • the SSB resource set may be set to ⁇ SSBx1, SSBx2, SSBx3, SSBx4,.. ⁇ .
  • the SSB index may be defined from 0 to 63.
  • the terminal receives the SSB resource from the base station based on the CSI-SSB-ResourceSetList.
  • the terminal (beam) reports the best SSBRI and L1-RSRP corresponding thereto to the base station.
  • the UE reports the best SSBRI and the corresponding L1-RSRP to the base station.
  • the terminal determines that the CSI-RS and the SSB are 'QCL-TypeD' ' can be assumed to be quasi co-located in terms of
  • the QCL TypeD may mean that it is QCL between antenna ports from the viewpoint of the spatial Rx parameter.
  • the same reception beam may be applied.
  • the UE does not expect the CSI-RS to be configured in the RE overlapping the RE of the SSB.
  • At least one CSI-RS resource in the NZP-CSI-RS-ResourceSet is the same downlink spatial domain transmission filter It can be assumed that the transmission is transmitted by .
  • At least one CSI-RS resource in the NZP-CSI-RS-ResourceSet is transmitted through the same Tx beam.
  • at least one CSI-RS resource in the NZP-CSI-RS-ResourceSet may be transmitted through another OFDM symbol or may be transmitted in another frequency domain (ie, through FDM).
  • the case where at least one CSI-RS resource is the target of FDM is when the terminal is a multi-panel terminal.
  • repetition when repetition is set to "ON", it is related to the Rx beam sweeping procedure of the UE.
  • the UE does not expect to receive different periodicities in periodicityAndOffset from all CSI-RS resources in the NZP-CSI-RS-ResourceSet.
  • the UE does not assume that at least one CSI-RS resource in the NZP-CSI-RS-ResourceSet is transmitted with the same downlink spatial domain transmission filter.
  • At least one CSI-RS resource in the NZP-CSI-RS-ResourceSet is transmitted through another TX bam.
  • the case where repetition is set to "OFF" is related to the transmission beam sweeping procedure of the base station.
  • parameter repetition may be set only for the CSI-RS resource set associated with the CSI-ReportConfig with L1 RSRP or "No report or None".
  • CSI-ResourceConfig When the UE receives a CSI-ResourceConfig with reportQuantity set to “cri-RSRP” or “none”, the UE may be configured with the same number of ports (1-port or 2-port), and NZP-CSI-RS- Defines the "nrofPorts" parameter for all CSI-RS resources in the ResourceSet.
  • CSI-RS if parameter repetition is configured in a specific CSI-RS resource set and TRS_info is not configured, CSI-RS is used for beam management.
  • the CSI-RS is used as a Tracking Reference Signal (TRS).
  • CSI-RS is used for CSI acquisition.
  • FIG. 9 is a diagram illustrating an example of a DL BM procedure using a CSI-RS.
  • FIG. 9(a) shows an Rx beam determination (or refinement) procedure of a UE
  • FIG. 9(b) shows a transmission beam determination procedure of a base station.
  • FIG. 9(a) shows a case where parameter repetition is set to "on”
  • FIG. 9(b) shows a case where it is set to "OFF”.
  • FIG. 10 is a diagram for describing a procedure for determining a reception beam in a downlink beam management procedure using a CSI-RS.
  • the UE receives the NZP CSI-RS resource set IE including the upper layer parameter repetition from the base station through RRC signaling (S1010).
  • Parameter repeat is set to "ON".
  • the UE repeatedly receives a CSI resource from another OFDM symbol through the same Tx beam (or DL spatial domain transmission filter) in a CSI-RS resource set in which repetition is set to “ON” (S1020).
  • the terminal determines its own reception beam (S1030).
  • the UE may omit the CSI report, or may transmit the CSI report including the CRI/L1-RSRP to the base station (S1040).
  • the reportQuantity of the CSI report Config may be configured as "No report (or None)" or "CRI and L1-RSRP".
  • the UE may omit CSI reporting or report ID information (CRI) of a beam pair related priority beam and its quality value (L1-RSRP).
  • CRI report ID information
  • L1-RSRP quality value
  • a transmission beam determination process of the base station will be described below with reference to FIGS. 9(b) and 11 .
  • 11 is a flowchart illustrating an example of a transmission beam determination procedure of a base station.
  • the UE receives the NZP CSI-RS resource set IE including the upper layer parameter repetition from the base station through RRC signaling (S1110).
  • the parameter repetition is set to “OFF” and is related to the Tx beam sweeping procedure of the base station.
  • the UE receives a CSI resource from a CSI-RS resource set with repetition set to “OFF” through another Tx beam (DL spatial domain transmission filter) (S1120).
  • Tx beam DL spatial domain transmission filter
  • the terminal selects (or determines) an optimal beam (S1130), and reports ID and quality information (eg, L1-RSRP) of the selected beam to the base station (S1140).
  • ID and quality information eg, L1-RSRP
  • the reportQuantity of the CSI report Config may be configured as "CRI + L1-RSRP". That is, when CSI-RS is transmitted for BM, the UE reports CSI and L1-RSRP corresponding thereto to the base station.
  • the terminal may receive RRC configuration for a list of at least M candidates for the purpose of a Quasi Co-location (QCL) indication, Transmission Configuration Indication (TCI) states.
  • M may be 64.
  • Each TCI state may be configured as one RS set.
  • At least each ID of DL RS for spatial QCL purpose (QCL Type D) in the RS set may refer to one of DL RS types such as SSB, P-CSI RS, SP-CSI RS, and A-CSI RS. .
  • initialization/update of IDs of DL RS(s) in the RS set used for spatial QCL purposes may be performed through at least explicit signaling.
  • the TCI-State IE associates one or two DL reference signals (RS) with corresponding quasi co-location (QCL) types.
  • RS DL reference signals
  • QCL quasi co-location
  • the bwp-Id parameter of the QCL-Info parameter indicates the DL BWP where the RS is located, the cell parameter indicates the carrier where the RS is located, and the referencesignal parameter is the source of quasi co-location for the target antenna port(s). It indicates a reference antenna port(s) or a reference signal including it.
  • the target antenna port(s) may be CSI-RS, PDCCH DMRS, or PDSCH DMRS.
  • the corresponding TCI state ID may be indicated in the NZP CSI-RS resource configuration information.
  • the TCI state ID may be indicated in each CORESET setting.
  • the TCI state ID may be indicated through DCI to indicate QCL reference information for the PDSCH DMRS antenna port(s).
  • An antenna port is defined such that a channel on which a symbol on an antenna port is carried can be inferred from a channel on which another symbol on the same antenna port is carried.
  • the two antenna ports are QC/QCL (quasi co-located or quasi co-location) ) can be said to be in a relationship.
  • the channel characteristics include delay spread, Doppler spread, frequency/Doppler shift, average received power, and received timing/average delay. delay) and one or more of Spatial RX parameters.
  • the Spatial Rx parameter means a spatial (reception) channel characteristic parameter such as angle of arrival.
  • a list of up to M TCI-State configurations in the higher layer parameter PDSCH-Config may be set.
  • the M depends on UE capability.
  • Each TCI-State includes parameters for establishing a quasi co-location relationship between one or two DL reference signals and the DM-RS port of the PDSCH.
  • the quasi co-location relationship is set with the higher layer parameter qcl-Type1 for the first DL RS and qcl-Type2 (if set) for the second DL RS.
  • the QCL type is not the same regardless of whether the reference is the same DL RS or different DL RSs.
  • the quasi co-location type corresponding to each DL RS is given by the higher layer parameter qcl-Type of QCL-Info, and can take one of the following values:
  • the corresponding NZP CSI-RS antenna ports are indicated/configured to be QCL with a specific TRS from a QCL-Type A perspective and a specific SSB from a QCL-Type D perspective. have.
  • the UE receiving this instruction/configuration receives the corresponding NZP CSI-RS using the Doppler and delay values measured in QCL-TypeA TRS, and applies the reception beam used for QCL-TypeD SSB reception to the corresponding NZP CSI-RS reception. can do.
  • the UE may receive an activation command by MAC CE signaling used to map up to 8 TCI states to the codepoint of the DCI field 'Transmission Configuration Indication'.
  • beam reciprocity (or beam correspondence) between Tx beams and Rx beams may or may not be established according to UE implementation. If the reciprocity between the Tx beam and the Rx beam is established in both the base station and the terminal, the UL beam pair may be aligned through the DL beam pair. However, when the reciprocity between the Tx beam and the Rx beam is not established in either of the base station and the terminal, a UL beam pair determination process is required separately from the DL beam pair determination.
  • the base station can use the UL BM procedure for determining the DL Tx beam without the terminal requesting a report of a preferred beam.
  • UL BM may be performed through beamformed UL SRS transmission, and whether the UL BM of the SRS resource set is applied is set by (higher layer parameter) usage. If usage is set to 'BeamManagement (BM)', only one SRS resource may be transmitted to each of a plurality of SRS resource sets at a given time instant.
  • BM BeamManagement
  • the terminal may receive one or more Sounding Reference Symbol (SRS) resource sets configured by the SRS-ResourceSet (through higher layer signaling, RRC signaling, etc.).
  • SRS Sounding Reference Symbol
  • the UE K 1 SRS resources (higher later parameter SRS-resource) may be configured.
  • K is a natural number, and the maximum value of K is indicated by SRS_capability.
  • the UL BM procedure can be divided into Tx beam sweeping of the UE and Rx beam sweeping of the base station.
  • the terminal receives RRC signaling (eg, SRS-Config IE) including a usage parameter set to 'beam management' (higher layer parameter) from the base station.
  • RRC signaling eg, SRS-Config IE
  • the SRS-Config IE is used for SRS transmission configuration.
  • the SRS-Config IE includes a list of SRS-Resources and a list of SRS-ResourceSets.
  • Each SRS resource set means a set of SRS-resources.
  • the network may trigger the transmission of the SRS resource set using the configured aperiodicSRS-ResourceTrigger (L1 DCI).
  • usage indicates a higher layer parameter indicating whether the SRS resource set is used for beam management, codebook-based or non-codebook-based transmission.
  • the usage parameter corresponds to the L1 parameter 'SRS-SetUse'.
  • 'spatialRelationInfo' is a parameter indicating the setting of spatial relation between the reference RS and the target SRS.
  • the reference RS may be an SSB, CSI-RS, or SRS corresponding to the L1 parameter 'SRS-SpatialRelationInfo'.
  • the usage is set for each SRS resource set.
  • the UE determines the Tx beam for the SRS resource to be transmitted based on the SRS-SpatialRelation Info included in the SRS-Config IE.
  • SRS-SpatialRelation Info is set for each SRS resource, and indicates whether to apply the same beam as the beam used in SSB, CSI-RS, or SRS for each SRS resource.
  • SRS-SpatialRelationInfo may or may not be set in each SRS resource.
  • SRS-SpatialRelationInfo is configured in the SRS resource, the same beam as the beam used in SSB, CSI-RS or SRS is applied and transmitted. However, if the SRS-SpatialRelationInfo is not configured in the SRS resource, the UE arbitrarily determines a Tx beam and transmits the SRS through the determined Tx beam.
  • the UE applies the same spatial domain Rx filter (or generated from the filter) as the spatial domain Rx filter used for receiving the SSB/PBCH and applies the corresponding SRS resource transmits; or
  • the UE transmits the SRS resource by applying the same spatial domain transmission filter used for reception of periodic CSI-RS or SP CSI-RS;
  • beam determination and transmission operation may be applied similarly to the above.
