WO2023058872A1 - Dispositif et procédé d'exécution d'un transfert intercellulaire sur la base d'une position dans un système de communication sans fil - Google Patents

Dispositif et procédé d'exécution d'un transfert intercellulaire sur la base d'une position dans un système de communication sans fil Download PDF

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Publication number
WO2023058872A1
WO2023058872A1 PCT/KR2022/011526 KR2022011526W WO2023058872A1 WO 2023058872 A1 WO2023058872 A1 WO 2023058872A1 KR 2022011526 W KR2022011526 W KR 2022011526W WO 2023058872 A1 WO2023058872 A1 WO 2023058872A1
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Prior art keywords
handover
base station
information related
command message
information
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PCT/KR2022/011526
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English (en)
Korean (ko)
Inventor
하업성
장지환
이명희
오재기
정재훈
박재용
김성진
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엘지전자 주식회사
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Priority to KR1020247005387A priority Critical patent/KR20240070506A/ko
Publication of WO2023058872A1 publication Critical patent/WO2023058872A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters
    • H04W36/32—Reselection being triggered by specific parameters by location or mobility data, e.g. speed data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0055—Transmission or use of information for re-establishing the radio link
    • H04W36/0058—Transmission of hand-off measurement information, e.g. measurement reports
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
    • H04W36/00837—Determination of triggering parameters for hand-off
    • H04W36/008375—Determination of triggering parameters for hand-off based on historical data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
    • H04W36/0085—Hand-off measurements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/08—Reselecting an access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters

Definitions

  • the following description relates to a wireless communication system, and relates to an apparatus and method for performing a handover based on a location of a terminal in a wireless communication system.
  • a wireless access system is widely deployed to provide various types of communication services such as voice and data.
  • a wireless access system is a multiple access system capable of supporting communication with multiple users by sharing available system resources (bandwidth, transmission power, etc.).
  • Examples of the multiple access system include a code division multiple access (CDMA) system, a frequency division multiple access (FDMA) system, a time division multiple access (TDMA) system, an orthogonal frequency division multiple access (OFDMA) system, and a single carrier frequency (SC-FDMA) system. division multiple access) system.
  • CDMA code division multiple access
  • FDMA frequency division multiple access
  • TDMA time division multiple access
  • OFDMA orthogonal frequency division multiple access
  • SC-FDMA single carrier frequency division multiple access
  • eMBB enhanced mobile broadband
  • RAT radio access technology
  • a communication system considering reliability and latency-sensitive services/UE (user equipment) as well as mMTC (massive machine type communications) providing various services anytime and anywhere by connecting multiple devices and objects has been proposed. .
  • Various technical configurations for this have been proposed.
  • the present disclosure may provide an apparatus and method for effectively performing handover using location information in a wireless communication system.
  • the present disclosure may provide an apparatus and method for performing handover based on movement path prediction for a terminal in a wireless communication system.
  • the present disclosure may provide an apparatus and method for selecting target cells for handover based on movement path prediction for a terminal in a wireless communication system.
  • the present disclosure may provide an apparatus and method for selecting target cells having a high handover success rate in a wireless communication system.
  • the present disclosure may provide an apparatus and method for selecting target cells based on environment information of a terminal in a wireless communication system.
  • the present disclosure may provide an apparatus and method for selecting target cells using a Thomson sampling technique in a wireless communication system.
  • the present disclosure may provide an apparatus and method for performing a handover based on a probability distribution of successful handover at positions on a predicted movement path in a wireless communication system.
  • the present disclosure may provide an apparatus and method for optimizing a probability distribution model for selecting a target cell in a wireless communication system.
  • a method of operating a user equipment (UE) in a wireless communication system includes receiving configuration information related to measurement from a first base station, and reporting a measurement based on the configuration information ( measurement report) to the first base station, receiving a handover command message from the first base station, and performing handover to a second base station based on the handover command message.
  • the measurement report may include information related to a movement path prediction of the UE, and the handover command message may include information related to conditions for executing the handover.
  • a method of operating a first base station in a wireless communication system includes transmitting configuration information related to measurement to a user equipment (UE), and measurement generated based on the configuration information. Receiving a measurement report from the UE, transmitting a handover command message to the UE, and receiving a handover confirmation message transmitted in response to the UE handing over to a second base station.
  • the measurement report may include information related to a movement path prediction of the UE, and the handover command message may include information related to conditions for executing the handover.
  • a user equipment (UE) in a wireless communication system includes a transceiver and a processor connected to the transceiver, wherein the processor includes configuration information related to measurement from a first base station. and transmits a measurement report to the first base station based on the configuration information, receives a handover command message from the first base station, and transmits a handover command message to the second base station based on the handover command message.
  • Control to perform handover wherein the measurement report includes information related to prediction of a moving path of the UE, and the handover command message includes information related to conditions for executing the handover.
  • a first base station includes a transceiver and a processor connected to the transceiver, wherein the processor provides user equipment (UE) with configuration information related to measurement. transmits, receives a measurement report generated based on the configuration information from the UE, transmits a handover command message to the UE, and transmits a handover command message to the UE in response to handover to the second base station. Controls to receive an over confirmation message, wherein the measurement report includes information related to prediction of a moving path of the UE, and the handover command message includes information related to conditions for executing the handover. can do.
  • UE user equipment
  • an apparatus includes at least one processor, at least one computer memory connected to the at least one processor and storing instructions that direct operations as executed by the at least one processor, ,
  • the operations include, by the device, receiving configuration information related to measurement from a first base station, and transmitting a measurement report to the first base station based on the configuration information. , receiving a handover command message from the first base station, and performing handover to a second base station based on the handover command message.
  • the measurement report may include information related to a movement path prediction of the UE, and the handover command message may include information related to conditions for executing the handover.
  • a non-transitory computer-readable medium storing at least one instruction (instructions), the at least one instruction executable by a processor (executable) Including, the at least one command, the device receives configuration (configuration) information related to measurement (measurement) from the first base station, and based on the configuration information, the measurement report (measurement report) to the first base station transmits to, receives a handover command message from the first base station, controls to perform handover to a second base station based on the handover command message, and the measurement report is related to prediction of the movement path of the UE.
  • information, and the handover command message may include information related to conditions for executing the handover.
  • a target cell for handover can be effectively selected.
  • Effects obtainable in the embodiments of the present disclosure are not limited to the above-mentioned effects, and other effects not mentioned are technical fields to which the technical configuration of the present disclosure is applied from the description of the following embodiments of the present disclosure. can be clearly derived and understood by those skilled in the art. That is, unintended effects according to implementing the configuration described in the present disclosure may also be derived by those skilled in the art from the embodiments of the present disclosure.
  • FIG. 1 shows an example of a communication system applicable to the present disclosure.
  • FIG. 2 shows an example of a wireless device applicable to the present disclosure.
  • FIG. 3 illustrates another example of a wireless device applicable to the present disclosure.
  • FIG. 4 shows an example of a portable device applicable to the present disclosure.
  • FIG. 5 illustrates an example of a vehicle or autonomous vehicle applicable to the present disclosure.
  • AI Artificial Intelligence
  • FIG. 7 illustrates a method of processing a transmission signal applicable to the present disclosure.
  • FIG 8 illustrates an example of a communication structure that can be provided in a 6th generation (6G) system applicable to the present disclosure.
  • FIG. 10 illustrates a THz communication method applicable to the present disclosure.
  • 12A to 12D show examples of updating a probability distribution model applicable to the present disclosure.
  • FIG. 13 illustrates a concept of handover in a wireless communication system according to an embodiment of the present disclosure.
  • FIG. 14 illustrates a concept of target cell selection based on Thompson sampling (TS) in a wireless communication system according to an embodiment of the present disclosure.
  • TS Thompson sampling
  • FIG. 15 illustrates an example of an artificial intelligence model for predicting a moving path in a wireless communication system according to an embodiment of the present disclosure.
  • 16 illustrates examples of probability density functions of a beta distribution usable in a wireless communication system according to an embodiment of the present disclosure.
  • FIG. 17 illustrates an example of a procedure for performing handover in a wireless communication system according to an embodiment of the present disclosure.
  • FIG. 18 illustrates an example of a procedure for controlling handover in a wireless communication system according to an embodiment of the present disclosure.
  • FIG. 19 illustrates an example of signal exchange for handover based on movement path estimation and a probability model in a wireless communication system according to an embodiment of the present disclosure.
  • FIG. 20 illustrates an example of a procedure for handover based on movement path estimation and a probability model in a wireless communication system according to an embodiment of the present disclosure.
  • FIG. 21 illustrates a reward structure based on Thomson sampling in a wireless communication system according to an embodiment of the present disclosure.
  • each component or feature may be considered optional unless explicitly stated otherwise.
  • Each component or feature may be implemented in a form not combined with other components or features.
  • an embodiment of the present disclosure may be configured by combining some elements and/or features. The order of operations described in the embodiments of the present disclosure may be changed. Some components or features of one embodiment may be included in another embodiment, or may be replaced with corresponding components or features of another embodiment.
  • a base station has meaning as a terminal node of a network that directly communicates with a mobile station.
