WO2024207454A1 - Dispositifs et procédés de communication - Google Patents

Dispositifs et procédés de communication Download PDF

Info

Publication number
WO2024207454A1
WO2024207454A1 PCT/CN2023/086963 CN2023086963W WO2024207454A1 WO 2024207454 A1 WO2024207454 A1 WO 2024207454A1 CN 2023086963 W CN2023086963 W CN 2023086963W WO 2024207454 A1 WO2024207454 A1 WO 2024207454A1
Authority
WO
WIPO (PCT)
Prior art keywords
terminal device
radio link
link problem
determination
indication
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.)
Ceased
Application number
PCT/CN2023/086963
Other languages
English (en)
Inventor
Rao SHI
Zhen He
Gang Wang
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.)
NEC Corp
Original Assignee
NEC Corp
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 NEC Corp filed Critical NEC Corp
Priority to PCT/CN2023/086963 priority Critical patent/WO2024207454A1/fr
Publication of WO2024207454A1 publication Critical patent/WO2024207454A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0686—Hybrid systems, i.e. switching and simultaneous transmission
    • H04B7/0695—Hybrid systems, i.e. switching and simultaneous transmission using beam selection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W76/00—Connection management
    • H04W76/10—Connection setup
    • H04W76/19—Connection re-establishment