  • the terminal may or may not receive feedback on SRS from the base station as in the following three cases.
  • the UE transmits the SRS through the beam indicated by the base station.
  • the base station corresponds to the purpose of selecting the Rx beam.
  • Spatial_Relation_Info may not be set for all SRS resources in the SRS resource set.
  • the UE can freely transmit while changing the SRS beam. That is, in this case, the terminal corresponds to the use of sweeping the Tx beam.
  • Spatial_Relation_Info may be set only for some SRS resources in the SRS resource set.
  • the SRS may be transmitted with the indicated beam for the configured SRS resource, and the UE may arbitrarily apply the Tx beam to the SRS resource for which Spatial_Relation_Info is not configured.
  • CSI-RS channel state information-reference signal
  • time/frequency tracking time/frequency tracking
  • CSI calculation computation
  • CSI computation is related to CSI acquisition (acquisition)
  • L1-RSRP computation is related to beam management (BM).
  • CSI channel state information refers to information that can indicate the quality of a radio channel (or link) formed between a terminal and an antenna port.
  • FIG. 12 is a flowchart illustrating an example of a CSI-related procedure.
  • a UE in order to perform one of the uses of CSI-RS, a UE (eg, user equipment, UE) transmits configuration information related to CSI to a base station (eg, radio resource control) through RRC signaling: general Node B, gNB) (S1210).
  • a base station eg, radio resource control
  • RRC signaling general Node B, gNB
  • the CSI-related configuration information includes CSI-IM (interference management) resource-related information, CSI measurement configuration-related information, CSI resource configuration-related information, CSI-RS resource-related information. Alternatively, it may include at least one of CSI report configuration related information.
  • CSI-IM interference management
  • the CSI-IM resource-related information may include CSI-IM resource information, CSI-IM resource set information, and the like.
  • the CSI-IM resource set is identified by a CSI-IM resource set ID (identifier), and one resource set includes at least one CSI-IM resource.
  • Each CSI-IM resource is identified by a CSI-IM resource ID.
  • CSI resource configuration related information may be expressed as a CSI-ResourceConfig IE.
  • CSI resource configuration related information defines a group including at least one of a non zero power (NZP) CSI-RS resource set, a CSI-IM resource set, or a CSI-SSB resource set. That is, the CSI resource configuration related information includes a CSI-RS resource set list, and the CSI-RS resource set list is at least one of a NZP CSI-RS resource set list, a CSI-IM resource set list, or a CSI-SSB resource set list. may contain one.
  • the CSI-RS resource set is identified by the CSI-RS resource set ID, and one resource set includes at least one CSI-RS resource. Each CSI-RS resource is identified by a CSI-RS resource ID.
  • Table 5 shows an example of the NZP CSI-RS resource set IE.
  • parameters indicating the use of CSI-RS for each NZP CSI-RS resource set eg, BM-related 'repetition' parameter, tracking-related 'trs-Info' parameter
  • BM-related 'repetition' parameter e.g., BM-related 'repetition' parameter, tracking-related 'trs-Info' parameter
  • the repetition parameter corresponding to the higher layer parameter corresponds to 'CSI-RS-ResourceRep' of the L1 parameter.
  • CSI reporting configuration (report configuration) related information includes a report configuration type (reportConfigType) parameter indicating a time domain behavior and a report Quantity (reportQuantity) parameter indicating a CSI related quantity for reporting.
  • the time domain behavior may be periodic, aperiodic or semi-persistent.
  • CSI report configuration related information may be expressed as a CSI-ReportConfig IE, and Table 6 below shows an example of the CSI-ReportConfig IE.
  • the UE measures CSI based on the configuration information related to the CSI (S1220).
  • the CSI measurement may include (1) a process of receiving a CSI-RS by the UE (S1221), and (2) a process of calculating CSI through the received CSI-RS (S1222). will be described later.
  • the RE (resource element) mapping of the CSI-RS resource in the time and frequency domains is set by the higher layer parameter CSI-RS-ResourceMapping.
  • the density (density, D) represents the density of the CSI-RS resource measured in RE/port/PRB (physical resource block), and nrofPorts represents the number of antenna ports.
  • the terminal reports the measured CSI to the base station (S1230).
  • the terminal may omit the report.
  • the terminal may report to the base station.
  • the report of the terminal may be omitted.
  • the NR system supports more flexible and dynamic CSI measurement and reporting.
  • the CSI measurement may include a procedure of receiving a CSI-RS and acquiring CSI by computing the received CSI-RS.
  • CM periodic/semi-persistent/periodic channel measurement
  • IM interference measurement
  • CSI-IM configuration a 4-port NZP CSI-RS RE pattern is used.
  • CSI-IM based IMR of NR has a design similar to CSI-IM of LTE, and is configured independently of ZP CSI-RS resources for PDSCH rate matching. And, in the NZP CSI-RS-based IMR, each port emulates an interference layer with a (preferred channel and) precoded NZP CSI-RS. This is for intra-cell interference measurement for a multi-user case, and mainly targets MU interference.
  • the base station transmits the precoded NZP CSI-RS to the terminal on each port of the configured NZP CSI-RS based IMR.
  • the terminal assumes a channel / interference layer for each port in the resource set and measures the interference.
  • the base station or network For the channel, if there is no PMI and RI feedback, a plurality of resources are set in the set, and the base station or network indicates a subset of NZP CSI-RS resources through DCI for channel / interference measurement.
  • Each CSI resource setting 'CSI-ResourceConfig' is (given by the higher layer parameter csi-RS-ResourceSetList) Includes configuration for CSI resource set.
  • CSI resource setting corresponds to CSI-RS-resourcesetlist.
  • S represents the number of configured CSI-RS resource sets.
  • the configuration for the CSI resource set includes an SS/PBCH block (SSB) resource used for each CSI resource set and L1-RSRP computation including CSI-RS resources (consisting of NZP CSI-RS or CSI-IM). .
  • SSB SS/PBCH block
  • Each CSI resource setting is located in the DL BWP (bandwidth part) identified by the higher layer parameter bwp-id. And, all CSI resource settings linked to the CSI reporting setting have the same DL BWP.
  • the time domain behavior of the CSI-RS resource within the CSI resource setting included in the CSI-ResourceConfig IE is indicated by a higher layer parameter resourceType, and may be set to aperiodic, periodic or semi-persistent.
  • resourceType For Periodic and semi-persistent CSI resource setting, the number of configured CSI-RS resource sets (S) is limited to '1'.
  • S For Periodic and semi-persistent CSI resource settings, the set periodicity and slot offset are given in the numerology of the associated DL BWP, as given by bwp-id.
  • the same time domain behavior is configured for the CSI-ResourceConfig.
  • the same time domain behavior is configured for the CSI-ResourceConfig.
  • CM channel measurement
  • IM interference measurement
  • a channel measurement resource may be an NZP CSI-RS for CSI acquisition
  • an interference measurement resource may be a CSI-IM and an NZP CSI-RS for IM.
  • CSI-IM (or ZP CSI-RS for IM) is mainly used for inter-cell interference measurement.
  • the NZP CSI-RS for IM is mainly used for intra-cell interference measurement from multi-users.
  • the UE may assume that CSI-RS resource(s) for channel measurement and CSI-IM / NZP CSI-RS resource(s) for interference measurement configured for one CSI reporting are 'QCL-TypeD' for each resource. .
  • resource setting can mean a resource set list.
  • each trigger state set using the higher layer parameter CSI-AperiodicTriggerState is one or more CSI-ReportConfig and each CSI-ReportConfig linked to a periodic, semi-persistent or aperiodic resource setting.
  • One reporting setting can be connected with up to three resource settings.
  • the resource setting (given by the higher layer parameter resourcesForChannelMeasurement) is for channel measurement for L1-RSRP computation.
  • the first resource setting (given by the higher layer parameter resourcesForChannelMeasurement) is for channel measurement, and the second resource (given by csi-IM-ResourcesForInterference or nzp-CSI-RS -ResourcesForInterference)
  • the setting is for interference measurement performed on CSI-IM or NZP CSI-RS.
  • the first resource setting (given by resourcesForChannelMeasurement) is for channel measurement
  • the second resource setting (given by csi-IM-ResourcesForInterference) is for CSI-IM based interference measurement
  • the third resource setting (given by nzp-CSI-RS-ResourcesForInterference) is for NZP CSI-RS based interference measurement.
  • each CSI-ReportConfig is linked to a periodic or semi-persistent resource setting.
  • the resource setting is for channel measurement for L1-RSRP computation.
  • the first resource setting (given by resourcesForChannelMeasurement) is for channel measurement
  • the second resource setting (given by the higher layer parameter csi-IM-ResourcesForInterference) is performed on CSI-IM It is used for interference measurement.
  • each CSI-RS resource for channel measurement is associated with CSI-IM resource and resource by the order of CSI-RS resources and CSI-IM resources in the corresponding resource set. .
  • the number of CSI-RS resources for channel measurement is the same as the number of CSI-IM resources.
  • the UE does not expect to be set to one or more NZP CSI-RS resources in the resource set associated with the resource setting for channel measurement.
  • the UE in which the higher layer parameter nzp-CSI-RS-ResourcesForInterference is set does not expect that 18 or more NZP CSI-RS ports will be set in the NZP CSI-RS resource set.
  • the UE assumes the following.
  • Each NZP CSI-RS port configured for interference measurement corresponds to an interfering transport layer.
  • NZP CSI-RS resource for channel measurement NZP CSI-RS resource for measuring interference
  • CSI-IM resource for measuring interference CSI-IM resource for measuring interference
  • time and frequency resources available to the UE are controlled by the base station.
  • CSI channel state information
  • CQI channel quality indicator
  • precoding matrix indicator precoding matrix indicator
  • PMI CSI-RS resource indicator
  • SSBRI SS / PBCH block resource indicator
  • layer It may include at least one of indicator (LI), rank indicator (RI) or L1-RSRP.
  • the terminal For CQI, PMI, CRI, SSBRI, LI, RI, L1-RSRP, the terminal is CSI-ReportConfig reporting setting, CSI-ResourceConfig resource setting and a list of one or two trigger states (provided by aperiodicTriggerStateList and semiPersistentOnPUSCH-TriggerStateList) is set by a higher layer.
  • each trigger state includes a channel and optionally an associated CSI-ReportConfigs list indicating resource set IDs for interference.
  • semiPersistentOnPUSCH-TriggerStateList each trigger state includes one associated CSI-ReportConfig.
  • time domain behavior of CSI reporting supports periodic, semi-persistent, and aperiodic.
  • Periodic CSI reporting period (periodicity) and slot offset (slot offset) may be set in RRC, refer to the CSI-ReportConfig IE.
  • SP sin-periodic CSI reporting is performed on short PUCCH, long PUCCH, or PUSCH.
  • SP CSI on PUSCH periodicity of SP CSI reporting is set to RRC, but slot offset is not set to RRC, and SP CSI reporting is activated/deactivated by DCI (format 0_1).
  • DCI format 0_1
  • SP-CSI C-RNTI SP-CSI C-RNTI
  • the initial CSI reporting timing follows the PUSCH time domain allocation value indicated by DCI, and the subsequent CSI reporting timing follows the cycle set by the RRC.
  • DCI format 0_1 includes a CSI request field, and can activate/deactivation a specific configured SP-CSI trigger state.
