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WO2024031697A1 - Capacité de dispositif et surveillance de performance pour un modèle - Google Patents

Capacité de dispositif et surveillance de performance pour un modèle Download PDF

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Publication number
WO2024031697A1
WO2024031697A1 PCT/CN2022/112294 CN2022112294W WO2024031697A1 WO 2024031697 A1 WO2024031697 A1 WO 2024031697A1 CN 2022112294 W CN2022112294 W CN 2022112294W WO 2024031697 A1 WO2024031697 A1 WO 2024031697A1
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WIPO (PCT)
Prior art keywords
model
communication device
time
measurement
similarity
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Ceased
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PCT/CN2022/112294
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English (en)
Inventor
Guozeng ZHENG
Zhaohua Lu
Wenfeng Liu
Huahua Xiao
Lun Li
Yong Li
Yuxin Wang
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ZTE Corp
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ZTE Corp
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Priority to KR1020247042014A priority Critical patent/KR20250016209A/ko
Priority to PCT/CN2022/112294 priority patent/WO2024031697A1/fr
Priority to JP2024571344A priority patent/JP2025525699A/ja
Priority to EP22954649.4A priority patent/EP4541047A4/fr
Priority to CN202280097847.9A priority patent/CN119487885A/zh
Publication of WO2024031697A1 publication Critical patent/WO2024031697A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W8/00—Network data management
    • H04W8/22—Processing or transfer of terminal data, e.g. status or physical capabilities
    • H04W8/24—Transfer of terminal data
    • 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
    • H04B17/373—Predicting channel quality or other radio frequency [RF] parameters
    • 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
    • H04B7/0626—Channel coefficients, e.g. channel state information [CSI]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0636—Feedback format
    • H04B7/0639—Using selective indices, e.g. of a codebook, e.g. pre-distortion matrix index [PMI] or for beam selection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • 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
    • H04W72/21—Control channels or signalling for resource management in the uplink direction of a wireless link, i.e. towards the network

Definitions

  • This disclosure is directed generally to digital wireless communications.
  • LTE Long-Term Evolution
  • 3GPP 3rd Generation Partnership Project
  • LTE-A LTE Advanced
  • 5G The 5th generation of wireless system, known as 5G, advances the LTE and LTE-A wireless standards and is committed to supporting higher data-rates, large number of connections, ultra-low latency, high reliability and other emerging business needs.
  • a wireless communication method comprising transmitting, by a communication device, a capability message indicating that the communication device is capable of using a model by the communication device to perform one or more wireless communication operations.
  • the capability message includes time requirements of the model. In some embodiments, the time requirements are associated with a model identifier.
  • the model can be received by the communication device from a network.
  • the capability message include one of the following: a computation time, a computation time offset, or a model activation time.
  • the computation time is a time the communication device needs to conduct an operation
  • the computation time offset is an additional time the communication device needs to conduct an operation
  • the model activation time is a time the communication device needs to activate an operation.
  • the computation time can be a constant independent of the model selected. In some embodiments, the value of the computation time offset depends on the model. In some embodiments, the capacity message indicates a measurement report is created after a time lapse T based on a reference signal received by the communication device, wherein T depends on the computation time offset and the computation time.
  • the method further comprising transmitting, by the communication device, an identifier message indicating the model is activated.
  • the identifier message can be sent through a PUCCH. In some embodiments.
  • the method further comprising receiving, by the communication device, a response indicating the identifier message was received successfully.
  • the method further comprising deactivating, by the communication device, the model when the response was not received by the communication device.
  • the capability message includes a capacity of the communication device, wherein the capacity includes concurrent capability for model-related and non-model related.
  • Another wireless communication method comprising reporting, by a communication device, a performance indication of a model.
  • the performance indication includes a relationship between an assistance information with an actual measurement information conducted by the communication device.
  • the relationship can be a similarity or a distance, wherein the similarity includes cosine similarity, generalization cosine similarity or square generalization cosine similarity, wherein the distance includes Euclidean distance or normalized mean square error.
  • the communication device is configured with at least a dedicated occasion used for the performance indication of the model.
  • the performance indication includes a predicated measurement and an actual measurement of the dedicated occasion.
  • the actual measurement includes RSRP (s) of a plurality of reference signal, wherein the predicted measurement includes predicted RSRP (s) of the reference signals based on the model.
