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WO2025015484A1 - Procédé et appareil de mise à jour de commutation de modèle - Google Patents

Procédé et appareil de mise à jour de commutation de modèle Download PDF

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Publication number
WO2025015484A1
WO2025015484A1 PCT/CN2023/107598 CN2023107598W WO2025015484A1 WO 2025015484 A1 WO2025015484 A1 WO 2025015484A1 CN 2023107598 W CN2023107598 W CN 2023107598W WO 2025015484 A1 WO2025015484 A1 WO 2025015484A1
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WIPO (PCT)
Prior art keywords
model
update
target
currently used
csi
Prior art date
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PCT/CN2023/107598
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English (en)
Chinese (zh)
Inventor
张博源
池连刚
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Beijing Xiaomi Mobile Software Co Ltd
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Beijing Xiaomi Mobile Software Co Ltd
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Priority to PCT/CN2023/107598 priority Critical patent/WO2025015484A1/fr
Publication of WO2025015484A1 publication Critical patent/WO2025015484A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition

Definitions

  • the present disclosure relates to the field of communication technology, and in particular to a model switching update method and device.
  • a terminal In a communication system, a terminal usually needs to report the channel state information (CSI) of the downlink to a network device so that the network device can determine the channel quality of the downlink based on the CSI.
  • CSI channel state information
  • 3GPP 3rd Generation Partnership Project
  • the present disclosure provides a model switching update method and device.
  • a model switching update method comprising:
  • the first device performs performance monitoring on a first model currently used by the first device and/or a second model currently used by the second device; the first model and the second model correspond to each other, and the first model and the second model are used to implement compression and reconstruction of channel state information CSI;
  • the performance monitoring result meets the first preset condition, and it is determined that a model switching update is required
  • the first device determines at least one of a target first model, a first model update parameter, a target second model, and a second model update parameter; wherein the target first model is a target model to be switched by the first device, and the target second model is a target model to be switched by the second device;
  • a model switching update method comprising:
  • the second device receives indication information sent by the first device, where the indication information indicates a target second model and/or a second model update parameter; wherein the target second model is a target model to be switched by the second device;
  • the second device performs model switching update based on the indication information.
  • a model switching update method is proposed for a communication system, the communication system comprising a first device and a second device, the method comprising at least one of the following:
  • the performance monitoring result meets the first preset condition, and the first device determines that a model switching update is required
  • the first device determines at least one of a target first model, a first model update parameter, a target second model, and a second model update parameter; wherein the target first model is a target model to be switched by the first device, and the target second model is a target model to be switched by the second device;
  • the first device performs a model switching update and sends indication information to the second device, where the indication information indicates the target second model and/or an update parameter of the second model;
  • the second device performs model switching update based on the indication information.
  • the processing module is further configured to determine that a model switching update is required when a performance monitoring result meets a first preset condition
  • the sending module is used to perform a model switching update and send indication information to the second device, where the indication information indicates the target second model and/or the second model update parameter.
  • a second device including:
  • a receiving module configured for the second device to receive indication information sent by the first device, wherein the indication information indicates a target second model and/or a second model update parameter; wherein the target second model is a target model to be switched by the second device;
  • a processing module is used for the second device to perform model switching update based on the indication information.
  • a communication device including:
  • One or more processors are One or more processors;
  • the processor is used to call instructions to enable the communication device to execute the model switching update method described in the first aspect or the second aspect.
  • a communication system includes a first device and a second device, wherein the first device is configured to implement the model switching update method described in the first aspect, and the second device is configured to implement the model switching update method described in the first aspect or the second aspect.
  • a storage medium stores instructions, and wherein when the instructions are executed on a communication device, the communication device executes the model switching update method as described in the first aspect or the second aspect.
  • FIG1A is a schematic diagram of the architecture of some communication systems provided by embodiments of the present disclosure.
  • FIG1B is a structural block diagram of a method for “CSI compression reporting and reconstruction based on a bilateral model” provided by an embodiment of the present disclosure
  • 2A-2B are interactive schematic diagrams of a model switching update method provided by an embodiment of the present disclosure
  • 3A-3C are flowchart diagrams of a model switching update method provided in yet another embodiment of the present disclosure.