  • a specific operation described as being performed by a base station in this document may be performed by an upper node of the base station in some cases.
  • the 'base station' is a term such as a fixed station, Node B, eNode B, gNode B, ng-eNB, advanced base station (ABS), or access point. can be replaced by
  • a terminal includes a user equipment (UE), a mobile station (MS), a subscriber station (SS), a mobile subscriber station (MSS), It may be replaced with terms such as mobile terminal or advanced mobile station (AMS).
  • UE user equipment
  • MS mobile station
  • SS subscriber station
  • MSS mobile subscriber station
  • AMS advanced mobile station
  • the transmitting end refers to a fixed and/or mobile node providing data service or voice service
  • the receiving end refers to a fixed and/or mobile node receiving data service or voice service. Therefore, in the case of uplink, the mobile station can be a transmitter and the base station can be a receiver. Similarly, in the case of downlink, the mobile station may be a receiving end and the base station may be a transmitting end.
  • Embodiments of the present disclosure are wireless access systems, such as an IEEE 802.xx system, a 3rd Generation Partnership Project (3GPP) system, a 3GPP Long Term Evolution (LTE) system, a 3GPP 5G (5th generation) NR (New Radio) system, and a 3GPP2 system. It may be supported by at least one disclosed standard document, and in particular, the embodiments of the present disclosure are supported by 3GPP technical specification (TS) 38.211, 3GPP TS 38.212, 3GPP TS 38.213, 3GPP TS 38.321 and 3GPP TS 38.331 documents It can be.
  • 3GPP technical specification TS 38.211, 3GPP TS 38.212, 3GPP TS 38.213, 3GPP TS 38.321 and 3GPP TS 38.331 documents It can be.
  • embodiments of the present disclosure may be applied to other wireless access systems, and are not limited to the above-described systems.
  • it may also be applicable to a system applied after the 3GPP 5G NR system, and is not limited to a specific system.
  • CDMA code division multiple access
  • FDMA frequency division multiple access
  • TDMA time division multiple access
  • OFDMA orthogonal frequency division multiple access
  • SC-FDMA single carrier frequency division multiple access
  • LTE is 3GPP TS 36.xxx Release 8 or later
  • LTE technology after 3GPP TS 36.xxx Release 10 is referred to as LTE-A
  • xxx Release 13 may be referred to as LTE-A pro.
  • 3GPP NR may mean technology after TS 38.xxx Release 15.
  • 3GPP 6G may mean technology after TS Release 17 and/or Release 18.
  • "xxx" means a standard document detail number.
  • LTE/NR/6G may be collectively referred to as a 3GPP system.
  • FIG. 1 is a diagram illustrating an example of a communication system applied to the present disclosure.
  • a communication system 100 applied to the present disclosure includes a wireless device, a base station, and a network.
  • the wireless device means a device that performs communication using a radio access technology (eg, 5G NR, LTE), and may be referred to as a communication/wireless/5G device.
  • the wireless device includes a robot 100a, a vehicle 100b-1 and 100b-2, an extended reality (XR) device 100c, a hand-held device 100d, and a home appliance. appliance) 100e, Internet of Thing (IoT) device 100f, and artificial intelligence (AI) device/server 100g.
  • a radio access technology eg, 5G NR, LTE
  • XR extended reality
  • IoT Internet of Thing
  • AI artificial intelligence
  • the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing inter-vehicle communication, and the like.
  • the vehicles 100b-1 and 100b-2 may include an unmanned aerial vehicle (UAV) (eg, a drone).
  • UAV unmanned aerial vehicle
  • the XR device 100c includes augmented reality (AR)/virtual reality (VR)/mixed reality (MR) devices, and includes a head-mounted device (HMD), a head-up display (HUD) installed in a vehicle, a television, It may be implemented in the form of smart phones, computers, wearable devices, home appliances, digital signage, vehicles, robots, and the like.
  • the mobile device 100d may include a smart phone, a smart pad, a wearable device (eg, a smart watch, a smart glass), a computer (eg, a laptop computer), and the like.
  • the home appliance 100e may include a TV, a refrigerator, a washing machine, and the like.
  • the IoT device 100f may include a sensor, a smart meter, and the like.
  • the base station 120 and the network 130 may also be implemented as a wireless device, and a specific wireless device 120a may operate as a base station/network node to other wireless devices.
  • the wireless devices 100a to 100f may be connected to the network 130 through the base station 120 .
  • AI technology may be applied to the wireless devices 100a to 100f, and the wireless devices 100a to 100f may be connected to the AI server 100g through the network 130.
  • the network 130 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 120/network 130, but communicate directly without going through the base station 120/network 130 (e.g., sidelink communication). You may.
  • the vehicles 100b-1 and 100b-2 may perform direct communication (eg, vehicle to vehicle (V2V)/vehicle to everything (V2X) communication).
  • the IoT device 100f eg, sensor
  • the IoT device 100f 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 120 and the base station 120/base station 120.
  • wireless communication/connection includes various types of uplink/downlink communication 150a, sidelink communication 150b (or D2D communication), and inter-base station communication 150c (eg relay, integrated access backhaul (IAB)). This can be done through radio access technology (eg 5G NR).
  • radio access technology eg 5G NR
  • a wireless device and a base station/wireless device, and a base station can transmit/receive radio signals to each other.
  • the wireless communication/connections 150a, 150b, and 150c may transmit/receive signals through various physical channels.
  • various configuration information setting processes for transmitting / receiving radio signals various signal processing processes (eg, channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.) At least a part of a resource allocation process may be performed.
  • FIG. 2 is a diagram illustrating an example of a wireless device applicable to the present disclosure.
  • a first wireless device 200a and a second wireless device 200b may transmit and receive radio signals through various wireless access technologies (eg, LTE and NR).
  • ⁇ the first wireless device 200a, the second wireless device 200b ⁇ denotes the ⁇ wireless device 100x and the base station 120 ⁇ of FIG. 1 and/or the ⁇ wireless device 100x and the wireless device 100x.
  • ⁇ can correspond.
  • the first wireless device 200a includes one or more processors 202a and one or more memories 204a, and may further include one or more transceivers 206a and/or one or more antennas 208a.
  • the processor 202a controls the memory 204a and/or the transceiver 206a and may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flow diagrams disclosed herein.
  • the processor 202a may process information in the memory 204a to generate first information/signal, and transmit a radio signal including the first information/signal through the transceiver 206a.
  • the processor 202a may receive a radio signal including the second information/signal through the transceiver 206a and store information obtained from signal processing of the second information/signal in the memory 204a.
  • the memory 204a may be connected to the processor 202a and may store various information related to the operation of the processor 202a.
  • memory 204a may perform some or all of the processes controlled by processor 202a, or instructions for performing the descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein. It may store software codes including them.
  • the processor 202a and the memory 204a may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR).
  • the transceiver 206a may be coupled to the processor 202a and may transmit and/or receive wireless signals through one or more antennas 208a.
  • the transceiver 206a may include a transmitter and/or a receiver.
  • the transceiver 206a may be used interchangeably with a radio frequency (RF) unit.
  • RF radio frequency
  • a wireless device may mean a communication modem/circuit/chip.
  • the second wireless device 200b includes one or more processors 202b, one or more memories 204b, and may further include one or more transceivers 206b and/or one or more antennas 208b.
  • the processor 202b controls the memory 204b and/or the transceiver 206b and may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flow diagrams disclosed herein.
  • the processor 202b may process information in the memory 204b to generate third information/signal, and transmit a radio signal including the third information/signal through the transceiver 206b.
  • the processor 202b may receive a radio signal including the fourth information/signal through the transceiver 206b and store information obtained from signal processing of the fourth information/signal in the memory 204b.
  • the memory 204b may be connected to the processor 202b and may store various information related to the operation of the processor 202b.
  • the memory 204b may perform some or all of the processes controlled by the processor 202b, or instructions for performing the descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein. It may store software codes including them.
  • the processor 202b and the memory 204b may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR).
  • the transceiver 206b may be coupled to the processor 202b and may transmit and/or receive wireless signals through one or more antennas 208b.
  • the transceiver 206b may include a transmitter and/or a receiver.
  • the transceiver 206b may be used interchangeably with an RF unit.
  • a wireless device may mean a communication modem/circuit/chip.
  • one or more protocol layers may be implemented by one or more processors 202a, 202b.
  • the one or more processors 202a and 202b may include one or more layers (eg, PHY (physical), MAC (media access control), RLC (radio link control), PDCP (packet data convergence protocol), RRC (radio resource) control) and functional layers such as service data adaptation protocol (SDAP).
  • One or more processors 202a, 202b may generate one or more protocol data units (PDUs) and/or one or more service data units (SDUs) according to the descriptions, functions, procedures, proposals, methods, and/or operational flow charts disclosed herein.
  • PDUs protocol data units
  • SDUs service data units
  • processors 202a, 202b may generate messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and/or operational flow diagrams disclosed herein.
  • One or more processors 202a, 202b generate PDUs, SDUs, messages, control information, data or signals (eg, baseband signals) containing information according to the functions, procedures, proposals and/or methods disclosed herein , may be provided to one or more transceivers 206a and 206b.