Definitions

  • Embodiments of the present disclosure generally relate to the field of telecommunication, and in particular, to devices and methods of communication for artificial intelligence (AI) -based mobility management.
  • AI artificial intelligence
  • embodiments of the present disclosure provide methods, devices and computer storage media of communication for AI-based mobility management.
  • a terminal device comprising a processor.
  • the processor is configured to cause the terminal device to: determine that a radio link problem occurs; determine information associated with an input of an artificial intelligence model for radio link measurements, an output of the artificial intelligence model being used for determination of an operation for the radio link problem; and perform the operation for the radio link problem.
  • a terminal device comprising a processor.
  • the processor is configured to cause the terminal device to: determine that a beam link problem occurs; determine information associated with an input of an artificial intelligence model for beam failure detection, an output of the artificial intelligence model being used for determination of an operation for the beam link problem; and perform the operation for the beam link problem.
  • a method of communication comprises: determining, at a terminal device, that a radio link problem occurs; determining information associated with an input of an artificial intelligence model for radio link measurements, an output of the artificial intelligence model being used for determination of an operation for the radio link problem; and performing the operation for the radio link problem.
  • a method of communication comprises: determining, at a terminal device, that a beam link problem occurs; determining information associated with an input of an artificial intelligence model for beam failure detection, an output of the artificial intelligence model being used for determination of an operation for the beam link problem; and performing the operation for the beam link problem.
  • a computer readable medium having instructions stored thereon.
  • the instructions when executed on at least one processor, cause the at least one processor to perform the method according to the third or fourth aspect of the present disclosure.
  • FIG. 1 illustrates an example communication network in which some embodiments of the present disclosure can be implemented
  • FIG. 2 illustrates a schematic diagram of an AI model inference and monitoring in which some embodiments of the present disclosure can be implemented
  • FIG. 3 illustrates a schematic diagram illustrating a process of communication for AI-based RLM according to embodiments of the present disclosure
  • FIG. 4 illustrates a schematic diagram of an example of AI-based RLM according to embodiments of the present disclosure
  • FIG. 5 illustrates a schematic diagram illustrating an process of communication for AI-based BFD according to embodiments of the present disclosure
  • FIG. 6 illustrates a schematic diagram of an example of AI-based BFD according to embodiments of the present disclosure
  • FIG. 7 illustrates an example method of communication implemented at a terminal device in accordance with some embodiments of the present disclosure
  • FIG. 8 illustrates another example method of communication implemented at a terminal device in accordance with some embodiments of the present disclosure.
  • FIG. 9 is a simplified block diagram of a device that is suitable for implementing embodiments of the present disclosure.
  • terminal device refers to any device having wireless or wired communication capabilities.
  • the terminal device include, but not limited to, user equipment (UE) , personal computers, desktops, mobile phones, cellular phones, smart phones, personal digital assistants (PDAs) , portable computers, tablets, wearable devices, internet of things (IoT) devices, Ultra-reliable and Low Latency Communications (URLLC) devices, Internet of Everything (IoE) devices, machine type communication (MTC) devices, device on vehicle for V2X communication where X means pedestrian, vehicle, or infrastructure/network, devices for Integrated Access and Backhaul (IAB) , Space borne vehicles or Air borne vehicles in Non-terrestrial networks (NTN) including Satellites and High Altitude Platforms (HAPs) encompassing Unmanned Aircraft Systems (UAS) , eXtended Reality (XR) devices including different types of realities such as Augmented Reality (AR) , Mixed Reality (MR) and Virtual Reality (VR) , the unmanned aerial vehicle (UAV)
  • UE user equipment
  • the ‘terminal device’ can further has ‘multicast/broadcast’ feature, to support public safety and mission critical, V2X applications, transparent IPv4/IPv6 multicast delivery, IPTV, smart TV, radio services, software delivery over wireless, group communications and IoT applications. It may also incorporate one or multiple Subscriber Identity Module (SIM) as known as Multi-SIM.
  • SIM Subscriber Identity Module
  • the term “terminal device” can be used interchangeably with a UE, a mobile station, a subscriber station, a mobile terminal, a user terminal or a wireless device.
  • the term “network device” may refer to a device which is capable of providing or hosting a cell or coverage where terminal devices can communicate.
  • Examples of an access network device include, but not limited to, a satellite, a unmanned aerial systems (UAS) platform, a Node B (NodeB or NB) , an evolved NodeB (eNodeB or eNB) , a next generation NodeB (gNB) , a transmission reception point (TRP) , a remote radio unit (RRU) , a radio head (RH) , a remote radio head (RRH) , an IAB node, a low power node such as a femto node, a pico node, a reconfigurable intelligent surface (RIS) , and the like.
  • UAS unmanned aerial systems
  • NodeB Node B
  • eNodeB or eNB evolved NodeB
  • gNB next generation NodeB
  • TRP transmission reception point
  • RRU remote radio unit
  • the terminal device or the network device may have AI or machine learning (ML) capability. It generally includes a model which has been trained from numerous collected data for a specific function, and can be used to predict some information.
  • AI machine learning
  • the terminal or the network device may work on several frequency ranges, e.g. FR1 (410 MHz to 7125 MHz) , FR2 (24.25GHz to 71GHz) , frequency band larger than 100GHz as well as Tera Hertz (THz) . It can further work on licensed/unlicensed/shared spectrum.
  • the terminal device may have more than one connection with the network devices under Multi-Radio Dual Connectivity (MR-DC) application scenario.
  • MR-DC Multi-Radio Dual Connectivity
  • the terminal device or the network device can work on full duplex, flexible duplex and cross division duplex modes.
  • test equipment e.g. signal generator, signal analyzer, spectrum analyzer, network analyzer, test terminal device, test network device, channel emulator.
  • the terminal device may be connected with a first network device and a second network device.
  • One of the first network device and the second network device may be a master node and the other one may be a secondary node.
  • the first network device and the second network device may use different radio access technologies (RATs) .
  • the first network device may be a first RAT device and the second network device may be a second RAT device.
  • the first RAT device is eNB and the second RAT device is gNB.
  • Information related with different RATs may be transmitted to the terminal device from at least one of the first network device or the second network device.