  • 4A-4C are flowchart diagrams of a model switching update method provided in yet another embodiment of the present disclosure.
  • 5A-5C are flowchart diagrams of a model switching update method provided in yet another embodiment of the present disclosure.
  • FIG6A is a schematic diagram of the structure of a network device provided by an embodiment of the present disclosure.
  • FIG6B is a schematic diagram of the structure of a terminal provided by an embodiment of the present disclosure.
  • FIG7A is a schematic diagram of the structure of a communication device provided by an embodiment of the present disclosure.
  • FIG. 7B is a schematic diagram of the structure of a chip provided by an embodiment of the present disclosure.
  • the disclosed embodiments provide a model switching update method and device, equipment, system and computer storage medium.
  • an embodiment of the present disclosure provides a model switching update method, the method comprising:
  • the first device performs performance monitoring on a first model currently used by the first device and/or a second model currently used by the second device; the first model and the second model correspond to each other, and the first model and the second model are used to implement compression and reconstruction of channel state information CSI;
  • the performance monitoring result meets the first preset condition, and it is determined that a model switching update is required
  • the first device determines at least one of a target first model, a first model update parameter, a target second model, and a second model update parameter; wherein the target first model is a target model to be switched by the first device, and the target second model is a target model to be switched by the second device;
  • the first device performs a model switching update and sends indication information to the second device, where the indication information indicates the target second model and/or an update parameter of the second model.
  • the first device determines whether a model switching update is required by determining whether the performance monitoring results of the first model currently used by the first device and/or the second model currently used by the second device meet the first preset condition, and when it is determined that a model switching update is required, at least one of the target first model, the first model update parameter, the target second model, and the second model update parameter is determined, and then the first device performs a model switching update, and also sends an indication message to the second device to instruct the second device to also perform a model switching update.
  • the first device can adaptively switch the model based on the real-time performance monitoring results of the first model currently used by the first device, and can also adaptively update the model parameters of the model, and the first device can also adaptively switch the model based on the real-time performance monitoring results of the second model currently used by the second device, and/or adaptively update the model parameters of the model, so as to ensure that the models used by the first device and/or the second device in real time are matched with the current channel scenario or data distribution characteristics, thereby improving the accuracy of CSI feedback.
  • the method before the first device performs performance monitoring on a first model currently used by the first device and/or a second model currently used by the second device, the method further includes:
  • the model-related information includes at least one of the following:
  • the model structure corresponding to at least one second model deployed by the second device.
  • determining the model-related information corresponding to the second device includes:
  • the first device before the first device performs performance monitoring on the currently used first model and/or the currently used second model, it first obtains model-related information corresponding to the second device, so that the first device can know the second model currently used by the second device, which second models are deployed on the second device, and the model structure of the second model deployed on the second device based on the model-related information.
  • the first device can subsequently successfully monitor the performance of the second model currently used by the second device, and can successfully determine the second model update parameters and/or the target second model, thereby successfully instructing the second device to switch and update the model, so that the second device can adaptively switch the model and/or update the model parameters of the model based on the instruction of the first device, so that the model used by the second device in real time can match the current channel scenario or data distribution characteristics, thereby improving the accuracy of CSI feedback.
  • the first device determines a first parameter value, where the first parameter value is used to determine whether a model switching update is required;
  • the first preset condition includes: the performance monitoring result is less than the first parameter value.
  • the first device determines the first parameter value configured by itself
  • the first device determines the first parameter value based on a protocol agreement.
  • the first device before the first device performs performance monitoring on the currently used first model and/or the currently used second model, it first determines the first parameter value, so that the first device can determine whether a model switch update is currently required based on the first parameter value, and when it is determined that a model switch update is currently required, the subsequent model switch update process is executed, thereby ensuring the integrity and smooth execution of the model switch update process.
  • the first device determines at least one of a target first model, a first model update parameter, a target second model, and a second model update parameter, including:
  • the first model currently used by the first device is the first model uniquely deployed by the first device, and/or the second model currently used by the second device is the second model uniquely deployed by the second device, and sample data is determined;
  • the model parameters of the trained first model are determined as the first model update parameters, and/or the model parameters of the trained second model are determined as the second model update parameters.
