[go: up one dir, main page]

WO2025005772A1 - Procédé de modification de modèle d'intelligence artificielle dans un système de communication sans fil, et dispositif associé - Google Patents

Procédé de modification de modèle d'intelligence artificielle dans un système de communication sans fil, et dispositif associé Download PDF

Info

Publication number
WO2025005772A1
WO2025005772A1 PCT/KR2024/095696 KR2024095696W WO2025005772A1 WO 2025005772 A1 WO2025005772 A1 WO 2025005772A1 KR 2024095696 W KR2024095696 W KR 2024095696W WO 2025005772 A1 WO2025005772 A1 WO 2025005772A1
Authority
WO
WIPO (PCT)
Prior art keywords
model
information
terminal
dci
base station
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/KR2024/095696
Other languages
English (en)
Korean (ko)
Inventor
백정석
김서욱
송수은
이재홍
이창성
장현덕
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Samsung Electronics Co Ltd
Original Assignee
Samsung Electronics Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Samsung Electronics Co Ltd filed Critical Samsung Electronics Co Ltd
Publication of WO2025005772A1 publication Critical patent/WO2025005772A1/fr
Anticipated expiration legal-status Critical
Pending legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/318—Received signal strength
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/318—Received signal strength
    • H04B17/328—Reference signal received power [RSRP]; Reference signal received quality [RSRQ]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/0632—Channel quality parameters, e.g. channel quality indicator [CQI]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/20—Control channels or signalling for resource management
    • H04W72/23—Control channels or signalling for resource management in the downlink direction of a wireless link, i.e. towards a terminal
    • H04W72/232—Control channels or signalling for resource management in the downlink direction of a wireless link, i.e. towards a terminal the control data signalling from the physical layer, e.g. DCI signalling
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Definitions