  • first information may be transmitted to the terminal device from the first network device and second information may be transmitted to the terminal device from the second network device directly or via the first network device.
  • information related with configuration for the terminal device configured by the second network device may be transmitted from the second network device via the first network device.
  • Information related with reconfiguration for the terminal device configured by the second network device may be transmitted to the terminal device from the second network device directly or via the first network device.
  • the singular forms ‘a’ , ‘an’ and ‘the’ are intended to include the plural forms as well, unless the context clearly indicates otherwise.
  • the term ‘includes’ and its variants are to be read as open terms that mean ‘includes, but is not limited to. ’
  • the term ‘based on’ is to be read as ‘at least in part based on. ’
  • the term ‘one embodiment’ and ‘an embodiment’ are to be read as ‘at least one embodiment. ’
  • the term ‘another embodiment’ is to be read as ‘at least one other embodiment. ’
  • the terms ‘first, ’ ‘second, ’ and the like may refer to different or same objects. Other definitions, explicit and implicit, may be included below.
  • values, procedures, or apparatus are referred to as ‘best, ’ ‘lowest, ’ ‘highest, ’ ‘minimum, ’ ‘maximum, ’ or the like. It will be appreciated that such descriptions are intended to indicate that a selection among many used functional alternatives can be made, and such selections need not be better, smaller, higher, or otherwise preferable to other selections.
  • AI may be interchangeably used with “machine learning (ML) ” or “AI/ML” .
  • AI model may be interchangeably used with “ML model” or “AI/ML model” .
  • Embodiments of the present disclosure provide solutions of communication for AI-based mobility management.
  • a terminal device upon determination that a radio link problem occurs, a terminal device determines information associated with an input of an AI model for RLM. An output of the AI model is used for determination of an operation for the radio link problem. The terminal device performs the determined operation for the radio link problem. In this way, an RLF procedure may be accelerated. Thus, latency of connection may be reduced and user experience may be improved.
  • a terminal device upon determination that a beam link problem occurs, determines information associated with an input of an AI model for BFD. An output of the AI model is used for determination of an operation for the beam link problem. The terminal device performs the operation for the beam link problem. In this way, a beam failure recovery (BFR) procedure may be accelerated. Thus, latency of connection may be reduced and user experience may be improved.
  • BFR beam failure recovery
  • FIG. 1 illustrates a schematic diagram of an example communication network 100 in which some embodiments of the present disclosure can be implemented.
  • the communication network 100 may include a terminal device 110 and a network device 120.
  • the network device 120 may provide one or more serving cells (not shown) to serve the terminal device 110.
  • the terminal device 110 may have a plurality of beams (not shown)
  • the network device 120 may have a plurality of beams (not shown)
  • a channel (or called as a sub-channel in this case) may be formed between one of the plurality of beams of the terminal device 110 and one of the plurality of beams of the network device 120.
  • the terminal device 110 may transmit information to the network device 120 or receive information from the network device 120 via one or more sub-channels.
  • the communication network 100 may include any suitable number of network devices and/or terminal devices and/or other network elements adapted for implementing implementations of the present disclosure.
  • the terminal device 110 and the network device 120 may communicate with each other via a channel such as a wireless communication channel.
  • the communications in the communication network 100 may conform to any suitable standards including, but not limited to, Global System for Mobile Communications (GSM) , Long Term Evolution (LTE) , LTE-Evolution, LTE-Advanced (LTE-A) , New Radio (NR) , Wideband Code Division Multiple Access (WCDMA) , Code Division Multiple Access (CDMA) , GSM EDGE Radio Access Network (GERAN) , Machine Type Communication (MTC) and the like.
  • GSM Global System for Mobile Communications
  • LTE Long Term Evolution
  • LTE-Evolution LTE-Advanced
  • NR New Radio
  • WCDMA Wideband Code Division Multiple Access
  • CDMA Code Division Multiple Access
  • GERAN GSM EDGE Radio Access Network
  • MTC Machine Type Communication
  • Examples of the communication protocols include, but not limited to, the first generation (1G) , the second generation (2G) , 2.5G, 2.75G, the third generation (3G) , the fourth generation (4G) , 4.5G, the fifth generation (5G) communication protocols, 5.5G, 5G-Advanced networks, or the sixth generation (6G) networks.
  • the network device 120 may configure a set of RLM-reference signal (RS) resources for the terminal device 110.
  • the terminal device 110 may receive, from the network device 120, a set of RLM-RSs on the set of RLM-RS resources. Based on measurements on the set of RLM-RSs, the terminal device 110 may determine radio link quality on all the configured RLM-RS resources. When the radio link quality is worse than a threshold quality (e.g., Q out ) , layer 1 (L1) of the terminal device 110 may send an out-of-synchronization indication to higher layers. When the radio link quality is better than a threshold quality (e.g., Q in ) , L1 of the terminal device 110 may send an in-synchronization indication to the higher layers.
  • a threshold quality e.g., Q out
  • the terminal device 110 may start a timer (e.g., T310) for RLF detection.
  • a predetermined number e.g., N311
  • the terminal device 110 may stop the timer for RLF detection.
  • the terminal device 110 may consider RLF to be detected.
  • the network device 120 may configure a set of BFD-RS resources for the terminal device 110.
  • the terminal device 110 may receive, from the network device 120, a set of BFD-RSs on the set of BFD-RS resources. Based on measurements on the set of BFD-RSs, the terminal device 110 may determine radio link quality on all the configured BFD-RS resources. When the radio link quality is worse than a threshold quality (e.g., Q out_LR ) , L1 of the terminal device 110 may send a beam failure instance indication to the higher layers.
  • a threshold quality e.g., Q out_LR
  • the terminal device 110 may start a timer (e.g., beamFailureDetectionTimer) for BFD, and increment a value of a counter (e.g., BFI_COUNTER) . If the value of the counter is above a threshold value (e.g., beamFailureInstanceMaxCount) , the terminal device 110 may trigger a BFR.
  • a timer e.g., beamFailureDetectionTimer
  • BFI_COUNTER e.g., beamFailureInstanceMaxCount
  • FIG. 2 illustrates a schematic diagram 200 of an AI model inference and monitoring in which some embodiments of the present disclosure can be implemented.
  • model training may be performed based on training data.
  • An AI model may be deployed or updated by the model training.
  • an input data set as an input of the AI model
  • an output data set may be obtained by model inference. This is an AI model inference procedure.