  • the first device performs a model switching update and sends indication information to the second device, including at least one of the following:
  • the first device updates the model parameters of the first model currently in use to the first model update parameters
  • the first device sends indication information to the second device, where the indication information includes second model update parameters and/or first indication; the first indication is used to instruct the second device to update model parameters of the second model currently in use to the second model update parameters.
  • the first device determines at least one of a target first model, a first model update parameter, a target second model, and a second model update parameter, including:
  • the first model in the model group with the highest performance monitoring result is determined as the target first model, and the second model in the model group with the highest performance monitoring result is determined as the target second model.
  • the first device sends indication information to the second device, where the indication information includes a second indication and/or relevant information of a target second model; the second indication is used to indicate at least one of the following:
  • the second device deactivates the second model currently in use
  • the second device switches from the currently used second model to the target second model.
  • the first device determines at least one of a target first model, a first model update parameter, a target second model, and a second model update parameter, including:
  • the first device deploys at least two first models, and/or the second device deploys at least two second models, and at least one model group is determined, wherein the model group includes the first model and the second model corresponding to each other;
  • the first model in the model group with the highest performance monitoring result is determined as the target first model, and the second model in the model group with the highest performance monitoring result is determined as the target second model;
  • the model parameters of the trained target first model are determined as the first model update parameters, and/or the model parameters of the trained target second model are determined as the second model update parameters.
  • the first device performs a model switching update and sends indication information to the second device, including at least one of the following:
  • the first device deactivates the currently used first model, switches from the currently used first model to the target first model, and updates the model parameters of the target first model to the first model update parameters;
  • the first device sends indication information to the second device, where the indication information includes at least one of relevant information of the target second model, a second model update parameter, and a third indication, where the third indication is used to indicate at least one of the following:
  • the second device deactivates the second model currently in use
  • the second device switches from the currently used second model to the target second model
  • the second device updates the model parameters of the target second model to the second model update parameters.
  • the second preset condition includes: the performance monitoring result is greater than or equal to a first parameter value.
  • a method for how a first device determines a target first model, a first model update parameter, a target second model, and a second model update parameter, so that the first device successfully determines at least one of the target first model, the first model update parameter, the target second model, and the second model update parameter.
  • the prerequisite for the first device to determine the target first model, the first model update parameter, the target second model, and the second model update parameter is that the performance monitoring result of the first model and/or the second model meets the second preset condition.
  • the second preset condition is that the performance monitoring result is greater than or equal to the first parameter value (i.e., the performance monitoring result is higher).
  • a method for how the first device performs model switching update and how to instruct the second device to perform model switching update, so that the first device can successfully implement model switching update, and the first device can successfully instruct the second device to perform model switching update, so that both the first device and the second device can adaptively switch models and/or update model parameters of the models, so that the models used in real time by the first device and the second device can both match the current channel scenario or data distribution characteristics, thereby improving the accuracy of CSI feedback.
  • the method further includes:
  • the first device is a terminal, and the second device is a network device;
  • the first model is used to compress the CSI
  • the second model is used to reconstruct the CSI.
  • the first device performs performance monitoring on a first model currently used by the first device and/or a second model currently used by the second device, including:
  • the first device uses the CSI data to monitor the performance of a first model currently used by the first device and/or a second model currently used by the second device.
  • the method further includes:
  • the first model after switching update is used for CSI compression.
  • the first device can be a terminal
  • the second device can be a network device, that is, the terminal determines at least one of the target first model, the first model update parameters, the target second model, and the second model update parameters. Since the CSI data on the terminal side is usually required when determining the target first model, the first model update parameters, the target second model, and the second model update parameters, the present disclosure uses the terminal to determine at least one of the target first model, the first model update parameters, the target second model, and the second model update parameters without using other devices (such as network devices) to determine them. This can avoid the situation where "when using other devices to determine at least one of the target first model, the first model update parameters, the target second model, and the second model update parameters, the terminal needs to send CSI data to other devices", thereby saving communication overhead and simplifying the process.
  • the first device is a network device, and the second device is a terminal;
  • the second model is used to compress the CSI
  • the first model is used to reconstruct the CSI.