  • the present disclosure relates to a method for changing an artificial intelligence (AI) model in a wireless communication system and a device therefor.
  • AI artificial intelligence
  • the maximum transmission speed in the 6G communication system which is expected to be realized around 2030, is tera (i.e., 1,000 giga) bps, and the wireless delay time is 100 microseconds ( ⁇ sec). In other words, the transmission speed in the 6G communication system is 50 times faster than that of the 5G communication system, and the wireless delay time is reduced to one-tenth.
  • 6G communication systems are being considered for implementation in terahertz bands (e.g., from 95 gigahertz (95 GHz) to 3 terahertz (3 THz) bands).
  • terahertz bands e.g., from 95 gigahertz (95 GHz) to 3 terahertz (3 THz) bands.
  • mmWave millimeter wave
  • 6G communication systems are being developed with full duplex technology that utilizes the same frequency resources for uplink and downlink at the same time, network technology that comprehensively utilizes satellites and HAPS (high-altitude platform stations), network structure innovation technology that supports mobile base stations and enables optimization and automation of network operation, dynamic spectrum sharing technology through collision avoidance based on spectrum usage prediction, AI-based communication technology that utilizes artificial intelligence (AI) from the design stage and internalizes end-to-end AI support functions to realize system optimization, and next-generation distributed computing technology that realizes services with complexity that exceeds the limits of terminal computing capabilities by utilizing ultra-high-performance communication and computing resources (mobile edge computing (MEC), cloud, etc.).
  • MEC mobile edge computing
  • 6G communication systems will enable a new level of hyper-connected experience through the hyper-connectivity of 6G communication systems that includes not only connections between things but also connections between people and things.
  • 6G communication systems will enable the provision of services such as truly immersive extended reality (truly immersive XR), high-fidelity mobile holograms, and digital replicas.
  • services such as remote surgery, industrial automation, and emergency response through enhanced security and reliability will be provided through 6G communication systems, which will be applied in various fields such as industry, medicine, automobiles, and home appliances.
  • a method for changing an AI model set in a terminal and a base station in a wireless communication system and a device therefor are provided.
  • a method for operating a terminal in a wireless communication system includes an operation of receiving, from a base station, configuration information including information on a criterion for changing an AI model according to a channel state, wherein the AI model is used to obtain DCI (downlink control information) prediction information for at least one prediction unit, and the DCI prediction information indicates whether a DCI exists in a corresponding prediction unit; and an operation of identifying whether the criterion is satisfied based on the information on the criterion and measurement information of the terminal, wherein the changing of the AI model corresponds to a change from one AI model to another AI model among a plurality of preset AI models, and the number of prediction units per inference for each of the plurality of AI models can be set differently.
  • configuration information including information on a criterion for changing an AI model according to a channel state
  • the AI model is used to obtain DCI (downlink control information) prediction information for at least one prediction unit, and the DCI prediction information indicates whether a DCI exists in a corresponding prediction unit
  • a method of operating a base station in a wireless communication system includes: an operation of generating configuration information including information on a criterion for changing an AI model according to a channel state, wherein the AI model is used to obtain DCI prediction information for at least one prediction unit, and the DCI prediction information indicates whether a DCI exists in the corresponding prediction unit; and an operation of transmitting the configuration information to a terminal, wherein the changing of the AI model corresponds to a change from one AI model to another AI model among a plurality of preset AI models, and the number of prediction units per inference for each of the plurality of AI models can be set differently.
  • a terminal in a wireless communication system includes: a transceiver; and at least one processor connected to the transceiver and including a processing circuit, wherein the at least one processor is configured to: receive, from a base station, configuration information including information on a criterion for changing an AI model according to a channel state, wherein the AI model is used to obtain DCI prediction information for at least one prediction unit, and the DCI prediction information indicates whether a DCI exists in the corresponding prediction unit; and identify, based on the information on the criterion and measurement information of the terminal, whether the criterion is satisfied, wherein the changing of the AI model corresponds to a change from one AI model to another AI model within a plurality of preset AI models, and the number of prediction units per inference for each of the plurality of AI models can be set differently.
  • a base station in a wireless communication system includes a transceiver; and at least one processor connected to the transceiver and including a processing circuit, wherein the at least one processor is configured to: generate configuration information including information on a criterion for changing an AI model according to a channel condition, wherein the AI model is used to obtain DCI prediction information for at least one prediction unit, and the DCI prediction information indicates whether a DCI exists in the corresponding prediction unit; and transmit the configuration information to a terminal, wherein the changing of the AI model corresponds to a change from one AI model to another AI model among a plurality of preset AI models, and the number of prediction units per inference for each of the plurality of AI models can be set differently.
  • a wireless communication system by changing an AI model set in a terminal and a base station according to the state of a channel, power consumption can be reduced and/or prediction accuracy can be increased.
  • FIG. 1 illustrates an exemplary wireless communication system according to one embodiment of the present disclosure.
  • FIG. 2 illustrates an exemplary structure of a wireless communication system for supporting wireless communication according to one embodiment of the present disclosure.
  • FIG. 3 exemplarily illustrates a signal transmission method of a wireless communication system according to one embodiment of the present disclosure.
  • FIG. 4 illustrates an exemplary wireless frame structure according to one embodiment of the present disclosure.
  • FIG. 5 illustrates an example of a physical downlink control channel (PDCCH) signaling procedure according to one embodiment of the present disclosure.
  • PDCCH physical downlink control channel
  • FIG. 6 illustrates an example of an AI-based PDCCH signaling procedure according to one embodiment of the present disclosure.
  • FIG. 7 illustrates an example of an AI model used in an AI-based PDCCH signaling procedure according to one embodiment of the present disclosure.
  • FIG. 8 is a flowchart of an AI-based PDCCH signaling procedure according to one embodiment of the present disclosure.
  • FIG. 9 illustrates an example of an AI model for an AI-based PDCCH signaling procedure according to an embodiment of the present disclosure.
  • FIG. 10 is a diagram comparing a PDCCH monitoring ratio when AI-based PDCCH monitoring is performed and a PDCCH monitoring ratio when AI-based PDCCH monitoring is not performed according to one embodiment of the present disclosure.
  • FIG. 11 illustrates an example of an AI model set in a base station and a terminal according to one embodiment of the present disclosure.
  • FIG. 12a illustrates an example of a method in which a base station and a terminal perform PDCCH prediction using an AI model according to one embodiment of the present disclosure.
  • FIG. 12b illustrates an example of a method in which a base station and a terminal perform PDCCH prediction using an AI model according to one embodiment of the present disclosure.
  • FIG. 12c illustrates an example of a method in which a base station and a terminal perform PDCCH prediction using an AI model according to one embodiment of the present disclosure.
  • FIG. 13 illustrates an example of output mismatch due to input mismatch of an AI model according to one embodiment of the present disclosure.
  • FIG. 14 illustrates the PDCCH failure probability according to the number of prediction units per inference according to one embodiment of the present disclosure.
  • FIG. 15 is a flowchart of a procedure for a base station to set criteria for changing an AI model according to one embodiment of the present disclosure.
  • FIG. 16A is a flowchart illustrating an example of a method by which a terminal initiates a procedure for changing an AI model according to one embodiment of the present disclosure.
  • FIG. 16b is a flowchart illustrating an example of a method by which a terminal initiates a procedure for changing an AI model according to one embodiment of the present disclosure.
  • FIG. 17 is a flowchart illustrating a method by which a base station initiates a procedure for changing an AI model according to one embodiment of the present disclosure.
  • FIG. 18 is a flowchart of a procedure for changing and synchronizing an AI model according to one embodiment of the present disclosure.
  • FIG. 19 is a flowchart of a procedure for changing and synchronizing an AI model according to one embodiment of the present disclosure.
  • FIG. 20 illustrates an example of a synchronization adjustment method according to one embodiment of the present disclosure.
  • FIG. 21 is a flowchart of an AI model change procedure according to one embodiment of the present disclosure.
  • FIG. 22 is a flowchart of an AI model change procedure according to one embodiment of the present disclosure.
  • FIG. 23 is a flowchart of an AI model change procedure according to one embodiment of the present disclosure.
  • FIG. 24 is a flowchart of an AI model change procedure according to one embodiment of the present disclosure.
  • FIG. 25 is a flowchart of a method of operating a terminal according to one embodiment of the present disclosure.
  • FIG. 26 is a flowchart of a method of operating a base station according to one embodiment of the present disclosure.
  • FIG. 27 is a flowchart of a method of operating a terminal according to one embodiment of the present disclosure.
  • FIG. 28 is a flowchart of a method of operating a base station according to one embodiment of the present disclosure.
  • FIG. 29 is a flowchart of a method of operating a terminal according to one embodiment of the present disclosure.
  • FIG. 30 is a flowchart of a method of operating a base station according to one embodiment of the present disclosure.
  • Figure 31 shows the configuration of a terminal according to one embodiment of the present disclosure.
  • FIG. 32 illustrates a configuration of a base station according to one embodiment of the present disclosure.
  • FIG. 1 illustrates an exemplary wireless communication system according to one embodiment of the present disclosure.
  • a wireless communication system may include at least one base station (110) and at least one terminal (e.g., at least one of a plurality of terminals (120a, 120b, 120c, 120d)).
  • the base station (110) and/or the terminal (120a, 120b, 120c, 120d) may be a wireless communication device or an end device that performs wireless communication.
  • a plurality of terminals (120a, 120b, 120c, 120d) included in a wireless communication system (100) may be in the same or different operation states (e.g., active state, inactive state, or idle state).
  • the plurality of terminals (120a, 120b, 120c, 120d) may perform operations according to the operation states.
  • the plurality of terminals (120a, 120b, 120c, 120d) may include one or more terminals in the same operation state.
  • the reference numeral '120' below may be used to refer to all of the plurality of terminals (120a, 120b, 120c, 120d) or to refer to at least one specific terminal. At least one specific terminal may be a terminal having substantially the same or similar operation state.
  • the base station (110) is a network infrastructure that provides wireless access to the terminal (120).
  • the base station (110) has coverage defined as a certain geographical area based on the distance at which a signal can be transmitted. The coverage may be a service area corresponding to the cell.
  • the base station (110) may be referred to as an 'access point (AP)', an 'eNodeB (eNB)', a '5th generation node', a 'next Generation NodeB (gNB)', a '5G NodeB (5gNB)', a 'wireless point', a 'transmission/reception point (TRP)', a 'digital unit (DU)', a 'radio unit (RU), a remote radio head (RRH)', or other terms having an equivalent technical meaning thereto.
  • AP 'access point
  • eNB eNodeB
  • gNB '5th generation node'
  • gNB 'next Generation Node
  • the terminal (120) is a device used by a user and performs communication with the base station (110) through a wireless channel. In some cases, the terminal (120) may be operated without the involvement of the user. That is, the terminal (120) is a device that performs machine type communication (MTC) and may not be carried by the user.
  • the terminal (120) may be referred to as a 'user equipment (UE)', a 'mobile station', a 'subscriber station', a 'remote terminal', a 'wireless terminal', an 'electronic device', or a 'user device' or other terms having an equivalent technical meaning thereto.
  • the terminal (120) may include, for example, at least one of a cellular phone, a smart phone, a computer, a tablet PC, a mobile phone, a video phone, an e-book reader, a desktop PC, a laptop PC, a netbook computer, a workstation, a server, a PDA, a portable multimedia player (PMP), an MP3 player, a medical device, a camera, a wearable device, or a multimedia system capable of performing a communication function.
  • the type of the terminal is not limited to the above examples.
  • the base station (110) or the terminal (120) may perform beamforming.
  • the beamforming may include transmission beamforming or reception beamforming.
  • the base station (110) may perform transmission beamforming for signal transmission through downlink, and may perform reception beamforming for signal reception through uplink.
  • the terminal (120) may perform reception beamforming for signal reception through downlink, and may perform transmission beamforming for signal transmission through uplink. That is, the base station (110) or the terminal (120) may provide directionality to the transmission signal or the reception signal.
  • the base station (110) or the terminal (120) may select serving beams through a beam search or beam management procedure.
  • the base station (110) or terminal (120) can perform communication through resources that are in a QCL (quasi co-located) relationship with the resources that transmitted the serving beams.
  • the base station (110) or terminal (120) can transmit and receive wireless signals in a millimeter wave (mmWave) band (e.g., 28 GHz, 30 GHz, 38 GHz, 60 GHz).
  • mmWave millimeter wave
  • the terminal (120) can perform a synchronization process and/or a cell search procedure through a primary synchronization signal (PSS), a secondary synchronization signal (SSS), and a physical broadcast channel (PBCH).
  • PSS primary synchronization signal
  • SSS secondary synchronization signal
  • PBCH physical broadcast channel
  • the terminal (120) can perform an access procedure to complete connection to a network through a base station (110).
  • the terminal (120) can transmit a preamble through a physical random access channel (PRACH) and receive a response message to the preamble.
  • PRACH physical random access channel
  • the terminal that has performed the procedure as described above can then receive a downlink control channel (e.g., a physical downlink control channel (PDCCH)) or a downlink data channel (e.g., a physical downlink shared channel (PDSCH)) as an uplink/downlink signal transmission procedure, or transmit an uplink control channel (e.g., a physical uplink control channel (PUCCH)) or an uplink data channel (e.g., a physical uplink shared channel (PUSCH)).
  • a downlink control channel e.g., a physical downlink control channel (PDCCH)
  • a downlink data channel e.g., a physical downlink shared channel (PUSCH)
  • PUCCH physical uplink control channel
  • PUSCH physical uplink shared channel
  • the AI model in the wireless communication system (100) can perform training using information input from the base station (110) and/or information input from terminals (120).
  • the AI model can evolve through learning through training.
  • the AI model can be defined for each function implemented in the wireless communication system (100) and/or for each terminal.
  • an AI model for obtaining downlink control information may be implemented.
  • the AI model for obtaining DCI may be implemented for each terminal, for example, or may be implemented as one for multiple terminals (e.g., all terminals).
  • the AI model for obtaining DCI may also be referred to as an AI model for PDCCH prediction.
  • an AI model for obtaining DCI prediction information may use channel quality indication (CQI) information that may be obtained or provided by a terminal (120) and scheduling information that may be obtained or provided by a base station (110) as input information.
  • CQI channel quality indication
  • the AI model may evolve to service a corresponding function through learning by CQI information and scheduling information corresponding to input information. Evolution means that the accuracy of DCI prediction information may be gradually increased through repetitive learning through training.
  • an artificial intelligence model for obtaining DCI may be applied (or set) to each of the base station (110) and the terminal (120).
  • the base station (110) and the terminal (120) must be able to share input information for the corresponding AI model with each other.
  • the terminal (120) may measure channel quality and report CQI information corresponding to the measured channel quality to the base station (110).
  • the PF (proportional fair) scheduler of the base station (110) may obtain scheduling information using the CQI information.
  • the base station (110) may transmit the obtained scheduling information to the terminal (120).
  • the terminal (120) may obtain scheduled information, which is scheduling information transmitted from the base station (110).
  • the base station (110) can input CQI information and scheduling information of each terminal (120) into an AI model activated for each terminal to obtain DCI prediction information for the terminal.
  • the terminal (120) can input its own CQI information and scheduling information into an AI model activated to perform the corresponding function to obtain DCI prediction information for itself. If the AI models in the base station (110) and the terminal (120) have been learned through the same training process and the input information is synchronized, the base station (110) and the terminal (120) will obtain substantially the same DCI prediction information.
  • the DCI prediction information from the AI model can indicate whether a DCI for the terminal exists in a specific prediction unit (e.g., slot) (e.g., '1' if a DCI exists or '0' if a DCI does not exist).
  • a specific prediction unit e.g., slot
  • FIG. 2 illustrates an exemplary structure of a wireless communication system for supporting wireless communication according to one embodiment of the present disclosure.
  • a wireless communication system may include one or more base stations (210a, 210b) (e.g., base station (110) of FIG. 1) or a core network (CN) (220) (e.g., evolved packet core (EPC) or New Generation Core (NGC)).
  • CN core network
  • EPC evolved packet core
  • NGC New Generation Core
  • One or more base stations (210a, 210b) may be connected to the CN (220).
  • the base stations (210a, 210b) may include eNBs corresponding to base stations in an LTE network or gNBs corresponding to base stations in an NR network, and may include base stations with new names that may be introduced in a 6G network.