  • a model monitoring may be performed to monitor performance of the model inference of the AI model.
  • the model monitoring may be classified into three types: comparison between inference results and ground-truth results; evaluation for system performance (e.g., throughout, block error rate (BLER) , reference signal receiving power (RSRP) , positive acknowledgement (ACK) /NACK) ; distribution detection for the input or output data set.
  • BLER block error rate
  • RSRP reference signal receiving power
  • ACK positive acknowledgement
  • NACK distribution detection for the input or output data set.
  • Embodiments of the present disclosure provide solutions of AI-based RLM and AI-based BFD so as to fast a RLF procedure and a BFR procedure. More details of the solutions will be described with reference to FIGs. 3 to 6 below.
  • FIG. 3 illustrates a schematic diagram illustrating a process 300 of communication for AI-based RLM according to embodiments of the present disclosure.
  • the process 300 may involve the terminal device 110 and the network device 120 as illustrated in FIG. 1.
  • the steps and the order of the steps in FIG. 3 are merely for illustration, and not for limitation. For example, the order of the steps may be changed. Some of the steps may be omitted or any other suitable additional steps may be added. It is assumed that model training and deployment of an AI model for RLM is finished, and the AI model is deployed at the terminal device 110 or the network device 120.
  • the terminal device 110 determines 310 whether a radio link problem occurs. In some embodiments, if a predetermined number (e.g., N310) of consecutive out-of-synchronization indications are received, the terminal device 110 may determine that the radio link problem occurs. It is to be understood that the predetermined number may be any suitable number and may be determined in any suitable ways.
  • a predetermined number e.g., N310
  • the terminal device 110 may determine that the radio link problem occurs. It is to be understood that the timer may be any suitable timers existing or to be developed in future. For example, the timer may be T312 or the like.
  • the terminal device 110 determines 320 information associated with an input of the AI model.
  • An output of the AI model is used for determination of an operation for the radio link problem.
  • the AI model may be applied for RLM.
  • the information may indicate historical measurement results of RLM-RSs.
  • the information may indicate a configuration for RLM (e.g., RLM-RS configuration) .
  • the information may indicate a location of the terminal device 110.
  • the information may indicate a speed of the terminal device 110.
  • the information may indicate an angle of the terminal device 110. It is to be understood that the information may be any suitable information associated with the input of the AI model.
  • the terminal device 110 performs 330 the operation for the radio link problem.
  • the terminal device 110 may determine 331, based on the output of the AI model, future measurement results of RLM-RSs by using the determined information as the input of the AI model. For example, the terminal device 110 may derive the future measurement results at least within the timer (e.g., T310) configured for RLF detection. Based on the future measurement results, the terminal device 110 may perform 332 the operation for the radio link problem.
  • the timer e.g., T310
  • the terminal device 110 may determine, from the future measurement results, that a predetermined number (e.g., N311) of consecutive in-synchronization indications is assumed. In other words, the terminal device 110 may determine, from the future measurement results, that an RLF will not happen in a future period of time.
  • the operation for the radio link problem may comprise stopping a timer (e.g., T310) configured for RLF detection. In this case, there is no need to perform any other actions. That is, the terminal device 110 may do nothing and just wait for a handover command if a handover procedure is triggered. Thus, overhead for RLF detection may be reduced.
  • the terminal device 110 may determine, from the future measurement results, that a predetermined number (e.g., N311) of consecutive in-synchronization indications is not assumed. In other words, the terminal device 110 may determine, from the future measurement results, that an RLF will happen in a future period of time.
  • the operation for the radio link problem may comprise stopping a timer configured for RLF detection.
  • the operation for the radio link problem may comprise storing information of the radio link problem caused by AI prediction in an RLF report.
  • the operation for the radio link problem may comprise initiating a master cell group (MCG) failure information reporting.
  • the operation for the radio link problem may comprise initiating a connection re-establishment procedure.
  • the terminal device 110 may receive, from the network device 120, an indication of a time offset.
  • the time offset is associated with the performing of the operation for the radio link problem.
  • the terminal device 110 may perform the operation for the radio link problem after the time offset with respect to reception of the indication of the time offset.
  • FIG. 4 illustrates a schematic diagram 400 of an example of AI-based RLM according to embodiments of the present disclosure.
  • 3 consecutive measured BLER of RLM-RSs are above Q out . That is, 3 consecutive out-of-synchronization indications are received.
  • the terminal device 110 determines that a radio link problem occurs.
  • An AI model for RLM is performed, and future measured BLER of RLM-RSs in future T310 (from the time point A to a time point B) are predicted. It can be seen from FIG. 4 that there is no 3 consecutive measured BLER of RLM-RSs below Q in . Thus, 3 consecutive in-synchronization indications are not assumed.
  • the operation for the radio link problem may comprise at least one of the following: stopping a timer configured for RLF detection; storing information of the radio link problem in an RLF report; initiating an MCG failure information reporting; or initiating a connection re-establishment procedure.
  • the UE shall:
  • RLM i.e., UE derive the future RLM-RS configured in RadioLinkMonitoringConfig
  • timer T304 for the NR PSCell is not running in case of NR-DC or timer T307 of the E-UTRA PSCell is not running as specified in TS 36.331 [10] , clause 5.3.10.10, in NE-DC) :
  • the terminal device 110 may transmit 333, to the network device 120, the information associated with the input of the AI model.
  • the network device 120 may determine 334 future measurement results of RLM-RSs based on the output of the AI model.
  • the network device 120 may transmit 335, to the terminal device 110, an indication of the operation for the radio link problem. In some embodiments, if the network device 120 determines that an RLF will not happen, the network device 120 may transmit an indication indicating stopping of the timer configured for RLF detection. In some embodiments, if the network device 120 determines that an RLF will happen, the network device 120 may transmit an indication indicating at least one of the following: initiating of a connection re-establishment procedure; initiating of an MCG failure information reporting; an occurrence of an RLF; or a handover.
  • the terminal device 110 may perform 336 the operation for the radio link problem based on the indication.
  • the terminal device 110 may stop the timer.