  • SP CSI reporting has the same or similar activation/deactivation as the mechanism with data transmission on the SPS PUSCH.
  • aperiodic CSI reporting is performed on PUSCH and is triggered by DCI.
  • information related to the trigger of aperiodic CSI reporting may be delivered/indicated/configured through the MAC-CE.
  • AP CSI-RS timing is set by RRC, and timing for AP CSI reporting is dynamically controlled by DCI.
  • NR For NR, a method of dividing and reporting CSI in multiple reporting instances applied to PUCCH-based CSI reporting in LTE (eg, transmitted in the order of RI, WB PMI/CQI, SB PMI/CQI) is not applied. Instead, NR restricts the setting of a specific CSI report in short/long PUCCH, and a CSI omission rule is defined. And, in relation to AP CSI reporting timing, PUSCH symbol/slot location is dynamically indicated by DCI. And, candidate slot offsets are set by RRC. For CSI reporting, slot offset (Y) is set for each reporting setting. For UL-SCH, slot offset K2 is configured separately.
  • DL and UL transmission/reception operations may be applied/used in combination with the method and/or embodiments proposed in the present specification, or proposed in this specification. It can be supplemented to clarify the technical characteristics of the methods.
  • '/' may mean including (and) all of the content separated by / or including only a part of the separated content (or).
  • 5G new radio (NR) transmission technology enables various advantages such as increase in data transmission rate, reduction in communication delay, utilization of a very large number of communication devices, and enhancement of spectrum efficiency.
  • a millimeter wave band with a wide available bandwidth is used for ultra-low-latency wideband data transmission required in the NR communication system, and it is necessary to perform high-gain precision beamforming using a large-scale antenna array to compensate for the path loss that occurs.
  • Massive multiple-input multiple-output (massive MIMO) transmission technology combined with the utilization of high-frequency bands is one of the core technologies of the 5G NR system, and requires very precise beam forming and management methods.
  • FR1 provides coverage of a wide area including a mobile receiver, and FR2, such as millimeter wave, performs inter-infrastructure communication or high-speed data transmission to a fixed-position receiver.
  • FR2 such as millimeter wave
  • DL deep learning
  • RL reinforcement learning
  • AI artificial intelligence
  • FIG. 13 shows an example of basic operations of a user terminal (UE) and a 5G network in a 5G communication system.
  • UE user terminal
  • the UE transmits specific information to the 5G network (S1). Then, the 5G network performs 5G processing on the specific information (S2).
  • 5G processing may include AI processing. Then, the 5G network transmits a response including the AI processing result to the UE (S3).
  • FIG. 14 is a block diagram of an AI device according to an embodiment of the present invention.
  • the AI device 20 may include an electronic device including an AI module capable of performing AI processing, or a server including an AI module.
  • the AI apparatus 20 may be a client device that directly uses the AI processing result or a device in a cloud environment that provides the AI processing result to other devices.
  • the AI device 20 is a computing device capable of learning a neural network, and may be implemented in various electronic devices such as a server, a desktop PC, a notebook PC, and a tablet PC.
  • the AI device 20 may include an AI processor 21 , a memory 25 and/or a communication unit 27 .
  • the AI processor 21 may learn the neural network using a program stored in the memory 25 .
  • the AI processor 21 may include a plurality of network nodes having weights that simulate neurons of a human neural network.
  • the plurality of network modes may transmit and receive data according to a connection relationship, respectively, so as to simulate a synaptic activity of a neuron in which a neuron sends and receives a signal through a synapse.
  • the neural network may include a deep learning model developed from a neural network model. In a deep learning model, a plurality of network nodes may exchange data according to a convolutional connection relationship while being located in different layers.
  • neural network models include deep neural networks (DNN), convolutional deep neural networks (CNN), Recurrent Boltzmann Machine (RNN), Restricted Boltzmann Machine (RBM), deep trust It includes various deep learning techniques such as neural networks (DBN, deep belief networks) and deep Q-networks.
  • DNN deep neural networks
  • CNN convolutional deep neural networks
  • RNN Recurrent Boltzmann Machine
  • RBM Restricted Boltzmann Machine
  • DNN deep trust It includes various deep learning techniques such as neural networks (DBN, deep belief networks) and deep Q-networks.
  • the processor performing the above-described function may be a general-purpose processor (eg, CPU), but may be an AI-only processor (eg, GPU) for artificial intelligence learning.
  • a general-purpose processor eg, CPU
  • an AI-only processor eg, GPU
  • the memory 25 may store various programs and data necessary for the operation of the AI device 20 .
  • the memory 25 may be implemented as a non-volatile memory, a volatile memory, a flash-memory, a hard disk drive (HDD), or a solid state drive (SDD).
  • the memory 25 is accessed by the AI processor 21 , and reading/writing/modification/deletion/update of data by the AI processor 21 may be performed.
  • the memory 25 may store a neural network model (eg, the deep learning model 26 ) generated through a learning algorithm for data classification/recognition according to an embodiment of the present invention.
  • the AI processor 21 may include a data learning unit 22 that learns a neural network for data classification/recognition.
  • the data learning unit 22 may learn a criterion regarding which training data to use to determine data classification/recognition and how to classify and recognize data using the training data.
  • the data learning unit 22 may learn the deep learning model by acquiring learning data to be used for learning and applying the acquired learning data to the deep learning model.
  • the data learning unit 22 may be manufactured in the form of at least one hardware chip and mounted on the AI device 20 .
  • the data learning unit 22 may be manufactured in the form of a dedicated hardware chip for artificial intelligence (AI), or may be manufactured as a part of a general-purpose processor (CPU) or a graphics-only processor (GPU) to the AI device 20 . may be mounted.
  • the data learning unit 22 may be implemented as a software module.
  • the software module When implemented as a software module (or a program module including instructions), the software module may be stored in a computer-readable non-transitory computer readable medium.
  • the at least one software module may be provided by an operating system (OS) or may be provided by an application.
  • OS operating system
  • the data learning unit 22 may include a training data acquiring unit 23 and a model learning unit 24 .
  • the training data acquisition unit 23 may acquire training data required for a neural network model for classifying and recognizing data.
  • the model learning unit 24 may use the acquired training data to learn so that the neural network model has a criterion for determining how to classify predetermined data.
  • the model learning unit 24 may train the neural network model through supervised learning using at least a portion of the training data as a criterion for determination.
  • the model learning unit 24 may learn the neural network model through unsupervised learning for discovering a judgment criterion by self-learning using learning data without guidance.
  • the model learning unit 24 may train the neural network model through reinforcement learning using feedback on whether the result of the situation determination according to the learning is correct.
  • the model learning unit 24 may train the neural network model by using a learning algorithm including an error back-propagation method or a gradient decent method.
  • Supervised learning is performed using a series of learning data and corresponding labels (target output values), and a neural network model based on supervised learning can be a model that infers a function from training data. have.
  • Supervised learning receives a series of training data and a corresponding target output value, finds errors through learning that compares the actual output value with the target output value for the input data, and can revise the model based on the results.
  • Supervised learning can be further divided into regression, classification, detection, and semantic segmentation according to the shape of the result.
  • the function derived through supervised learning can again be used to predict new results.
  • the neural network model based on supervised learning can optimize the parameters of the neural network model through learning of numerous training data.
  • the model learning unit 24 may store the learned neural network model in a memory.
  • the model learning unit 24 may store the learned neural network model in the memory of the server connected to the AI device 20 through a wired or wireless network.
  • the data learning unit 22 further includes a training data preprocessing unit (not shown) and a training data selection unit (not shown) to improve the analysis result of the recognition model or to save resources or time required for generating the recognition model You may.
  • the learning data preprocessor may preprocess the acquired data so that the acquired data can be used for learning for situation determination.
  • the training data preprocessor may process the acquired data into a preset format so that the model learning unit 24 may use the acquired training data for image recognition learning.
  • the learning data selection unit may select data required for learning from among the learning data acquired by the learning data acquiring unit 23 or the training data preprocessed by the preprocessing unit.
  • the selected training data may be provided to the model learning unit 24 .
  • the data learning unit 22 may further include a model evaluation unit (not shown) in order to improve the analysis result of the neural network model.
  • the model evaluator may input evaluation data to the neural network model and, when an analysis result output from the evaluation data does not satisfy a predetermined criterion, may cause the model learning unit 22 to learn again.
  • the evaluation data may be predefined data for evaluating the recognition model.
  • the model evaluator may evaluate as not satisfying a predetermined criterion when, among the analysis results of the learned recognition model for the evaluation data, the number or ratio of evaluation data for which the analysis result is not accurate exceeds a preset threshold. have.
  • the communication unit 27 may transmit the AI processing result by the AI processor 21 to an external electronic device.
  • the external electronic device may include a Bluetooth device, an autonomous vehicle, a robot, a drone, an AR device, a mobile device, a home appliance, and the like.
  • the AI device 20 shown in FIG. 14 has been functionally divided into the AI processor 21, the memory 25, the communication unit 27, and the like, but the above-described components are integrated into one module and the AI module Note that it may also be called
  • a part or all of the configuration of the AI device 20 shown in FIG. 14 is included in a device such as a Bluetooth device, an autonomous vehicle, a robot, a drone, an AR device, a mobile device, a home appliance, etc. may be included in a device such as a Bluetooth device, an autonomous vehicle, a robot, a drone, an AR device, a mobile device, a home appliance, etc. may be included in a device such as a Bluetooth device, an autonomous vehicle, a robot, a drone, an AR device, a mobile device, a home appliance, etc.
  • a beam management method using artificial intelligence, neural network learning, and the like in a wireless communication system is proposed.
  • a "beam signature” and multiple beam signatures generated by extending a beamforming vector in a time dimension as well as a 3D space We define a “beambook” composed of
  • a useful reference signal transmission method and a beam management operation based thereon to utilize the beam signature we propose a useful reference signal transmission method and a beam management operation based thereon to utilize the beam signature.
  • the base station may refer to an object that transmits and receives data to and from the UE.
  • the base station may be a concept including one or more TPs (Transmission Points), one or more TRPs (Transmission and Reception Points), and the like.
  • the TP and/or TRP may include a panel of a base station, a transmission and reception unit, and the like.
  • the UE may refer to a generic term for an object having mobility that transmits/receives data to and from a base station.
  • a UE may be represented as a mobile device, vehicle, robot, drone, UAV, or the like.
  • it will be expressed as a receiver for convenience of description.
  • the use of these terms does not limit the technical scope of the present invention.
  • 15 shows an example of signaling between a base station and a receiver to which the method proposed in this specification can be applied. 15 is only an example for convenience of description, and does not limit the technical scope of the present invention.
  • the base station may determine the beam signature length L and the beambook size J (S1510).
  • a beam signature is a concept in which a positional dimension that changes with time of a receiver (eg, UE) is added to a code vector used for existing beamforming, and a plurality of positions where a receiver (eg, UE) can exist on one path It means a set of beamforming vectors for .
  • the beambook refers to a set of beam signatures for a plurality of paths. A detailed description of the beam signature and the beambook will be described later.
  • the base station may initialize the beambook for the J paths (S1520).
  • the J paths may correspond to the size of the beambook.
  • the J paths may be randomly selected by the base station or determined using a standard pattern.
  • the J routes may be determined based on route information previously reported to the base station by a receiver (eg, UE) (ie, a mobile device) (eg, route information determined by navigation).
  • a receiver eg, UE
  • a mobile device eg, route information determined by navigation.