  • the base stations (210a, 210b) may be devices unrelated to a radio access technology (RAT), such as APs in a WiFi network.
  • RAT radio access technology
  • One or more base stations may be configured as one unit or may be divided into various units.
  • Various units configuring one base station may support mobile communication functions corresponding to the role of the base station.
  • the mobile communication functions may include functions for each PDCP/RLC/MAC/PHY/RF layer, for example.
  • One unit may support one or more functions. Multiple units may support one or more functions in a distributed manner.
  • One or more base stations (210a, 210b) may be interconnected with other base stations via an interface such as X2 or Xn.
  • the base stations (210a, 210b) may be interconnected with each other via an X2 or Xn interface.
  • the base stations (210a, 210b) may be interconnected with a CN (220) (e.g., NGC) via an interface such as S1 or NG.
  • the interface such as S1 or NG may be an interface between the base station and the core network.
  • the one or more base stations (210a, 210b) may be interconnected with an Access and Mobility Management Function (AMF) via an N2 interface.
  • AMF Access and Mobility Management Function
  • the one or more base stations (210a, 210b) may be interconnected with a User Plane Function (UPF) via an N3 interface.
  • UPF User Plane Function
  • FIG. 3 exemplarily illustrates a signal transmission method of a wireless communication system according to one embodiment of the present disclosure.
  • a terminal (UE) (120) may receive information from a base station (BS) (110) (e.g., the base station (110) of FIG. 1) via a downlink.
  • the terminal (120) may transmit information to the base station (110) via an uplink.
  • the information transmitted and received by the base station (110) and/or the terminal (120) may include data and/or various control information.
  • the physical channels in the base station (110) and/or the terminal (120) may exist in various ways depending on the type and/or purpose of the information transmitted and received.
  • the terminal (120) may perform an initial cell search to synchronize with the base station (110).
  • the terminal (120) may perform the initial cell search, for example, in response to being powered on or entering a new cell.
  • the terminal (120) may receive a synchronization signal (e.g., a primary synchronization signal (PSS) and/or a secondary synchronization signal (SSS)) from the base station (110) to synchronize with the base station (110).
  • PSS primary synchronization signal
  • SSS secondary synchronization signal
  • the terminal (120) may obtain information such as a cell identifier that may identify the base station (110).
  • the terminal (120) may receive a physical broadcast channel (PBCH) from the base station (110) identified based on the synchronization signal.
  • PBCH physical broadcast channel
  • the terminal (120) may obtain broadcast information (e.g., a master information block (MIB)) of the cell transmitted by the base station (110) through the PBCH.
  • the terminal (120) can identify the downlink channel status by receiving a downlink reference signal (DL RS) during the initial cell search. As a result, the terminal (120) can complete downlink synchronization with the base station (110).
  • broadcast information e.g., a master information block (MIB)
  • MIB master information block
  • DL RS downlink reference signal
  • the terminal (120) can receive system information (SI) through a downlink physical channel (e.g., PDCCH or PDSCH).
  • SI system information
  • the terminal (120) can identify a radio resource to which a PDSCH is to be transmitted through the PDCCH, and receive system information through the PDSCH that uses the identified radio resource.
  • the system information can include, for example, a lot of information that the terminal (120) requires for wireless communication with the base station (110).
  • the terminal (120) When the terminal (120) obtains system information through synchronization for the downlink, it can perform a random access procedure for the base station (110) using the system information.
  • the random access procedure can include a plurality of operations (e.g., operation 303, operation 304, operation 305, and operation 306) for implementing the random access.
  • the terminal (120) can transmit a preamble corresponding to a specific sequence through a physical random access channel (PRACH) (msg1).
  • the terminal (120) can obtain resources for the PRACH from a first system information block (SIB1: system information block 1) received through a PDSCH.
  • SIB1 system information block 1
  • the preamble is a unique signal used for an initial connection between the terminal (120) and the base station (110) in wireless communication. Based on the preamble, the terminal (120) can inform the base station (110) of its existence and the start of wireless communication.
  • the terminal (120) may receive a random access response (RAR) for the preamble signal via the PDCCH or the corresponding PDSCH (msg2).
  • RAR random access response
  • the RAR may be a response that notifies the terminal (120) that the base station (110) is ready to start communication in response to recognizing the preamble of the terminal (120).
  • the terminal (120) may transmit an RRC connection request message (msg3) to the base station (110) using scheduling information in the RAR.
  • the terminal (120) may receive a response message (msg4) corresponding to the RRC connection request message (msg3) from the base station (110).
  • the terminal (120) may transmit a PUSCH to the base station (110) using information obtained from the response message (msg4).
  • the scheduling information may include, for example, information indicating when or which frequency band the base station (110) will use to transmit data to the terminal (120).
  • the terminal (120) may complete uplink synchronization with the base station (110).
  • the terminal (120) may additionally perform a contention resolution procedure in operation 306 in the case of a contention-based RACH. Specifically, the terminal (120) may perform a contention resolution procedure such as reception of a PDCCH and a PDSCH corresponding thereto. This may be a process for determining whether to grant priority to a specific terminal when multiple terminals attempt to access the base station (110) using the same preamble at the same time. This process is a procedure necessary for efficiently using resources in wireless communications.
  • the terminal (120) that has performed at least some of operations 301 to 306 may perform a procedure for receiving or transmitting a signal via uplink/downlink.
  • the terminal (120) may perform a downlink signal reception procedure for receiving a signal via PDCCH and/or PDSCH in operation 307.
  • the terminal (120) may perform an uplink signal transmission procedure for transmitting a signal via PUSCH and/or PUCCH in operation 308.
  • the terminal (120) can receive downlink control information (DCI) via a PDCCH.
  • the terminal (120) can obtain control information such as resource allocation information from the DCI.
  • the DCI can be configured in different formats depending on the intended use.
  • the intended use may include, but is not limited to, PUSCH scheduling within a cell, one or multiple PUSCH scheduling within a cell, or indication of cell group downlink feedback information for the terminal (120), PUSCH scheduling within a cell, PDSCH scheduling within a downlink cell, and/or PDCSH scheduling within a cell.
  • the terminal (120) may also transmit uplink control information (UCI) to the base station (110).
  • UCI may include at least one of hybrid automatic repeat and request acknowledgement/negative-ACK (HARQ-ACK/NACK), scheduling request (SR), channel quality indication (CQI), precoding matrix indication (PMI), rank indication (RI), and/or beam indication (BI).
  • HARQ-ACK/NACK hybrid automatic repeat and request acknowledgement/negative-ACK
  • an AI model can be used.
  • the AI model can be used in at least a part of the signal transmission procedure.
  • At least one function of the base station (110) and/or the terminal (120) of the wireless communication system (100) can be performed instead by the AI model or supported by the AI model.
  • FIG. 4 illustrates an exemplary wireless frame structure according to one embodiment of the present disclosure.
  • each radio frame may have a length of, for example, 10 ms.
  • the radio frame may be divided into two half frames.
  • Each half frame may have a length of, for example, 5 ms.
  • the half frame may be divided into five subframes.
  • Each subframe may have a length of, for example, 1 ms.
  • a subframe can be divided into one or more slots.
  • the number of slots within a subframe is called the numerology ( ) (or, subcarrier spacing (SCS)).
  • SCS subcarrier spacing
  • a subframe can contain 1 slot.
  • a subframe can contain two slots.
  • a subframe can contain 4 slots.
  • a subframe can contain 8 slots.
  • the subframe may include 16 slots.
  • a slot may contain 12 or 14 OFDM symbols depending on the cyclic prefix (CP). For example, when a normal CP is used, each slot may contain 14 OFDM symbols. For example, when an extended CP is used, each slot may contain 12 OFDM symbols.
  • CP cyclic prefix
  • the structure of the wireless frame of FIG. 4 is only an example, and the number of subframes, the number of slots, the number of symbols, and the length of frames/subframes/slots/symbols within the frame may be changed in various ways.
  • the PDCCH signaling procedure may also be referred to as a PDCCH monitoring procedure.
  • PDCCH can carry DCI.
  • Scheduling information for uplink data (or PUSCH) or downlink data (or PDSCH) can be included in DCI and transmitted from the base station to the terminal.
  • the terminal can monitor a DCI format for fallback and a DCI format for non-fallback for PUSCH or PDSCH.
  • the fallback DCI format can be composed of fixed fields predefined between the base station and the terminal, and the non-fallback DCI format can include configurable fields.
  • DCI can be transmitted through PDCCH after channel coding and modulation process.
  • a cyclic redundancy check (CRC) is attached to the DCI message payload, and the CRC can be scrambled with a radio network temporary identifier (RNTI) corresponding to the identity of the UE.
  • RNTI radio network temporary identifier
  • Different RNTIs can be used depending on the purpose of the DCI message, such as UE-specific data transmission, power control command, or random access response. That is, RNTI is not transmitted explicitly, but is scrambled in the CRC after calculating the CRC, or is included in the CRC after being operated on a bit-by-bit basis (e.g., OR, AND, XOR, etc.).
  • the UE checks the CRC using the allocated RNTI, and if the CRC check result is correct, the UE can know that the message was transmitted to the UE.
  • a DCI scheduling a PDSCH for system information (SI) may be scrambled with SI-RNTI.
  • SI system information
  • a DCI scheduling a PDSCH for a random access response (RAR) message may be scrambled with RA-RNTI.
  • RAR random access response
  • a DCI scheduling a PDSCH for a paging message may be scrambled with P-RNTI.
  • a DCI notifying a slot format indicator (SFI) may be scrambled with SFI-RNTI.
  • a DCI notifying a transmit power control (TPC) may be scrambled with TPC-RNTI.
  • a DCI scheduling a UE-specific PDSCH or PUSCH may be scrambled with C-RNTI (cell RNTI).
  • a PDCCH may be composed of, for example, 1, 2, 4, 8, or 16 CCEs (control channel elements) depending on an AL (aggregation level).
  • a CCE is a logical allocation unit used to provide a PDCCH with a preset code rate depending on a wireless channel condition.
  • a CCE may be composed of, for example, 6 REGs (resource element groups).
  • a REG may be defined by, for example, one OFDM symbol and one (P)RB.
  • CORESET control resource set
  • CORESET can be defined as a REG set with a given pneumologue, for example. Multiple CORESETs for one UE can overlap in the time/frequency domain.
  • CORESET can be configured via system information (e.g., MIB) or UE-specific higher layer (e.g., RRC layer) signaling. For example, the number of RBs and the number of OFDM symbols (up to 3) composing the CORESET can be configured via higher layer signaling.
  • the UE may monitor PDCCH candidates.
  • the PDCCH candidates may indicate, for example, CCE(s) that the UE should monitor for PDCCH detection.
  • each PDCCH candidate may be defined as 1, 2, 4, 8, or 16 CCEs according to AL.
  • Monitoring may include (blind) decoding the PDCCH candidates.
  • a set of PDCCH candidates that the UE monitors is defined as a PDCCH search space.
  • the search space includes a common search space (CSS) or a UE-specific search space (USS).
  • the UE may acquire DCI by monitoring PDCCH candidates in one or more search (search) spaces established by MIB or higher layer signaling.
  • Each CORESET is associated with one or more search (search) spaces, and each search space may be associated with one CORESET.
  • FIG. 5 illustrates an example of a PDCCH signaling procedure according to one embodiment of the present disclosure.
  • the PDCCH signaling procedure of the embodiment of FIG. 5 may be a non-AI based PDCCH signaling procedure that does not utilize an AI model.
  • the non-AI based PDCCH signaling procedure may be referred to as a PDCCH monitoring procedure, or a non-AI based PDCCH monitoring procedure.
  • the non-AI PDCCH signaling procedure may be, for example, a signaling procedure using an always awake scheme or a signaling procedure using a discontinuous reception (DRX) scheme (e.g., C(connected mode)-DRX).
  • DRX discontinuous reception
  • the terminal (120) In the always awake mode, the terminal (120) always monitors the PDCCH in the awake state, regardless of whether the PDCCH carrying the DCI is transmitted. For example, as illustrated in FIG. 5, in the always awake mode, the terminal (120) monitors the PDCCH in every slot, regardless of whether the PDCCH carrying the DCI is transmitted. For example, the terminal (120) monitors all possible PDCCH candidates in every slot, regardless of whether the DCI is transmitted (blind decoding). This causes large power consumption.
  • the base station (110) sets a DRX cycle to the terminal (120), and the terminal (120) can wake up periodically and monitor the PDCCH for the period set by the onDurationTimer. If the PDCCH is successfully received, the terminal (120) can extend the active time based on the period set by the inactive timer. Even when the DRX method is used, the terminal (120) monitors the PDCCH in every slot while it is awake, regardless of whether the PDCCH carrying the DCI is transmitted. That is, the terminal (120) cannot skip PDCCH monitoring for slots in which the PDCCH is not transmitted. This may cause unnecessary power consumption of the terminal (120).
  • the DRX method e.g., C-DRX
  • FIG. 6 illustrates an example of an AI-based PDCCH signaling procedure according to an embodiment of the present disclosure.
  • FIG. 7 illustrates an example of an AI model used in an AI-based PDCCH signaling procedure according to an embodiment of the present disclosure.
  • the PDCCH signaling procedure of the embodiment of FIG. 6 may be an AI-based PDCCH signaling procedure using an AI model.
  • the AI-based PDCCH signaling procedure may also be referred to as an AI-based PDCCH monitoring procedure, or an AI-driven PDCCH signaling procedure, or an AI-driven PDCCH monitoring procedure.
  • a terminal (120) and a base station (110) can perform PDCCH prediction (or inference) using a preset AI model.
  • the AI model may be initially set upon cell connection, or may be additionally set when performance deteriorates during operation.
  • the AI model may be set from the base station (110) to the terminal (120), for example, via upper layer signaling (e.g., RRC message).
  • activation of the set AI model may be performed, for example, via PHY layer signaling (e.g., DCI).
  • a plurality of AI models may be set by the base station (110) to the terminal (120) via the RRC message, and at least one of the plurality of AI models set via the DCI may be activated in the terminal (120).
  • this is merely an example, and there may be various methods for setting and/or activating the AI model.
  • the terminal (120) may perform PDCCH prediction (or inference) using the first AI model (710).
  • information related to a channel state and/or a PDCCH may be used as an input of the first AI model (710), and first DCI prediction information (711) for at least one prediction unit may be generated as an output of the first AI model (710).
  • the first DCI prediction information (711) may be referred to as first DCI output information or first DCI presence information.
  • the base station (110) may perform PDCCH prediction (or inference) using a second AI model (720) associated with the first AI model (710).
  • information related to a channel state and/or a PDCCH may be used as an input of the second AI model (720), and second DCI prediction information (721) for at least one prediction unit may be generated as an output of the second AI model (720).
  • the second DCI prediction information (721) may be referred to as second DCI output information, or second DCI presence information.
  • the prediction unit corresponds to a unit that performs PDCCH prediction using an AI model.
  • the length of the prediction unit may be, for example, the same as the length of one slot. That is, each prediction unit may correspond to each slot. However, this is merely an example, and the length of the prediction unit may vary.
  • the length of the prediction unit may correspond to the length of a preset time period or a predefined scheduling unit (e.g., a scheduling unit for non-slot-based scheduling (e.g., a scheduling unit for scheduling one or more symbol units, a scheduling unit corresponding to a mini-slot, a scheduling unit newly defined in a communication system after 5G (e.g., a communication system after 6G or later))) or the length of a unit (or time period) scheduled by PDCCH/DCI.
  • a scheduling unit for non-slot-based scheduling e.g., a scheduling unit for scheduling one or more symbol units, a scheduling unit corresponding to a mini-slot, a scheduling unit newly defined in a communication system after 5G (e.g., a communication system after 6G or later)
  • a scheduling unit for scheduling one or more symbol units e.g., a scheduling unit corresponding to a mini-slot, a scheduling unit newly defined in a communication system after 5G (e
  • the terminal (120) may perform inference to obtain first DCI prediction information (711) for at least one prediction unit using the first AI model (710).
  • the first AI model (710) is an AI model for generating first DCI prediction information (711) for one prediction unit with one inference (an AI model set to have one prediction unit per inference)
  • the terminal (120) may repeatedly (or continuously) perform inference to obtain first DCI prediction information (711) for each of a plurality of prediction units.
  • the terminal (120) can perform a single inference to obtain first DCI prediction information (711) for each of the multiple prediction units.
  • the first DCI prediction information (711) may indicate whether DCI (or a PDCCH carrying the DCI) is transmitted within the corresponding prediction unit (or whether the DCI exists within the corresponding prediction unit).
  • the terminal (120) when the first DCI prediction information (711) is set to a first value indicating that DCI (or PDCCH carrying DCI) is not transmitted within the corresponding prediction unit, the terminal (120) may be in a sleep state and may not monitor the PDCCH to acquire the DCI. For example, as illustrated in FIG. 6, the terminal (120) may enter a sleep state in the 2nd, 4th, 6th and 8th prediction units having the first DCI prediction information (711) set to a value of '0' and may not monitor the PDCCH. Through this, power of the terminal (120) may be saved.
  • the terminal (120) when the first DCI prediction information (711) is set to a second value indicating that DCI (or PDCCH carrying DCI) is not transmitted within the corresponding prediction unit, the terminal (120) may be in an awake state and may monitor the PDCCH to acquire the DCI. In this case, the terminal (120) may receive the PDSCH using the acquired DCI. For example, as illustrated in FIG. 6, the terminal (120) may enter the awake state in the 1st, 3rd, 5th and 7th prediction units having the first DCI prediction information (711) set to a value of '1' and may monitor the PDCCH.
  • the base station (110) can perform inference to obtain second DCI prediction information (721) for at least one prediction unit using the second AI model (720) associated with the first AI model (710).