  • the terminal device 110 may initiate the connection re-establishment procedure.
  • the terminal device 110 may initiate the MCG failure information reporting.
  • the terminal device 110 may perform a procedure associated with the RLF.
  • the terminal device 110 may perform the handover.
  • the terminal device 110 may receive, from the network device 120, an indication of a time offset.
  • the time offset is associated with the performing of the operation for the radio link problem.
  • the terminal device 110 may perform the operation for the radio link problem after the time offset with respect to reception of the indication of the time offset.
  • the UE shall:
  • the UE behavior is based on implementation, e.g., sending measurement report, RRCReestablishment, random access to other cell and so on...
  • a RLF procedure may be accelerated, connection latency may be reduced, and user experience may be improved.
  • FIG. 5 illustrates a schematic diagram illustrating a process 500 of communication for AI-based BFD according to embodiments of the present disclosure.
  • the process 500 may involve the terminal device 110 and the network device 120 as illustrated in FIG. 1.
  • the steps and the order of the steps in FIG. 5 are merely for illustration, and not for limitation. For example, the order of the steps may be changed. Some of the steps may be omitted or any other suitable additional steps may be added. It is assumed that model training and deployment of an AI model for BFD is finished, and the AI model is deployed at the terminal device 110 or the network device 120.
  • the terminal device 110 determines 510 whether a beam link problem occurs. In some embodiments, if a beam failure instance (BFI) is received, the terminal device 110 may determine that the beam link problem occurs. In some embodiments, if a timer (e.g., beamFailureDetectionTimer) for BFD starts, the terminal device 110 may determine that the beam link problem occurs. It is to be understood that the timer may be any suitable timers existing or to be developed in future.
  • BFI beam failure instance
  • a timer e.g., beamFailureDetectionTimer
  • the terminal device 110 determines 520 information associated with an input of the AI model.
  • An output of the AI model is used for determination of an operation for the beam link problem.
  • the AI model may be applied for RLM.
  • the information may indicate historical measurement results of BFD-RSs.
  • the information may indicate a configuration for BFD (e.g., BFD-RS configuration) .
  • the information may indicate a location of the terminal device 110.
  • the information may indicate a speed of the terminal device 110.
  • the information may indicate an angle of the terminal device 110. It is to be understood that the information may be any suitable information associated with the input of the AI model.
  • the terminal device 110 performs 530 the operation for the beam link problem.
  • the terminal device 110 may determine 531, based on the output of the AI model, future measurement results of BFD-RSs by using the determined information as the input of the AI model.
  • the terminal device 110 may derive the future measurement results at least within (beamFailureDetectionTimer ⁇ TIndication_interval_BFD ⁇ beamFailureInstanceMaxCount) , where beamFailureDetectionTimer denotes a value of the timer for BFD, TIndication_interval_BFD denotes a time interval between two successive BFIs, and beamFailureInstanceMaxCount denotes the threshold value for the counter. Based on the future measurement results, the terminal device 110 may perform 532 the operation for the beam link problem.
  • the terminal device 110 may determine, from the future measurement results, that a value of a counter (e.g., BFI_COUNTER) for BFI above a threshold value (e.g., beamFailureInstanceMaxCount) is not assumed. In other words, the terminal device 110 may determine that a beam failure will not be detected in a future period of time.
  • the operation for the beam link problem may comprise resetting the counter and stopping a timer (e.g., beamFailureDetectionTimer) for BFD.
  • the terminal device 110 may determine, from the future measurement results, that the value of the counter for BFI above the threshold value is assumed. In other words, the terminal device 110 may determine that a beam failure will be detected in a future period of time.
  • the operation for the beam link problem may comprise triggering a BFR for a secondary cell (SCell) .
  • the operation for the beam link problem may comprise indicating a beam failure of a primary secondary cell (PSCell) to upper layers of the terminal device 110.
  • the operation for the beam link problem may comprise initiating a random access (RA) procedure on a serving cell (e.g., a special cell (SpCell) ) .
  • RA random access
  • FIG. 6 illustrates a schematic diagram 600 of an example of AI-based BFD according to embodiments of the present disclosure.
  • one BLER measurement 610 of BFD-RSs above Q out is measured.
  • the terminal device 110 determines that a beam link problem occurs, and BFI_COUNTER is set to 1, and a BFD timer is started at a time point C.
  • An AI model for BFD is performed.
  • the terminal device 110 may transmit 533, to the network device 120, the information associated with the input of the AI model.
  • the network device 120 may determine 534 future measurement results of BFD-RSs based on the output of the AI model.
  • the network device 120 may transmit 535, to the terminal device 110, an indication of the operation for the beam link problem.
  • the network device 120 may transmit an indication indicating resetting of the counter for BFI (e.g., to 0) and stopping of the timer configured for BFD.
  • the network device 120 may transmit an indication indicating initiating of an RA procedure on a serving cell. For example, the network device 120 may trigger an RA procedure by a physical downlink control channel (PDCCH) order or a radio resource control (RRC) signaling.
  • PDCCH physical downlink control channel
  • RRC radio resource control
  • the network device 120 may transmit an indication indicating update of a quasi co-location (QCL) relationship for beam management.
  • QCL quasi co-location
  • the network device 120 may update the QCL relationship by downlink control information (DCI) , a medium access control control element (MAC CE) or an RRC signaling.
  • DCI downlink control information
  • MAC CE medium access control control element
  • RRC Radio Resource Control
  • the network device 120 may transmit an indication indicating an occurrence of a beam failure. For example, the network device 120 may transmit information that a beam failure will happen.
  • the terminal device 110 may perform 536 the operation for the beam link problem based on the indication.
  • the indication indicates stopping of a timer configured for BFD
  • the terminal device 110 may stop the timer.
  • the indication indicates resetting of a counter for BFI
  • the terminal device 110 may reset the counter (e.g., to 0) .
  • the indication indicates initiating of an RA procedure
  • the terminal device 110 may initiate the RA procedure on a serving cell.
  • the indication indicates update of a QCL relationship
  • the terminal device 110 may update the QCL relationship.
  • the indication indicates an occurrence of a beam failure
  • the terminal device 110 may perform a procedure associated with the beam failure.
  • the terminal device 110 may receive, from the network device 120, an indication of a time offset.