  • the actual measurement includes codebook-based precoding matrix indication (PMI) .
  • PMI codebook-based precoding matrix indication
  • the performance indication further includes a relationship between the output of the model and an input of the model.
  • the performance indication includes a relationship between M-N remaining measurement results of N measurement results and a part of the model output, wherein the N measurement results are based on the reference signals, wherein the M measurement results are based on the N measurement results and are used for an input of the model, wherein M ⁇ N.
  • the relationship can be a similarity or a distance, wherein the similarity can be at least one of the following: cosine similarity, generalization cosine similarity or square generalization cosine similarity, wherein the distance can be at least one of the following: Euclidean distance or normalized mean square error.
  • the above-described methods are embodied in the form of processor-executable code and stored in a non-transitory computer-readable storage medium.
  • the code included in the computer readable storage medium when executed by a processor, causes the processor to implement the methods described in this patent document.
  • a device that is configured or operable to perform the above-described methods is disclosed.
  • FIG. 1 shows an example of Artificial Intelligence/Machine Learning (AI/ML) model.
  • FIG. 2 shows an example of computation time and computation time offset.
  • FIG. 3 shows an example the relationship between the assistance channel information and the actual channel measurement.
  • FIG. 4 shows an example of indicating a dedicated channel measurement occasion to monitor the model performance.
  • FIG. 5 shows an example of adopting the relationship between the part of AI model output and the AI model input for model performance monitoring.
  • FIG. 6 shows an exemplary block diagram of a hardware platform that may be a part of a network device or a communication device.
  • FIG. 7 shows an example of wireless communication including a base station (BS) and user equipment (UE) based on some implementations of the disclosed technology.
  • BS base station
  • UE user equipment
  • Section headings are used in the present document only to improve readability and do not limit scope of the disclosed embodiments and techniques in each section to only that section.
  • 3GPP Third Generation Partnership Project
  • NR New Radio
  • 5G New Radio
  • an AI/ML model is an exemplary scenario, and the technical solutions described in this application can be generalized or applicable to any model that determines a relationship between an input and an output.
  • AI/ML Artificial Intelligence/Machine Learning
  • an AI/ML model can be used to increase the accuracy of channel state information (CSI) .
  • AI/ML models can predict beam information.
  • UEs in the network should have corresponding capabilities.
  • This application discloses some procedures are proposed to report UE capabilities involving AI/ML models. Due to the limited model generalization and dynamic environments, the model may degrade its performance over time. Accordingly, in this application, several solutions are discussed to monitor the model performance.
  • AI/ML has been studied and used in various fields to extract features that cannot be derived by other mathematical methods.
  • AI/ML model is a data driven algorithm that applies AI/ML techniques to generate a set of outputs based on a set of inputs, which includes at least three parts as shown in FIG. 1:
  • AI model an algorithm to derive the relationship between AI model input and AI model output
  • AI model training is a process to train an AI/ML model by learning the input/output relationship in a data driven manner and to obtain the trained AI/ML model for inference.
  • AI model Inference is a process of adopting a trained AI/ML model to produce a set of outputs based on a set of inputs.
  • a measurement report e.g., a CSI report or beam report
  • a UE may use an AI model to derive the measurement report.
  • UE should have corresponding capabilities.
  • a UE may report its model (e.g., AI/ML model) related information to a network through a UE capability signaling or an assistance information signaling.
  • model e.g., AI/ML model
  • the model related information can be reported after a network sends a request message to a UE, where the request message may include a model identifier.
  • model related information can be reported without firstly requesting a message from the network.
  • the model related information includes time requirements for a model.
  • the model related information may include a model identifier to associate with the corresponding time requirements.
  • the time requirements may include one of the following:
  • the computation time may include the time that a UE needs to conduct/operate/execute a model and/or the time a UE needs to prepare a measurement report associated with the model.
  • the computation time is the same to all models.
  • the computation time is different for different report quantities in a measurement report.
  • the computation time depends on the content included in the measurement report.
  • the computation time for beam report and that for CSI report may be different.
  • the computation time is not required to be reported by a UE.
  • the computation time reuses the value defined for non-model based measurement report.
  • computation time offset may include the additional time that a UE needs to conduct/operate/execute for a model.