  • the second AI model (720) is an AI model for generating second DCI prediction information (721) for one prediction unit with one inference (an AI model configured to have one prediction unit per inference)
  • the base station (110) can perform inference iteratively (or continuously) to obtain second DCI prediction information (721) for each of the plurality of prediction units.
  • the base station (110) can perform one inference to obtain second DCI prediction information (721) for each of the multiple prediction units.
  • the second AI model (720) may be the same AI model as the associated first AI model (710).
  • the same AI model may mean that when the same input information is input, the same output information (or substantially the same) is output.
  • the first AI model (710) may be a neural network model that is substantially the same or similar to the second AI model (720).
  • the first AI model (710) and the second AI model (720) may be trained to output substantially the same or similar results based on the same input being applied.
  • the first AI model (710) and the second AI model (720) may have substantially the same or similar neural network structures.
  • the first AI model (710) and the second AI model (720) may have substantially the same or similar input layers, hidden layers (s), and/or output layers, respectively. Nodes of each layer constituting the first AI model (710) or the second AI model (720) and/or correlations between nodes may be substantially the same or similar in both AI models.
  • the first AI model (710) and the second AI model (720) have substantially the same or similar neural network structures, the first AI model (710) and the second AI model (720) can generate substantially the same or similar outputs.
  • the first AI model (710) and the second AI model (720) generate substantially the same or similar outputs, it can be said that synchronization has been achieved for the first AI model (710) and the second AI model (720).
  • the second DCI prediction information (721) may indicate whether DCI (or a PDCCH carrying the DCI) is transmitted within the corresponding prediction unit (or whether the DCI exists within the corresponding prediction unit).
  • the base station (110) may not transmit (or schedule) the PDCCH carrying the DCI within the corresponding prediction unit. For example, as illustrated in FIG. 6, the base station (110) may not transmit the PDCCH in the 2nd, 4th, 6th, and 8th prediction units having the second DCI prediction information (721) set to a value of '0'.
  • the base station (110) may transmit (or schedule) the PDCCH carrying the DCI within the corresponding prediction unit. For example, as illustrated in FIG. 6, the base station (110) may transmit the PDCCH in the 1st, 3rd, 5th, and 7th prediction units having the second DCI prediction information (721) set to a value of '1'.
  • the terminal (120) monitors the PDCCH only when it is predicted that the PDCCH carrying the DCI within the corresponding prediction unit will be transmitted using the DCI prediction information obtained using the AI model, thereby reducing the power of the terminal (120) required for PDCCH monitoring compared to the embodiment of FIG. 5.
  • FIG. 8 is a flowchart of an AI-based PDCCH signaling procedure according to an embodiment of the present disclosure.
  • FIG. 9 illustrates an example of an AI model for an AI-based PDCCH signaling procedure according to an embodiment of the present disclosure.
  • the AI-based PDCCH signaling procedure may include an AI model setup step/operation (810), a PDCCH prediction step/operation (820), and/or a PDCCH monitoring step/operation (830).
  • the AI model setting step/operation (810) may include an operation (811) of training an AI model for PDCCH prediction and/or an operation (812) of delivering (e.g., distributing) the AI model.
  • the operation (811) of learning an AI model may be performed by the base station (110) (or, the core network).
  • the base station (110) may train the AI model (910) by using training data to train the AI model (910).
  • the training data may be collected from, for example, all terminals (120) belonging to the base station (110).
  • the base station (110) can train at least one AI model (910).
  • the at least one AI model (910) can include, but is not limited to, at least one of an AI model (Model A) for generating DCI prediction information for one prediction unit with a single inference, an AI model (Model B) for generating DCI prediction information for each of n (e.g., 3) prediction units with a single inference, or an AI model (Model C) for generating DCI prediction information for each of m (e.g., 5) prediction units greater than n with a single inference.
  • an AI model (Model A) for generating DCI prediction information for one prediction unit with a single inference
  • an AI model (Model B) for generating DCI prediction information for each of n (e.g., 3) prediction units with a single inference
  • an AI model (Model C) for generating DCI prediction information for each of m (e.g., 5) prediction units greater than n with a single inference.
  • the base station (110) can train the AI model (910) on a per-terminal and/or per-function basis.
  • input data for training or learning may include scheduling information (911) and/or CQI information (912).
  • scheduling information (911) may be acquired or provided by the base station (110).
  • the scheduling information (911) may include, for example, information for scheduling at least one terminal (120a, 120b) associated with the base station (110).
  • the scheduling information (911) may include information for scheduling all terminals (120) belonging to the base station (110).
  • CQI information (912) may be acquired or provided by at least one terminal (120a, 120b).
  • CQI information (912) may include, for example, a CQI value acquired by at least one terminal (120a, 120b) associated with a base station.
  • CQI information may include CQI values acquired by all terminals (120) belonging to the base station.
  • the PF scheduler of the base station (110) can generate scheduling information using channel state information (e.g., CQI) reported from the terminal.
  • the base station (110) can transmit the generated scheduling information to the terminal (120), and the terminal (120) can receive the scheduling information transmitted from the base station (110).
  • the operation (812) of transmitting the AI model may include an operation of the base station (110) transmitting (or setting) data related to the learned AI model to at least one terminal (120a, 120b).
  • the base station (110) may transmit at least one learned AI model or parameter(s) specifying at least one learned AI model to the terminal (120).
  • the terminal (120a, 120b) may store at least one received AI model or update at least one pre-stored AI model based on parameter(s) specifying at least one AI model.
  • the PDCCH prediction step/operation (720) may include an inference operation for generating DCI prediction information for at least one prediction unit using a stored AI model by the terminal (120) and the base station (110).
  • the terminal (120a, 120b) may generate first DCI prediction information (911a-1, 911a-2) for at least one prediction unit using the first AI model (910a-1, 910a-2).
  • the first terminal (120a) may input, for example, scheduled information received at the first terminal (120a) and CQI information measured at the first terminal (120a), as inputs of the first AI model (910a-1), and may obtain, for example, first DCI prediction information (911a-1) for at least one prediction unit as outputs of the first AI model (910a-1).
  • the second terminal (120b) can input, for example, scheduled information received from the second terminal (120b) and CQI information measured from the second terminal (120b) as inputs of the first AI model (910a-2), and can obtain, for example, first DCI prediction information (911a-2) for at least one prediction unit as outputs of the first AI model (910a-2).
  • the terminal (120a, 120b) may determine whether to perform monitoring for a PDCCH carrying DCI in a corresponding prediction unit using the first DCI prediction information (911a-1, 911a-2).
  • the first terminal (120a) may determine whether to perform monitoring for a PDCCH carrying DCI in a corresponding prediction unit using the first DCI prediction information (911a-1).
  • the second terminal (120b) may determine whether to perform monitoring for a PDCCH carrying DCI in a corresponding prediction unit using the first DCI prediction information (911a-2).
  • the base station (110) can generate second DCI prediction information (911b-1, 911b-2) for at least one prediction unit using the second AI model (910b-1, 910b-2) associated with the first AI model (910a-1, 910a-2).
  • the base station (110) can input, as an input of the second AI model (910b-1) for the first terminal (120a), scheduling information generated by the base station (110) and CQI information measured by the first terminal (120a) and reported to the base station (110), and can obtain, as an output of the second AI model (910b-1), second DCI prediction information (911b-1) for at least one prediction unit.
  • the base station (110) may input scheduling information generated by the base station (110) and CQI information measured by the second terminal (120b) and reported to the base station (110) as inputs of the second AI model (910b-2) for the second terminal (120b), and may obtain second DCI prediction information (911b-2) for at least one prediction unit as outputs of the second AI model (910b-2).
  • the base station (110) may use the second DCI prediction information (911b-1, 911b-2) to determine whether to transmit a PDCCH carrying DCI in the corresponding prediction unit.
  • the base station (110) may use the second DCI prediction information (911b-1) for the first terminal (120a) to determine whether to transmit a PDCCH carrying DCI associated with the first terminal (120a) in the corresponding prediction unit.
  • the base station (110) may use the second DCI prediction information (911b-2) for the second terminal (120b) to determine whether to transmit a PDCCH carrying DCI associated with the second terminal (120b) in the corresponding prediction unit.
  • the first AI model (910a-1, 910a-2) may be the same AI model as the associated second AI model (910b-1, 910b-2).
  • the first AI model (910a-1) of the first terminal (120a) and the second AI model (910b-1) of the base station (110), which are used for PDCCH prediction in the same prediction unit may be the same AI model.
  • the first AI model (910a-2) of the second terminal (120b) and the second AI model (910b-2) of the base station (110), which are used for PDCCH prediction in the same prediction unit may be the same AI model.
  • the first AI model (910a-1, 910a-2) and the input of the second AI model (910b-1, 910b-2) for the corresponding prediction unit are the same, the first AI model (910a-1, 910a-2) and the second AI model (910b-1, 910b-2) can each generate DCI prediction information set to the same value as output.
  • the PDCCH monitoring step/operation (830) may include an operation of the base station (110) transmitting or not transmitting a PDCCH in a corresponding prediction slot based on PDCCH prediction (e.g., DCI prediction information) using the second AI model (910b-1, 910b-2).
  • PDCCH prediction e.g., DCI prediction information
  • the PDCCH monitoring step/operation (830) may include an operation of the terminal (120a, 120b) to monitor or not monitor the PDCCH in a corresponding prediction slot based on PDCCH prediction (e.g., DCI prediction information) using the first AI model (910a-1, 910a-2).
  • PDCCH prediction e.g., DCI prediction information
  • the terminal (120a, 120b) when a terminal (120a, 120b) performs PDCCH monitoring, the terminal (120a, 120b) may transmit an ACK message to the base station (110) in response to receiving (or obtaining) a PDCCH in a corresponding prediction unit, and may transmit a NACK message to the base station (110) in response to not receiving (or obtaining) a PDCCH in a corresponding prediction unit.
  • the base station (110) may check whether the PDCCH prediction has been performed accurately. If the PDCCH prediction is determined to be inaccurate, the base station (110) may initiate a procedure for changing the AI model.
  • ACK/NACK for PDCCH reception may be used as an input for learning/training the AI model.
  • FIG. 10 is a diagram comparing a PDCCH monitoring ratio when AI-based PDCCH monitoring is performed and a PDCCH monitoring ratio when AI-based PDCCH monitoring is not performed according to one embodiment of the present disclosure.
  • the PDCCH monitoring ratio in the case where AI-based PDCCH monitoring is not performed corresponds to the PDCCH monitoring ratio in the case where PDCCH monitoring is performed using the C-DRX method. Referring to Fig. 10, it can be confirmed that in the case where AI-based PDCCH monitoring is performed, the average PDCCH monitoring ratio is reduced by about 35% compared to the case where AI-based PDCCH monitoring is not performed.
  • FIG. 11 illustrates an example of an AI model set in a base station and a terminal according to one embodiment of the present disclosure.
  • the base station (110) and the terminal (120) may include at least one AI model for AI-based PDCCH prediction.
  • the terminal (120) may include at least one first AI model.
  • the at least one first AI model may include, but is not limited to, at least one of a first AI model (first AI model A) (1120a) for generating DCI prediction information for one prediction unit with a single inference, a first AI model (first AI model B) (1120b) for generating DCI prediction information for each of n (e.g., 3) prediction units with a single inference, or a first AI model (first AI model C) (1120c) for generating DCI prediction information for each of m (e.g., 5) prediction units greater than n with a single inference.
  • the base station (110) may include at least one second AI model associated with at least one first AI model.
  • the at least one second AI model may include, but is not limited to, at least one of a second AI model (second AI model A) (1110a) for generating DCI prediction information for one prediction unit in a single inference, a second AI model (second AI model B) (1110b) for generating DCI prediction information for each of n (e.g., 3) prediction units in a single inference, or a second AI model (second AI model C) (1110c) for generating DCI prediction information for each of m (e.g., 5) prediction units greater than n in a single inference.
  • the first AI model A (1120a), the first AI model B (1120b), and the first AI model C (1120c) of the terminal (120) may be associated with the second AI model A (1110a), the second AI model B (1110b), and the second AI model C (1110c) of the base station (110), respectively.
  • the first AI model A (1120a), the first AI model B (1120b), and the first AI model C (1120c) may be the same AI models as the second AI model A (1110a), the second AI model B (1110b), and the second AI model C (1110c), respectively.
  • each AI model may be identified by an AI model identifier (ID).
  • ID an AI model identifier
  • the first AI model A (1120a), the first AI model B (1120b), and the first AI model C (1120c) may each have a unique AI model ID.
  • the second AI model A (1110a), the second AI model B (1110b), and the second AI model C (1110c) may each have a unique AI model ID.
  • the second AI model associated with the first AI model may have the same AI model ID or different AI model IDs.
  • the first AI model A (1120a), the first AI model B (1120b), and the first AI model C (1120c) may have the same AI model ID or different AI model IDs as the associated second AI model A (1110a), the second AI model B (1110b), and the second AI model C (1110c), respectively.
  • FIG. 12a illustrates an example of a method for a base station and a terminal to perform PDCCH prediction using an AI model according to an embodiment of the present disclosure.
  • FIG. 12b illustrates an example of a method for a base station and a terminal to perform PDCCH prediction using an AI model according to an embodiment of the present disclosure.
  • FIG. 12c illustrates an example of a method for a base station and a terminal to perform PDCCH prediction using an AI model according to an embodiment of the present disclosure.
  • a first AI model A (e.g., the first AI model A (1120a) of FIG. 11) and an associated second AI model A (e.g., the second AI model A (1110a) of FIG. 11) may be used for PDCCH prediction.
  • the first AI model A (1120a) and the second AI model A (1110a) may be the same AI model, which may be a model that generates DCI prediction information for one prediction unit (one prediction unit per inference) through one inference.
  • the terminal (120) can input first input information (1221a) into the first AI model A (1120a).
  • the first input information (1221a) may include, but is not limited to, measured CQI information, scheduling probability information, average throughput information, and/or last scheduled information.
  • the measured CQI information may include, for example, a CQI value measured at the terminal based on DL RS (e.g., CSI (channel state information)-RS).
  • DL RS e.g., CSI (channel state information)-RS.
  • the scheduling probability information may, for example, represent a scheduling probability for a preset period (e.g., a period corresponding to the previous n slots from the current slot).
  • the current slot may be a slot on which inference is performed, and may be a slot preceding a slot (e.g., slot i-1) on which PDCCH prediction is performed (e.g., slot i).
  • the average transmission rate information may, for example, represent an average of the transmission rate over a preset period of time (e.g., a period corresponding to the current slot to the previous m slots).
  • the last scheduled information may be, for example, the last scheduled information acquired by the terminal.
  • information related to channel status and/or PDCCH can be used as input information.
  • information such as ACK/NACK information for PDCCH/DCI reception, H-ARQ (hybrid automatic repeat request) feedback, channel status conditions, DCI grant size, frequency range, numerology value, BWP setting, CORESET information, search space information, AL information, and/or duplex mode information (e.g., TDD/FDD) can be used as first input information (1221a).
  • the terminal (120) can generate first output information (1222a) as an output of the first AI model A (1120a).
  • the first output information (1222a) may include first DCI prediction information for prediction unit i (e.g., slot i).
  • the terminal (120) can generate first DCI prediction information for each prediction unit by continuously performing inference using the first AI model A (1120a).
  • the base station (110) can input second input information (1211a) into the second AI model A (1110a).
  • the second input information (1211a) may include, but is not limited to, reported CQI information, scheduling probability information, average throughput information, and/or last scheduled information.
  • the reported CQI information may include, for example, a CQI value measured at the terminal and reported to the base station based on DL RS (e.g., CSI-RS).
  • DL RS e.g., CSI-RS
  • the scheduling probability information may, for example, represent a scheduling probability for a preset period (e.g., a period corresponding to the previous n slots from the current slot).
  • the current slot may be a slot on which inference is performed, and may be a slot preceding a slot (e.g., slot i-1) on which PDCCH prediction is performed (e.g., slot i).
  • the average transmission rate information may, for example, represent an average of the transmission rate over a preset period of time (e.g., a period corresponding to the current slot to the previous m slots).
  • the last scheduled information may be, for example, the last scheduled information transmitted by the base station.
  • information related to channel status and/or PDCCH can be used as input information.
  • information such as ACK/NACK information for PDCCH/DCI reception, H-ARQ (hybrid automatic repeat request) feedback, channel status conditions, DCI grant size, frequency range, numerology value, BWP setting, CORESET information, search space information, AL information, and/or duplex mode information (e.g., TDD/FDD) can be used as second input information (1211a).
  • the base station (110) can generate second output information (1212a) as an output of the second AI model A (1110a).
  • the second output information (1212a) may include second DCI prediction information for prediction unit i (e.g., slot i).
  • the base station (110) can generate second DCI prediction information for each prediction unit by continuously performing inference using the second AI model A (1110a).