  • One or more processors 202a, 202b may receive signals (eg, baseband signals) from one or more transceivers 206a, 206b, and descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein PDUs, SDUs, messages, control information, data or information can be obtained according to these.
  • signals eg, baseband signals
  • One or more processors 202a, 202b may be referred to as a controller, microcontroller, microprocessor or microcomputer.
  • One or more processors 202a, 202b 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 may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, and the like.
  • Firmware or software configured to perform the descriptions, functions, procedures, proposals, methods and/or operational flow charts disclosed in this document may be included in one or more processors 202a or 202b or stored in one or more memories 204a or 204b. It can be driven by the above processors 202a and 202b.
  • the descriptions, functions, procedures, suggestions, methods and/or operational flow charts disclosed in this document may be implemented using firmware or software in the form of codes, instructions and/or sets of instructions.
  • One or more memories 204a, 204b may be coupled to one or more processors 202a, 202b and may store various types of data, signals, messages, information, programs, codes, instructions and/or instructions.
  • One or more memories 204a, 204b may include read only memory (ROM), random access memory (RAM), erasable programmable read only memory (EPROM), flash memory, hard drive, registers, cache memory, computer readable storage media, and/or It may consist of a combination of these.
  • One or more memories 204a, 204b may be located internally and/or externally to one or more processors 202a, 202b.
  • one or more memories 204a, 204b may be connected to one or more processors 202a, 202b through various technologies such as wired or wireless connections.
  • One or more transceivers 206a, 206b may transmit user data, control information, radio signals/channels, etc. referred to in the methods and/or operational flow charts of this document to one or more other devices.
  • One or more transceivers 206a, 206b may receive user data, control information, radio signals/channels, etc. referred to in descriptions, functions, procedures, proposals, methods and/or operational flow charts, etc. disclosed herein from one or more other devices. there is.
  • one or more transceivers 206a and 206b may be connected to one or more processors 202a and 202b and transmit and receive radio signals.
  • one or more processors 202a, 202b may control one or more transceivers 206a, 206b to transmit user data, control information, or radio signals to one or more other devices.
  • one or more processors 202a, 202b may control one or more transceivers 206a, 206b to receive user data, control information, or radio signals from one or more other devices.
  • one or more transceivers 206a, 206b may be coupled to one or more antennas 208a, 208b, and one or more transceivers 206a, 206b may be connected to one or more antennas 208a, 208b to achieve the descriptions, functions disclosed in this document.
  • one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (eg, antenna ports).
  • One or more transceivers (206a, 206b) in order to process the received user data, control information, radio signal / channel, etc. using one or more processors (202a, 202b), the received radio signal / channel, etc. in the RF band signal It can be converted into a baseband signal.
  • One or more transceivers 206a and 206b may convert user data, control information, and radio signals/channels processed by one or more processors 202a and 202b from baseband signals to RF band signals.
  • one or more transceivers 206a, 206b may include (analog) oscillators and/or filters.
  • FIG. 3 is a diagram illustrating another example of a wireless device applied to the present disclosure.
  • a wireless device 300 corresponds to the wireless devices 200a and 200b of FIG. 2, and includes various elements, components, units/units, and/or modules. ) can be configured.
  • the wireless device 300 may include a communication unit 310, a control unit 320, a memory unit 330, and an additional element 340.
  • the communication unit may include communication circuitry 312 and transceiver(s) 314 .
  • communication circuitry 312 may include one or more processors 202a, 202b of FIG. 2 and/or one or more memories 204a, 204b.
  • transceiver(s) 314 may include one or more transceivers 206a, 206b of FIG.
  • the control unit 320 is electrically connected to the communication unit 310, the memory unit 330, and the additional element 340 and controls overall operations of the wireless device. For example, the control unit 320 may control electrical/mechanical operations of the wireless device based on programs/codes/commands/information stored in the memory unit 330. In addition, the control unit 320 transmits the information stored in the memory unit 330 to the outside (eg, another communication device) through the communication unit 310 through a wireless/wired interface, or transmits the information stored in the memory unit 330 to the outside (eg, another communication device) through the communication unit 310. Information received through a wireless/wired interface from other communication devices) may be stored in the memory unit 330 .
  • the additional element 340 may be configured in various ways according to the type of wireless device.
  • the additional element 340 may include at least one of a power unit/battery, an input/output unit, a driving unit, and a computing unit.
  • the wireless device 300 may be a robot (FIG. 1, 100a), a vehicle (FIG. 1, 100b-1, 100b-2), an XR device (FIG. 1, 100c), a mobile device (FIG. 1, 100d) ), home appliances (FIG. 1, 100e), IoT devices (FIG.
  • Wireless devices can be mobile or used in a fixed location depending on the use-case/service.
  • various elements, components, units/units, and/or modules in the wireless device 300 may be entirely interconnected through a wired interface or at least partially connected wirelessly through the communication unit 310 .
  • the control unit 320 and the communication unit 310 are connected by wire, and the control unit 320 and the first units (eg, 130 and 140) are connected wirelessly through the communication unit 310.
  • each element, component, unit/unit, and/or module within wireless device 300 may further include one or more elements.
  • the control unit 320 may be composed of one or more processor sets.
  • control unit 320 may include 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.
  • memory unit 330 may include RAM, dynamic RAM (DRAM), ROM, flash memory, volatile memory, non-volatile memory, and/or combinations thereof. can be configured.
  • FIG. 4 is a diagram illustrating an example of a portable device applied to the present disclosure.
  • a portable device may include a smart phone, a smart pad, a wearable device (eg, smart watch, smart glasses), and a portable computer (eg, a laptop computer).
  • a mobile device may be referred to as a mobile station (MS), a user terminal (UT), a mobile subscriber station (MSS), a subscriber station (SS), an advanced mobile station (AMS), or a wireless terminal (WT).
  • MS mobile station
  • UT user terminal
  • MSS mobile subscriber station
  • SS subscriber station
  • AMS advanced mobile station
  • WT wireless terminal
  • a portable device 400 includes an antenna unit 408, a communication unit 410, a control unit 420, a memory unit 430, a power supply unit 440a, an interface unit 440b, and an input/output unit 440c. ) may be included.
  • the antenna unit 408 may be configured as part of the communication unit 410 .
  • Blocks 410 to 430/440a to 440c respectively correspond to blocks 310 to 330/340 of FIG. 3 .
  • the communication unit 410 may transmit/receive signals (eg, data, control signals, etc.) with other wireless devices and base stations.
  • the controller 420 may perform various operations by controlling components of the portable device 400 .
  • the controller 420 may include an application processor (AP).
  • the memory unit 430 may store data/parameters/programs/codes/commands necessary for driving the portable device 400 . Also, the memory unit 430 may store input/output data/information.
  • the power supply unit 440a supplies power to the portable device 400 and may include a wired/wireless charging circuit, a battery, and the like.
  • the interface unit 440b may support connection between the mobile device 400 and other external devices.
  • the interface unit 440b may include various ports (eg, audio input/output ports and video input/output ports) for connection with external devices.
  • the input/output unit 440c may receive or output image information/signal, audio information/signal, data, and/or information input from a user.
  • the input/output unit 440c may include a camera, a microphone, a user input unit, a display unit 440d, a speaker, and/or a haptic module.
  • the input/output unit 440c acquires information/signals (eg, touch, text, voice, image, video) input from the user, and the acquired information/signals are stored in the memory unit 430.
  • the communication unit 410 may convert the information/signal stored in the memory into a wireless signal, and directly transmit the converted wireless signal to another wireless device or to a base station.
  • the communication unit 410 may receive a radio signal from another wireless device or base station and then restore the received radio signal to original information/signal. After the restored information/signal is stored in the memory unit 430, it may be output in various forms (eg, text, voice, image, video, or haptic) through the input/output unit 440c.
  • FIG. 5 is a diagram illustrating an example of a vehicle or autonomous vehicle to which the present disclosure applies.
  • a vehicle or an autonomous vehicle may be implemented as a mobile robot, vehicle, train, manned/unmanned aerial vehicle (AV), ship, etc., and is not limited to a vehicle type.
  • AV unmanned aerial vehicle
  • a vehicle or autonomous vehicle 500 includes an antenna unit 508, a communication unit 510, a control unit 520, a driving unit 540a, a power supply unit 540b, a sensor unit 540c, and an autonomous driving unit.
  • a portion 540d may be included.
  • the antenna unit 550 may be configured as a part of the communication unit 510 .
  • Blocks 510/530/540a to 540d respectively correspond to blocks 410/430/440 of FIG. 4 .
  • the communication unit 510 may transmit/receive signals (eg, data, control signals, etc.) with external devices such as other vehicles, base stations (eg, base stations, roadside base units, etc.), servers, and the like.
  • the controller 520 may perform various operations by controlling elements of the vehicle or autonomous vehicle 500 .
  • the controller 520 may include an electronic control unit (ECU).
  • ECU electronice control unit
  • AI devices include TVs, projectors, smartphones, PCs, laptops, digital broadcasting terminals, tablet PCs, wearable devices, set-top boxes (STBs), radios, washing machines, refrigerators, digital signage, robots, vehicles, etc. It may be implemented as a device or a movable device.