  • each model has its own computation time offset.
  • the computation time offset is not required to be reported by a UE.
  • the value of computation time offset is set zero by default.
  • a UE can report a zero value for the computation time offset.
  • a UE may only report a measurement report based on reference signal (s) that is received T time units earlier than the time when the measurement report is transmitted, where the T time units are computation time plus computation time offset.
  • a UE when a UE receives a Media Access Control (MAC) signaling including an indication command to use a model, the UE may need some time to activate the model. For example, when a UE transmits a PUCCH with HARQ-ACK information in time n corresponding to the PDSCH carrying the indication command, the UE assumes that the corresponding model to be ready/activated for inference should be applied at time n + model activation time.
  • MAC Media Access Control
  • a UE when a UE receives a Downlink Control Indication (DCI) including an indication command to use a model, UE may need some time to activate the model. For example, when a UE receives a PDCCH carrying an indication command in time n, the UE assumes that the corresponding model to be ready/activated for inference should be applied at time n + model activation time.
  • DCI Downlink Control Indication
  • a UE can send a signaling to inform a network that a model is ready/activated for inference. In some embodiments, if a UE can send a signaling to inform a network that a model is ready/activated for inference and if the UE is indicated to use this model to do operations, the UE doesn’t need extra time (e.g., activation time) to activate this model.
  • the signaling can be transmitted by a PUCCH, where each model may be associated with a dedicated PUCCH resource.
  • the signaling can be transmitted by a PRACH, where each model may be associated with a dedicated PRACH.
  • a UE can receive the response from a network to confirm that the signaling has been received successfully by the network.
  • the response is transmitted by a MAC signaling.
  • a UE detects a PDCCH scrambled by a dedicated RNTI the UE is confirmed that the signaling has been received successfully by the network.
  • the UE may deactivate the model automatically if a UE doesn’t receive the response.
  • a UE may deactivate the model automatically when the waiting time, starting from the UE sending the signaling, exceeds a time threshold.
  • a UE may report concurrent/mixed UE capabilities for model related and non-model related.
  • a UE may report that UE can be configured with the number of measurement report (s) using model and the number of measurement report (s) not using model.
  • a UE may report that when the UE is configured with M CSI reports using a model in a Bandwidth Part/Component Carrier (BWP/CC) , the UE is not expected to be configured more than N other CSI reports not using model.
  • BWP/CC Bandwidth Part/Component Carrier
  • the value of N is either not required to be reported or can be reported by a value zero, both of which indicate that when configured with CSI reports using a model, the UE cannot be configured with other CSI reports not using model.
  • a UE may report that when the UE is configured with M beam reports using a model in a BWP/CC (Bandwidth Part/Component Carrier) , the UE is not expected to be configured more than N other beam reports not using model.
  • the value of N is not required to be reported or can be reported by a value zero, both of which indicate that when configured with beam reports using a model, the UE cannot be configured with other beam reports not using model.
  • a UE may report that the UE can process simultaneously for measurement report (s) using model and measurement report (s) not using model.
  • a UE can process simultaneously for M beam reports using model and N beam reports not using model in a CC (or across all CCs) .
  • the value of N is not required to be reported or can be reported by a value zero.
  • UE can process simultaneously for M beam report using model and N CSI reports not using model in a CC (or across all CCs) .
  • the value of N is not required to be reported or can be reported by a value zero.
  • the performance of a deployed model may not always be good. Due to the limited model generalization and dynamic environment, the performance of the model may degrade. Therefore, both the network and the UE should constantly monitor the performance of the model.
  • Method 1 Network provides assistance channel information to assist model performance monitoring.
  • a UE may report the relationship between the assistance channel information and the actual channel measurement.
  • an assistance channel information and an actual channel measurement are denoted by H assist and H respectively.
  • the first AI model output is and its AI model input is based on assistance channel information.
  • the second AI model output is and its AI model input is based on actual channel measurement
  • One metric to evaluate the model performance is similarity, e.g., a cosine similarity, a generalization cosine similarity, or a square generalization cosine similarity. Assume the similarity between H assist and H is S input and the similarity between and is S output . In an example, if the value of S input equals to (or approximately equals to) that of S output , the AI model still works well with some guaranteed performance.