  • the first AI model A (1120a) and the second AI model A (1110a) correspond to the same AI model, if the first input information (1221a) and the second input information (1211a) are the same, the first output information (1222a) and the second output information (1212a) may be the same.
  • a first AI model B (e.g., the first AI model B (1120b) of FIG. 11) and an associated second AI model A (e.g., the second AI model B (1110b) of FIG. 11) may be used for PDCCH prediction.
  • the first AI model B (1120b) and the second AI model B (1110b) may be the same AI model, which may be a model that generates DCI prediction information for n (e.g., 3) prediction units through a single inference (n prediction units per inference).
  • the embodiment of Fig. 12b can perform inference/prediction on the same principle as the embodiment of Fig. 12a, except that DCI prediction information for multiple prediction units is generated with a single inference. Therefore, redundant description is omitted.
  • the terminal (120) may apply the first input information (1221b) as an input of the first AI model B (1120b) and generate the first output information (1222b) as an output of the first AI model B (1120b).
  • the first output information (1222b) may include, for example, the first DCI prediction information for prediction unit i (e.g., slot i), the first DCI prediction information for prediction unit i+1 (e.g., slot i+1), and the first DCI prediction information for prediction unit i+2 (e.g., slot i+2).
  • the terminal (120) may generate the first DCI prediction information for each prediction unit by continuously performing inference using the first AI model B (1120b).
  • the base station (110) can input the second input information (1211b) as an input of the second AI model B (1110b) and generate the second output information (1212b) as an output of the second AI model B (1110b).
  • the second output information (1212b) can include, for example, second DCI prediction information for prediction unit i (e.g., slot i), second DCI prediction information for prediction unit i+1 (e.g., slot i+1), and second DCI prediction information for prediction unit i+2 (e.g., slot i+2).
  • the base station (110) can generate the second DCI prediction information for each prediction unit by continuously performing inference using the second AI model B (1110b).
  • a first AI model C (e.g., the first AI model C (1120c) of FIG. 11) and an associated second AI model C (e.g., the second AI model C (1110c) of FIG. 11) may be used for PDCCH prediction.
  • the first AI model C (1120c) and the second AI model C (1110c) may be the same AI model, which may be a model that generates DCI prediction information for m (e.g., 5) prediction units through a single inference (m prediction units per inference).
  • the embodiment of Fig. 12c can perform inference/prediction on the same principle as the embodiment of Fig. 12b, except that DCI prediction information for a larger number of prediction units is generated with a single inference. Therefore, redundant description is omitted.
  • the terminal (120) may apply first input information (1221c) as an input of the first AI model C (1120c) and generate first output information (1222c) as an output of the first AI model C (1120c).
  • the first output information (1222c) may include, for example, first DCI prediction information for prediction unit i (e.g., slot i), first DCI prediction information for prediction unit i+1 (e.g., slot i+1), first DCI prediction information for prediction unit i+2 (e.g., slot i+2), first DCI prediction information for prediction unit i+3 (e.g., slot i+3), and first DCI prediction information for prediction unit i+4 (e.g., slot i+4).
  • the terminal (120) can generate first DCI prediction information for each prediction unit by continuously performing inference using the first AI model C (1120c).
  • the base station (110) can input the second input information (1211c) as an input of the second AI model C (1110c) and generate the second output information (1212c) as an output of the second AI model C (1110c).
  • the second output information (1212c) can include, for example, second DCI prediction information for prediction unit i (e.g., slot i), second DCI prediction information for prediction unit i+1 (e.g., slot i+1), second DCI prediction information for prediction unit i+2 (e.g., slot i+2), second DCI prediction information for prediction unit i+3 (e.g., slot i+3), and second DCI prediction information for prediction unit i+4 (e.g., slot i+4).
  • the base station (110) can generate the second DCI prediction information for each prediction unit by continuously performing inference using the second AI model C (1110c).
  • FIG. 13 illustrates an example of output mismatch due to input mismatch of an AI model according to one embodiment of the present disclosure.
  • the first input information (1321) input to the first AI model of the terminal e.g., the first AI model A (1120a) and the first AI model C (1120c) of FIG. 11
  • the second input information (1311) input to the associated second AI model of the base station e.g., the second AI model A (1110a) and the second AI model C (1110c) of FIG. 11
  • input mismatch may occur.
  • the failure probability of DCI decoding and CSI report decoding increases, and thus, the input information of each AI model of the base station and the terminal may be different.
  • the measured CQI value of the first input information (1321) and the reported CQI value of the second input information (1311) may be different.
  • the last scheduled information of the first input information (1321) and the last scheduled information of the second input information (1311) may be different.
  • Such inconsistency in input information may cause inconsistency in output information in the AI model.
  • the first output information (1322c) of the first AI model C (1120c) and the second output information (1312c) of the second AI model C (1110c) may be inconsistent.
  • the values of the first DCI prediction information for the second and fifth prediction units according to the first inference of the first AI model C (1120c) and the values of the first DCI prediction information for the second and fifth prediction units according to the first inference of the second AI model C (1110c) may be different.
  • the mismatch of the output information may not occur.
  • the first output information (1322a) of the first AI model A (1120a) and the second output information (1312a) of the second AI model A (1110a) may match despite the mismatch of the input information.
  • the first AI model A (1120a) and the second AI model A (1110a), which perform PDCCH prediction for one prediction unit with one inference may have prediction accuracy that is less dependent on the channel state than the first AI model C (1120c) and the second AI model C (1110c) (or the first AI model B (1120b) and the second AI model B (1110b)) that perform PDCCH prediction for multiple prediction units with one inference.
  • FIG. 14 illustrates the PDCCH failure probability according to the number of prediction units per inference according to one embodiment of the present disclosure.
  • a method needs to be considered to reduce power consumption due to unnecessary inference without performance degradation by using an appropriate AI model depending on the channel condition (e.g., DL channel condition).
  • an AI model having a relatively large number of prediction units per inference among the configured AI models (e.g., AI model B (1110b, 1120b) or AI model C (1110c, 1120c) of FIG. 11) may be used to reduce the number of inferences, thereby reducing power consumption of the terminal and reducing scheduling overhead of the base station.
  • an AI model having a relatively small number of prediction units per inference among the configured AI models may be used to prevent performance degradation.
  • AI model A (1110a, 1120a) of FIG. 11 may be used to prevent performance degradation.
  • FIG. 15 is a flowchart of a procedure for a base station to set criteria for changing an AI model according to one embodiment of the present disclosure.
  • a base station may generate configuration information (AI model change criterion configuration information) for setting a criterion used for changing an AI model according to a channel state (e.g., a DL channel state).
  • a channel state e.g., a DL channel state
  • the criterion used for changing an AI model may be referred to as a change criterion or a measurement criterion.
  • the AI model change criterion configuration information may be referred to as configuration information, change criterion configuration information, or measurement criterion configuration information.
  • the AI model change criteria setting information may include, but is not limited to, information on at least one of the following change criteria (measurement criteria):
  • SINR signal-to-interference-plus-noise ratio
  • the AI model change criterion setting information may include information about change criterion 1, information about change criterion 2, information about change criterion 3, and/or information about change criterion 4.
  • Information about change criterion 1 may include, but is not limited to, information for identifying change criterion 1, information indicating an RSRP threshold of change criterion 1, and/or information indicating a TTT.
  • Information about change criterion 2 may include, but is not limited to, information for identifying change criterion 2 and/or information indicating a threshold of change criterion 2.
  • Information about change criterion 3 may include, but is not limited to, information for identifying change criterion 3, information indicating an SINR threshold of change criterion 3, and/or information indicating a TTT.
  • Information about change criterion 4 may include, but is not limited to, information for identifying change criterion 4, and/or information indicating a threshold for change criterion 4.
  • the change criterion may also be used as a criterion for determining (or selecting) the AI model to be changed.
  • a separate AI model may correspond to each range distinguished by each threshold. For example, if the measurement result falls within a first range of the change criterion (measurement criterion) (e.g., a range smaller than the first threshold), AI model A (e.g., model A (1110a, 1120a) of FIG.
  • AI model 11 may be selected as the AI model to be changed, if the measurement result falls within a second range of the change criterion (measurement criterion) (e.g., a range between the first threshold and the second threshold), AI model B (e.g., model B (1110b, 1120b) of FIG. 11) may be selected as the AI model to be changed, and if the measurement result falls within a third range of the change criterion (measurement criterion) (e.g., a range larger than the second threshold), AI model C (e.g., model C (1110c, 1120c) of FIG. 11) may be selected as the AI model to be changed.
  • a second range of the change criterion e.g., a range between the first threshold and the second threshold
  • AI model B e.g., model B (1110b, 1120b) of FIG. 11
  • AI model C e.g., model C (1110c, 1120c) of FIG. 11
  • the base station can transmit AI model change criterion setting information to a terminal (e.g., terminal (120) of FIG. 1).
  • the base station may transmit AI model change criterion setting information to the terminal using RRC layer signaling (e.g., RRC message), MAC layer signaling, or PHY layer signaling (e.g., DCI message).
  • RRC layer signaling e.g., RRC message
  • MAC layer signaling e.g., MAC layer signaling
  • PHY layer signaling e.g., DCI message
  • the base station may transmit to the terminal a report configuration (e.g., ReportConfig or CSI-ReportConfig IE (information element)) including AI model change criteria setting information.
  • a report configuration e.g., ReportConfig or CSI-ReportConfig IE (information element)
  • the base station may include the AI model change criteria setting information in a report configuration for a measurement report (e.g., a measurement report for CSI-RS) and transmit it to the terminal.
  • the report configuration may be transmitted by being included in an RRC message (e.g., an RRC configuration message, an RRC reconfiguration message).
  • the base station may not transmit AI model change criterion setting information to the terminal. That is, operation 15020 may be omitted.
  • UL RS e.g., SRS (sounding reference signal)
  • FIG. 16a is a flowchart illustrating an example of a method by which a terminal initiates a procedure for changing an AI model according to an embodiment of the present disclosure.
  • FIG. 16b is a flowchart illustrating an example of a method by which a terminal initiates a procedure for changing an AI model according to an embodiment of the present disclosure.
  • the terminal may acquire (or receive) a DL RS.
  • the DL RS may be, for example, a CSI-RS, but is not limited thereto.
  • the terminal can perform measurements (e.g., measurement of received signal strength, measurement of received signal quality, measurement of signal-to-noise ratio, measurement of signal-to-interference-noise ratio, etc.) on the received DL RS.
  • the terminal can measure (or obtain) RSRP, RSRQ (reference signal received quality) and/or SINR for the CSI-RS based on the received CSI-RS, but is not limited thereto.
  • various measurements that can indicate channel status (or quality) may also be used for this.
  • the terminal can determine whether the change criteria of the AI model are satisfied based on the AI model change criteria setting information.
  • the AI model change criteria setting information can be set in the terminal by a procedure for setting criteria for changing the AI model (e.g., the procedure for setting criteria for changing the AI model of FIG. 15).
  • the terminal can determine whether the change criteria of the AI model are satisfied by comparing the measurement result of the received DL RS (e.g., CSI-RS) with the change criteria of the AI model based on the AI model change criteria setting information.
  • the measurement result of the received DL RS e.g., CSI-RS
  • the terminal may determine that the change criterion of the AI model is satisfied when the RSRP for the measured CSI-RS satisfies the RSRP threshold TTT or more (e.g., when the change criterion 1 of FIG. 15 is satisfied).
  • the terminal may determine that the change criterion of the AI model is satisfied when the average of the time series data of RSRP for the measured CSI-RS satisfies the threshold (e.g., when the change criterion 2 of FIG. 15 is satisfied).
  • the terminal may determine that the change criterion of the AI model is satisfied when the SINR for the measured CSI-RS satisfies the SINR threshold for a certain period of time, TTT (e.g., when the change criterion 3 of FIG. 15 is satisfied).
  • the terminal may determine that the change criterion of the AI model is satisfied when the average of the time series data of the SINR for the measured CSI-RS satisfies the threshold (e.g., when the change criterion 4 of FIG. 15 is satisfied).
  • the terminal can initiate a procedure for changing the AI model.
  • the terminal may transmit a message requesting a change of the AI model (e.g., a model change request (MCR) message) to the base station, as in operation 16030a.
  • a message requesting a change of the AI model e.g., a model change request (MCR) message
  • MCR model change request
  • the terminal When using DL RS such as CSI-RS to check channel status, it may be granting too much authority to the terminal to change the AI model directly. Therefore, if the change criteria are satisfied, instead of changing the AI model directly, the terminal can transmit an MCR message to the base station requesting a change of the AI model.
  • DL RS such as CSI-RS
  • the MCR message may include information about a measurement result for a CSI-RS and/or a comparison result of a change criterion of an AI model based on the measurement result and AI model change criterion setting information.
  • the terminal may transmit an MCR message to the base station using RRC layer signaling (e.g., an RRC message), MAC layer signaling, and/or PHY layer signaling (e.g., a UCI message).
  • RRC layer signaling e.g., an RRC message
  • MAC layer signaling e.g., a MAC layer signaling
  • PHY layer signaling e.g., a UCI message
  • the base station may determine whether to change the AI model (e.g., change the AI model to an AI model with a larger number of prediction units per inference) based on the MCR message (or information included in the MCR message) and/or traffic information of the base station (e.g., current traffic).
  • the AI model e.g., change the AI model to an AI model with a larger number of prediction units per inference
  • the terminal may determine whether to change the AI model. If necessary, the base station may grant the terminal permission to directly change the AI model. In this case, the terminal may directly determine whether to change the AI model (e.g., change the AI model to an AI model having a larger number of prediction units per inference or an AI model having a smaller number of prediction units) by using, for example, a comparison result of the change criterion for the AI model based on the measurement result for the CSI-RS and the AI model change criterion setting information. If it is determined to change the AI model, the terminal may perform a procedure for changing the AI model (e.g., a procedure for changing and synchronizing the AI model of FIG. 19). If it is determined not to change the AI model, the terminal may perform operation 16020b again.
  • the AI model e.g., change the AI model to an AI model having a larger number of prediction units per inference or an AI model having a smaller number of prediction units
  • FIG. 17 is a flowchart illustrating a method by which a base station initiates a procedure for changing an AI model according to one embodiment of the present disclosure.
  • the base station may acquire (or receive) a UL RS.
  • the UL RS may be, for example, an SRS, but is not limited thereto.
  • the base station can perform measurements (e.g., measured signal strength, measured signal quality, measured signal-to-noise ratio, measured signal-to-interference-noise ratio, etc.) on the received UL RS.
  • the terminal can measure (or obtain) RSRP and/or SINR for the SRS based on the received SRS, but is not limited thereto.
  • various measurements that can indicate channel conditions (or quality) can also be used for this.
  • the base station can determine whether the change criteria of the AI model are satisfied based on the AI model change criteria setting information.
  • the AI model change criteria setting information can be generated in the base station by a procedure for setting criteria for changing the AI model (e.g., the procedure for setting criteria for changing the AI model of FIG. 15).
  • the base station can determine whether the change criteria of the AI model are satisfied by comparing the measurement result for the received UL RS (e.g., SRS) with the change criteria of the AI model based on the AI model change criteria setting information.
  • the measurement result for the received UL RS e.g., SRS
  • the base station may determine that the change criterion of the AI model is satisfied when the RSRP for the measured SRS satisfies the RSRP threshold TTT or more (e.g., when the change criterion 1 of FIG. 15 is satisfied).
  • the base station may determine that the change criterion of the AI model is satisfied when the average of the time series data of the RSRP for the measured SRS satisfies the threshold (e.g., when the change criterion 2 of FIG. 15 is satisfied).
  • the base station may determine that the change criterion of the AI model is satisfied when the SINR for the measured SRS satisfies the SINR threshold for a certain time period, TTT (e.g., when the change criterion 3 of FIG. 15 is satisfied).
  • the base station may determine that the change criterion of the AI model is satisfied when the average of the time series data of the SINR for the measured SRS satisfies the threshold (e.g., when the change criterion 4 of FIG. 15 is satisfied).
  • the base station can initiate a procedure for changing the AI model.
  • the base station may determine whether to change the AI model, as in operation 17030. If it is determined to change the AI model, the base station may perform a procedure for changing the AI model (e.g., the procedure for changing and synchronizing the AI model of FIG. 18). If it is determined not to change the AI model, the base station may perform operation 17020b again.
  • a procedure for changing the AI model e.g., the procedure for changing and synchronizing the AI model of FIG. 18. If it is determined not to change the AI model, the base station may perform operation 17020b again.