  • the AI device 600 includes a communication unit 610, a control unit 620, a memory unit 630, an input/output unit 640a/640b, a running processor unit 640c, and a sensor unit 640d.
  • a communication unit 610 can include Blocks 610 to 630/640a to 640d may respectively correspond to blocks 310 to 330/340 of FIG. 3 .
  • the communication unit 610 communicates wired and wireless signals (eg, sensor information, user data) with external devices such as other AI devices (eg, FIG. 1, 100x, 120, and 140) or AI servers (Fig. input, learning model, control signal, etc.) can be transmitted and received. To this end, the communication unit 610 may transmit information in the memory unit 630 to an external device or transmit a signal received from the external device to the memory unit 630 .
  • external devices eg, sensor information, user data
  • AI devices eg, FIG. 1, 100x, 120, and 140
  • AI servers Fig. input, learning model, control signal, etc.
  • the controller 620 may determine at least one executable operation of the AI device 600 based on information determined or generated using a data analysis algorithm or a machine learning algorithm. And, the controller 620 may perform the determined operation by controlling components of the AI device 600 . For example, the control unit 620 may request, retrieve, receive, or utilize data from the learning processor unit 640c or the memory unit 630, and may perform a predicted operation among at least one feasible operation or one determined to be desirable. Components of the AI device 600 may be controlled to execute an operation. In addition, the control unit 620 collects history information including user feedback on the operation contents or operation of the AI device 600 and stores it in the memory unit 630 or the running processor unit 640c, or the AI server ( 1, 140) can be transmitted to an external device. The collected history information can be used to update the learning model.
  • the memory unit 630 may store data supporting various functions of the AI device 600 .
  • the memory unit 630 may store data obtained from the input unit 640a, data obtained from the communication unit 610, output data of the learning processor unit 640c, and data obtained from the sensing unit 640.
  • the memory unit 630 may store control information and/or software codes required for operation/execution of the controller 620 .
  • the input unit 640a may obtain various types of data from the outside of the AI device 600.
  • the input unit 620 may obtain learning data for model learning and input data to which the learning model is to be applied.
  • the input unit 640a may include a camera, a microphone, and/or a user input unit.
  • the output unit 640b may generate an output related to sight, hearing, or touch.
  • the output unit 640b may include a display unit, a speaker, and/or a haptic module.
  • the sensing unit 640 may obtain at least one of internal information of the AI device 600, surrounding environment information of the AI device 600, and user information by using various sensors.
  • the sensing unit 640 may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, and/or a radar. there is.
  • the learning processor unit 640c may learn a model composed of an artificial neural network using learning data.
  • the running processor unit 640c may perform AI processing together with the running processor unit of the AI server (FIG. 1, 140).
  • the learning processor unit 640c may process information received from an external device through the communication unit 610 and/or information stored in the memory unit 630 .
  • the output value of the learning processor unit 640c may be transmitted to an external device through the communication unit 610 and/or stored in the memory unit 630.
  • the transmitted signal may be processed by a signal processing circuit.
  • the signal processing circuit 700 may include a scrambler 710, a modulator 720, a layer mapper 730, a precoder 740, a resource mapper 750, and a signal generator 760.
  • the operation/function of FIG. 7 may be performed by the processors 202a and 202b and/or the transceivers 206a and 206b of FIG. 2 .
  • blocks 710 to 760 may be implemented in the processors 202a and 202b and/or the transceivers 206a and 206b of FIG. 2 .
  • blocks 710 to 760 may be implemented in the processors 202a and 202b of FIG. 2 .
  • blocks 710 to 750 may be implemented in the processors 202a and 202b of FIG. 2 and block 760 may be implemented in the transceivers 206a and 206b of FIG. 2 , and are not limited to the above-described embodiment.
  • the codeword may be converted into a radio signal through the signal processing circuit 700 of FIG. 7 .
  • a codeword is an encoded bit sequence of an information block.
  • Information blocks may include transport blocks (eg, UL-SCH transport blocks, DL-SCH transport blocks).
  • Radio signals may be transmitted through various physical channels (eg, PUSCH, PDSCH).
  • the codeword may be converted into a scrambled bit sequence by the scrambler 710.
  • 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.
  • the scrambled bit sequence may be modulated into a modulation symbol sequence by modulator 720.
  • 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 730. Modulation symbols of each transport layer may be mapped to corresponding antenna port(s) by the precoder 740 (precoding).
  • the output z of the precoder 740 can be obtained by multiplying the output y of the layer mapper 730 by the N*M precoding matrix W.
  • N is the number of antenna ports and M is the number of transport layers.
  • the precoder 740 may perform precoding after transform precoding (eg, discrete fourier transform (DFT)) on complex modulation symbols. Also, the precoder 740 may perform precoding without performing transform precoding.
  • transform precoding eg, discrete fourier transform (DFT)
  • the resource mapper 750 may map modulation symbols of each antenna port to time-frequency resources.
  • the time-frequency resource may include a plurality of symbols (eg, CP-OFDMA symbols and DFT-s-OFDMA symbols) in the time domain and a plurality of subcarriers in the frequency domain.
  • the signal generator 760 generates a radio signal from the mapped modulation symbols, and the generated radio signal can be transmitted to other devices through each antenna.
  • 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 to the signal processing process 710 to 760 of FIG. 7 .
  • a wireless device eg, 200a and 200b of FIG. 2
  • 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 de-scramble process.
  • a signal processing circuit for a received signal may include a signal restorer, a resource demapper, a postcoder, a demodulator, a descrambler, and a decoder.
  • 6G (radio communications) systems are characterized by (i) very high data rates per device, (ii) very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) battery- It aims to lower energy consumption of battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities.
  • the vision of the 6G system can be four aspects such as “intelligent connectivity”, “deep connectivity”, “holographic connectivity”, and “ubiquitous connectivity”, and the 6G system can satisfy the requirements shown in Table 1 below. That is, Table 1 is a table showing the requirements of the 6G system.
  • the 6G system is enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), mMTC (massive machine type communications), AI integrated communication, tactile Internet (tactile internet), high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion and improved data security ( can have key factors such as enhanced data security.
  • eMBB enhanced mobile broadband
  • URLLC ultra-reliable low latency communications
  • mMTC massive machine type communications
  • AI integrated communication e.g., AI integrated communication
  • tactile Internet tactile internet
  • high throughput high network capacity
  • high energy efficiency high backhaul and access network congestion
  • improved data security can have key factors such as enhanced data security.
  • FIG. 10 is a diagram illustrating an example of a communication structure that can be provided in a 6G system applicable to the present disclosure.
  • a 6G system is expected to have 50 times higher simultaneous wireless communication connectivity than a 5G wireless communication system.
  • URLLC a key feature of 5G, is expected to become a more mainstream technology by providing end-to-end latency of less than 1 ms in 6G communications.
  • the 6G system will have much better volume spectral efficiency, unlike the frequently used area spectral efficiency.
  • 6G systems can provide very long battery life and advanced battery technology for energy harvesting, so mobile devices in 6G systems may not need to be charged separately.
  • AI The most important and newly introduced technology for the 6G system is AI.
  • AI was not involved in the 4G system.
  • 5G systems will support partial or very limited AI.
  • the 6G system will be AI-enabled for full automation.
  • Advances in machine learning will create more intelligent networks for real-time communication in 6G.
  • Introducing AI in communications can simplify and enhance real-time data transmission.
  • AI can use a plethora of analytics to determine how complex target tasks are performed. In other words, AI can increase efficiency and reduce processing delays.
  • AI can also play an important role in machine-to-machine, machine-to-human and human-to-machine communications.
  • AI can be a rapid communication in the brain computer interface (BCI).
  • BCI brain computer interface
  • AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.
  • AI-based physical layer transmission means applying a signal processing and communication mechanism based on an AI driver rather than a traditional communication framework in fundamental signal processing and communication mechanisms. For example, deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based multiple input multiple output (MIMO) mechanism, It may include AI-based resource scheduling and allocation.
  • MIMO multiple input multiple output
  • Machine learning may be used for channel estimation and channel tracking, and may be used for power allocation, interference cancellation, and the like in a downlink (DL) physical layer. Machine learning can also be used for antenna selection, power control, symbol detection, and the like in a MIMO system.
  • DL downlink
  • AI algorithms based on deep learning require a lot of training data to optimize training parameters.
  • a lot of training data is used offline. This is because static training on training data in a specific channel environment may cause a contradiction between dynamic characteristics and diversity of a radio channel.
  • Machine learning refers to a set of actions that train a machine to create a machine that can do tasks that humans can or cannot do.
  • Machine learning requires data and a running model.
  • data learning methods can be largely classified into three types: supervised learning, unsupervised learning, and reinforcement learning.
  • Neural network training is aimed at minimizing errors in the output.
  • Neural network learning repeatedly inputs training data to the neural network, calculates the output of the neural network for the training data and the error of the target, and backpropagates the error of the neural network from the output layer of the neural network to the input layer in a direction to reduce the error. ) to update the weight of each node in the neural network.