  • the first device performs performance monitoring on a first model currently used by the first device and/or a second model currently used by the second device, including:
  • the first device sends a first request to the second device, where the first request is used to request CSI data;
  • the first device receives the CSI data sent by the second device
  • the first device uses the CSI data to monitor the performance of a first model currently used by the first device and/or a second model currently used by the second device.
  • determining the sample data includes:
  • the first device receives the sample data sent by the second device.
  • the method further includes:
  • the CSI reconstruction is performed using the first model after switching updates.
  • the first device can be a network device
  • the second device can be a terminal, that is, the network device determines at least one of the target first model, the first model update parameters, the target second model, and the second model update parameters. Since the computing resource configuration and hardware configuration of the network device are relatively high, when the network device is used to determine the target first model, the first model update parameters, the target second model, and the second model update parameters, it can better provide hardware support for model training and updating.
  • the CSI data is obtained by the terminal through measuring a pilot signal sent by a network device and estimating a channel.
  • the sample data is: CSI data measured and estimated by the terminal within a first preset time period.
  • the performance monitoring result is used to reflect the CSI feedback accuracy of the first model and the second model.
  • an embodiment of the present disclosure proposes a model switching update method, the method comprising:
  • the second device receives indication information sent by the first device, where the indication information indicates a target second model and/or a second model update parameter; wherein the target second model is a target model to be switched by the second device;
  • the second device performs model switching update based on the indication information.
  • the first device sends an indication message to the second device to instruct the second device to also switch and update the model. It can be seen that in the embodiment of the present disclosure, the first device can instruct the second device to adaptively switch the model and/or adaptively update the model parameters of the model, thereby ensuring that the model used by the second device in real time is matched to the current channel scenario or data distribution characteristics, thereby improving the accuracy of CSI feedback.
  • a first model is deployed on the first device, and a second model is deployed on the second device; the first model and the second model correspond to each other, and the first model and the second model are used to realize compression and reconstruction of CSI.
  • model-related information corresponding to the second device to the first device includes at least one of the following:
  • the model structure corresponding to at least one second model deployed by the second device.
  • the indication information includes a second model update parameter and/or a first indication; the first indication is used to instruct the second device to update a model parameter of a second model currently in use to the second model update parameter;
  • the performing model switching update based on the indication information includes:
  • the second device updates the model parameters of the second model currently in use to the second model update parameters.
  • the indication information includes relevant information of the second indication and/or the target second model; the second indication is used to indicate at least one of the following: the second device deactivates the currently used second model, the second device switches from the currently used second model to the target second model;
  • the performing model switching update based on the indication information includes:
  • the second device deactivates the currently used second model and switches from the currently used second model to the target second model.
  • the indication information includes at least one of relevant information of the target second model, second model update parameters, and a third indication, wherein the third indication is used to indicate at least one of the following: the second device deactivates the currently used second model, the second device switches from the currently used second model to the target second model, and the second device updates the model parameters of the target second model to the second model update parameters;
  • the performing model switching update based on the indication information includes:
  • the second device deactivates the currently used second model, switches from the currently used second model to the target second model, and updates the model parameters of the target second model to the second model update parameters.
  • the first device is a terminal, and the second device is a network device;
  • the first model is used to compress the CSI
  • the second model is used to reconstruct the CSI.
  • the method further includes:
  • the second model is used to compress the CSI
  • the first model is used to reconstruct the CSI.
  • the method further includes:
  • the method further includes:
  • the second model after switching and updating is used for CSI compression.
  • the first device performs performance monitoring on a first model currently used by the first device and/or a second model currently used by the second device; the first model and the second model correspond to each other, and the first model and the second model are used to implement compression and reconstruction of channel state information CSI;
  • the performance monitoring result meets the first preset condition, and the first device determines that a model switching update is required
  • the first device determines at least one of a target first model, a first model update parameter, a target second model, and a second model update parameter;
  • the second device receives the indication information sent by the first device
  • the second device performs model switching update based on the indication information.