  • Another metric to evaluate the model performance is distance, e.g., Euclidean distance or normalized mean squared error. Assume the distance between H assist and H is D input and the distance between and is D output . In an example, if the value of D input equals to (or approximately equals to) that of D output , the AI model still works well with some guaranteed performance.
  • UE needs to report a performance indication to network about the result of performance monitoring.
  • the performance indication includes an indicator in a measurement report.
  • the value of 0 and 1 mean the AI model is invalid and valid respectively. More specifically, the value 0 means S output /S input or D output /D input is lower than a threshold.
  • the performance indication includes the value of similarity (S input and S output ) or a distance (D input and D output ) in a measurement report. In some embodiments, the performance indication includes the value of S output /S input or S output /D input .
  • network may provide a bunch of assistance channel information.
  • each assistance channel information may be associated with an performance indication.
  • Method 2 Dedicated channel measurement occasion is used to monitor the model performance.
  • a network may indicate at least one dedicated channel measurement occasion to monitor the model performance.
  • the AI model input is based on actual channel measurements in some previous occasions (e.g., observation occasions) .
  • the AI model output is the predicted channel measurements that can be used in some future occasions (e.g., predication occasions) . Therefore, a UE doesn’t have to acquire actual channel measurements of prediction occasions. However, if an occasion is indicated as a dedicated channel measurement occasion (e.g., monitoring occasion) to monitor the model performance, as shown in FIG. 4, a UE may need to acquire both actual and predicated channel measurements. Then, a UE/network can compare actual channel measurement and predicated channel measurement to check whether the channel prediction is accurate enough.
  • a UE has to report both actual channel measurement and predicated channel measurement of the monitoring occasion in a measurement report.
  • an AI model is adopted for beam prediction.
  • the actual channel measurement includes RSRP (s) of a plurality of reference signals
  • the predicated channel measurement includes RSRP (s) of the plurality of reference signals
  • a network may indicate the numbers of RSRP (s) included in actual and predicated channel measurement
  • the number of RSRP (s) included in actual channel measurement and the that included in predicated channel measurement are the same.
  • each RSRP is associated with a reference signal index.
  • the AI model can be adopted for CSI prediction.
  • the actual channel measurement includes a traditional codebook-based precoding matrix indication (PMI) .
  • PMI codebook-based precoding matrix indication
  • Method 3 Dedicated module of an AI module is for model performance monitoring
  • a part of AI model output can be used for model performance monitoring.
  • the part of AI model output at least includes an indicator showing whether the AI model is valid or not.
  • the indicator can be reported in a measurement report.
  • the relationship between the part of AI model output and the AI model input can be used for model performance monitoring.
  • the AI model input is channel information H
  • the AI model output of first sub-module is the compressed channel information
  • the AI model input of second sub-module is the reconstructed channel information
  • a measurement report may include an indication of the relationship.
  • the relationship can be the similarity/distance between the channel information and the reconstructed channel information.
  • a measurement report may include both an indication to the relationship and the compressed channel information.
  • a UE gets N measurement results based on the received reference signal (s) .
  • An AI model input may include only M measurement results of the N measurement results with M ⁇ N. In this scenario, the remaining N-M measurement results can be used for model performance monitoring.
  • the N measurement results are N RSRP values, where each RSRP value corresponds to a beam between a network and a UE.
  • An AI model input only includes M RSRP values out of the N RSRP values with M ⁇ N. Accordingly, the remaining N-M RSRP values can be used for model performance monitoring. The remaining N-M RSRP values correspond to a set of beams between a network and a UE. Furthermore, the part of AI model output includes the predicted N-M RSRP values correspond to the set of beams between a network and a UE.
  • a UE may report a validity indicator based on the remaining N-M RSRP values with the predicted N-M RSRP values.
  • the validity indicator can be the similarity between the remaining N-M RSRP values and the predicted N-M RSRP values.
  • the validity indicator can be the difference (or distance) between the remaining N-M RSRP values and the predicted N-M RSRP values.
  • a UE may report both the remaining N-M RSRP values and the predicted N-M RSRP values in a measurement report.
  • one of the predicted N-M RSRP values is reported relative to one of the remaining N-M RSRP values corresponding the same beam between a network and a UE.
  • the N measurement results are N channel measurement results corresponding to a N-port reference signal.