  • Supervised learning uses training data in which correct answers are labeled in the learning data, and unsupervised learning may not have correct answers labeled in the learning data. That is, for example, learning data in the case of supervised learning related to data classification may be data in which each learning data is labeled with a category. Labeled training data is input to the neural network, and an error may be calculated by comparing the output (category) of the neural network and the label of the training data. The calculated error is back-propagated in a reverse direction (ie, from the output layer to the input layer) in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back-propagation.
  • a reverse direction ie, from the output layer to the input layer
  • the amount of change in the connection weight of each updated node may be determined according to a learning rate.
  • the neural network's computation of input data and backpropagation of errors can constitute a learning cycle (epoch).
  • the learning rate may be applied differently according to the number of iterations of the learning cycle of the neural network. For example, a high learning rate is used in the early stages of neural network learning to increase efficiency by allowing the neural network to quickly achieve a certain level of performance, and a low learning rate can be used in the late stage to increase accuracy.
  • the learning method may vary depending on the characteristics of the data. For example, in a case where the purpose of the receiver is to accurately predict data transmitted by the transmitter in a communication system, it is preferable to perform learning using supervised learning rather than unsupervised learning or reinforcement learning.
  • the learning model corresponds to the human brain, and the most basic linear model can be considered. ) is called
  • the neural network cord used as a learning method is largely divided into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent boltzmann machine (RNN). and this learning model can be applied.
  • DNN deep neural networks
  • CNN convolutional deep neural networks
  • RNN recurrent boltzmann machine
  • THz communication can be applied in 6G systems.
  • the data transmission rate can be increased by increasing the bandwidth. This can be done using sub-THz communication with wide bandwidth and applying advanced massive MIMO technology.
  • THz waves also known as sub-millimeter radiation
  • THz waves generally represent a frequency band between 0.1 THz and 10 THz with corresponding wavelengths in the range of 0.03 mm-3 mm.
  • the 100 GHz-300 GHz band range (sub THz band) is considered a major part of the THz band for cellular communications. Adding to the sub-THz band mmWave band will increase 6G cellular communications capacity.
  • 300 GHz-3 THz is in the far infrared (IR) frequency band.
  • the 300 GHz-3 THz band is part of the broad band, but is at the border of the wide band, just behind the RF band. Thus, this 300 GHz-3 THz band exhibits similarities to RF.
  • THz communications include (i) widely available bandwidth to support very high data rates, and (ii) high path loss at high frequencies (highly directional antennas are indispensable).
  • the narrow beamwidth produced by the highly directional antenna reduces interference.
  • the small wavelength of the THz signal allows a much larger number of antenna elements to be incorporated into devices and BSs operating in this band. This enables advanced adaptive array technology to overcome range limitations.
  • THz Terahertz
  • FIG. 10 is a diagram illustrating a THz communication method applicable to the present disclosure.
  • THz waves are located between RF (Radio Frequency)/millimeter (mm) and infrared bands, and (i) transmit non-metal/non-polarizable materials better than visible light/infrared rays, and have a shorter wavelength than RF/millimeter waves and have high straightness. Beam focusing may be possible.
  • Multi-arm bandits (MAB) and Thompson sampling (TS)
  • MAB refers to a system in which one candidate can be selected at a time in an environment where a plurality of selectable candidates exist, and the degree of compensation provided in response to the selection is different for each candidate.
  • the selectable candidate may be referred to as an arm.
  • the MAB problem is to find an answer about how to make a selection to maximize the sum of rewards when given a limited number of selection opportunities.
  • the MAB problem can be solved through exploration and exploitation.
  • Use is a method of selecting the best candidate based on existing observations
  • search is a method of selecting new candidates to obtain more observation results. If too few searches are accumulated, choices based on incorrect information may be made. Conversely, if too many searches are conducted, unnecessary opportunity costs may be incurred to obtain more information even though there is sufficient information.
  • utilization and discovery are in a trade-off relationship with each other, and optimizing them is the key to solving the MAB problem.
  • Thomson sampling expresses the probability that a positive reward is given when each arm is selected as a beta distribution.
  • the beta distribution is a probability distribution model represented by two parameters ⁇ and ⁇ .
  • selection of a candidate is performed by randomly sampling values on the x-axis for each of the beta distributions and identifying the candidate corresponding to the largest value.
  • the reward value according to the selection of the corresponding candidate is used to update ⁇ and ⁇ constituting the beta distribution of the corresponding code. For example, a positive result increases ⁇ by 1, and a negative result increases ⁇ by 1.
  • the beta distribution used to express the probability distribution of each candidate in Thomson sampling is defined as in [Equation 1].
  • the beta distribution is a continuous probability distribution defined in the interval [0, 1] by two parameters ⁇ and ⁇ .
  • FIG. 11 shows examples of probability density functions of beta distributions applicable to this disclosure. 11 shows that ( ⁇ , ⁇ ) is (1/3,1), (10,30), (20,20), (1,3), (2,6), (4,4), (2/ 3,2/3), (2,1), and (1,1) beta distributions.
  • ⁇ /( ⁇ + ⁇ ) is (1/3,1), (10,30), (20,20), (1,3), (2,6), (4,4), (2/ 3,2/3), (2,1), and (1,1) beta distributions.
  • a reward distribution of each candidate is estimated using existing data, and a candidate to be given the highest reward is selected according to the estimated distribution.
  • one candidate is selected probabilistically by random sampling based on a beta distribution.
  • ⁇ or ⁇ of the selected candidate is updated based on a result of performing an action according to the selected candidate.
  • the corresponding beta distribution will change to a form that is more concentrated in the center position. It gets lower. If the number of candidates selected is small, the beta distribution will change to a widely distributed form, and the possibility of being selected in the future will arise.
  • FIGS. 12A to 12D Specific examples of updating beta distributions are shown in FIGS. 12A to 12D.
  • 12A to 12D show examples of updating a probability distribution model applicable to the present disclosure.
  • 12A to 12D illustrate changes in three beta distributions (eg, Arm 1, Arm 2, and Arm 3) when selections are made about 1500 times.
  • ( ⁇ , ⁇ ) of the first arm 1, arm 2, and arm 3 are the same as (1,1), (1,1), and (1,1). Since ( ⁇ , ⁇ ) is (1,1), the beta distribution has a uniform distribution with the same probability (eg 1) for all values of x. Since all three arms have the same probability distribution, the search starts with the same probability.
  • ( ⁇ , ⁇ ) of Arm 1, Arm 2, and Arm 3 are (3,2), (2,3), and (2,2).
  • the beta distribution is updated, the probability of each arm being selected is also updated. A clear difference between cancers has not yet been identified.
  • arm 3 since the value selected from the beta distribution of arm 3 is the largest, arm 3 will be selected. The selection of values follows the corresponding beta distribution and is performed by random sampling considering probability.
  • a (2,2) beta distribution such as Arm 3 if random sampling is performed considering the probability, 0.5 with the highest probability will be selected with the highest frequency, but other values other than 0.5 are also less frequent.
  • the y-axis value of 0.5 is about 1.5 and the y-axis value of 0.2 is about 1, so the frequency at which 0.5 is selected through random sampling is 0.2. It can be understood that it is about 1.5 times the frequency of being.
  • ( ⁇ , ⁇ ) of arm 1, arm 2, and arm 3 are (4,3), (2,3), and (5,2).
  • the probability of being selected in the order of arm 3, arm 1, and arm 2 decreases.
  • one value is sampled on the x-axis based on the probability for each of Arm 1, Arm 2, and Arm 3, and since the value selected from the beta distribution of Arm 3 is the largest, Arm 3 will be selected.
  • FIG. 12D after 1496 selections, ( ⁇ , ⁇ ) of Arm 1, Arm 2, and Arm 3 are (33,100), (100,223), and (436,611). Since about 1500 searches have been performed sufficiently, the probability that arm 3 is selected becomes overwhelmingly high.
  • the present disclosure is for performing location-based handover in a wireless communication system, and relates to a technique for selecting a target cell for conditional handover (CHO). Specifically, the present disclosure proposes a predictive mobility-based conditional handover technology that maximizes target cell selection accuracy for conditional handover.
  • a conditional handover technique may be applied.
  • the base station informs the terminal of candidate target cells and handover start conditions for starting handover, and the terminal can start handover without additional reporting when the condition is satisfied.
  • conditional handover proceeds as follows. After connecting to the cellular network, the terminal measures the received signal strength (RSS) values for the serving cell and the neighboring cell according to the measurement configuration, and the received signal strength value is a preset event trigger (event trigger) ) If the condition is satisfied, a measurement report is transmitted. Then, the serving cell base station selects target cells for conditional handover based on the measured received signal strength, and transmits a handover request to the selected target cells in advance. When a response to the handover request is received, the serving cell base station transmits a handover command including information on selected target cells and information on conditions for performing handover to the target cells to the terminal in advance. . After receiving the handover command, the terminal does not immediately perform handover and continues measurement.
  • RSS received signal strength
  • event trigger a preset event trigger
  • the terminal measures the received signal strength values of the serving cell and the target cells, and if a target cell that satisfies the conditions for performing handover is found in the handover command, handover is performed to the found target cell. .