  • an embodiment of the present disclosure provides a first device, including:
  • a processing module configured to perform performance monitoring on a first model currently used by the first device and/or a second model currently used by the second device; the first model and the second model correspond to each other, and the first model and the second model are used to implement compression and reconstruction of channel state information CSI;
  • the processing module is further configured to determine that a model switching update is required when a performance monitoring result meets a first preset condition
  • the processing module is further used to determine at least one of a target first model, a first model update parameter, a target second model, and a second model update parameter; wherein the target first model is a target model to be switched by the first device, and the target second model is a target model to be switched by the second device;
  • the sending module is used to perform a model switching update and send indication information to the second device, where the indication information indicates the target second model and/or the second model update parameter.
  • the apparatus before the first device performs performance monitoring on a first model currently used by the first device and/or a second model currently used by the second device, the apparatus is further configured to:
  • the model-related information includes at least one of the following:
  • the model structure corresponding to at least one second model deployed by the second device.
  • the device is further used for:
  • the apparatus before determining that a model switching update is required, the apparatus is further configured to:
  • the first device determines a first parameter value, where the first parameter value is used to determine whether a model switching update is required;
  • the first preset condition includes: the performance monitoring result is less than the first parameter value.
  • the device is further used for at least one of the following:
  • the first device receives the first parameter value sent by the second device
  • the first device determines the first parameter value configured by itself
  • the first device determines the first parameter value based on a protocol agreement.
  • the processing module is further used to:
  • the first model currently used by the first device is the first model that is uniquely deployed by the first device, and/or the first model currently used by the second device
  • the second model used is the second model uniquely deployed by the second device, and sample data is determined
  • the model parameters of the trained first model are determined as the first model update parameters, and/or the model parameters of the trained second model are determined as the second model update parameters.
  • the sending module is further used for at least one of the following:
  • the first device updates the model parameters of the first model currently in use to the first model update parameters
  • the first device sends indication information to the second device, where the indication information includes second model update parameters and/or first indication; the first indication is used to instruct the second device to update the model parameters of the second model currently in use to the second model update parameters.
  • the processing module is further used to:
  • the first device deploys at least two first models, and/or the second device deploys at least two second models, and at least one model group is determined, wherein the model group includes the first model and the second model corresponding to each other;
  • the first model in the model group with the highest performance monitoring result is determined as the target first model, and the second model in the model group with the highest performance monitoring result is determined as the target second model.
  • the sending module is further used for at least one of the following:
  • the first device deactivates the currently used first model and switches from the currently used first model to the target first model
  • the first device sends indication information to the second device, where the indication information includes a second indication and/or relevant information of a target second model; the second indication is used to indicate at least one of the following:
  • the second device deactivates the second model currently in use
  • the second device switches from the currently used second model to the target second model.
  • the processing module is further used to:
  • the first model in the model group with the highest performance monitoring result is determined as the target first model, and the second model in the model group with the highest performance monitoring result is determined as the target second model;
  • the model parameters of the trained target first model are determined as the first model update parameters, and/or the model parameters of the trained target second model are determined as the second model update parameters.
  • the sending module is further used for at least one of the following:
  • the first device deactivates the currently used first model, switches from the currently used first model to the target first model, and updates the model parameters of the target first model to the first model update parameters;
  • the first device sends indication information to the second device, where the indication information includes at least one of relevant information of the target second model, a second model update parameter, and a third indication, where the third indication is used to indicate at least one of the following:
  • the second device deactivates the second model currently in use
  • the second device switches from the currently used second model to the target second model
  • the second device updates the model parameters of the target second model to the second model update parameters.
  • the second preset condition includes: the performance monitoring result is greater than or equal to the first parameter value.
  • the apparatus is further configured to:
  • the first device is a terminal, and the second device is a network device;
  • the first model is used to compress the CSI
  • the second model is used to reconstruct the CSI.
  • the processing module is further used to:
  • the first device uses the CSI data to monitor the performance of a first model currently used by the first device and/or a second model currently used by the second device.
  • the apparatus is further configured to:
  • the first model after switching update is used for CSI compression.
  • the first device is a network device, and the second device is a terminal;
  • the second model is used to compress the CSI
  • the first model is used to reconstruct the CSI.
  • the processing module is further used to:
  • the first device sends a first request to the second device, where the first request is used to request CSI data;
  • the first device receives the CSI data sent by the second device
  • the first device uses the CSI data to monitor the performance of a first model currently used by the first device and/or a second model currently used by the second device.