  • the base station may determine whether to change the AI model based on a comparison result of a measurement result for a UL RS (e.g., SRS) and a change criterion of the AI model based on AI model change criterion setting information.
  • a UL RS e.g., SRS
  • the base station may determine whether to change the AI model (e.g., to change the AI model to an AI model having a larger number of prediction units per inference or to an AI model having a smaller number of prediction units) based on a comparison result of a measurement result for an UL RS (e.g., SRS) and a change criterion for the AI model based on AI model change criterion setting information and traffic information of the base station (e.g., current traffic).
  • an UL RS e.g., SRS
  • a change criterion for the AI model based on AI model change criterion setting information and traffic information of the base station (e.g., current traffic).
  • a procedure for synchronizing (e.g., time synchronization) is required so that the terminal and the base station can change the AI model to the same AI model at the same time and perform inference for PDCCH prediction based on the changed AI model.
  • the following describes a procedure for changing and synchronizing an AI model.
  • FIG. 18 is a flowchart of a procedure for changing and synchronizing an AI model according to one embodiment of the present disclosure.
  • FIG. 20 illustrates an example of a synchronization adjustment method according to one embodiment of the present disclosure.
  • the base station may transmit a message for model change and synchronization (e.g., a model change and synchronous alignment (MCSA) message) to the terminal.
  • a message for model change and synchronization e.g., a model change and synchronous alignment (MCSA) message
  • the base station may transmit the MCSA message to the terminal in response to the base station determining a change of an AI model (e.g., determining an AI model change according to operation 17030 of FIG. 17).
  • MCSA model change and synchronous alignment
  • the MCSA message may include synchronization information for changing the AI model.
  • the synchronization information may be used, for example, for the terminal and the base station to change the AI model at the same time (or timing). Based on this synchronization information, the terminal and the base station may change the AI model to the same AI model in synchronization.
  • the synchronization information may be set in units of presets.
  • the synchronization information may be set in units of slots.
  • the synchronization information may indicate after which slot an AI model is to be changed based on a slot in which an MCSA message is transmitted (and/or received).
  • the present invention is not limited thereto, and the units in which the synchronization information is set may be variously changed.
  • the synchronization information may be set in units of non-slots (e.g., a scheduling unit for scheduling one or more symbol units, a scheduling unit corresponding to a mini-slot, a scheduling unit newly defined in a communication system after 5G (e.g., a communication system after 6G or later)).
  • the MCSA message may include information about the AI model.
  • the MCSA message may include information for identifying the AI model to be changed (e.g., an AI model ID).
  • the MCSA message may include information about the difference in the number of prediction units per inference of the current AI model and the AI model to be changed (hereinafter referred to as prediction unit count difference information).
  • prediction unit count difference information information about the difference in the number of prediction units per inference of the current AI model and the AI model to be changed.
  • the MCSA message may include prediction unit count difference information that indicates a difference (e.g., 2) between two values (e.g., 3 and 1).
  • the MCSA message may include prediction unit number difference information indicating the difference (e.g., -2) between two values (e.g., 1 and 3).
  • the terminal may identify the AI model to be changed based on the difference information in the number of prediction units in the MCSA message. For example, if the current AI model is an AI model that obtains DCI prediction information for one prediction unit per inference (e.g., the first AI model A (1120a) of FIG. 11) and the difference information in the number of prediction units indicates 2, the terminal may identify that the AI model to be changed is an AI model that obtains DCI prediction information for three prediction units per inference (e.g., the first AI model B (1120b) of FIG. 11).
  • the terminal may identify that the AI model to be changed is an AI model that obtains DCI prediction information for three prediction units per inference (e.g., the first AI model B (1120b) of FIG. 11).
  • the base station may transmit the MCSA message to the terminal using RRC layer signaling (e.g., an RRC message), MAC layer signaling, and/or PHY layer signaling (e.g., a DCI message).
  • RRC layer signaling e.g., an RRC message
  • MAC layer signaling e.g., a MAC layer signaling
  • PHY layer signaling e.g., a DCI message
  • the base station can transmit the MCSA message according to a preset transmission method.
  • the base station may transmit (or unicast) an MCSA message to a terminal that requires a change in the AI model.
  • the base station may transmit an MCSA message to a terminal that transmitted an MCR message.
  • the base station may transmit (or broadcast) AI model information (e.g., a table including AI model information (e.g., AI model ID) for all terminals belonging to the base station) to all terminals belonging to the base station.
  • AI model information e.g., a table including AI model information (e.g., AI model ID) for all terminals belonging to the base station
  • the terminals belonging to the base station may check their own AI models included in the AI model information.
  • each terminal may identify whether a change in the AI model is required.
  • each terminal may identify whether a change in the AI model is required by comparing the current AI model of the terminal with the AI model of the terminal included in the received AI model information.
  • the base station may periodically transmit (or broadcast) AI model information for all terminals belonging to the base station (e.g., a table including AI model information for all terminals belonging to the base station).
  • the base station may periodically transmit (or broadcast) AI model information for all terminals belonging to the base station (e.g., a table including AI model information for all terminals belonging to the base station) regardless of whether a change in the AI model is required.
  • the terminals belonging to the base station may periodically check their own AI models included in the AI model information. Through this, each terminal may identify whether a change in the AI model is required. For example, each terminal may compare the current AI model of the terminal with the AI model of the terminal included in the received AI model information to identify whether a change in the AI model is required.
  • the terminal may perform synchronization coordination based on the received MCSA message. For example, the terminal may perform synchronization coordination using synchronization information included in the received MCSA message. For example, as illustrated in FIG. 20, if the synchronization information instructs to change the AI model after x slots from a slot (e.g., slot index n) in which the MCSA message is transmitted (and/or received), the terminal may maintain the current AI model (e.g., model A) for x slots (e.g., slot index n+x) from the slot in which the MCSA message is received, and change the AI model (e.g., to model B) in the slot after the x slots (e.g., slot index n+x+1).
  • the synchronization information instructs to change the AI model after x slots from a slot (e.g., slot index n) in which the MCSA message is transmitted (and/or received)
  • the terminal may maintain the current AI model (e.g., model A) for x slots (e.g
  • the terminal and the base station can change the AI model at the same time.
  • the terminal and the base station can change the AI model at the start time of the slot specified by the synchronization information (or, at a time separated by a preset offset from the start time).
  • FIG. 19 is a flowchart of a procedure for changing and synchronizing an AI model according to one embodiment of the present disclosure.
  • the terminal may transmit an MCSA message to the base station.
  • the terminal may transmit the MCSA message to the base station in response to identifying that a change in an AI model is determined by the terminal (e.g., an AI model change decision according to operation 16030b of FIG. 16b).
  • the MCSA message may include synchronization information for changing the AI model.
  • the synchronization information may be used, for example, for the terminal and the base station to change the AI model at the same time (or timing). Based on this synchronization information, the terminal and the base station may change the AI model to the same AI model in synchronization.
  • the synchronization information may be set in units of presets.
  • the synchronization information may be set in units of slots.
  • the synchronization information may indicate after which slot an AI model is to be changed based on a slot in which an MCSA message is transmitted (and/or received).
  • the synchronization information may be set in various units.
  • the synchronization information may be set in units of non-slot scheduling units (e.g., scheduling units for scheduling in units of one or more symbols, scheduling units corresponding to mini-slots, scheduling units newly defined in communication systems after 5G (e.g., communication systems after 6G or later)).
  • the MCSA message may include information about the AI model.
  • the MCSA message may include information for identifying the AI model to be changed (e.g., an AI model ID).
  • the MCSA message may include information about the difference in the number of prediction units per inference of the current AI model and the AI model to be changed (hereinafter referred to as prediction unit count difference information).
  • prediction unit count difference information information about the difference in the number of prediction units per inference of the current AI model and the AI model to be changed.
  • the MCSA message may include prediction unit count difference information that indicates a difference (e.g., 2) between two values (e.g., 3 and 1).
  • the MCSA message may include prediction unit number difference information indicating the difference (e.g., -2) between two values (e.g., 1 and 3).
  • the base station can identify the AI model to be changed based on the difference information in the number of prediction units in the MCSA message. For example, if the current AI model is an AI model that obtains DCI prediction information for one prediction unit per inference (e.g., the second AI model A (1110a) of FIG. 11) and the difference information in the number of prediction units indicates 2, the base station can identify that the AI model to be changed is an AI model that obtains DCI prediction information for three prediction units per inference (e.g., the second AI model B (1110b) of FIG. 11).
  • the base station can identify that the AI model to be changed is an AI model that obtains DCI prediction information for three prediction units per inference (e.g., the second AI model B (1110b) of FIG. 11).
  • the terminal may transmit an MCSA message to the base station using RRC layer signaling (e.g., an RRC message), MAC layer signaling, and/or PHY layer signaling (e.g., a UCI message).
  • RRC layer signaling e.g., an RRC message
  • MAC layer signaling e.g., a MAC layer signaling
  • PHY layer signaling e.g., a UCI message
  • the base station can perform synchronization coordination based on the received MCSA message. For example, the base station can perform synchronization coordination using synchronization information included in the received MCSA message. For example, if the synchronization information instructs to change the AI model after x slots from a slot (e.g., slot index n) in which the MCSA message is transmitted (or received), the base station can maintain the current AI model for x slots (e.g., slot index n+x) from the slot in which the MCSA message is received, and change the AI model in the slot after the x slots (e.g., slot index n+x+1).
  • a slot e.g., slot index n
  • the base station can maintain the current AI model for x slots (e.g., slot index n+x) from the slot in which the MCSA message is received, and change the AI model in the slot after the x slots (e.g., slot index n+x+1).
  • the terminal and the base station can change the AI model at the same time.
  • the terminal and the base station can change the AI model at the start time of the slot specified by the synchronization information (or, at a time separated by a preset offset from the start time).
  • FIG. 21 is a flowchart of an AI model change procedure according to one embodiment of the present disclosure.
  • the embodiment of FIG. 21 may be an example of an AI model change procedure when DL RS (e.g., CSI-RS) is used for measuring channel conditions and the authority to change the AI model is granted to the base station.
  • DL RS e.g., CSI-RS
  • the base station may transmit a report setting including a change criterion (or, measurement criterion) to the terminal.
  • a change criterion or, measurement criterion
  • the terminal For a description of generation and transmission of the measurement criterion, see, for example, the description of FIG. 15.
  • operation 21010 may include operation 15010 and/or operation 15020 of FIG. 15.
  • the base station and the terminal may perform an AI-based PDCCH monitoring procedure.
  • AI-based PDCCH monitoring procedure For a description of the AI-based PDCCH monitoring procedure, reference may be made to the descriptions of, for example, FIGS. 6 to 12c.
  • operation 21020 may include operation 8010, operation 8020, and/or operation 8030 of FIG. 8.
  • the terminal may identify that the measurement criteria are satisfied and transmit an MCR message to the base station. For a description of determining whether the measurement criteria are satisfied and transmitting the MCR message, see, for example, the description of FIG. 16a.
  • operation 21030 may include operations 16010a and/or 16020a of FIG. 16a
  • operation 21040 may include operation 16030a of FIG. 16a.
  • the base station may identify that a model change is determined and transmit an MCSA message to the terminal. For a description of the determination of whether to change the model and the transmission of the MCSA message, see, for example, the description of FIG. 18. As an example, operations 21050 and 21060 may include operation 18010 of FIG. 18.
  • the terminal performs synchronization adjustment, and the terminal and the base station can change the AI model.
  • the terminal and the base station can change the AI model.
  • operation 21070 may include operation 18020 of FIG. 18, and operation 21080 may include operation 18030 of FIG. 18.
  • FIG. 22 is a flowchart of an AI model change procedure according to one embodiment of the present disclosure.
  • the embodiment of FIG. 22 may be an example of an AI model change procedure when DL RS (e.g., CSI-RS) is used for measuring channel status and the terminal is granted permission to change the AI model.
  • DL RS e.g., CSI-RS
  • the base station may transmit a report setting including a change criterion (or, measurement criterion) to the terminal.
  • a change criterion or, measurement criterion
  • operation 22010 may include operation 15010 and/or operation 15020 of FIG. 15.
  • the base station and the terminal may perform an AI-based PDCCH monitoring procedure.
  • AI-based PDCCH monitoring procedure see, for example, the descriptions of FIGS. 6 to 12c.
  • operation 22020 may include operation 8010, operation 8020, and/or operation 8030 of FIG. 8.
  • the terminal can identify that the measurement criterion is satisfied and that a model change is determined. For a description of the determination of whether the measurement criterion is satisfied and the determination of whether to change the model, see, for example, the description of FIG. 16b.
  • operation 22030 can include operation 16010b and/or operation 16020b of FIG. 16b
  • operation 22040 can include operation 16030b of FIG. 16b.
  • the terminal may transmit an MCSA message to the base station.
  • MCSA message For a description of transmission of the MCSA message, see, for example, the description of FIG. 19.
  • operation 22050 may include operation 19010 of FIG. 19.
  • the base station performs synchronization adjustment, and the terminal and the base station can change the AI model.
  • the synchronization adjustment and the AI model change see, for example, the description of FIG. 19.
  • operation 22070 may include operation 19020 of FIG. 19, and operation 22080 may include operation 19030 of FIG. 19.
  • FIG. 23 is a flowchart of an AI model change procedure according to one embodiment of the present disclosure.
  • the embodiment of FIG. 23 may be an example of an AI model change procedure when UL RS (e.g., SRS) is used for measuring channel conditions and the authority to change the AI model is granted to the base station.
  • UL RS e.g., SRS
  • operation 23010 the base station and the terminal may perform an AI-based PDCCH monitoring procedure.
  • AI-based PDCCH monitoring procedure For a description of the AI-based PDCCH monitoring procedure, reference may be made to the descriptions of FIGS. 6 to 12c, for example.
  • operation 23020 may include operation 8010, operation 8020, and/or operation 8030 of FIG. 8.
  • the base station can identify that the measurement criteria are satisfied and that a model change is determined. For a description of the determination of whether the measurement criteria are satisfied and the determination of whether to change the model, see, for example, the description of FIG. 17.
  • operation 23020 can include operations 17010 and/or 17020 of FIG. 17, and operation 23030 can include operation 17030 of FIG. 17.
  • the base station may transmit an MCSA message to the terminal.
  • MCSA message For a description of transmission of the MCSA message, see, for example, the description of FIG. 18.
  • operation 23040 may include operation 18010 of FIG. 18.
  • operation 23050 and 23060 the terminal performs synchronization adjustment, and the terminal and the base station can change the AI model.
  • the terminal and the base station can change the AI model.
  • operation 23050 may include operation 18020 of FIG. 18, and operation 23060 may include operation 18030 of FIG. 18.
  • FIG. 24 is a flowchart of an AI model change procedure according to one embodiment of the present disclosure.
  • the embodiment of FIG. 24 may be an example of an AI model change procedure when UL RS (e.g., SRS) is used for measuring channel conditions and the authority to change the AI model is granted to the base station.
  • UL RS e.g., SRS
  • operation 24010 the base station and the terminal may perform an AI-based PDCCH monitoring procedure.
  • AI-based PDCCH monitoring procedure For a description of the AI-based PDCCH monitoring procedure, reference may be made to the descriptions of FIGS. 6 to 12c, for example.
  • operation 24010 may include operation 8010, operation 8020, and/or operation 8030 of FIG. 8.
  • the base station may periodically broadcast an MCSA message.
  • MCSA message For a description of the periodic broadcasting of the MCSA message, see, for example, the description of FIG. 18.
  • operation 24020 may include operation 18010 of FIG. 18.
  • the base station can identify that the measurement criteria are satisfied and that a model change is determined. For a description of the determination of whether the measurement criteria are satisfied and the determination of whether to change the model, see, for example, the description of FIG. 17.
  • operation 24030 can include operations 17010 and/or 17020 of FIG. 17, and operation 24040 can include operation 17030 of FIG. 17.
  • the terminal performs synchronization adjustment, and the terminal and the base station can change the AI model.
  • the terminal and the base station can change the AI model.
  • operation 24050 can include operation 18020 of FIG. 18, and operation 24060 can include operation 18030 of FIG. 18.
  • FIG. 25 is a flowchart of a method of operating a terminal according to one embodiment of the present disclosure.
  • the embodiment of Fig. 25 may be, for example, an example of the operation of the terminal in the embodiment of Fig. 21.
  • the terminal can receive a configuration message from the base station (25010).
  • a configuration message from the base station (25010).