  • a beambook for a path may be initialized.
  • the base station may transmit a reference signal to a receiver (eg, UE) (S1530).
  • a receiver eg, UE
  • the reference signal may be transmitted periodically, semi-continuously, or aperiodically.
  • the reference signal may be one of CSI-RS or SSB.
  • a receiver may measure CSI for beam reporting based on the reference signal (S1540).
  • the operation of measuring CSI for beam reporting may be performed with reference to the above-described beam management and CSI-related operations.
  • the receiver may transmit a beam report/CSI report to the base station (S1550).
  • a receiver eg, UE may feed back information on its preferred beam through the beam report.
  • the base station may learn the deep learning neural network by using the reported beam information and update the beam signature (S1560). Also, the beambook may be updated.
  • step S1550 may correspond to step S1 of FIG. 13
  • step S1560 may correspond to step S2 of FIG. 13
  • the base station may transmit a response including a learning result based on AI processing to the receiver. For example, updated beam signature information may be transmitted to the receiver, or a reference signal to which beamforming optimized based on the updated beam signature and beambook is applied may be transmitted to the receiver.
  • the base station may include all or part of the AI device 20 of FIG. 14 as a configuration, and may be provided to perform at least a part of AI processing together.
  • the base station may perform AI processing on the reported beam information to process/determine and generate a control signal.
  • the AI processor 21 of FIG. 14 may control and/or perform neural network learning of a base station, and may be designed to simulate a human brain structure on a computer.
  • the learning data selector may select only data of a threshold value or higher (or less than or equal to) among beam reports received by the base station as the learning data.
  • the directional vector from the base station to the target receiver may be expressed as a zenith angle and an azimuth angle using a spherical coordinate system in a three-dimensional space.
  • the direction vector also changes according to the moving position, so the position index It is possible to display zenith angle and azimuth angle information at L different positions using .
  • L means the number of possible positions of the receiver in one path, and the L value may be set by using a predefined value or by a base station (or a receiver).
  • a trajectory matrix indicating such location information for a total of J different paths that the target receiver can move can be expressed as Equation (3).
  • Equation 3 j is a path index, and a row vector is the zenith angle for the lth position of the jth path and azimuth includes
  • Equation 4 is the path matrix shown in Equation 3 represents a set of
  • the path matrix set T includes location information of the mobile receiver for J paths.
  • the path matrix set may be defined in advance or may be generated using received signal information collected from a base station.
  • the number of paths J is a parameter determined in consideration of beamforming performance and operational complexity, and the parameter J is a value set by the base station, a predetermined value, or a value preferred by the receiver as UE capability information to the base station. It may be reported and determined as one of the values recommended by the receiver based on the UE capability information.
  • a beamforming vector that generates a beam in the direction indicated by , the beam signature related to the j-th path may be defined as in Equation 5.
  • the beam signature related to the j-th path denotes a set of beamforming vectors for L positions of the j-th path.
  • the beamforming vector is and where i) may be regarded as the number of Tx antenna ports of the transmitter or ii) the number of one beamformed (or virtualized) ports.
  • Equation 6 represents a beambook that is a set of beam signatures of J paths.
  • a beam signature is a concept in which a positional dimension that changes with time is added to an existing code vector, and through this dimension increase, a beam generation and tracking pattern according to the path trajectory of a moving object can be defined.
  • Various types of paths may be generated according to the moving direction of the receiver, but representative path trajectories that occur frequently, such as an example of a vehicle turning left/straight/right at an intersection, are limited.
  • the number of paths J which is the size of the beambook
  • Beam signatures for performing beamforming while tracking a representative path can be defined and utilized. That is, each beam signature has an elemental technical characteristic capable of performing sophisticated beamforming for a mobile receiver in space and time.
  • Beam signature can be generated by an initial value by pre-definition. Also, beamforming performance is performed by performing a continuous learning function by using path information of the receiver (moving object)(s) collected from the base station, beam report, etc. can improve
  • RS reference signal
  • the reference signal includes i) a user-specific reference signal that generates and transmits a signal beam specialized for each user, ii) a UE group-specific reference signal that transmits a beam specialized for each user group, and iii) is common to all users in the cell It can be defined by dividing it into a universal reference signal applied as .
  • the beam signature element For user-specific reference signals, the beam signature element Among them, an element having the best reception quality at the current user's location may be selected and used as a reference signal (RS) beam.
  • the position index of the beam corresponding to the best quality among the elements of the j-th beam signature. If indicated as is a beam set used for a user reference signal.
  • the receiver After receiving the reference signal, the receiver may provide information necessary for path selection to the base station by determining and feeding back a preferred beam (with good performance).
  • the receiver moves to the next position of the beam signature element may be included as an additional reference signal, and in this case, 2J reference signals may be transmitted to the receiver.
  • the number of reference signals increases to 3J, 4J, ..., and the like.
  • the reference signals grouped into J may be mapped to different virtual cell IDs for each group.
  • a reference signal In the case of multi-user transmission, a reference signal must also be transmitted for each user, and in order to reduce interference between reference signals, a transmission method using a precoding method such as zero-forcing (ZF) is possible.
  • ZF zero-forcing
  • a UE group-specific reference signal In the case of a UE group-specific reference signal, one or more UEs are grouped and a beam specialized for each group is transmitted. The same reference signal may be transmitted to UEs in the same group.
  • the reference signal for each path is based on the beamforming vector of the best quality for the UE group or the beamforming vector with the best quality for the representative UE of the group among the beamforming vectors constituting the beam signature. can be transmitted. For example, when a plurality of vehicles are operated in a group as in platooning technology, the base station transmits a UE group specific reference signal to a plurality of vehicles included in one group, or a representative (or master) in one group ) can be transferred to the vehicle.
  • a reference signal beam is arranged and transmitted so as to cover an area where a receiver is distributed within a cell. It is also possible to arrange the beams in a uniform form or to arrange the beams in a non-uniform form by applying a vector quantization (VQ) algorithm or the like according to receiver distribution probability.
  • VQ vector quantization
  • the receiver determines a preferred beam after receiving the reference signal and provides the path selection information to the base station by feedback.
  • the receiver may measure CSI for beam report based on the received reference signal (S1540), determine a preferred beam based on the measurement result, and feed it back to the base station through beam report (S1550). Through the beam report, information necessary for the base station to select a path may be provided.
  • the receiver may measure at least one of CQI, PMI, CRI, RI, LI, L1-RSRP, and L1-SINR based on the received reference signal.
  • the receiver may report at least one of CQI, PMI, CRI, RI, LI, L1-RSRP, or L1-SINR to the base station.
  • the beam report may be transmitted periodically, semi-continuously or aperiodically.
  • the base station may perform neural network learning based on the received beam report and update the beam signature/beambook using the learning result (S1560).
  • the beambook for the moving path of the receiver and beam signatures for positions in the corresponding path may be updated.
  • beamforming optimized based on the updated beam signature and beambook may be applied to communicate with the receiver.
  • 16 is a diagram illustrating an artificial neural network according to an embodiment of the present specification.
  • FIG. 16 (a) is a diagram showing the general structure of an artificial neural network
  • FIG. 16 (b) is a diagram illustrating an autoencoder that performs decoding after encoding and a reconstruction step in an artificial neural network. to be.
  • An artificial neural network generally consists of an input layer, a hidden lyaer, and an output layer, and neurons included in each layer can be connected through weights. Through the linear combination of weights and neuron values and non-linear activation functions, artificial neural networks can have a form that can approximate complex functions. The purpose of artificial neural network learning is to find a weight that minimizes the difference between the calculated output and the actual output in the output layer.
  • the deep neural network may refer to an artificial neural network composed of several hidden layers between an input layer and an output layer. Complex nonlinear relationships can be modeled by using many hidden layers, and a neural network structure that can be highly abstracted by increasing the number of layers is called deep learning. Deep learning learns a very large amount of data, and when new data is input, it can select the most probabilistic answer based on the learning result. Therefore, deep learning can operate adaptively according to input, and can automatically find characteristic factors in the process of learning a model based on data.
  • Deep learning-based models include deep neural networks (DNN), convolutional deep neural networks (CNN), Recurrent Boltzmann Machine (RNN), Restricted Boltzmann Machine (RBM), and deep It may include, but is not limited to, various deep learning techniques such as deep belief networks (DBN) and deep Q-networks. In addition, it may include a machine learning method other than deep learning. For example, a machine learning-based model may be applied to extract features of input data by applying a deep learning-based model, and to classify or recognize input data based on the extracted features. The machine learning-based model may include, but is not limited to, a support vector machine (SVM), an AdaBoost, and the like.
  • SVM support vector machine
  • AdaBoost AdaBoost
  • an artificial neural network may include an input layer, a hidden layer, an output layer, and weights.
  • FIG. 16A shows the structure of an artificial neural network in which the size of the input layer is 3, the size of the first and second hidden layers is 4, and the size of the output layer is 1.
  • the neurons included in the hidden layer may be connected by linear coupling between the neurons included in the input layer and individual weights included in the weight.
  • Neurons included in the output layer may be connected by linear coupling between neurons included in the hidden layer and individual weights included in the weight.
  • the artificial neural network can find a value that minimizes the difference between the output calculated in the output layer and the actual output.
  • the artificial neural network may have an artificial neural network structure in which the size of the input layer is 10 and the size of the output layer is 4, and the size of the hidden layer is not limited.
  • data determined as learning data based on data (eg, CSI, beam report) received from the receiver may be input to the input layer as input data.
  • data eg, CSI, beam report
  • at least one of CQI, PMI, CRI, RI, LI, L1-RSRP, or L1-SINR may be input to the input layer, and in the output layer, the expected movement path of the receiver and the expected position information in the corresponding path, respectively.
  • a corresponding output value may be output.
  • the CSI content input to the input layer is an example, and is not limited thereto.
  • the content (or parameter) representing the CSI measured based on the reference signal is the input of the input layer.
  • the reference signal eg, the average/rate of change of each of CQI, PMI, CRI, RI, LI, L1-RSRP, or L1-SINR, etc.
  • location information of the receiver eg, a location or movement path from receiving a reference signal and measuring CSI to performing beam report
  • information may be additionally input to the input layer.
  • the artificial neural network may include an autoencoder.
  • the autoencoder inputs original data into an artificial neural network to encode, and if the encoded data is restored by decoding it, there may be some difference between the restored data and the input data, and it is an artificial neural network that uses this difference.
  • the size of the input layer and the size of the output layer are each equal to 5, the size of the first hidden layer may be 3, the size of the second hidden layer may be 2, and the size of the third hidden layer may be 3. It may have a structure in which the number of nodes gradually decreases toward the middle layer and gradually increases as the number of nodes approaches the output layer.
  • the autoencoder compares the input value of the original data with the output value of the restored data, and if the difference is large, it can be determined that the data has not been learned, and if the difference between the input value and the output value is small, the data is judged as pre-learned can do. Therefore, the reliability of data can be increased by using an autoencoder.
  • a mean square error may be used as a method of comparing an input value and an output value.
  • MSE mean square error
  • the mean square error value is less than a preset threshold value, it is determined as pre-learned data, the test target data is input to the input layer of the artificial neural network model, and the beam is based on the output data of the artificial neural network model. Signature and beambook can be updated.
  • an operation example between one base station and a receiver has been mainly described, but is not limited thereto. Therefore, it can be applied even when the receiver is supported by multiple base stations.