  • An AI model input only includes M (M ⁇ N) channel measurement results out of the N channel measurement results. Accordingly, the remaining N-M channel measurement results can be used for model performance monitoring. The remaining N-M channel measurement results correspond to a set of ports of the N-port reference signal. Furthermore, the part of AI model output includes the predicted N-M channel measurement results correspond to the set of ports of the N-port reference signal.
  • a UE may report a validity indicator based on the remaining N-M channel measurement results and the predicted N-M channel measurement results.
  • the validity indicator can be the similarity between the remaining N-M channel measurement results and the predicted N-M channel measurement results.
  • the validity indicator can be the difference (or distance) between the remaining N-M channel measurement results and the predicted N-M channel measurement results.
  • FIG. 6 shows an exemplary block diagram of a hardware platform 600 that may be a part of a network device (e.g., base station) or a communication device (e.g., a user equipment (UE) ) .
  • the hardware platform 600 includes at least one processor 610 and a memory 605 having instructions stored thereupon. The instructions upon execution by the processor 610 configure the hardware platform 600 to perform the operations described in FIGS. 1 to 5 and 7 and in the various embodiments described in this patent document.
  • the transmitter 615 transmits or sends information or data to another device.
  • a network device transmitter can send a message to a user equipment.
  • the receiver 620 receives information or data transmitted or sent by another device.
  • a user equipment can receive a message from a network device.
  • FIG. 7 shows an example of a wireless communication system (e.g., a 5G or NR cellular network) that includes a base station 720 and one or more user equipment (UE) 711, 712 and 713.
  • the UEs access the BS (e.g., the network) using a communication link to the network (sometimes called uplink direction, as depicted by dashed arrows 731, 732, 733) , which then enables subsequent communication (e.g., shown in the direction from the network to the UEs, sometimes called downlink direction, shown by arrows 741, 742, 743) from the BS to the UEs.
  • a wireless communication system e.g., a 5G or NR cellular network
  • the UEs access the BS (e.g., the network) using a communication link to the network (sometimes called uplink direction, as depicted by dashed arrows 731, 732, 733) , which then enables subsequent communication (e.
  • the BS send information to the UEs (sometimes called downlink direction, as depicted by arrows 741, 742, 743) , which then enables subsequent communication (e.g., shown in the direction from the UEs to the BS, sometimes called uplink direction, shown by dashed arrows 731, 732, 733) from the UEs to the BS.
  • the UE may be, for example, a smartphone, a tablet, a mobile computer, a machine to machine (M2M) device, an Internet of Things (IoT) device, and so on.
  • M2M machine to machine
  • IoT Internet of Things
  • a wireless communication method comprising transmitting, by a communication device, a capability message indicating that the communication device is capable of using a model by the communication device to perform one or more wireless communication operations.
  • the capability message includes time requirements of the model. In some embodiments, the time requirements are associated with a model identifier.
  • the model can be received by the communication device from a network.
  • the capability message include one of the following: a computation time, a computation time offset, or a model activation time.
  • the computation time is a time the communication device needs to conduct an operation
  • the computation time offset is an additional time the communication device needs to conduct an operation
  • the model activation time is a time the communication device needs to activate an operation.
  • the computation time can be a constant independent of the model selected. In some embodiments, the value of the computation time offset depends on the model. In some embodiments, the capacity message indicates a measurement report is created after a time lapse T based on a reference signal received by the communication device, wherein T depends on the computation time offset and the computation time.
  • the method further comprising transmitting, by the communication device, an identifier message indicating whether the model is activated.
  • the identifier message can be sent through a PUCCH. In some embodiments.
  • the method further comprising receiving, by the communication device, a response indicating the identifier message was received successfully.
  • the method further comprising deactivating, by the communication device, the model when the response was not received by the communication device.
  • the capability message includes a capacity of the communication device, wherein the capacity includes concurrent capability for model-related and non-model related.
  • Another wireless communication method comprising reporting, by a communication device, a performance indication of a model.
  • the performance indication includes a relationship between an assistance information with an actual measurement information conducted by the communication device.
  • the relationship can be a similarity or a distance, wherein the similarity includes cosine similarity, generalization cosine similarity or square generalization cosine similarity, wherein the distance includes Euclidean distance or normalized mean square error.