  • the configuration message may include configuration information for PDCCH prediction using an AI model and/or configuration information related to a change of an AI model used for PDCCH prediction.
  • setting information related to a change in an AI model may include information on change criteria for changing the AI model.
  • the terminal can determine whether the change criteria are satisfied (25020). For a description of determining whether the change criteria are satisfied, see, for example, the description of FIG. 16a.
  • the terminal may determine whether a change criterion is satisfied based on measurement results (e.g., RSRP, SINR) based on received DL RS (e.g., CSI-RS) and information about the change criterion included in the configuration information.
  • measurement results e.g., RSRP, SINR
  • received DL RS e.g., CSI-RS
  • the terminal can transmit an MCR message to the base station (25030).
  • the base station 25030
  • the MCR message may include information about a measurement result for a CSI-RS and/or a comparison result between the measurement result and a change criterion.
  • the terminal can receive an MCSA message from the base station (25040).
  • MCSA message For a description of transmitting and receiving an MCSA message, refer to, for example, the description of FIG. 18.
  • the base station when the base station determines to change the AI model based on the MCR message, the base station may transmit an MCSA message to the terminal, and the terminal may receive the MCSA message.
  • the MCSA message may include synchronization information for changing the AI model.
  • the synchronization information may be used, for example, for the terminal and the base station to change the AI model at the same time (or timing). Based on this synchronization information, the terminal and the base station may change the AI model to the same AI model in synchronization.
  • the synchronization information may be set in units of presets.
  • the synchronization information may be set in units of slots.
  • the synchronization information may indicate after how many slots the AI model is to be changed based on the slot in which the MCSA message is transmitted (and/or received).
  • the MCSA message may include information about the AI model.
  • the MCSA message may include information for identifying the AI model to be changed (e.g., an AI model ID).
  • the terminal can change the AI model based on the MCSA message (25050).
  • the AI model change see, for example, the description of Fig. 18.
  • FIG. 26 is a flowchart of an operation method of a base station according to one embodiment of the present disclosure.
  • the embodiment of FIG. 26 may be, for example, an example of the operation of the base station in the embodiment of FIG. 21.
  • the base station can transmit a configuration message to the terminal (26010).
  • a description of the generation and transmission/reception of the configuration message (information) refer to, for example, the description of FIG. 15.
  • the configuration message may include configuration information for PDCCH prediction using an AI model and/or configuration information related to a change of an AI model used for PDCCH prediction.
  • setting information related to a change in an AI model may include information on change criteria for changing the AI model.
  • the base station can receive an MCR message from the terminal (26020).
  • MCR message For a description of transmission of the MCR message, refer to, for example, the description of Fig. 16a.
  • the terminal can determine whether a change criterion is satisfied based on the configuration message, and if the change criterion is satisfied, can transmit an MCR message to the base station.
  • the base station can receive the MCR message transmitted from the terminal.
  • the terminal may determine whether a change criterion is satisfied based on measurement results (e.g., RSRP, SINR) based on received DL RS (e.g., CSI-RS) and information about the change criterion included in the configuration information.
  • measurement results e.g., RSRP, SINR
  • received DL RS e.g., CSI-RS
  • the MCR message may include information about a measurement result for a CSI-RS and/or a comparison result between the measurement result and a change criterion.
  • the base station can decide whether to change the AI model based on the MCR message (26030).
  • the base station can transmit an MCSA message to the terminal (26040).
  • MCSA message For a description of the transmission and reception of the MCSA message, refer to the description of FIG. 18, for example.
  • the MCSA message may include synchronization information for changing the AI model.
  • the synchronization information may be used, for example, for the terminal and the base station to change the AI model at the same time (or timing). Based on this synchronization information, the terminal and the base station may change the AI model to the same AI model in synchronization.
  • the synchronization information may be set in units of presets.
  • the synchronization information may be set in units of slots.
  • the synchronization information may indicate after how many slots the AI model is to be changed based on the slot in which the MCSA message is transmitted (and/or received).
  • the MCSA message may include information about the AI model.
  • the MCSA message may include information for identifying the AI model to be changed (e.g., an AI model ID).
  • the base station can change the AI model (25050).
  • AI model change see, for example, the description of Fig. 18.
  • FIG. 27 is a flowchart of a method of operating a terminal according to one embodiment of the present disclosure.
  • the embodiment of Fig. 27 may be, for example, an example of the operation of the terminal in the embodiment of Fig. 22.
  • the terminal can receive a configuration message from the base station (27010).
  • a configuration message from the base station (27010).
  • the configuration message may include configuration information for PDCCH prediction using an AI model and/or configuration information related to a change of an AI model used for PDCCH prediction.
  • setting information related to a change in an AI model may include information on change criteria for changing the AI model.
  • the terminal can determine whether the change criteria are satisfied (27020). For a description of determining whether the change criteria are satisfied, see, for example, the description of FIG. 16b.
  • the terminal may determine whether a change criterion is satisfied based on measurement results (e.g., RSRP, SINR) based on received DL RS (e.g., CSI-RS) and information about the change criterion included in the configuration information.
  • measurement results e.g., RSRP, SINR
  • received DL RS e.g., CSI-RS
  • the terminal can decide whether to change the AI model (27030). For a description of the decision on whether to change the model, see, for example, the description of Fig. 16b.
  • the terminal can transmit an MCSA message to the base station (27040).
  • MCSA message For a description of the transmission and reception of the MCSA message, refer to the description of FIG. 19, for example.
  • the MCSA message may include synchronization information for changing the AI model.
  • the synchronization information may be used, for example, for the terminal and the base station to change the AI model at the same time (or timing). Based on this synchronization information, the terminal and the base station may change the AI model to the same AI model in synchronization.
  • the synchronization information may be set in units of presets.
  • the synchronization information may be set in units of slots.
  • the synchronization information may indicate after how many slots the AI model is to be changed based on the slot in which the MCSA message is transmitted (and/or received).
  • the MCSA message may include information about the AI model.
  • the MCSA message may include information for identifying the AI model to be changed (e.g., an AI model ID).
  • the terminal can change the AI model (27050).
  • AI model for a description of changing the AI model, refer to the description of Fig. 19, for example.
  • FIG. 28 is a flowchart of an operation method of a base station according to one embodiment of the present disclosure.
  • the embodiment of FIG. 28 may be, for example, an example of the operation of the base station in the embodiment of FIG. 22.
  • the base station can transmit a configuration message to the terminal (28010).
  • a description of the generation and transmission/reception of the configuration message (information) refer to, for example, the description of Fig. 15.
  • the configuration message may include configuration information for PDCCH prediction using an AI model and/or configuration information related to a change of an AI model used for PDCCH prediction.
  • setting information related to a change in an AI model may include information on change criteria for changing the AI model.
  • the base station can receive an MCSA message from the terminal (28020).
  • MCSA message For a description of transmitting and receiving an MCSA message, refer to, for example, the description of FIG. 19.
  • the MCSA message may include synchronization information for changing the AI model.
  • the synchronization information may be used, for example, for the terminal and the base station to change the AI model at the same time (or timing). Based on this synchronization information, the terminal and the base station may change the AI model to the same AI model in synchronization.
  • the synchronization information may be set in units of presets.
  • the synchronization information may be set in units of slots.
  • the synchronization information may indicate after how many slots the AI model is to be changed based on the slot in which the MCSA message is transmitted (and/or received).
  • the MCSA message may include information about the AI model.
  • the MCSA message may include information for identifying the AI model to be changed (e.g., an AI model ID).
  • the base station can change the AI model based on the MCSA message (28030).
  • the AI model change see, for example, the description of FIG. 19.
  • FIG. 29 is a flowchart of a method of operating a terminal according to one embodiment of the present disclosure.
  • the embodiment of FIG. 29 may be an example of the operation of the terminal in the embodiment of FIG. 23 or 24, for example.
  • the terminal can receive an MCSA message from the base station.
  • MCSA message For a description of transmission of the MCSA message, refer to, for example, the description of FIG. 18.
  • the MCSA message may include synchronization information for changing the AI model.
  • the synchronization information may be used, for example, for the terminal and the base station to change the AI model at the same time (or timing). Based on this synchronization information, the terminal and the base station may change the AI model to the same AI model in synchronization.
  • the synchronization information may be set in units of presets.
  • the synchronization information may be set in units of slots.
  • the synchronization information may indicate after how many slots the AI model is to be changed based on the slot in which the MCSA message is transmitted (and/or received).
  • the MCSA message may include information about the AI model.
  • the MCSA message may include information for identifying the AI model to be changed (e.g., an AI model ID).
  • the terminal can change the AI model based on the MCSA message (29020).
  • the AI model change see, for example, the description of Fig. 18.
  • FIG. 30 is a flowchart of an operation method of a base station according to one embodiment of the present disclosure.
  • the embodiment of FIG. 30 may be an example of the operation of the base station in the embodiment of FIG. 23 or 24, for example.
  • the base station can determine whether the change criteria are satisfied (30010).
  • the change criteria For a description of determining whether the change criteria are satisfied, see, for example, the description of FIG. 17.
  • the base station can determine whether a change criterion is satisfied based on measurement results (e.g., RSRP, SINR) based on received UL RS (e.g., SRS) and information about a change criterion generated by the base station.
  • measurement results e.g., RSRP, SINR
  • received UL RS e.g., SRS
  • the base station can decide whether to change the AI model (30020). For a description of the decision on whether to change the model, see, for example, the description of FIG. 17.
  • the base station can transmit an MCSA message to the terminal (30030).
  • MCSA message For a description of the transmission and reception of the MCSA message, refer to the description of FIG. 18, for example.
  • the MCSA message may include synchronization information for changing the AI model.
  • the synchronization information may be used, for example, for the terminal and the base station to change the AI model at the same time (or timing). Based on this synchronization information, the terminal and the base station may change the AI model to the same AI model in synchronization.
  • the synchronization information may be set in units of presets.
  • the synchronization information may be set in units of slots.
  • the synchronization information may indicate after how many slots the AI model is to be changed based on the slot in which the MCSA message is transmitted (and/or received).
  • the MCSA message may include information about the AI model.
  • the MCSA message may include information for identifying the AI model to be changed (e.g., an AI model ID).
  • the base station can change the AI model (30040).
  • AI model change see, for example, the description in Fig. 18.
  • Figure 31 shows the configuration of a terminal according to one embodiment of the present disclosure.
  • the terminal of Fig. 31 may be, for example, the terminal (120) of Fig. 1, or may include the terminal (120) of Fig. 1, or may be an electronic device included in the terminal (120) of Fig. 1.
  • the terminal may include a transceiver (3110), a control unit (3120), and a storage unit (3130).
  • the control unit (3110) may be defined as a circuit or an application-specific integrated circuit or at least one processor.
  • the transceiver (3110) can transmit and receive signals with other entities.
  • the transceiver (3110) can transmit and receive data for changing an AI model for PDCCH prediction, for example.
  • the control unit (3120) can control the overall operation of the terminal according to the embodiment proposed in the present disclosure.
  • the control unit (3120) can control the signal flow between each block to perform the operation according to the flow chart described above.
  • the control unit (3120) can control the operation of the terminal described with reference to FIGS. 1 to 30, for example.
  • the storage unit (3130) can store at least one of the information transmitted and received through the transceiver unit (3110) and the information generated through the control unit (3120).
  • the storage unit (3130) can store data and information necessary for changing the AI model for PDCCH prediction described with reference to FIGS. 1 to 30.
  • Figure 32 shows the configuration of a base station according to one embodiment of the present disclosure.
  • the base station of FIG. 32 may be, for example, the base station (110) of FIG. 1, or may include the base station (110) of FIG. 1, or may be an electronic device included in the base station (110) of FIG. 1.
  • the base station may include a transceiver (3210), a control unit (3220), and a storage unit (3230).
  • the control unit (3210) may be defined as a circuit or an application specific integrated circuit or at least one processor.
  • the transceiver (3210) can transmit and receive signals with other entities.
  • the transceiver (3210) can transmit and receive data for changing an AI model for PDCCH prediction, for example.
  • the control unit (3220) can control the overall operation of the base station according to the embodiment proposed in the present disclosure.
  • the control unit (3220) can control the signal flow between each block to perform the operation according to the flow chart described above.
  • the control unit (3220) can control the operation of the base station described with reference to FIGS. 1 to 30, for example.
  • the storage unit (3230) can store at least one of the information transmitted and received through the transceiver unit (3210) and the information generated through the control unit (3220).
  • the storage unit (3230) can store data and information necessary for changing the AI model for PDCCH prediction described with reference to FIGS. 1 to 30.
  • Electronic devices may be devices of various forms.
  • the electronic devices may include, for example, display devices, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliance devices.
  • Electronic devices according to embodiments of the present disclosure are not limited to the above-described devices.
  • phrases “A or B”, “at least one of A and B”, “at least one of A or B”, “A, B or C”, “at least one of A, B and C”, and “at least one of A, B, or C” can each include any one of the items listed together in that phrase, or all possible combinations thereof.
  • Terms such as “first”, “second”, or “first” or “second” may be used merely to distinguish the corresponding element from other corresponding elements, and do not limit the corresponding elements in any other respect (e.g., importance or order).
  • part or module used in various embodiments of the present specification may include units implemented by hardware, software or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit.
  • the "part” or “module” may be an integrally configured component or a minimum unit of the component that performs one or more functions, or a part thereof.
  • the “part” or “module” may be implemented in the form of an application-specific integrated circuit (ASIC).
  • ASIC application-specific integrated circuit
  • if as used in various embodiments of the present specification can be interpreted to mean “when”, or “upon”, or “in response to determining”, or “in response to detecting,” depending on the context. Similarly, “if it is determined that”, or “if ⁇ is detected” can be interpreted to mean “upon determining”, or “in response to determining”, or “upon detecting”, or “in response to detecting,” depending on the context.
  • the program executed by the electronic device described herein may be implemented as hardware components, software components, and/or a combination of hardware components and software components.
  • the program may be executed by any system capable of executing computer-readable instructions.
  • the software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing device to perform a desired operation or may independently or collectively command the processing device.
  • the software may be implemented as a computer program including instructions stored on a computer-readable storage medium. Examples of the computer-readable storage medium include magnetic storage media (e.g., Read-Only Memory (ROM), Random-Access Memory (RAM), floppy disks, hard disks, etc.) and optical readable media (e.g., CD-ROMs, Digital Versatile Discs (DVDs)).
  • the computer-readable storage medium may be distributed across network-connected computer systems so that the computer-readable code may be stored and executed in a distributed manner.
  • the computer program may be distributed online (e.g., by download or upload) via an application store (e.g., Play StoreTM) or directly between two user devices (e.g., smart phones).
  • an application store e.g., Play StoreTM
  • two user devices e.g., smart phones
  • at least a part of the computer program product may be temporarily stored or temporarily created in a machine-readable storage medium, such as a memory of a manufacturer's server, a server of the application store, or an intermediary server.
  • each component e.g., a module or a program of the above-described components may include a single or multiple entities, and some of the multiple entities may be separately arranged in other components.
  • one or more components or operations of the above-described corresponding components may be omitted, or one or more other components or operations may be added.
  • the multiple components e.g., a module or a program
  • the integrated component may perform one or more functions of each of the multiple components identically or similarly to those performed by the corresponding component of the multiple components before the integration.
  • the operations performed by the module, program, or other component may be executed sequentially, in parallel, repeatedly, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