  • the quality of a received signal may be improved by sharing a beam signature/beambook and a reference signal between the base stations.
  • the shape of the antenna array is designed and applied in three dimensions, such as a cylinder and a sphere, there is an advantage of facilitating beam tracking for a path with high curvature. Therefore, it may be desirable to install a three-dimensional antenna array by reflecting the characteristics of the topography and the receiver movement trajectory within the cell.
  • a Doppler frequency shift occurs according to the moving speed of the receiver, and accordingly, the reception phase angle is additionally rotated. Since this phase change is common to the antenna elements, it does not affect the determination of the beamforming vector defined by the relative phase difference from the reference antenna. However, the density of elements used in the beam signature may be changed according to the increase or decrease of the moving speed of the receiver, which may be adjusted through feedback information on the reference signal as described above.
  • 17 is an example of a vehicle route traveling through a six-lane crossroad in a city center for explaining the present embodiment.
  • An 8-Tx uniform linear array (ULA) to which cross-polarization is applied has a structure in which a 10 degree downtilt is applied with the cross-center direction as the boresight direction.
  • FIG. 18 is a two-dimensional diagram illustrating the crossroad of FIG. 17 and shows an example of a moving path of a vehicle. Specifically, the vehicle enters the intersection from four directions, east, west, north, and south, and proceeds by turning left/straight/right, and thus follows a total of 12 movement paths.
  • Equation 7 is an example illustrating the zenith angle and the azimuth angle of the receiver directional vector at intervals of 1 m for 12 paths.
  • Each path corresponds to a total distance of 80 m from the position 40 m away from the center of the intersection to the position 40 m away through the intersection.
  • columns 1, 2, and 3 indicate left turn, straight line, and right turn progress directions, respectively.
  • Beam signatures for each of these 12 path matrices can be determined, and the beambook consisting of to be.
  • FIG. 20 illustrates a reference signal transmission scheme
  • (a) of FIG. 20 is an example in which the base station transmits a user-specific reference signal according to a user location
  • (b) of FIG. 20 is a common reference signal the base station transmits is an example to
  • the base station transmits a reference signal beam corresponding to the same position index and the next position index based on the current receiver's position on the left/straight/right rotation path.
  • the moving path information of the receiver may be transmitted to the base station through feedback of the receiver.
  • the base station transmits beams for regularly distributed positions on the crossroad road, and the user feeds back information on the preferred beam of the best quality.
  • a beam may be formed and transmitted through zero forced precoding.
  • 21 is an example illustrating an evaluation of a signal-to-interference and noise ratio according to an increase in the number of users. It is assumed that users are randomly distributed on the crossroads, and the noise power is set to -20dB compared to the transmit power for each antenna. As a result of evaluation for the case where the antenna radio wave radiation form is uniform in all directions and the case where the 3GPP radiation pattern is applied, it can be confirmed that the SINR is maintained at a high level.
  • FIG. 22 is an example of evaluation results for SINR and sum data rate according to a change in distance between two arbitrary receivers.
  • FIG. 22 (a) shows the SINR
  • FIG. 22 (b) shows the sum rate result. Referring to FIG. 22 , it can be seen that a high level of signal quality is maintained regardless of the distance between users.
  • 23 shows an example of beam management for each path according to the number of base stations.
  • each of the plurality of base stations operates independently.
  • 23A is an example of a case in which there are two base stations (eg, A and B). It is assumed that the paths indicated by the solid line in FIG. 23A are managed by the base station A, and the paths indicated by the dotted line are managed by the base station B.
  • FIG. 23B is an example of a case in which there are four base stations (eg, A, B, C, D).
  • Each base station can independently manage vehicles traveling on a specific route.
  • base station B may manage vehicles on a path entering from the west direction
  • base station C may manage vehicles on a path entering from the east direction.
  • a separate base station for each path may be independently utilized, and as the number of base stations increases, beam transmission is concentrated on a specific path.
  • 24 shows an example in which a plurality of base stations cooperatively perform beam signature transmission.
  • 24A is an example in which two base stations (eg, A and B) cooperate to transmit a beam signature.
  • 24B is an example in which four base stations (eg, A, B, C, and D) cooperate to transmit a beam signature. All or some of the multiple base stations may operate cooperatively. When a plurality of base stations cooperate to transmit a beam signature, higher quality transmission may be possible through signal aggregation.
  • a learning-evolving beam management scheme may be desirable.
  • excellent beamforming performance and beam tracking can be achieved within a limited feedback payload by performing beamforming based on adaptive learning using location information according to the movement path and movement trajectory of the receiver.
  • FIG. 25 shows an example of a beam management operation flowchart of a base station (BS) to which the methods proposed in the present specification can be applied.
  • the base station may mean a generic term for an object that transmits and receives data with the terminal.
  • the base station may be a concept including one or more TPs (Transmission Points), one or more TRPs (Transmission and Reception Points), and the like.
  • the TP and/or TRP may include a panel of a base station, a transmission and reception unit, and the like.
  • the TRP may be classified according to information (eg, index, ID) on the CORESET group (or CORESET pool).
  • information eg, index, ID
  • the CORESET group or CORESET pool
  • CORESET group (or CORESET pool) may be performed through higher layer signaling (eg, RRC signaling, etc.). 25 is only for convenience of description, and does not limit the scope of the present invention. Also, some step(s) shown in FIG. 25 may be omitted depending on circumstances and/or settings.
  • higher layer signaling eg, RRC signaling, etc.
  • the base station (BS) may transmit configuration information to the terminal (UE).
  • the configuration information may be received through higher layer signaling (eg, RRC).
  • the configuration information may include system information, scheduling information, CSI-related configuration (eg, CSI-ReportConfig, CSI-ResourceConfig, etc.).
  • the setting information is preset, the corresponding step may be omitted.
  • the base station may determine parameters related to beam management (S2510).
  • the parameters related to the beam management may include i) location information of the terminal and ii) movement path information of the terminal.
  • the location information of the terminal may be expressed as a zenith angle and an azimuth angle.
  • determining the parameters related to the beam management may include i) determining candidates of a movement path of the terminal and ii) determining position candidates of the terminal in each candidate movement path.
  • beamforming vectors for position candidates of the terminal in each candidate movement path may constitute a beam signature
  • sets of beam signatures of candidates of movement paths of the terminal may constitute a beambook. That is, the step of determining the parameters related to the beam management may be interpreted as a step of determining (or initializing) a beam signature and a beambook.
  • the number of location candidates of the terminal in each candidate movement path may be predefined.
  • the number of candidates of the movement paths of the terminal may be predefined or determined based on the capability of the UE.
  • the base station may receive capability information including a predefined movement path of the terminal from the terminal, and the number of candidates for the movement paths may be determined based on the movement path received through the capability information.
  • the plurality of base stations may control or support the candidates of the movement paths by dividing them.
  • some of the candidates for the movement paths may be supported by the first base station and some may be supported by the second base station in consideration of the coverage of each base station, the distance to the receiving device, processing capability, and the like.
  • the beam signature for a specific base station may be composed of beamforming vectors for some movement path candidates supported by the beam signature.
  • the operation of the base station ( 100/200 in FIGS. 26 to 31 ) determining the parameters related to beam management in step S2510 may be implemented by the apparatus of FIGS. 26 to 31 to be described below.
  • one or more processors 102 may control one or more transceivers 106 and/or one or more memories 104 to determine parameters related to the beam management.
  • the base station may transmit a reference signal for beam management to a user equipment (UE) based on the parameters related to the beam management (S2520).
  • the reference signal may be one of SSB, CSI-RS, TRS, or PT-RS.
  • the reference signal may be transmitted periodically, semi-continuously, or aperiodically.
  • the reference signal may be a UE-specific reference signal, a UE group-specific reference signal, or a common reference signal.
  • a reference signal for each path may be transmitted based on a beamforming vector of the best quality among beamforming vectors constituting a beam signature.
  • the reference signal for each path is based on the beamforming vector of the best quality for the UE group or the beamforming vector with the best quality for the representative UE of the group among the beamforming vectors constituting the beam signature. can be transmitted.
  • one reference signal ie, when the total number of paths is J, J reference signals
  • J reference signals may be transmitted for each path.
  • a plurality of reference signals may be transmitted for each path in consideration of the current location of the terminal and a location to be moved thereafter. In this case, an integer multiple of J may be transmitted.
  • the operation of transmitting the reference signal by the base station ( 100/200 in FIGS. 26 to 31 ) in step S2520 described above may be implemented by the apparatus of FIGS. 26 to 31 to be described below.
  • one or more processors 102 may control one or more transceivers 106 and/or one or more memories 104 to transmit the reference signal, and the one or more transceivers 106 may transmit the reference signal. have.
  • the base station may receive, from the terminal, a beam related report calculated based on the reference signal (S2530).
  • the beam-related report may include at least one of CQI, PMI, L1-RSRP, and L1-SINR.
  • the beam-related report may be received periodically, semi-continuously, or aperiodically.
  • location information of the terminal may be received together with the beam-related report.
  • the operation of receiving the beam related report by the base station ( 100/200 in FIGS. 26 to 31 ) in step S2530 described above may be implemented by the apparatus of FIGS. 26 to 31 , which will be described below.
  • one or more processors 102 may control one or more transceivers 106 and/or one or more memories 104 to receive the beam-related report, and the one or more transceivers 106 may receive the beam-related report. can receive
  • the base station may perform artificial neural network learning based on the beam-related report (S2540).
  • artificial neural network learning based on the beam-related report sets at least one of CQI, PMI L1-RSRP, or L1-SINR as input data, and sets the location information of the terminal and the movement path information of the terminal as output data. By setting it, it can be performed in a way to learn the artificial neural network model.
  • the operation of the base station (100/200 in FIGS. 26 to 31 ) performing artificial neural network learning in step S2540 may be implemented by the apparatus of FIGS. 26 to 31 to be described below.
  • one or more processors 102 may perform artificial neural network learning or may control one or more memories 104 to perform artificial neural network learning.
  • the base station may update the parameters related to the beam management based on the neural network learning result (S2550). For example, a beambook for a moving path of the terminal and a beam signature for positions in the corresponding path may be updated using the learning result.
  • one or more processors 102 may control one or more transceivers 106 and/or one or more memories 104 to update parameters related to the beam management.
  • the base station may communicate with the terminal by applying optimized beamforming based on the updated beam signature and beambook.
  • the above-described base station/UE signaling and operation may be implemented by an apparatus (e.g. FIGS. 26 to 31 ) to be described below.
  • the base station may correspond to the first radio device, the UE may correspond to the second radio device, and vice versa may be considered in some cases.
  • the base station may correspond to the first radio device, the UE may correspond to the second radio device, and vice versa may be considered in some cases.
  • the UE may be the vehicle of FIGS. 30 and 31 .
  • the above-described base station/UE signaling and operation may be processed by one or more processors eg 102 and 202 of FIGS. 26 to 31 , and the above-described base station/UE signaling and operation may be performed in FIGS. 26 to 31 . It may be stored in one or more memories (eg 104, 204) in the form of an instruction/program (eg instruction, executable code) for driving at least one processor (eg 102, 202).
  • an instruction/program eg instruction, executable code
  • an apparatus comprising one or more memories and one or more processors operatively coupled to the one or more memories, wherein the one or more processors enable the apparatus to determine parameters related to beam management, and a terminal ( USER EQUIPMENT, UE), transmits a reference signal for beam management based on parameters related to the beam management, and receives, from the terminal, a beam related report calculated based on the reference signal, It is possible to control to perform artificial neural network learning based on the beam-related report, and to update parameters related to the beam management based on the neural network learning result.