  • the terminal transmits a confirmation signal to the previous serving cell base station, and the previous serving cell base station requests handover cancellation to at least one remaining target cell.
  • Conditional handover is designed so that a UE transmits a measurement report and a base station transmits a handover command in advance in a situation where the signal environment is good, in order to reduce the probability of transmission failure of the measurement report and handover command.
  • the handover is determined in advance, the probability of occurrence of a target cell selection error increases, so it is common to select a plurality of neighbor cells as target cells.
  • the base station may select a plurality of neighboring cells as target cells. When a plurality of target cells are selected, signaling overhead increases, and thus radio resources may be wasted. Accordingly, the present disclosure proposes a technique capable of improving communication performance of the entire system and increasing energy efficiency by maximizing target cell selection accuracy for conditional handover.
  • FIG. 13 illustrates a concept of handover in a wireless communication system according to an embodiment of the present disclosure.
  • a terminal 1310 is located within a cell of a first base station 1320-1, and a second base station 1320-2 and a third base station 1320-3 exist as neighbor base stations.
  • the serving cell of the terminal 1310 is the first base station 1320 - 1 , and the terminal 1320 is predicted to move along the movement path 1302 .
  • a handover procedure may be controlled based on a predicted movement path 1302 .
  • the first base station 1320-1 has information on handover history of terminals in a border area with neighboring cells.
  • the border area is divided into a plurality of unit areas, and the first base station 1320 - 1 retains information about a handover success rate to each neighbor cell using conditional handover in each unit area.
  • information about a handover success rate may include probability distribution models for neighboring cells. Accordingly, if the movement path 1302 of the terminal is predicted, unit areas belonging to the predicted movement path 1302 may be identified, and optimal target cells may be selected according to the handover success rate in the identified unit areas. can
  • the movement path 1302 may be predicted by the terminal 1310 or the first base station 1320-1.
  • the UE 1310 predicts the movement path 1302 and the UE 1310 transmits a measurement report for conditional handover, the UE 1310 returns the predicted movement path 1302.
  • Information on can be transmitted to the first base station 1320-1 together.
  • the first base station 1320-1 predicts a target cell based on handover history information of past terminals at a location representing each unit area included in the predicted movement path 1302, At least one cell having a handover success rate is selected as at least one target cell. Inside the cell of the first base station 1320-1, the first base station 1320-1 may retain information related to handover prediction of all locations.
  • the terminal 1310 when the terminal 1310 reaches the first position 1304 within the cell of the first base station 1320-1 where the measurement report triggering condition is satisfied, the terminal 1310 returns to the first base station 1320-1. send a measurement report to
  • the measurement report may include information about the predicted movement path 1302.
  • the movement path 1302 includes 8 unit areas, a handover success rate to neighboring cells in each of the 8 unit areas can be predicted, and among cells having a handover success rate of a certain level or higher, At least some of them may be selected as target cell(s) for conditional handover of the UE 1310.
  • the terminal 1310 can change the serving cell by starting conditional handover at the second position 1306 adjacent to the cell boundary.
  • handover is a technology that can improve the accuracy of target cell selection for conditional handover by combining movement path prediction and AI-based handover prediction.
  • AI-based handover prediction provides a prediction result for a target cell as an output.
  • a technique for determining a target cell for handover according to various embodiments may be based on a Thomson sampling method-based selection algorithm that guarantees excellent performance among MAB problem solving methods.
  • MAB is a technique that balances exploitation and exploration in recommendation. Use recommends a neighbor cell with a relatively high handover success rate to the UE, while discovery recommends a new neighbor cell in a balanced manner. By applying the MAB technique, a new neighbor cell is appropriately recommended through discovery, and feedback on the selection can be efficiently reflected in the terminal and all base stations. There is a trade-off between use and search, and controlling use and search may temporarily appear as a loss to the UE, but since several neighboring cells are identified through search, it is more efficient overall.
  • FIG. 14 illustrates a concept of target cell selection based on Thomson sampling in a wireless communication system according to an embodiment of the present disclosure.
  • a plurality of neighboring cells 1404-1 to 1404-N are identified based on a movement path prediction result 1402 of the UE.
  • the MAB-Thomson sampling technique 1496 based on use and search is applied, at least one target cell 1408 may be selected.
  • data used in the MAB-Thomson sampling technique 1420 is a probability distribution for each of the neighboring cells 1404-1 to 1404-N.
  • a moving path of a terminal may be predicted using various techniques according to a given environment and available technology. For example, moving path prediction methods may be divided into two types depending on whether or not a moving path is specified.
  • a route guidance program such as a navigation system is running
  • a movement route may be obtained from the corresponding program since the movement route is set.
  • the movement path in a state where the movement path is not designated, the movement path may be predicted using movement history information and environment information of the terminal based on an artificial intelligence model. For example, an artificial intelligence model as shown in FIG. 15 below may be used to predict a movement path.
  • a neural network 15 illustrates an example of an artificial intelligence model for predicting a moving path in a wireless communication system according to an embodiment of the present disclosure.
  • 15 is a neural network capable of effectively predicting a moving path of a terminal so as to respond to the environment and situation by applying environment and situation information of the terminal and inferring/predicting using a weight value learned from the network or base station.
  • a neural network 1510 includes a plurality of layers, specifically, an input layer, at least one hidden layer, and an output layer.
  • the moving path prediction algorithm implemented by the neural network 1510 includes mobility information (eg, global positioning system (GPS) value per time, speed, direction, etc.) of the terminal, received signal strength of a neighboring cell
  • mobility information eg, global positioning system (GPS) value per time, speed, direction, etc.
  • Various variables such as information may be taken as inputs, and a movement path of a terminal may be predicted based on an optimal weight value corresponding to a network or a base station.
  • the optimal weight is a variable for reflecting the environment in the cell.
  • an artificial intelligence model for predicting a movement path as shown in FIG. 15 may be referred to as a 'mobility prediction model'.
  • At least one target cell is selected from locations on the predicted movement path.
  • At least one target cell may be selected based on Thomson sampling.
  • Thomson sampling is an algorithm that estimates the reward distribution of neighboring cells for handover progress based on data observed in the past, and selects a candidate to give the highest reward in the future with high probability based on the estimated distribution. .
  • a reward given to each candidate has a value of 0 or 1 with a probability of p by a Bernoulli trial, and a prior probability of p may follow a beta distribution.
  • the beta distribution is a continuous probability distribution defined on the interval [0, 1] by two parameters ⁇ and ⁇ . If the beta distribution is expressed as a graph, it is shown in FIG. 16 below.
  • 16 illustrates examples of probability density functions of a beta distribution usable in a wireless communication system according to an embodiment of the present disclosure.
  • 16 illustrates beta distributions where ( ⁇ , ⁇ ) are (0.5,0.5), (5,1), (1,3), (2,2), (2,5).
  • ⁇ /( ⁇ + ⁇ ) increases, the center position of the beta distribution approaches 1, and as the value of ⁇ /( ⁇ + ⁇ ) increases, the center position of the beta distribution approaches 0.
  • the reward probability p of the candidate can be estimated to have a distribution of Beta(5, 3).
  • selectable target cells correspond to beta distributions illustrated in FIG. 16 .
  • the target cell is selected using probability matching based on the given exclusive distributions, that is, the estimated distribution, which is a method of maximizing the probability that the base station will receive positive compensation for the selected target cell. .
  • 17 illustrates an example of a procedure for performing handover in a wireless communication system according to an embodiment of the present disclosure. 17 illustrates an operation method of a terminal (eg, terminal 1310 of FIG. 13) performing handover.
  • a terminal eg, terminal 1310 of FIG. 13
  • the terminal receives configuration information related to measurement.
  • the measurement is the signal quality (e.g., RSS, signal to interference noise ratio (SINR), reference signal received power (RSRP), reference signal received quality (RSRQ) for the serving cell or neighboring cells required to perform handover etc.)
  • the setting information includes setting information (eg, information on a measurement report event) related to measurement performance and measurement reporting.
  • the configuration information may further include at least one of a report request for a movement path of the terminal and a report request for information necessary for predicting the movement path.
  • the terminal transmits a measurement report including information related to path prediction.
  • Information related to route prediction may include at least one of a predicted movement route or information necessary to predict a movement route.
  • the movement path indicates a trajectory from the current location of the terminal to the boundary of the serving cell.
  • a trajectory passes through at least one unit area, and may be expressed by representative location values of the unit area in a base station or a terminal.
  • the information required to predict the moving route is indirect information used to determine the moving route, information necessary to request route information to a third-party server that provides navigation services, and an artificial intelligence model for predicting the moving route. It may include at least one of the input data to .
  • the measurement report may include measurement information (eg, a signal quality value) for at least one neighboring cell.
  • the measurement report may be transmitted when an event indicated by the measurement information is satisfied.
  • the terminal receives a handover command message.
  • the terminal may receive a message instructing to perform handover from the base station.
  • the handover command message may be an RRC reconfiguration message including information on at least one target cell for conditional handover.
  • the handover command message may indicate at least one neighbor cell selected based on prediction as at least one target cell.
  • the handover command message may include information related to conditions for executing handover to at least one target cell.