  • the device is further used for:
  • the first device sends a second request to the second device, where the second request is used to request the sample data;
  • the first device receives the sample data sent by the second device.
  • the apparatus is further configured to:
  • the CSI reconstruction is performed using the first model after switching updates.
  • the CSI data is obtained by the terminal through measuring a pilot signal sent by a network device and estimating a channel.
  • the sample data is: CSI data measured and estimated by the terminal within a first preset time period.
  • the performance monitoring results are used to reflect the CSI feedback accuracy of the first model and the second model.
  • an embodiment of the present disclosure provides a second device, including:
  • a receiving module configured for a second device to receive indication information sent by a first device, wherein the indication information indicates a target second model and/or a second model update parameter; wherein the target second model is a target model to be switched by the second device;
  • a processing module is used for the second device to perform model switching update based on the indication information.
  • the apparatus before the second device receives the indication information sent by the first device, the apparatus is further configured to:
  • model-related information corresponding to the second device to the first device includes at least one of the following:
  • the apparatus before the second device receives the indication information sent by the first device, the apparatus is further configured to:
  • a first parameter value is sent to the first device, where the first parameter value is used to determine whether a model switching update is required.
  • the indication information includes a second model update parameter and/or a first indication; the first indication is used to instruct the second device to update a model parameter of a second model currently in use to the second model update parameter;
  • the processing module is also used for:
  • the second device updates the model parameters of the second model currently in use to the second model update parameters.
  • the indication information includes relevant information of the second indication and/or the target second model; the second indication is used to indicate at least one of the following: the second device deactivates the currently used second model, the second device switches from the currently used second model to the target second model;
  • the processing module is also used for:
  • the second device deactivates the currently used second model and switches from the currently used second model to the target second model.
  • the indication information includes at least one of relevant information of the target second model, a second model update parameter, and a third indication, wherein the third indication is used to indicate at least one of the following: the second device deactivates the currently used second model, the second device switches from the currently used second model to the target second model, and the second device updates the model parameters of the target second model to the second model update parameters;
  • the processing module is also used for:
  • the second device deactivates the currently used second model, switches from the currently used second model to the target second model, and updates the model parameters of the target second model to the second model update parameters.
  • the apparatus is further used to:
  • the first device is a terminal, and the second device is a network device;
  • the first model is used to compress the CSI
  • the second model is used to reconstruct the CSI.
  • the method further includes:
  • the first device is a network device, and the second device is a terminal;
  • the second model is used to compress the CSI
  • the first model is used to reconstruct the CSI.
  • the device is further used for:
  • the device is further used for:
  • the apparatus is further used to:
  • the second model after switching and updating is used for CSI compression.
  • an embodiment of the present disclosure proposes a communication device, wherein the communication device includes: one or more processors; one or more memories for storing instructions; wherein the processor is used to call the instructions so that the communication device executes the model switching update method described in the first aspect, the optional implementation of the first aspect, the second aspect, and the optional implementation of the second aspect.
  • an embodiment of the present disclosure proposes a communication system, which includes: a terminal and a network device; wherein the terminal is configured to execute the method described in the first aspect and the optional implementation of the first aspect, and the network device is configured to execute the method described in the second aspect and the optional implementation of the second aspect.
  • an embodiment of the present disclosure proposes a program product.
  • the communication device executes the method described in the first aspect, the optional implementation of the first aspect, the second aspect, and the optional implementation of the second aspect.
  • an embodiment of the present disclosure proposes a computer program, which, when executed on a computer, enables the computer to execute the method described in the first aspect, the optional implementation of the first aspect, the second aspect, and the optional implementation of the second aspect.
  • the present disclosure proposes the name of the invention.
  • the terms such as sending method and receiving method, information sending method and information receiving method can be replaced with each other, the terms such as communication device and information processing device, information sending device and information receiving device can be replaced with each other, and the terms such as information processing system, communication system, information sending system and information receiving system can be replaced with each other.
  • each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined.
  • a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged.
  • the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined, for example, some or all of the steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.
  • elements expressed in the singular form such as “a”, “an”, “the”, “above”, “said”, “aforementioned”, “this”, etc., may mean “one and only one", or “one or more”, “at least one”, etc.