  • the parameters related to the beam management include i) location information of the terminal and ii) movement path information of the terminal, and the location information of the terminal may be expressed as a zenith angle and an azimuth angle.
  • one or more non-transitory computer-readable media storing one or more instructions, the one or more instructions executable by one or more processors.
  • the first device determines parameters related to beam management, and transmits to the second device a reference signal for beam management based on the parameters related to the beam management, from the second device,
  • a command to receive a beam-related report calculated based on the reference signal, perform artificial neural network learning based on the beam-related report, and update parameters related to beam management based on the neural network learning result may include
  • the parameters related to the beam management include i) location information of the terminal and ii) movement path information of the terminal, and the location information of the terminal may be expressed as a zenith angle and an azimuth angle.
  • 26 illustrates a communication system 1 applied to the present invention.
  • the communication system 1 applied to the present invention includes a wireless device, a base station, and a network.
  • the wireless device refers to a device that performs communication using a radio access technology (eg, 5G NR (New RAT), LTE (Long Term Evolution)), and may be referred to as a communication/wireless/5G device.
  • a radio access technology eg, 5G NR (New RAT), LTE (Long Term Evolution)
  • the wireless device includes a robot 100a, a vehicle 100b-1, 100b-2, an eXtended Reality (XR) device 100c, a hand-held device 100d, and a home appliance 100e. ), an Internet of Things (IoT) device 100f, and an AI device/server 400 .
  • the vehicle may include a vehicle equipped with a wireless communication function, an autonomous driving vehicle, a vehicle capable of performing inter-vehicle communication, and the like.
  • the vehicle may include an Unmanned Aerial Vehicle (UAV) (eg, a drone).
  • UAV Unmanned Aerial Vehicle
  • XR devices include AR (Augmented Reality)/VR (Virtual Reality)/MR (Mixed Reality) devices, and include a Head-Mounted Device (HMD), a Head-Up Display (HUD) provided in a vehicle, a television, a smartphone, It may be implemented in the form of a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, and the like.
  • the portable device may include a smart phone, a smart pad, a wearable device (eg, a smart watch, smart glasses), a computer (eg, a laptop computer), and the like.
  • Home appliances may include a TV, a refrigerator, a washing machine, and the like.
  • the IoT device may include a sensor, a smart meter, and the like.
  • the base station and the network may be implemented as a wireless device, and a specific wireless device 200a may operate as a base station/network node to other wireless devices.
  • the wireless devices 100a to 100f may be connected to the network 300 through the base station 200 .
  • AI Artificial Intelligence
  • the network 300 may be configured using a 3G network, a 4G (eg, LTE) network, or a 5G (eg, NR) network.
  • the wireless devices 100a to 100f may communicate with each other through the base station 200/network 300, but may also communicate directly (e.g. sidelink communication) without passing through the base station/network.
  • the vehicles 100b-1 and 100b-2 may perform direct communication (e.g. Vehicle to Vehicle (V2V)/Vehicle to everything (V2X) communication).
  • the IoT device eg, sensor
  • the IoT device may directly communicate with other IoT devices (eg, sensor) or other wireless devices 100a to 100f.
  • Wireless communication/connection 150a, 150b, and 150c may be performed between the wireless devices 100a to 100f/base station 200 and the base station 200/base station 200 .
  • the wireless communication/connection includes uplink/downlink communication 150a and sidelink communication 150b (or D2D communication), and communication between base stations 150c (eg relay, IAB (Integrated Access Backhaul)).
  • This can be done through technology (eg 5G NR)
  • Wireless communication/connection 150a, 150b, 150c allows the wireless device and the base station/radio device, and the base station and the base station to transmit/receive wireless signals to each other.
  • the wireless communication/connection 150a, 150b, and 150c may transmit/receive signals through various physical channels.
  • various signal processing processes eg, channel encoding/decoding, modulation/demodulation, resource mapping/demapping, etc.
  • resource allocation processes etc.
  • the first wireless device 100 and the second wireless device 200 may transmit/receive wireless signals through various wireless access technologies (eg, LTE, NR).
  • ⁇ first wireless device 100, second wireless device 200 ⁇ is ⁇ wireless device 100x, base station 200 ⁇ of FIG. 26 and/or ⁇ wireless device 100x, wireless device 100x) ⁇ can be matched.
  • the first wireless device 100 includes one or more processors 102 and one or more memories 104 , and may further include one or more transceivers 106 and/or one or more antennas 108 .
  • the processor 102 controls the memory 104 and/or the transceiver 106 and may be configured to implement the descriptions, functions, procedures, suggestions, methods, and/or flow charts disclosed herein.
  • the processor 102 may process the information in the memory 104 to generate the first information/signal, and then transmit a wireless signal including the first information/signal through the transceiver 106 .
  • the processor 102 may receive the radio signal including the second information/signal through the transceiver 106 , and then store the information obtained from the signal processing of the second information/signal in the memory 104 .
  • the memory 104 may be connected to the processor 102 and may store various information related to the operation of the processor 102 .
  • the memory 104 may provide instructions for performing some or all of the processes controlled by the processor 102 , or for performing the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed herein. may store software code including
  • the processor 102 and the memory 104 may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR).
  • a wireless communication technology eg, LTE, NR
  • the transceiver 106 may be coupled with the processor 102 , and may transmit and/or receive wireless signals via one or more antennas 108 .
  • the transceiver 106 may include a transmitter and/or a receiver.
  • the transceiver 106 may be used interchangeably with a radio frequency (RF) unit.
  • RF radio frequency
  • a wireless device may refer to a communication modem/circuit/chip.
  • the second wireless device 200 includes one or more processors 202 , one or more memories 204 , and may further include one or more transceivers 206 and/or one or more antennas 208 .
  • the processor 202 controls the memory 204 and/or the transceiver 206 and may be configured to implement the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed herein.
  • the processor 202 may process the information in the memory 204 to generate third information/signal, and then transmit a wireless signal including the third information/signal through the transceiver 206 .
  • the processor 202 may receive the radio signal including the fourth information/signal through the transceiver 206 and then store information obtained from signal processing of the fourth information/signal in the memory 204 .
  • the memory 204 may be connected to the processor 202 and may store various information related to the operation of the processor 202 .
  • the memory 204 may provide instructions for performing some or all of the processes controlled by the processor 202 , or for performing the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed herein. may store software code including
  • the processor 202 and the memory 204 may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR).
  • a wireless communication technology eg, LTE, NR
  • the transceiver 206 may be coupled to the processor 202 and may transmit and/or receive wireless signals via one or more antennas 208 .
  • the transceiver 206 may include a transmitter and/or a receiver.
  • the transceiver 206 may be used interchangeably with an RF unit.
  • a wireless device may refer to a communication modem/circuit/chip.
  • one or more protocol layers may be implemented by one or more processors 102 , 202 .
  • one or more processors 102 , 202 may implement one or more layers (eg, functional layers such as PHY, MAC, RLC, PDCP, RRC, SDAP).
  • the one or more processors 102, 202 may be configured to process one or more Protocol Data Units (PDUs) and/or one or more Service Data Units (SDUs) according to the description, function, procedure, proposal, method, and/or operational flowcharts disclosed herein.
  • PDUs Protocol Data Units
  • SDUs Service Data Units
  • One or more processors 102, 202 may generate messages, control information, data, or information according to the description, function, procedure, proposal, method, and/or flow charts disclosed herein.
  • the one or more processors 102 and 202 generate a signal (eg, a baseband signal) including PDUs, SDUs, messages, control information, data or information according to the functions, procedures, proposals and/or methods disclosed in this document. , to one or more transceivers 106 and 206 .
  • the one or more processors 102 , 202 may receive signals (eg, baseband signals) from one or more transceivers 106 , 206 , and may be described, functions, procedures, proposals, methods, and/or operational flowcharts disclosed herein.
  • PDUs, SDUs, messages, control information, data, or information may be acquired according to the above.
  • One or more processors 102 , 202 may be referred to as a controller, microcontroller, microprocessor, or microcomputer.
  • One or more processors 102, 202 may be implemented by hardware, firmware, software, or a combination thereof.
  • ASICs Application Specific Integrated Circuits
  • DSPs Digital Signal Processors
  • DSPDs Digital Signal Processing Devices
  • PLDs Programmable Logic Devices
  • FPGAs Field Programmable Gate Arrays
  • firmware or software which may be implemented to include modules, procedures, functions, and the like.
  • the descriptions, functions, procedures, proposals, methods, and/or flow charts disclosed herein provide that firmware or software configured to perform is included in one or more processors 102 , 202 , or stored in one or more memories 104 , 204 . It may be driven by the above processors 102 and 202 .
  • the descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein may be implemented using firmware or software in the form of code, instructions, and/or a set of instructions.
  • One or more memories 104 , 204 may be coupled with one or more processors 102 , 202 and may store various forms of data, signals, messages, information, programs, code, instructions, and/or instructions.
  • One or more memories 104 , 204 may be comprised of ROM, RAM, EPROM, flash memory, hard drives, registers, cache memory, computer readable storage media, and/or combinations thereof.
  • One or more memories 104 , 204 may be located inside and/or external to one or more processors 102 , 202 .
  • one or more memories 104 , 204 may be coupled to one or more processors 102 , 202 through various technologies, such as wired or wireless connections.
  • One or more transceivers 106 , 206 may transmit user data, control information, radio signals/channels, etc. referred to in the methods and/or operational flowcharts herein, to one or more other devices.
  • One or more transceivers 106, 206 may receive user data, control information, radio signals/channels, etc. referred to in the descriptions, functions, procedures, suggestions, methods and/or flow charts, etc. disclosed herein, from one or more other devices. have.
  • one or more transceivers 106 , 206 may be coupled to one or more processors 102 , 202 and may transmit and receive wireless signals.
  • one or more processors 102 , 202 may control one or more transceivers 106 , 206 to transmit user data, control information, or wireless signals to one or more other devices.
  • one or more processors 102 , 202 may control one or more transceivers 106 , 206 to receive user data, control information, or wireless signals from one or more other devices.
  • one or more transceivers 106, 206 may be coupled to one or more antennas 108, 208, and the one or more transceivers 106, 206 may be coupled via one or more antennas 108, 208 to the descriptions, functions, and functions disclosed herein. , procedures, suggestions, methods and/or operation flowcharts, etc.
  • one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (eg, antenna ports).
  • the one or more transceivers 106, 206 convert the received radio signal/channel, etc. from the RF band signal to process the received user data, control information, radio signal/channel, etc. using the one or more processors 102, 202. It can be converted into a baseband signal.
  • One or more transceivers 106 and 206 may convert user data, control information, radio signals/channels, etc. processed using one or more processors 102 and 202 from baseband signals to RF band signals.
  • one or more transceivers 106 , 206 may include (analog) oscillators and/or filters.
  • the signal processing circuit 1000 may include a scrambler 1010 , a modulator 1020 , a layer mapper 1030 , a precoder 1040 , a resource mapper 1050 , and a signal generator 1060 .
  • the operations/functions of FIG. 28 may be performed by the processors 102 , 202 and/or transceivers 106 , 206 of FIG. 27 .