  • the time offset is associated with the performing of the operation for the beam link problem.
  • the terminal device 110 may perform the operation for the beam link problem after the time offset with respect to reception of the indication of the time offset.
  • the UE behavior is based on implementation, e.g., perform beam measurement, send BFR MAC CE, initiate RA and so on.
  • a BFR procedure may be accelerated, connection latency may be reduced, and user experience may be improved.
  • embodiments of the present disclosure provide methods of communication implemented at a terminal device. These methods will be described below with reference to FIGs. 7 to 8.
  • FIG. 7 illustrates an example method 700 of communication implemented at a terminal device in accordance with some embodiments of the present disclosure.
  • the method 700 may be performed at the terminal device 110 as shown in FIG. 1.
  • the method 700 will be described with reference to FIG. 1. It is to be understood that the method 700 may include additional blocks not shown and/or may omit some blocks as shown, and the scope of the present disclosure is not limited in this regard.
  • the terminal device 110 determines that a radio link problem occurs. In some embodiments, if a predetermined number of consecutive out-of-synchronization indications are received, the terminal device 110 may determine that the radio link problem occurs. In some embodiments, if a timer configured for RLF detection is started, the terminal device 110 may determine that the radio link problem occurs.
  • the terminal device 110 determines information associated with an input of an AI model for RLM.
  • An output of the AI model is used for determination of an operation for the radio link problem.
  • the information may indicate at least one of the following: historical measurement results of RSs for RLM; a configuration for the RLM; a location of the terminal device 110; a speed of the terminal device 110; or an angle of the terminal device 110. It is to be understood that any other suitable information is also feasible.
  • the terminal device 110 performs the operation for the radio link problem.
  • the terminal device 110 may determine, based on the output of the AI model, future measurement results of RSs for RLM by using the information as the input of the AI model. Based on the future measurement results, the terminal device 110 may perform the operation for the radio link problem.
  • the terminal device 110 may determine, from the future measurement results, that a predetermined number of consecutive in-synchronization indications is assumed. In this case, the terminal device 110 may stop a timer configured for RLF detection.
  • the terminal device 110 may determine, from the future measurement results, that a predetermined number of consecutive in-synchronization indications is not assumed. In this case, the terminal device 110 may perform the operation comprising at least one of the following: stopping a timer configured for radio link failure detection; storing information of the radio link problem in an RLF report; initiating an MCG failure information reporting; or initiating a connection re-establishment procedure.
  • the terminal device 110 may stop the timer. In some embodiments, if the indication indicates initiating of a connection re-establishment procedure, initiating the connection re-establishment procedure. In some embodiments, if the indication indicates initiating of an MCG failure information reporting, the terminal device 110 may initiate the MCG failure information reporting. In some embodiments, if the indication indicates an occurrence of an RLF, the terminal device 110 may perform a procedure associated with the RLF. In some embodiments, if the indication indicates a handover, the terminal device 110 may perform the handover.
  • the terminal device 110 may perform the operation for the radio link problem after the time offset with respect to reception of the indication of the time offset.
  • an RLF procedure may be accelerated.
  • latency of connection may be reduced and user experience may be improved.
  • FIG. 8 illustrates another example method 800 of communication implemented at a terminal device in accordance with some embodiments of the present disclosure.
  • the method 800 may be performed at the terminal device 110 as shown in FIG. 1.
  • the method 800 will be described with reference to FIG. 1. It is to be understood that the method 800 may include additional blocks not shown and/or may omit some blocks as shown, and the scope of the present disclosure is not limited in this regard.
  • the terminal device 110 determines that a beam link problem occurs. In some embodiments, if a BFI is received, the terminal device 110 may determine that the beam link problem occurs. In some embodiments, if a timer for BFD starts, the terminal device 110 may determine that the beam link problem occurs.
  • the terminal device 110 determines information associated with an input of an AI model for BFD.
  • An output of the AI model is used for determination of an operation for the beam link problem.
  • the information may indicate at least one of the following: historical measurement results of RSs for BFD; a configuration for the BFD; a location of the terminal device 110; a speed of the terminal device 110; or an angle of the terminal device 110.
  • the terminal device 110 performs the operation for the beam link problem.
  • the terminal device 110 may determine, based on the output of the AI model, future measurement results of RSs for BFD by using the information as the input of the AI model. Based on the future measurement results, the terminal device 110 may perform the operation for the beam link problem.
  • the terminal device 110 may determine, from the future measurement results, that a value of a counter for BFI above a threshold value is not assumed. In this case, the terminal device 110 may reset the counter and stop a timer for BFD.
  • the terminal device 110 may determine, from the future measurement results, that a value of a counter for BFI above a threshold value is assumed. In this case, the terminal device 110 may perform the operation comprising at least one of the following: triggering a BFR for an SCell; indicating a beam failure of a PSCell to upper layers of the terminal device 110; or initiating an RA procedure on a serving cell.
  • the terminal device 110 may transmit, to the network device 120, the information as the input of the AI model, and receive, from the network device 120, an indication of the operation for the beam link problem. Based on the indication, the terminal device 110 may perform the operation for the beam link problem.
  • the terminal device 110 may stop the timer. In some embodiments, if the indication indicates resetting of a counter for beam failure indication, the terminal device 110 may reset the counter. In some embodiments, if the indication indicates initiating of an RA procedure, the terminal device 110 may initiate the RA procedure on a serving cell. In some embodiments, if the indication indicates update of a QCL relationship, the terminal device 110 may update the QCL relationship. In some embodiments, if the indication indicates an occurrence of a beam failure, the terminal device 110 may perform a procedure associated with the beam failure.
  • the terminal device 110 may perform the operation for the beam link problem after the time offset with respect to reception of the indication of the time offset.
  • a BFR procedure may be accelerated.