  • the noun after the article may be understood as a singular expression or a plural expression.
  • plurality refers to two or more.
  • the terms “at least one of”, “at least one of”, “at least one of”, “one or more”, “a plurality of”, “multiple”, etc. can be used interchangeably.
  • descriptions such as “at least one of A, B, C...”, “A and/or B and/or C...”, etc. include the situation where any one of A, B, C... exists alone, and also include the situation where any multiple of A, B, C... exist in any combination, and each situation can exist alone; for example, “at least one of A, B, C” includes the situation where A exists alone, B exists alone, C exists alone, the combination of A and B, the combination of A and C, the combination of B and C, and the combination of A, B and C; for example, A and/or B includes the situation where A exists alone, B exists alone, and the combination of A and B.
  • the description methods such as “in one case A, in another case B", “in response to one case A, in response to another case B”, etc. may include the following technical solutions according to the situation: A is executed independently of B, that is, in some embodiments A; B is executed independently of A, that is, in some embodiments B; A and B are selectively executed, that is, selected from A and B in some embodiments; A and B are both executed, that is, A and B in some embodiments.
  • branches such as A, B, C, etc., it is similar to the above.
  • prefixes such as “first” and “second” in the embodiments of the present disclosure are only used to distinguish different description objects, and do not constitute restrictions on the position, order, priority, quantity or content of the description objects.
  • the statement of the description object refers to the description in the context of the claims or embodiments, and should not constitute unnecessary restrictions due to the use of prefixes.
  • the description object is a "field”
  • the ordinal number before the "field” in the "first field” and the "second field” does not limit the position or order between the "fields”
  • the "first” and “second” do not limit whether the "fields” they modify are in the same message, nor do they limit the order of the "first field” and the "second field”.
  • the description object is a "level”
  • the ordinal number before the "level” in the “first level” and the “second level” does not limit the priority between the "levels”.
  • the number of description objects is not limited by the ordinal number, and can be one or more. Taking the "first device” as an example, the number of "devices” can be one or more.
  • the objects modified by different prefixes may be the same or different. For example, if the description object is "device”, then the “first device” and the “second device” may be the same device or different devices, and their types may be the same or different. For another example, if the description object is "information”, then the "first information” and the “second information” may be the same information or different information, and their contents may be the same or different.
  • “including A”, “comprising A”, “used to indicate A”, and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.
  • terms such as “greater than”, “greater than or equal to”, “not less than”, “more than”, “more than or equal to”, “not less than”, “higher than”, “higher than or equal to”, “not lower than”, and “above” can be replaced with each other, and terms such as “less than”, “less than or equal to”, “not greater than”, “less than”, “less than or equal to”, “no more than”, “lower than”, “lower than or equal to”, “not higher than”, and “below” can be replaced with each other.
  • devices and the like may be interpreted as physical or virtual, and their names are not limited to the names described in the embodiments, such as “device”, “equipment”, “device”, “circuit”, “network element”, “node”, “function”, “unit”,
  • the terms “section”, “system”, “network”, “chip”, “chip system”, “entity”, and “subject” may be used interchangeably.
  • network may be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).
  • terminal In some embodiments, the terms "terminal”, “terminal device”, “user equipment (UE)”, “user terminal” “mobile station (MS)”, “mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client and the like can be used interchangeably.
  • the terminal may be replaced by an access network device, a core network device, or a network device.
  • the access network device, the core network device, or the network device may also be configured to have a structure that has all or part of the functions of the terminal.
  • acquisition of data, information, etc. may comply with the laws and regulations of the country where the data is obtained.
  • data, information, etc. may be obtained with the user's consent.
  • each element, each row, or each column in the table of the embodiments of the present disclosure may be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns may also be implemented as an independent embodiment.
  • the corresponding relationships shown in the tables in the present disclosure can be configured or predefined.
  • the values of the information in each table are only examples and can be configured as other values, which are not limited by the present disclosure.
  • the corresponding relationships shown in some rows may not be configured.
  • appropriate deformation adjustments can be made based on the above table, such as splitting, merging, etc.
  • the names of the parameters shown in the titles of the above tables can also use other names that can be understood by the communication device, and the values or representations of the parameters can also be other values or representations that can be understood by the communication device.