  • at least one target cell indicated by the handover command message since at least one target cell indicated by the handover command message has not yet been determined as a final handover target cell, it may be referred to as a 'candidate cell' or a 'candidate target cell'.
  • the terminal performs handover.
  • the UE performs measurement on at least one indicated target cell, determines that one of the at least one target cell satisfies the condition for performing handover, and then performs handover to the target cell that satisfies the condition.
  • the terminal may perform random access to a base station corresponding to the target cell and transmit a handover confirmation message.
  • the handover confirmation message may be a handover success message.
  • the terminal may generate information for updating the prediction model according to the result of handover and transmit the generated information through a handover confirmation message.
  • Information for updating the prediction model includes information indicating whether to maintain service quality after handover, information indicating signal quality for the serving cell after handover, compensation value used in the prediction model, and cell-specific probability included in the prediction model. It may include at least one of information indicating distribution.
  • handover may be performed based on movement path prediction and target cell prediction.
  • the operation of predicting the target cell may be performed by the UE.
  • information required to perform a prediction operation using an artificial intelligence-based prediction model may be provided to the terminal from the base station.
  • the information required to perform the prediction operation is information related to a prediction model for each representative location of a unit area within a cell, information indicating the number of handovers performed by the prediction model and the number of selections for each cell, or It may include at least one of information indicating a probability distribution for each candidate cell included in the prediction model.
  • Information necessary for performing the prediction operation may be provided through configuration information received in step S1701, a handover command message received in step S1705, or a separate configuration message.
  • the terminal may reselect at least some of the plurality of target cells included in the handover command messages through a prediction operation and perform handover.
  • the measurement report transmitted in step S1703 may not include the predicted movement path and information necessary for predicting the movement path.
  • 18 illustrates an example of a procedure for controlling handover in a wireless communication system according to an embodiment of the present disclosure.
  • 18 illustrates an operating method of a base station (eg, the first base station 1320-1 of FIG. 13) controlling handover.
  • a base station eg, the first base station 1320-1 of FIG. 13
  • the base station transmits configuration information related to measurement.
  • the setting information includes setting information (eg, information on a measurement reporting event) related to measurement performance and measurement reporting.
  • the configuration information may further include at least one of a report request for a movement path of the terminal and a report request for information necessary for predicting the movement path.
  • the base station receives a measurement report including information related to path prediction.
  • Information related to route prediction may include at least one of a predicted movement route or information necessary to predict a movement route.
  • the movement path indicates a trajectory from the current location of the terminal to the boundary of the serving cell.
  • a trajectory passes through at least one unit area, and may be expressed by representative location values of the unit area in a base station or a terminal.
  • the information required to predict the moving route is indirect information used to determine the moving route, information necessary to request route information to a third-party server that provides navigation services, and an artificial intelligence model for predicting the moving route. It may include at least one of the input data to .
  • the measurement report may include measurement information (eg, a signal quality value) for at least one neighboring cell.
  • the measurement report may be transmitted when an event indicated by the measurement information is satisfied.
  • the base station transmits a handover command message.
  • the base station may determine handover of the UE based on the measurement value included in the measurement report. Accordingly, the base station may transmit a handover command message indicating at least one neighboring cell selected based on prediction to the terminal as at least one target cell. Specifically, the base station may predict target cell(s) in each of positions included in the predicted movement path of the terminal and transmit a handover command message indicating the predicted target cell(s).
  • the movement path may be predicted by the terminal or the base station.
  • the handover command message may be an RRC reconfiguration message including information on at least one target cell for conditional handover.
  • at least one target cell indicated by the handover command message since at least one target cell indicated by the handover command message has not yet been determined as a final handover target cell, it may be referred to as a 'candidate cell' or a 'candidate target cell'.
  • the base station receives a handover confirmation message.
  • the handover confirmation message is received from the base station of the target cell after the handover of the terminal is completed.
  • the handover confirmation message may include information for updating the prediction model.
  • Information for updating the prediction model includes information indicating whether to maintain service quality after handover, information indicating signal quality for the serving cell after handover, compensation value used in the prediction model, and cell-specific probability included in the prediction model. It may include at least one of information indicating distribution.
  • the base station updates the predictive model. That is, the base station may update the prediction model based on the information included in the handover confirmation message.
  • the base station determines a compensation value based on the observation information, and updates the predictive model using the determined compensation value.
  • the base station can replace at least a part of the existing prediction model with the updated prediction model.
  • handover may be performed based on movement path prediction and target cell prediction.
  • the operation of predicting the target cell may be performed by the UE.
  • information required to perform a prediction operation using an artificial intelligence-based prediction model may be provided to the terminal from the base station.
  • the information required to perform the prediction operation is information related to a prediction model for each representative location of a unit area within a cell, information indicating the number of handovers performed by the prediction model and the number of selections for each cell, or It may include at least one of information indicating a probability distribution for each candidate cell included in the prediction model.
  • Information necessary for performing the prediction operation may be provided through configuration information transmitted in step S1801, a handover command message transmitted in step S1805, or a separate configuration message.
  • the measurement report received in step S1803 may not include the predicted movement path and information necessary for predicting the movement path.
  • the handover procedure according to various embodiments described above may be expressed as a conditional handover procedure based on predictive mobility.
  • the serving cell base station holds beta distribution information of the Thomson sampling model for candidate cells that can be selected when the terminal performs handover in all locations within the cell. That is, the base station holds beta distribution information determined based on reward information determined according to whether or not the handover of a terminal that has selected each neighboring cell from all locations within its own cell is successful.
  • a handover procedure according to various embodiments can be largely divided into four steps.
  • the serving cell base station transmits a measurement configuration including information related to mobility prediction to the terminal in order to perform conditional handover.
  • the UE predicts a moving path and measures RSS values for a serving cell and a neighboring cell. If the measured RSS values satisfy the conditional handover triggering condition, the UE transmits a measurement report including predicted movement path information and mobility prediction-related information to be updated to the serving cell base station.
  • information related to mobility prediction may include at least one of the following two items.
  • Route information format Location information obtained by dividing a route from the location of a serving cell base station to a designated distance into N equal intervals of time or distance.
  • Weight value Weight value of the mobility prediction model to be used in the terminal. Reflects the environmental characteristics within the serving cell.
  • the serving cell base station performs handover prediction based on movement path information predicted by the terminal and handover history information of all terminals.
  • the serving cell base station selects cells with the highest handover success probability at each location on the movement path (e.g., when the Thomson sampling model is used, the cell with the maximum value among the values randomly sampled with the beta distribution of candidate cells).
  • At least one cell having a handover success rate of a predetermined value or more among the selected cells may be selected as at least one target cell for conditional handover.
  • the serving cell base station requests handover to the at least one selected target cell, and when a response is received from the at least one target cell, information about the selected at least one target cell and the handover to the at least one target cell.
  • a handover command including conditions for performing an overover is transmitted to the terminal in advance.
  • the UE Upon receiving the handover command, the UE does not immediately perform handover and continuously measures RSS values for the serving cell and at least one serving cell.
  • the terminal performs handover to the target cell.
  • the UE transfers whether or not QoS of the service is maintained and an RSS value for the new serving cell to the previous serving cell base station as handover ACK information.
  • information for updating the predictive model may be calculated by the UE, and the result of the calculation may be transmitted to the previous serving cell.
  • the base station of the previous serving cell updates the handover prediction model based on the received information and transmits a handover cancel command to the remaining target cells. If QoS cannot be maintained, the compensation value is zero. If QoS is maintained, the compensation value is determined based on a result of comparing the RSS value for the new serving cell and a threshold value (eg, the RSS value for the previous serving cell + a constant value) as shown in [Equation 2] below.
  • a threshold value eg, the RSS value for the previous serving cell + a constant value
  • R is a compensation value
  • RSS T is an RSS value for a new serving cell after handover
  • RSS S is an RSS value for a previous serving cell
  • ⁇ means a margin value for determining a threshold. If the compensation value is 1, the compensation parameter ⁇ is increased by +1, and if the compensation value is 0, the compensation parameter ⁇ is increased by +1.
  • 19 illustrates an example of signal exchange for handover based on an event-triggered measurement report and prediction operation of a terminal in a wireless communication system according to an embodiment of the present disclosure.
  • 19 shows an embodiment in which movement path prediction is performed by the terminal 1910 and target cell prediction is performed by the first base station 1920-1.
  • a first base station 1920-1 is a serving cell base station
  • a second base station 1920-2 and a second base station 1920-3 are target cell base stations.
  • the first base station 1920-1 transmits measurement configuration information to the UE 1910. That is, after the UE 1910 is connected to the cellular network, the first base station 1920 - 1 transmits measurement settings to the UE 1910 .
  • Measurement settings include information related to mobility prediction.
  • Information related to mobility prediction may include at least one of a format of movement path information and information on an artificial intelligence model for mobility prediction (eg, weight values).
  • the format of the moving path information indicates what type of data is used to represent the moving path, and may indicate, for example, a location value in units of time or a location value in units of distance.
  • the UE 1910 measures RSS values for the serving cell and neighboring cells. And, the UE 1910 predicts the movement path of the UE 1910.