  • the hardware elements of FIG. 28 may be implemented in processors 102 , 202 and/or transceivers 106 , 206 of FIG. 27 .
  • blocks 1010 to 1060 may be implemented in the processors 102 and 202 of FIG. 27 .
  • blocks 1010 to 1050 may be implemented in the processors 102 and 202 of FIG. 27
  • block 1060 may be implemented in the transceivers 106 and 206 of FIG. 27 .
  • the codeword may be converted into a wireless signal through the signal processing circuit 1000 of FIG. 28 .
  • the codeword is a coded bit sequence of an information block.
  • the information block may include a transport block (eg, a UL-SCH transport block, a DL-SCH transport block).
  • the radio signal may be transmitted through various physical channels (eg, PUSCH, PDSCH).
  • the codeword may be converted into a scrambled bit sequence by the scrambler 1010 .
  • a scramble sequence used for scrambling is generated based on an initialization value, and the initialization value may include ID information of a wireless device, and the like.
  • the scrambled bit sequence may be modulated by a modulator 1020 into a modulation symbol sequence.
  • the modulation method may include pi/2-Binary Phase Shift Keying (pi/2-BPSK), m-Phase Shift Keying (m-PSK), m-Quadrature Amplitude Modulation (m-QAM), and the like.
  • the complex modulation symbol sequence may be mapped to one or more transport layers by the layer mapper 1030 .
  • Modulation symbols of each transport layer may be mapped to corresponding antenna port(s) by the precoder 1040 (precoding).
  • the output z of the precoder 1040 may be obtained by multiplying the output y of the layer mapper 1030 by the precoding matrix W of N*M.
  • N is the number of antenna ports
  • M is the number of transmission layers.
  • the precoder 1040 may perform precoding after performing transform precoding (eg, DFT transform) on the complex modulation symbols. Also, the precoder 1040 may perform precoding without performing transform precoding.
  • the resource mapper 1050 may map modulation symbols of each antenna port to a time-frequency resource.
  • the time-frequency resource may include a plurality of symbols (eg, a CP-OFDMA symbol, a DFT-s-OFDMA symbol) in the time domain and a plurality of subcarriers in the frequency domain.
  • CP Cyclic Prefix
  • DAC Digital-to-Analog Converter
  • the signal processing process for the received signal in the wireless device may be configured in reverse of the signal processing process 1010 to 1060 of FIG. 28 .
  • the wireless device eg, 100 and 200 in FIG. 27
  • the received radio signal may be converted into a baseband signal through a signal restorer.
  • the signal restorer may include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a Fast Fourier Transform (FFT) module.
  • ADC analog-to-digital converter
  • FFT Fast Fourier Transform
  • the baseband signal may be restored to a codeword through a resource de-mapper process, a postcoding process, a demodulation process, and a descrambling process.
  • the codeword may be restored to the original information block through decoding.
  • the signal processing circuit (not shown) for the received signal may include a signal restorer, a resource de-mapper, a postcoder, a demodulator, a descrambler, and a decoder.
  • the wireless device may be implemented in various forms according to use-examples/services (refer to FIG. 26 ).
  • wireless devices 100 and 200 correspond to wireless devices 100 and 200 of FIG. 27 , and various elements, components, units/units, and/or modules ) can be composed of
  • the wireless devices 100 and 200 may include a communication unit 110 , a control unit 120 , a memory unit 130 , and an additional element 140 .
  • the communication unit may include communication circuitry 112 and transceiver(s) 114 .
  • communication circuitry 112 may include one or more processors 102,202 and/or one or more memories 104,204 of FIG. 26 .
  • transceiver(s) 114 may include one or more transceivers 106 , 206 and/or one or more antennas 108 , 208 of FIG. 27 .
  • the control unit 120 is electrically connected to the communication unit 110 , the memory unit 130 , and the additional element 140 , and controls general operations of the wireless device. For example, the controller 120 may control the electrical/mechanical operation of the wireless device based on the program/code/command/information stored in the memory unit 130 . In addition, the control unit 120 transmits the information stored in the memory unit 130 to the outside (eg, another communication device) through the communication unit 110 through a wireless/wired interface, or through the communication unit 110 to the outside (eg, Information received through a wireless/wired interface from another communication device) may be stored in the memory unit 130 .
  • the outside eg, another communication device
  • Information received through a wireless/wired interface from another communication device may be stored in the memory unit 130 .
  • the additional element 140 may be configured in various ways according to the type of the wireless device.
  • the additional element 140 may include at least one of a power unit/battery, an input/output unit (I/O unit), a driving unit, and a computing unit.
  • the wireless device may include a robot ( FIGS. 26 and 100a ), a vehicle ( FIGS. 26 , 100b-1 , 100b-2 ), an XR device ( FIGS. 26 and 100c ), a mobile device ( FIGS. 26 and 100d ), and a home appliance. (FIG. 26, 100e), IoT device (FIG.
  • digital broadcasting terminal digital broadcasting terminal
  • hologram device public safety device
  • MTC device medical device
  • fintech device or financial device
  • security device climate/environment device
  • It may be implemented in the form of an AI server/device ( FIGS. 26 and 400 ), a base station ( FIGS. 26 and 200 ), and a network node.
  • the wireless device may be mobile or used in a fixed location depending on the use-example/service.
  • various elements, components, units/units, and/or modules in the wireless devices 100 and 200 may be all interconnected through a wired interface, or at least some of them may be wirelessly connected through the communication unit 110 .
  • the control unit 120 and the communication unit 110 are connected by wire, and the control unit 120 and the first unit (eg, 130 and 140 ) are connected to the communication unit 110 through the communication unit 110 . It can be connected wirelessly.
  • each element, component, unit/unit, and/or module within the wireless device 100 , 200 may further include one or more elements.
  • the controller 120 may be configured with one or more processor sets.
  • the controller 120 may be configured as a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphic processing processor, a memory control processor, and the like.
  • the memory unit 130 may include random access memory (RAM), dynamic RAM (DRAM), read only memory (ROM), flash memory, volatile memory, and non-volatile memory. volatile memory) and/or a combination thereof.
  • the vehicle or autonomous driving vehicle may be implemented as a mobile robot, vehicle, train, manned/unmanned aerial vehicle (AV), ship, or the like.
  • the vehicle or autonomous driving vehicle 100 includes an antenna unit 108 , a communication unit 110 , a control unit 120 , a driving unit 140a , a power supply unit 140b , a sensor unit 140c , and autonomous driving. It may include a part 140d.
  • the antenna unit 108 may be configured as a part of the communication unit 110 .
  • Blocks 110/130/140a-140d correspond to blocks 110/130/140 of FIG. 29, respectively.
  • the communication unit 110 may transmit/receive signals (eg, data, control signals, etc.) to and from external devices such as other vehicles, base stations (eg, base stations, roadside units, etc.), servers, and the like.
  • the controller 120 may control elements of the vehicle or the autonomous driving vehicle 100 to perform various operations.
  • the controller 120 may include an Electronic Control Unit (ECU).
  • the driving unit 140a may cause the vehicle or the autonomous driving vehicle 100 to run on the ground.
  • the driving unit 140a may include an engine, a motor, a power train, a wheel, a brake, a steering device, and the like.
  • the power supply unit 140b supplies power to the vehicle or the autonomous driving vehicle 100 , and may include a wired/wireless charging circuit, a battery, and the like.
  • the sensor unit 140c may obtain vehicle status, surrounding environment information, user information, and the like.
  • the sensor unit 140c includes an inertial measurement unit (IMU) sensor, a collision sensor, a wheel sensor, a speed sensor, an inclination sensor, a weight sensor, a heading sensor, a position module, and a vehicle forward movement.
  • IMU inertial measurement unit
  • a collision sensor a wheel sensor
  • a speed sensor a speed sensor
  • an inclination sensor a weight sensor
  • a heading sensor a position module
  • a vehicle forward movement / may include a reverse sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, and the like.
  • the autonomous driving unit 140d includes a technology for maintaining a driving lane, a technology for automatically adjusting speed such as adaptive cruise control, a technology for automatically driving along a predetermined route, and a technology for automatically setting a route when a destination is set. technology can be implemented.
  • the communication unit 110 may receive map data, traffic information data, and the like from an external server.
  • the autonomous driving unit 140d may generate an autonomous driving route and a driving plan based on the acquired data.
  • the controller 120 may control the driving unit 140a to move the vehicle or the autonomous driving vehicle 100 along the autonomous driving path (eg, speed/direction adjustment) according to the driving plan.
  • the communication unit 110 may non/periodically acquire the latest traffic information data from an external server, and may acquire surrounding traffic information data from surrounding vehicles.
  • the sensor unit 140c may acquire vehicle state and surrounding environment information.
  • the autonomous driving unit 140d may update the autonomous driving route and driving plan based on the newly acquired data/information.
  • the communication unit 110 may transmit information about a vehicle location, an autonomous driving route, a driving plan, and the like to an external server.
  • the external server may predict traffic information data in advance using AI technology or the like based on information collected from the vehicle or autonomous vehicles, and may provide the predicted traffic information data to the vehicle or autonomous vehicles.
  • the vehicle 31 illustrates a vehicle to which the present invention is applied.
  • the vehicle may also be implemented as a means of transportation, a train, an aircraft, a ship, and the like.
  • the vehicle 100 may include a communication unit 110 , a control unit 120 , a memory unit 130 , an input/output unit 140a , and a position measurement unit 140b .
  • blocks 110 to 130/140a to 140b correspond to blocks 110 to 130/140 of FIG. 29, respectively.
  • the communication unit 110 may transmit and receive signals (eg, data, control signals, etc.) with other vehicles or external devices such as a base station.
  • the controller 120 may control components of the vehicle 100 to perform various operations.
  • the memory unit 130 may store data/parameters/programs/codes/commands supporting various functions of the vehicle 100 .
  • the input/output unit 140a may output an AR/VR object based on information in the memory unit 130 .
  • the input/output unit 140a may include a HUD.
  • the position measuring unit 140b may acquire position information of the vehicle 100 .
  • the location information may include absolute location information of the vehicle 100 , location information within a driving line, acceleration information, location information with a surrounding vehicle, and the like.
  • the position measuring unit 140b may include a GPS and various sensors.
  • the communication unit 110 of the vehicle 100 may receive map information, traffic information, and the like from an external server and store it in the memory unit 130 .
  • the position measuring unit 140b may obtain vehicle position information through GPS and various sensors and store it in the memory unit 130 .
  • the controller 120 may generate a virtual object based on map information, traffic information, and vehicle location information, and the input/output unit 140a may display the created virtual object on a window inside the vehicle ( 1410 and 1420 ).
  • the controller 120 may determine whether the vehicle 100 is normally operating within the driving line based on the vehicle location information. When the vehicle 100 deviates from the driving line abnormally, the controller 120 may display a warning on the windshield of the vehicle through the input/output unit 140a.
  • control unit 120 may broadcast a warning message regarding the driving abnormality to surrounding vehicles through the communication unit 110 .
  • control unit 120 may transmit the location information of the vehicle and information on driving/vehicle abnormality to the related organization through the communication unit 110 .
  • the wireless communication technology implemented in the wireless devices 100 and 200 of the present specification may include a narrowband Internet of Things for low-power communication as well as LTE, NR, and 6G.
  • the NB-IoT technology may be an example of a LPWAN (Low Power Wide Area Network) technology, and may be implemented in standards such as LTE Cat NB1 and/or LTE Cat NB2, and is limited to the above-mentioned names. no.