  • latency of connection may be reduced and user experience may be improved.
  • FIG. 9 is a simplified block diagram of a device 900 that is suitable for implementing embodiments of the present disclosure.
  • the device 900 can be considered as a further example implementation of the terminal device 110 or the network device 120 as shown in FIG. 1. Accordingly, the device 900 can be implemented at or as at least a part of the terminal device 110 or the network device 120.
  • the device 900 includes a processor 910, a memory 920 coupled to the processor 910, a suitable transceiver 940 coupled to the processor 910, and a communication interface coupled to the transceiver 940.
  • the memory 910 stores at least a part of a program 930.
  • the transceiver 940 may be for bidirectional communications or a unidirectional communication based on requirements.
  • the transceiver 940 may include at least one of a transmitter 942 or a receiver 944.
  • the transmitter 942 and the receiver 944 may be functional modules or physical entities.
  • the transceiver 940 has at least one antenna to facilitate communication, though in practice an Access Node mentioned in this application may have several ones.
  • the communication interface may represent any interface that is necessary for communication with other network elements, such as X2/Xn interface for bidirectional communications between eNBs/gNBs, S1/NG interface for communication between a Mobility Management Entity (MME) /Access and Mobility Management Function (AMF) /SGW/UPF and the eNB/gNB, Un interface for communication between the eNB/gNB and a relay node (RN) , or Uu interface for communication between the eNB/gNB and a terminal device.
  • MME Mobility Management Entity
  • AMF Access and Mobility Management Function
  • RN relay node
  • Uu interface for communication between the eNB/gNB and a terminal device.
  • the program 930 is assumed to include program instructions that, when executed by the associated processor 910, enable the device 900 to operate in accordance with the embodiments of the present disclosure, as discussed herein with reference to FIGs. 1 to 8.
  • the embodiments herein may be implemented by computer software executable by the processor 910 of the device 900, or by hardware, or by a combination of software and hardware.
  • the processor 910 may be configured to implement various embodiments of the present disclosure.
  • a combination of the processor 910 and memory 920 may form processing means 950 adapted to implement various embodiments of the present disclosure.
  • the memory 920 may be of any type suitable to the local technical network and may be implemented using any suitable data storage technology, such as a non-transitory computer readable storage medium, semiconductor based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory and removable memory, as non-limiting examples. While only one memory 920 is shown in the device 900, there may be several physically distinct memory modules in the device 900.
  • the processor 910 may be of any type suitable to the local technical network, and may include one or more of general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs) and processors based on multicore processor architecture, as non-limiting examples.
  • the device 900 may have multiple processors, such as an application specific integrated circuit chip that is slaved in time to a clock which synchronizes the main processor.
  • a terminal device comprises a circuitry configured to: determine that a radio link problem occurs; determine information associated with an input of an artificial intelligence model for radio link measurements, an output of the artificial intelligence model being used for determination of an operation for the radio link problem; and perform the operation for the radio link problem.
  • a terminal device comprises a circuitry configured to: determine that a beam link problem occurs; determine information associated with an input of an artificial intelligence model for beam failure detection, an output of the artificial intelligence model being used for determination of an operation for the beam link problem; and perform the operation for the beam link problem.
  • circuitry used herein may refer to hardware circuits and/or combinations of hardware circuits and software.
  • the circuitry may be a combination of analog and/or digital hardware circuits with software/firmware.
  • the circuitry may be any portions of hardware processors with software including digital signal processor (s) , software, and memory (ies) that work together to cause an apparatus, such as a terminal device or a network device, to perform various functions.
  • the circuitry may be hardware circuits and or processors, such as a microprocessor or a portion of a microprocessor, that requires software/firmware for operation, but the software may not be present when it is not needed for operation.
  • the term circuitry also covers an implementation of merely a hardware circuit or processor (s) or a portion of a hardware circuit or processor (s) and its (or their) accompanying software and/or firmware.
  • various embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device. While various aspects of embodiments of the present disclosure are illustrated and described as block diagrams, flowcharts, or using some other pictorial representation, it will be appreciated that the blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.
  • the present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer readable storage medium.
  • the computer program product includes computer-executable instructions, such as those included in program modules, being executed in a device on a target real or virtual processor, to carry out the process or method as described above with reference to FIGs. 1 to 8.
  • program modules include routines, programs, libraries, objects, classes, components, data structures, or the like that perform particular tasks or implement particular abstract data types.
  • the functionality of the program modules may be combined or split between program modules as desired in various embodiments.
  • Machine-executable instructions for program modules may be executed within a local or distributed device. In a distributed device, program modules may be located in both local and remote storage media.
  • Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowcharts and/or block diagrams to be implemented.
  • the program code may execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
  • the above program code may be embodied on a machine readable medium, which may be any tangible medium that may contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
  • the machine readable medium may be a machine readable signal medium or a machine readable storage medium.
  • a machine readable medium may include but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.
  • machine readable storage medium More specific examples of the machine readable storage medium would include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM) , a read-only memory (ROM) , an erasable programmable read-only memory (EPROM or Flash memory) , an optical fiber, a portable compact disc read-only memory (CD-ROM) , an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
  • RAM random access memory
  • ROM read-only memory
  • EPROM or Flash memory erasable programmable read-only memory
  • CD-ROM portable compact disc read-only memory
  • magnetic storage device or any suitable combination of the foregoing.