Landscapes

  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • Quality & Reliability (AREA)
  • Theoretical Computer Science (AREA)
  • Electromagnetism (AREA)
  • Software Systems (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Computing Systems (AREA)
  • Medical Informatics (AREA)
  • Data Mining & Analysis (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

La présente divulgation porte sur un système de communication 5G ou 6G destiné à prendre en charge un débit de transmission de données supérieur à celui du précédent système de communication 4G tel que LTE. Le procédé de fonctionnement d'un terminal dans un système de communication sans fil comprend les opérations consistant à recevoir, d'une station de base, des informations de configuration qui comprennent des informations de critères permettant de modifier un modèle d'IA selon un état de canal, ainsi qu'à identifier si les critères sont satisfaits d'après les informations de mesure du terminal et les informations de critères. Le modèle d'IA est utilisé pour obtenir des informations de prédiction de DCI pour au moins une unité de prédiction, et les informations de prédiction de DCI indiquent si des DCI sont présentes dans l'unité de prédiction correspondante.
PCT/KR2024/095696 2023-06-27 2024-04-12 Procédé de modification de modèle d'intelligence artificielle dans un système de communication sans fil, et dispositif associé Pending WO2025005772A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
KR10-2023-0082427 2023-06-27
KR1020230082427A KR20250000567A (ko) 2023-06-27 2023-06-27 무선 통신 시스템에서 인공지능 모델을 변경하기 위한 방법 및 이를 위한 장치

Publications (1)

Publication Number Publication Date
WO2025005772A1 true WO2025005772A1 (fr) 2025-01-02

Family

ID=93939395

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2024/095696 Pending WO2025005772A1 (fr) 2023-06-27 2024-04-12 Procédé de modification de modèle d'intelligence artificielle dans un système de communication sans fil, et dispositif associé

Country Status (2)

Country Link
KR (1) KR20250000567A (fr)
WO (1) WO2025005772A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119922599A (zh) * 2025-01-09 2025-05-02 中国联合网络通信集团有限公司 盲检方法、装置及存储介质

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190223098A1 (en) * 2016-09-29 2019-07-18 Intel IP Corporation Mobile device management based on predicted base station behavior

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190223098A1 (en) * 2016-09-29 2019-07-18 Intel IP Corporation Mobile device management based on predicted base station behavior

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
MODERATOR (APPLE): "Summary #5 on other aspects of AI/ML for CSI enhancement", 3GPP DRAFT; R1-2303983, 3RD GENERATION PARTNERSHIP PROJECT (3GPP), MOBILE COMPETENCE CENTRE ; 650, ROUTE DES LUCIOLES ; F-06921 SOPHIA-ANTIPOLIS CEDEX ; FRANCE, vol. 3GPP RAN 1, no. e-Meeting; 20230417 - 20230426, 26 April 2023 (2023-04-26), Mobile Competence Centre ; 650, route des Lucioles ; F-06921 Sophia-Antipolis Cedex ; France, XP052368667 *
MODERATOR (OPPO): "Summary#2 for other aspects on AI/ML for beam management", 3GPP DRAFT; R1-2303967, 3RD GENERATION PARTNERSHIP PROJECT (3GPP), MOBILE COMPETENCE CENTRE ; 650, ROUTE DES LUCIOLES ; F-06921 SOPHIA-ANTIPOLIS CEDEX ; FRANCE, vol. 3GPP RAN 1, no. e-Meeting; 20230417 - 20230426, 20 April 2023 (2023-04-20), Mobile Competence Centre ; 650, route des Lucioles ; F-06921 Sophia-Antipolis Cedex ; France, XP052457627 *
RAJEEV KUMAR, QUALCOMM INCORPORATED: "Discussion on AI/ML methods", 3GPP DRAFT; R2-2212659; TYPE DISCUSSION, 3RD GENERATION PARTNERSHIP PROJECT (3GPP), MOBILE COMPETENCE CENTRE ; 650, ROUTE DES LUCIOLES ; F-06921 SOPHIA-ANTIPOLIS CEDEX ; FRANCE, vol. RAN WG2, no. Toulouse, FR; 20221114 - 20221118, 4 November 2022 (2022-11-04), Mobile Competence Centre ; 650, route des Lucioles ; F-06921 Sophia-Antipolis Cedex ; France, XP052216728 *
SAKIRA HASSAN, NOKIA, NOKIA SHANGHAI BELL, T-MOBILE US: "AIML architecture", 3GPP DRAFT; R2-2302649; TYPE DISCUSSION; FS_NR_AIML_AIR, 3RD GENERATION PARTNERSHIP PROJECT (3GPP), MOBILE COMPETENCE CENTRE ; 650, ROUTE DES LUCIOLES ; F-06921 SOPHIA-ANTIPOLIS CEDEX ; FRANCE, vol. RAN WG2, no. Online; 20230417 - 20230426, 6 April 2023 (2023-04-06), Mobile Competence Centre ; 650, route des Lucioles ; F-06921 Sophia-Antipolis Cedex ; France, XP052289058 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119922599A (zh) * 2025-01-09 2025-05-02 中国联合网络通信集团有限公司 盲检方法、装置及存储介质

Also Published As

Publication number Publication date
KR20250000567A (ko) 2025-01-03

Similar Documents

Publication Publication Date Title
WO2022145995A1 (fr) Procédé et appareil de réglage de temporisation de liaison montante
WO2022154345A1 (fr) Procédé et appareil de positionnement dans un système de communication
WO2022005187A1 (fr) Mécanismes et conditions pour la prise en charge de répétitions pour une transmission de pucch
WO2018143776A1 (fr) Procédé de surveillance de liaison sans fil par un terminal dans un système de communication sans fil et dispositif le prenant en charge
EP3695679A1 (fr) Procédés et appareils pour déterminer et configurer une ressource temps-fréquence dans le processus d'accès aléatoire
WO2019050329A1 (fr) Procédé de transmission de signal de liaison montante en fonction de livre de codes dans un système de communication sans fil, et dispositif pour cela
WO2022182179A1 (fr) Procédé et appareil pour surveiller une occasion de radiomessagerie dans un système de communication sans fil
WO2018016921A1 (fr) Procédés destinés à la transmission et à la réception d'informations de commande de liaison descendante entre station de base et terminal dans un système de communication sans fil, et dispositifs destinés à prendre en charge lesdits procédés
WO2019066619A1 (fr) Procédé de notification d'informations d'état de canal apériodiques dans un système de communication sans fil et dispositif associé
WO2022182172A1 (fr) Procédé et appareil pour une procédure de radiomessagerie dans un système de communication sans fil
WO2024096650A1 (fr) Système et procédé de configuration de ressources cfra pour mobilité basée sur un signal de couche inférieure
WO2020091390A1 (fr) Procédé et appareil d'émission et de réception de radiomessagerie, détermination de fenêtre d'informations de système et sélection de porteuse de liaison montante
WO2019139407A1 (fr) Procédés et appareils pour déterminer et configurer une ressource temps-fréquence dans le processus d'accès aléatoire
WO2024025287A1 (fr) Procédé de communication, équipement utilisateur, station de base et support de stockage
WO2020130754A1 (fr) Procédé et appareil de gestion de plusieurs bwps actives
WO2024029965A1 (fr) Procédé et appareil pour économiser l'énergie dans un système de communication sans fil
WO2023101492A1 (fr) Procédé et appareil pour générer un ce mac pour une récupération de défaillance de faisceau dans un système de communication sans fil
WO2022186638A1 (fr) Appareil et procédé d'accès aléatoire utilisant une configuration rach spécifique à une tranche
WO2023106832A1 (fr) Système et procédé de récupération de défaillance de faisceau multi-trp pour hiérarchisation spcell et mac ce
WO2025005772A1 (fr) Procédé de modification de modèle d'intelligence artificielle dans un système de communication sans fil, et dispositif associé
WO2023128080A1 (fr) Procédé et dispositif de commande de mesure de signal d'interférence dans un système de communication sans fil
EP4573811A1 (fr) Procédé et appareil pour des économies d'énergie d'un système de communication sans fil
WO2019098799A1 (fr) Procédé de notification d'informations d'état de canal en fonction de priorités dans un système de communications sans fil et dispositif associé
WO2023211107A1 (fr) Procédé permettant une transmission et une réception de liaison montante dans un système de communication sans fil et dispositif correspondant
WO2023013915A1 (fr) Procédé et dispositif d'émission et de réception d'informations d'état de canal dans un système de communication sans fil

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 24832548

Country of ref document: EP

Kind code of ref document: A1