  • the wireless communication technology implemented in the wireless devices 100 and 200 of the present specification may perform communication based on the LTE-M technology.
  • the LTE-M technology may be an example of an LPWAN technology, and may be called by various names such as enhanced machine type communication (eMTC).
  • eMTC enhanced machine type communication
  • LTE-M technology is 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-Bandwidth Limited), 5) LTE-MTC, 6) LTE Machine It may be implemented in at least one of various standards such as Type Communication, and/or 7) LTE M, and is not limited to the above-described name.
  • the wireless communication technology implemented in the wireless devices 100 and 200 of the present specification is at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN) in consideration of low power communication.
  • LPWAN Low Power Wide Area Network
  • the ZigBee technology can create PAN (personal area networks) related to small/low-power digital communication based on various standards such as IEEE 802.15.4, and can be called by various names.
  • Embodiments according to the present invention may be implemented by various means, for example, hardware, firmware, software, or a combination thereof.
  • an embodiment of the present invention provides one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), FPGAs ( field programmable gate arrays), a processor, a controller, a microcontroller, a microprocessor, and the like.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGAs field programmable gate arrays
  • an embodiment of the present invention may be implemented in the form of modules, procedures, functions, etc. that perform the functions or operations described above.
  • the software code may be stored in the memory and driven by the processor.
  • the memory may be located inside or outside the processor, and may transmit/receive data to and from the processor by various known means.
  • the beam management method based on artificial intelligence in the wireless communication system of the present invention has been mainly described as an example applied to the 3GPP LTE/LTE-A system and the 5G system (New RAT system), it is difficult to apply it to various wireless communication systems. It is possible.

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Abstract

L'invention concerne un procédé permettant d'effectuer une gestion de faisceau dans un système de communication sans fil, ainsi qu'un appareil associé. En particulier, un procédé permettant d'effectuer une gestion de faisceau au moyen d'une station de base dans un système de communication sans fil comprend les étapes consistant à : déterminer des paramètres associés à la gestion de faisceau ; transmettre un signal de référence pour la gestion de faisceau à un équipement utilisateur (UE) d'après les paramètres liés à la gestion de faisceau ; recevoir, d'un terminal, un rapport lié au faisceau calculé d'après le signal de référence ; effectuer un apprentissage de réseau neuronal artificiel d'après le rapport relatif au faisceau ; et mettre à jour les paramètres liés à la gestion de faisceau d'après le résultat d'apprentissage du réseau neuronal, les paramètres liés à la gestion de faisceau comprenant i) les informations d'emplacement du terminal et ii) les informations de trajet de déplacement du terminal, et les informations d'emplacement du terminal étant exprimées par un angle de zénith et un angle d'azimut.
PCT/KR2020/017558 2019-12-03 2020-12-03 Procédé de gestion de faisceau basé sur l'intelligence artificielle dans un système de communication sans fil, et appareil associé WO2021112592A1 (fr)

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Cited By (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11304063B2 (en) * 2020-07-09 2022-04-12 Industry Foundation Of Chonnam National University Deep learning-based beamforming communication system and method
WO2023280380A1 (fr) * 2021-07-05 2023-01-12 Telefonaktiebolaget Lm Ericsson (Publ) Procédés et appareil de gestion de faisceau
CN115622596A (zh) * 2022-12-12 2023-01-17 深圳大学 一种基于多任务学习的快速波束对齐方法
CN115802480A (zh) * 2022-10-17 2023-03-14 武汉大学 基于5g多波束下行信号的指纹定位方法及系统
US20230170976A1 (en) * 2021-11-30 2023-06-01 Qualcomm Incorporated Beam selection and codebook learning based on xr perception
WO2023123502A1 (fr) * 2021-12-31 2023-07-06 Huawei Technologies Co., Ltd. Systèmes et procédés d'alignement de faisceau pour formation de faisceau analogique
WO2023134628A1 (fr) * 2022-01-14 2023-07-20 维沃移动通信有限公司 Procédé et appareil de transmission, et dispositif
WO2023134761A1 (fr) * 2022-01-17 2023-07-20 维沃移动通信有限公司 Procédé et appareil d'échange d'informations de faisceau, et dispositif et support de stockage
WO2023179651A1 (fr) * 2022-03-23 2023-09-28 维沃移动通信有限公司 Procédé et appareil de traitement de faisceau et dispositif
WO2023179653A1 (fr) * 2022-03-23 2023-09-28 维沃移动通信有限公司 Procédé et appareil de traitement de faisceau, et dispositif
WO2023211350A1 (fr) * 2022-04-29 2023-11-02 Telefonaktiebolaget Lm Ericsson (Publ) Informations d'assistance d'équipement utilisateur pour de meilleures prédictions de faisceau de réseau
WO2023206163A1 (fr) * 2022-04-27 2023-11-02 Oppo广东移动通信有限公司 Procédés de communication sans fil, dispositifs de réseau et dispositifs terminaux
WO2024031537A1 (fr) * 2022-08-11 2024-02-15 Qualcomm Incorporated Configurations nominales de signaux csi-rs pour prédiction de faisceau spatial
WO2024031664A1 (fr) * 2022-08-12 2024-02-15 北京小米移动软件有限公司 Procédé d'envoi d'indication de performance, procédé de réception et appareil d'envoi, dispositif, et support de stockage
WO2024046202A1 (fr) * 2022-08-30 2024-03-07 维沃移动通信有限公司 Procédé de transmission, équipement utilisateur, dispositif côté réseau et support de stockage lisible
WO2024051789A1 (fr) * 2022-09-08 2024-03-14 华为技术有限公司 Procédé de gestion de faisceau
WO2024074088A1 (fr) * 2022-10-08 2024-04-11 华为技术有限公司 Procédé et appareil de communication
WO2024098184A1 (fr) * 2022-11-07 2024-05-16 富士通株式会社 Procédé et appareil d'émission-réception d'informations
WO2024119381A1 (fr) * 2022-12-06 2024-06-13 北京小米移动软件有限公司 Procédé et appareil d'indication de faisceau, dispositif, et support d'enregistrement

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102669662B1 (ko) * 2022-10-20 2024-05-28 한양대학교 산학협력단 무선 통신 기지국 및 방법
KR102665621B1 (ko) 2022-10-24 2024-05-16 주식회사 크로스웍스 이동형 기지국의 머신러닝 기반 매시브 mimo 처리 방법
WO2024106947A1 (fr) * 2022-11-15 2024-05-23 경희대학교 산학협력단 Système de communication mimo et procédé de communication, et dispositif informatique destiné à le mettre en œuvre

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019112499A1 (fr) * 2017-12-07 2019-06-13 Telefonaktiebolaget Lm Ericsson (Publ) Formation de faisceau d'antenne basée sur une position
WO2019118291A1 (fr) * 2017-12-11 2019-06-20 Nokia Of America Corporation Localisation autonome dans des réseaux sans fil
US20190222275A1 (en) * 2017-12-22 2019-07-18 Samsung Electronics Co., Ltd. Methods of beam codebook generation for the 5g terminals
US20190253117A1 (en) * 2018-02-15 2019-08-15 Qualcomm Incorporated Techniques for assisted beam refinement
WO2019172813A1 (fr) * 2018-03-08 2019-09-12 Telefonaktiebolaget Lm Ericsson (Publ) Gestion de communication dans un réseau de communication sans fil

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019112499A1 (fr) * 2017-12-07 2019-06-13 Telefonaktiebolaget Lm Ericsson (Publ) Formation de faisceau d'antenne basée sur une position
WO2019118291A1 (fr) * 2017-12-11 2019-06-20 Nokia Of America Corporation Localisation autonome dans des réseaux sans fil
US20190222275A1 (en) * 2017-12-22 2019-07-18 Samsung Electronics Co., Ltd. Methods of beam codebook generation for the 5g terminals
US20190253117A1 (en) * 2018-02-15 2019-08-15 Qualcomm Incorporated Techniques for assisted beam refinement
WO2019172813A1 (fr) * 2018-03-08 2019-09-12 Telefonaktiebolaget Lm Ericsson (Publ) Gestion de communication dans un réseau de communication sans fil

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11304063B2 (en) * 2020-07-09 2022-04-12 Industry Foundation Of Chonnam National University Deep learning-based beamforming communication system and method
WO2023280380A1 (fr) * 2021-07-05 2023-01-12 Telefonaktiebolaget Lm Ericsson (Publ) Procédés et appareil de gestion de faisceau
US20230170976A1 (en) * 2021-11-30 2023-06-01 Qualcomm Incorporated Beam selection and codebook learning based on xr perception
WO2023123502A1 (fr) * 2021-12-31 2023-07-06 Huawei Technologies Co., Ltd. Systèmes et procédés d'alignement de faisceau pour formation de faisceau analogique
WO2023134628A1 (fr) * 2022-01-14 2023-07-20 维沃移动通信有限公司 Procédé et appareil de transmission, et dispositif
WO2023134761A1 (fr) * 2022-01-17 2023-07-20 维沃移动通信有限公司 Procédé et appareil d'échange d'informations de faisceau, et dispositif et support de stockage
WO2023179653A1 (fr) * 2022-03-23 2023-09-28 维沃移动通信有限公司 Procédé et appareil de traitement de faisceau, et dispositif
WO2023179651A1 (fr) * 2022-03-23 2023-09-28 维沃移动通信有限公司 Procédé et appareil de traitement de faisceau et dispositif
WO2023206163A1 (fr) * 2022-04-27 2023-11-02 Oppo广东移动通信有限公司 Procédés de communication sans fil, dispositifs de réseau et dispositifs terminaux
WO2023211350A1 (fr) * 2022-04-29 2023-11-02 Telefonaktiebolaget Lm Ericsson (Publ) Informations d'assistance d'équipement utilisateur pour de meilleures prédictions de faisceau de réseau
WO2024031537A1 (fr) * 2022-08-11 2024-02-15 Qualcomm Incorporated Configurations nominales de signaux csi-rs pour prédiction de faisceau spatial
WO2024031664A1 (fr) * 2022-08-12 2024-02-15 北京小米移动软件有限公司 Procédé d'envoi d'indication de performance, procédé de réception et appareil d'envoi, dispositif, et support de stockage
WO2024046202A1 (fr) * 2022-08-30 2024-03-07 维沃移动通信有限公司 Procédé de transmission, équipement utilisateur, dispositif côté réseau et support de stockage lisible
WO2024051789A1 (fr) * 2022-09-08 2024-03-14 华为技术有限公司 Procédé de gestion de faisceau
WO2024074088A1 (fr) * 2022-10-08 2024-04-11 华为技术有限公司 Procédé et appareil de communication
CN115802480A (zh) * 2022-10-17 2023-03-14 武汉大学 基于5g多波束下行信号的指纹定位方法及系统
CN115802480B (zh) * 2022-10-17 2024-03-08 武汉大学 基于5g多波束下行信号的指纹定位方法及系统
WO2024098184A1 (fr) * 2022-11-07 2024-05-16 富士通株式会社 Procédé et appareil d'émission-réception d'informations
WO2024119381A1 (fr) * 2022-12-06 2024-06-13 北京小米移动软件有限公司 Procédé et appareil d'indication de faisceau, dispositif, et support d'enregistrement
CN115622596A (zh) * 2022-12-12 2023-01-17 深圳大学 一种基于多任务学习的快速波束对齐方法

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