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

Des modes de réalisation de la présente divulgation se rapportent à des dispositifs et à des procédés de communication. Selon un aspect, lors de la détermination du fait qu'un problème de liaison radio se produit, un dispositif terminal détermine des informations associées à une entrée d'un modèle AI pour RLM. Une sortie du modèle AI est utilisée pour la détermination d'une opération pour le problème de liaison radio. Le dispositif terminal effectue l'opération déterminée pour le problème de liaison radio. De cette manière, une procédure RLF peut être accélérée. Ainsi, la latence de connexion peut être réduite et l'expérience de l'utilisateur peut être améliorée.
PCT/CN2023/086963 2023-04-07 2023-04-07 Dispositifs et procédés de communication Ceased WO2024207454A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/CN2023/086963 WO2024207454A1 (fr) 2023-04-07 2023-04-07 Dispositifs et procédés de communication

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2023/086963 WO2024207454A1 (fr) 2023-04-07 2023-04-07 Dispositifs et procédés de communication

Publications (1)

Publication Number Publication Date
WO2024207454A1 true WO2024207454A1 (fr) 2024-10-10

Family

ID=92970868

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2023/086963 Ceased WO2024207454A1 (fr) 2023-04-07 2023-04-07 Dispositifs et procédés de communication

Country Status (1)

Country Link
WO (1) WO2024207454A1 (fr)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108810937A (zh) * 2018-05-30 2018-11-13 北京邮电大学 一种移动性管理的控制平面优化方法和控制器
CN112514441A (zh) * 2020-11-02 2021-03-16 北京小米移动软件有限公司 一种通信方法、通信装置及存储介质
US20220182263A1 (en) * 2020-12-03 2022-06-09 Qualcomm Incorporated Model discovery and selection for cooperative machine learning in cellular networks
CN114846845A (zh) * 2019-10-04 2022-08-02 瑞典爱立信有限公司 使用强化学习的高效3d移动性支持
CN115150847A (zh) * 2021-03-31 2022-10-04 华为技术有限公司 模型处理的方法、通信装置和系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108810937A (zh) * 2018-05-30 2018-11-13 北京邮电大学 一种移动性管理的控制平面优化方法和控制器
CN114846845A (zh) * 2019-10-04 2022-08-02 瑞典爱立信有限公司 使用强化学习的高效3d移动性支持
CN112514441A (zh) * 2020-11-02 2021-03-16 北京小米移动软件有限公司 一种通信方法、通信装置及存储介质
US20220182263A1 (en) * 2020-12-03 2022-06-09 Qualcomm Incorporated Model discovery and selection for cooperative machine learning in cellular networks
CN115150847A (zh) * 2021-03-31 2022-10-04 华为技术有限公司 模型处理的方法、通信装置和系统

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
ZTE, CHINA UNICOM, CMCC: "Solution to AI based UE Trajectory prediction", 3GPP DRAFT; R3-213759, 3RD GENERATION PARTNERSHIP PROJECT (3GPP), MOBILE COMPETENCE CENTRE ; 650, ROUTE DES LUCIOLES ; F-06921 SOPHIA-ANTIPOLIS CEDEX ; FRANCE, vol. RAN WG3, no. Online; 20210816 - 20210826, 6 August 2021 (2021-08-06), Mobile Competence Centre ; 650, route des Lucioles ; F-06921 Sophia-Antipolis Cedex ; France , XP052035530 *

Similar Documents

Publication Publication Date Title
WO2023108470A1 (fr) Procédé, dispositif et support lisible par ordinateur pour des communications
US20250220518A1 (en) Method, device and computer storage medium of communication
WO2023201490A1 (fr) Procédé, dispositif et support de stockage informatique destinés à la communication
WO2024087233A1 (fr) Procédé, dispositif et support d'enregistrement informatique de communication
WO2023097430A1 (fr) Procédé, dispositif et support de stockage informatique de communication
WO2025030519A1 (fr) Dispositifs et procédés de communication
WO2024031260A1 (fr) Procédé, dispositif, et support de stockage informatique de communication
WO2023240484A1 (fr) Procédé, dispositif et support de stockage informatique de communication
WO2024168700A1 (fr) Dispositifs et procédés de communication
WO2024207454A1 (fr) Dispositifs et procédés de communication
WO2023123442A1 (fr) Procédé, dispositif et support lisible par ordinateur destinés à des communications
WO2026051003A1 (fr) Dispositifs et procédés de communication
WO2025260238A1 (fr) Dispositifs et procédés de communication
WO2025147922A1 (fr) Dispositifs et procédés de communication
WO2025161029A1 (fr) Dispositifs et procédés de communication
WO2026007041A1 (fr) Dispositifs et procédés de communication
WO2025208411A1 (fr) Dispositifs et procédés de communication
WO2024187475A1 (fr) Dispositifs et procédés de communication
WO2026060591A1 (fr) Dispositifs et procédés de communication
WO2024168618A1 (fr) Dispositifs et procédés de communication
WO2025175526A1 (fr) Dispositifs et procédés de communication
WO2025199983A1 (fr) Dispositifs et procédés de communication
WO2025086052A1 (fr) Dispositifs et procédés de communication
WO2025091345A1 (fr) Dispositifs et procédés de communication
WO2025097316A1 (fr) Dispositifs et procédés de communication

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: 23931515

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 23931515

Country of ref document: EP

Kind code of ref document: A1