  • the communication device is configured with at least a dedicated occasion used for the performance indication of the model.
  • the performance indication includes a predicated measurement and an actual measurement of the dedicated occasion.
  • the actual measurement includes RSRP (s) of a plurality of reference signal, wherein the predicted measurement includes predicted RSRP (s) of the reference signals based on the model.
  • the actual measurement includes codebook-based precoding matrix indication (PMI) .
  • PMI codebook-based precoding matrix indication
  • the performance indication further includes a relationship between the output of the model and an input of the model.
  • the performance indication includes a relationship between M-N remaining measurement results of N measurement results and a part of the model output, wherein the N measurement results are based on the reference signals, wherein the M measurement results are based on the N measurement results and are used for an input of the model, wherein M ⁇ N.
  • the relationship can be a similarity or a distance, wherein the similarity can be at least one of the following: cosine similarity, generalization cosine similarity or square generalization cosine similarity, wherein the distance can be at least one of the following: Euclidean distance or normalized mean square error.
  • the above-described methods are embodied in the form of processor-executable code and stored in a non-transitory computer-readable storage medium.
  • the code included in the computer readable storage medium when executed by a processor, causes the processor to implement the methods described in this patent document.
  • a device that is configured or operable to perform the above-described methods is disclosed.
  • a computer-readable medium may include removable and non-removable storage devices including, but not limited to, Read Only Memory (ROM) , Random Access Memory (RAM) , compact discs (CDs) , digital versatile discs (DVD) , etc. Therefore, the computer-readable media can include a non-transitory storage media.
  • program modules may include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types.
  • Computer-or processor-executable instructions, associated data structures, and program modules represent examples of program code for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps or processes.
  • a hardware circuit implementation can include discrete analog and/or digital components that are, for example, integrated as part of a printed circuit board.
  • the disclosed components or modules can be implemented as an Application Specific Integrated Circuit (ASIC) and/or as a Field Programmable Gate Array (FPGA) device.
  • ASIC Application Specific Integrated Circuit
  • FPGA Field Programmable Gate Array
  • DSP digital signal processor
  • the various components or sub-components within each module may be implemented in software, hardware or firmware.
  • the connectivity between the modules and/or components within the modules may be provided using any one of the connectivity methods and media that is known in the art, including, but not limited to, communications over the Internet, wired, or wireless networks using the appropriate protocols.

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Abstract

L'invention concerne des techniques d'utilisation de modèles d'intelligence artificielle/apprentissage automatique (IA/AA) pour augmenter la précision d'information d'état de canal (CSI). En raison de la généralisation limitée de modèles limitée et de l'environnement sans fil dynamique, plusieurs procédés sont proposés pour surveiller les performances des modèles. Un procédé de communication sans fil, comprenant la transmission, par un dispositif de communication, d'un message de capacité indiquant que le dispositif de communication est capable d'utiliser un modèle par le dispositif de communication pour effectuer une ou plusieurs opérations de communication sans fil.
PCT/CN2022/112294 2022-08-12 2022-08-12 Capacité de dispositif et surveillance de performance pour un modèle Ceased WO2024031697A1 (fr)

Priority Applications (5)

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KR1020247042014A KR20250016209A (ko) 2022-08-12 2022-08-12 모델에 대한 디바이스 기능 및 성능 모니터링
PCT/CN2022/112294 WO2024031697A1 (fr) 2022-08-12 2022-08-12 Capacité de dispositif et surveillance de performance pour un modèle
JP2024571344A JP2025525699A (ja) 2022-08-12 2022-08-12 モデルのためのデバイス能力および性能監視
EP22954649.4A EP4541047A4 (fr) 2022-08-12 2022-08-12 Capacité de dispositif et surveillance de performance pour un modèle
CN202280097847.9A CN119487885A (zh) 2022-08-12 2022-08-12 针对模型的设备能力和性能监测

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WO2022008037A1 (fr) * 2020-07-07 2022-01-13 Nokia Technologies Oy Aptitude et incapacité d'ue ml
CN114091679A (zh) * 2020-08-24 2022-02-25 华为技术有限公司 一种更新机器学习模型的方法及通信装置
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JP2025525699A (ja) 2025-08-07
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CN119487885A (zh) 2025-02-18
KR20250016209A (ko) 2025-02-03

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