  • the movement route may be obtained from an application related to the movement route (eg, navigation) or may be determined using an artificial intelligence model.
  • the UE 1910 transmits a measurement report to the first base station 1920-1. That is, the UE 1910 determines that an event for a measurement report is satisfied when the RSS value of the serving cell measured according to the measurement configuration information is smaller than a predetermined value. Accordingly, the UE 1910 transmits a measurement report to the first base station 1920-1.
  • the measurement report may include at least one of a predicted movement path of the UE 1910 or a weight value of an updated mobility prediction model.
  • the first base station 1920-1 determines handover of the UE 1910.
  • the first base station 1920-1 determines handover based on the measurement report.
  • the first base station 1920-1 performs prediction based on the predicted movement path reported from the UE 1910, and assigns at least one neighboring cell having a handover prediction success rate of a certain value or more to at least one target cell. choose Based on the predicted movement path, prediction may be understood as prediction of target cells for handover at each of locations included in the movement path.
  • the second base station 1920-2 and the third base station 1920-3 are selected as target cells.
  • the first base station 1920-1 transmits a handover request message to the second base station 1920-2 and the third base station 1920-3, respectively. Accordingly, the second base station 1920-2 and the third base station 1920-3 determine whether to accept the UE 1910 or not. In this exemplary procedure, both the second base station 1920 - 2 and the third base station 1920 - 3 allow acceptance of the UE 1910 .
  • each of the second base station 1920-2 and the third base station 1920-3 transmits a handover request acknowledgment (ACK) message to the first base station 1920-1.
  • ACK handover request acknowledgment
  • the first base station 1920-1 transmits a handover command message to the UE 1910.
  • the handover command message may include information about target cells for conditional handover and information about conditional handover execution conditions. In this exemplary procedure, as target cells, the second base station 1920-2 and the third base station 1920-3 are indicated.
  • step S1919 The UE 1910 evaluates the execution condition of conditional handover.
  • the UE 1910 measures RSS values for the first base station 1920-1 as a serving cell, the second base station 1920-2 and the third base station 1920-3 as target cells, and performs conditional handover. Determine whether the condition is satisfied.
  • the execution condition of the conditional handover to the second base station 1920-2 is satisfied. Accordingly, the UE 1910 performs handover to the second base station 1920-2.
  • the UE 1910 transmits a handover confirmation message to the second base station 1920-2.
  • the handover confirmation message includes information for updating a predictive model (hereinafter referred to as 'model update information'). That is, the UE 1910 transmits model update information together with ACK information.
  • the UE 1910 determines predictive model information to be updated. That is, after performing handover to the target cell, the UE 1910 checks the RSS value of the new serving cell and whether the UE 1910 maintains the quality of service, and updates the prediction model based on the checked information (e.g., In the case of the Thomson sampling model, compensation or beta distribution information) can be determined.
  • the checked information e.g., In the case of the Thomson sampling model, compensation or beta distribution information
  • the second base station 1920-2 transmits a handover confirmation ACK message to the first base station 1920-1.
  • the handover confirmation ACK message indicates handover success and includes model update information received from the UE 1910.
  • the first base station 1920-1 updates the predictive model. That is, the first base station 1920-1 may update the prediction model based on the model update information included in the handover confirmation ACK message.
  • the first base station 1920-1 transmits a handover cancel message to the third base station 1920-3. Accordingly, the third base station 1920-3 may recognize that the UE 1910 has handed over to a base station other than the third base station 1920-3.
  • 20 illustrates an example of a procedure for handover based on movement path estimation and a probability model in a wireless communication system according to an embodiment of the present disclosure.
  • 20 illustrates an operation method of a terminal, a first base station operating as a serving base station, and a second base station operating as a target base station.
  • a terminal connects to a cellular network.
  • the first base station transmits measurement settings to the terminal.
  • the terminal detects an event for a measurement report and transmits the measurement report to the first base station.
  • the first base station determines whether to proceed with handover.
  • the terminal receives a command from the first base station serving cell base station, and measures RSS values for the serving cell and the target cell.
  • the terminal performs handover with a second base station that is a target cell that satisfies the conditions for conditional handover execution.
  • step S2013 the terminal transmits a handover confirmation message to the second base station serving as the target cell.
  • the handover confirmation message includes prediction model information to be updated.
  • step S2015 the second base station transmits a handover ACK message to the first base station serving the previous serving cell.
  • the handover ACK message includes prediction model information to be updated.
  • step S2017 the first base station updates the prediction model and transmits a handover cancellation message to the remaining target cell(s) other than the second base station.
  • FIG. 21 illustrates a reward structure based on Thomson sampling in a wireless communication system according to an embodiment of the present disclosure.
  • 21 shows a structure in which compensation is fed back based on an evaluation index (eg, whether to maintain QoS or not, battery consumption rate change information) after handover is completed.
  • evaluation index eg, whether to maintain QoS or not, battery consumption rate change information
  • sampling includes an operation of randomly sampling a value from a beta distribution 2110 of candidate cells. Among the sampled values, the maximum value is selected by optimization 2120 .
  • Action selects the neighbor cell with the highest handover success rate at each position on the movement path predicted by the UE from the serving cell base station, and among them, the neighbor cell whose success rate is higher than a certain value is selected as the target cell for conditional handover.
  • a cell with the highest success rate may be understood as a cell having the largest value among values randomly sampled from beta distributions of neighboring cells.
  • Observation includes an operation of updating parameters of the beta distribution for compensation based on evaluation 2130 for checking whether QoS is maintained and RSS values after completion of handover. The updated parameters are reflected in the beta distribution 2210.
  • Embodiments of the present disclosure may be applied to various wireless access systems.
  • various wireless access systems there is a 3rd Generation Partnership Project (3GPP) or 3GPP2 system.
  • 3GPP 3rd Generation Partnership Project
  • 3GPP2 3rd Generation Partnership Project2
  • Embodiments of the present disclosure may be applied not only to the various wireless access systems, but also to all technical fields to which the various wireless access systems are applied. Furthermore, the proposed method can be applied to mmWave and THz communication systems using ultra-high frequency bands.
  • embodiments of the present disclosure may be applied to various applications such as free-running vehicles and drones.

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Abstract

La présente divulgation est destinée à exécuter un transfert intercellulaire conditionnel dans un système de communication sans fil. Un procédé de fonctionnement d'un équipement utilisateur (UE) peut comprendre les étapes consistant à : recevoir des informations de configuration relatives à une mesure provenant d'une première station de base ; sur la base des informations de configuration, transmettre un rapport de mesure à la première station de base ; recevoir un message d'instruction de transfert intercellulaire provenant de la première station de base ; et, sur la base du message d'instruction de transfert intercellulaire, exécuter un transfert intercellulaire à une deuxième station de base.
PCT/KR2022/011526 2021-10-06 2022-08-04 Dispositif et procédé d'exécution d'un transfert intercellulaire sur la base d'une position dans un système de communication sans fil WO2023058872A1 (fr)

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Citations (1)

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US20180324623A1 (en) * 2017-05-05 2018-11-08 Motorola Mobility Llc Method and apparatus for transmitting a measurement report on a wireless network

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US20180324623A1 (en) * 2017-05-05 2018-11-08 Motorola Mobility Llc Method and apparatus for transmitting a measurement report on a wireless network

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INTEL CORPORATION: "AI/ML based mobility optimization", 3GPP DRAFT; R3-213471, 3RD GENERATION PARTNERSHIP PROJECT (3GPP), MOBILE COMPETENCE CENTRE ; 650, ROUTE DES LUCIOLES ; F-06921 SOPHIA-ANTIPOLIS CEDEX ; FRANCE, vol. RAN WG3, no. Electronic meeting; 20210816 - 20210826, 6 August 2021 (2021-08-06), Mobile Competence Centre ; 650, route des Lucioles ; F-06921 Sophia-Antipolis Cedex ; France , XP052035299 *
INTERDIGITAL: "Standardization impacts of Mobility Optimization Use Case for AI", 3GPP DRAFT; R3-213787, 3RD GENERATION PARTNERSHIP PROJECT (3GPP), MOBILE COMPETENCE CENTRE ; 650, ROUTE DES LUCIOLES ; F-06921 SOPHIA-ANTIPOLIS CEDEX ; FRANCE, vol. RAN WG3, no. Online; 20210816 - 20210826, 6 August 2021 (2021-08-06), Mobile Competence Centre ; 650, route des Lucioles ; F-06921 Sophia-Antipolis Cedex ; France , XP052035544 *
SAMSUNG: "Remaining Issues on Handover and Neighbor Search for an NTN", 3GPP DRAFT; R2-2107283, 3RD GENERATION PARTNERSHIP PROJECT (3GPP), MOBILE COMPETENCE CENTRE ; 650, ROUTE DES LUCIOLES ; F-06921 SOPHIA-ANTIPOLIS CEDEX ; FRANCE, vol. RAN WG2, no. e-Meeting; 20210809 - 20210827, 5 August 2021 (2021-08-05), Mobile Competence Centre ; 650, route des Lucioles ; F-06921 Sophia-Antipolis Cedex ; France , XP052032275 *
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