  • other data structures can also be used, such as arrays, queues, containers, stacks, linear lists, pointers, linked lists, trees, graphs, structures, classes, heaps, hash tables or hash tables.
  • the predefined in the present disclosure may be understood as defined, predefined, stored, pre-stored, pre-negotiated, pre-configured, solidified, or pre-burned.
  • FIG1A is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure.
  • a communication system 100 may include a first device 101 and a second device 102.
  • the first device may be, for example, a terminal
  • the second device may be, for example, a network device
  • the second device may be, for example, a terminal
  • the first device may be, for example, a network device
  • the network device may include an access network device and/or a core network device.
  • the terminal includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer (Pad), a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control (industrial control), a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid (smart grid), a wireless terminal device in transportation safety (transportation safety), a wireless terminal device in a smart city (smart city), and at least one of a wireless terminal device in a smart home (smart home), but is not limited to these.
  • a mobile phone a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer (Pad), a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device
  • the access network device is, for example, a node or device that accesses the terminal to the wireless network.
  • the access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a NodeB (NB), a home NodeB (HNB), At least one of a home evolved node B (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open RAN, a cloud RAN, a base station in other communication systems, and an access node in a wireless fidelity (WiFi) system, but not limited thereto.
  • eNB evolved NodeB
  • ng-eNB next generation evolved NodeB
  • gNB next generation NodeB
  • NB NodeB
  • HNB home
  • the technical solution of the present disclosure may be applicable to the Open RAN architecture.
  • the interfaces between access network devices or within access network devices involved in the embodiments of the present disclosure may become internal interfaces of Open RAN, and the processes and information interactions between these internal interfaces may be implemented through software or programs.
  • the access network device may be composed of a centralized unit (central unit, CU) and a distributed unit (distributed unit, DU), wherein the CU may also be called a control unit (control unit).
  • the CU-DU structure may be used to split the protocol layer of the access network device, with some functions of the protocol layer being centrally controlled by the CU, and the remaining part or all of the functions of the protocol layer being distributed in the DU, and the DU being centrally controlled by the CU, but not limited to this.
  • the core network device may be a device including one or more network elements, or may be a plurality of devices or a group of devices, each including all or part of one or more network elements.
  • the network element may be virtual or physical.
  • the core network may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC).
  • EPC Evolved Packet Core
  • 5GCN 5G Core Network
  • NGC Next Generation Core
  • the core network device may also be a location management function network element.
  • the location management function network element includes a location server (location server), which may be implemented as any one of the following: a location management function (LMF), an Enhanced Serving Mobile Location Centre (E-SMLC), a Secure User Plane Location (SUPL), and a Secure User Plane Location Platform (SUPLLP).
  • LMF location management function
  • E-SMLC Enhanced Serving Mobile Location Centre
  • SUPL Secure User Plane Location
  • SUPLLP Secure User Plane Location Platform
  • the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure.
  • a person of ordinary skill in the art can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.
  • the following embodiments of the present disclosure may be applied to the communication system 100 shown in FIG1A, or part of the subject, but are not limited thereto.
  • the subjects shown in FIG1A are examples, and the communication system may include all or part of the subjects in FIG1A, or may include other subjects other than FIG1A, and the number and form of the subjects are arbitrary, and the connection relationship between the subjects is an example, and the subjects may be connected or disconnected, and the connection may be in any manner, which may be a direct connection or an indirect connection, and may be a wired connection or a wireless connection.
  • LTE Long Term Evolution
  • LTE-A LTE-Advanced
  • LTE-B LTE-Beyond
  • SUPER 3G IMT-Advanced
  • 4G fourth generation mobile communication system
  • 5G 5G new radio
  • FRA future radio access
  • RAT new radio access technology
  • NR new radio
  • NX new radio access
  • FX future generation radio access
  • GSM Global System for Mobile communications
  • CDMA2000 Code Division Multiple Access 2000
  • UMB Ultra Mobile Broadband
  • IEEE 802.11 Wi-Fi (registered trademark)
  • IEEE 802.16 WiMAX (registered trademark)
  • IEEE 802.20 Ultra-WideBand (UWB)
  • Bluetooth registered trademark
  • PLMN Public Land Mobile Network
  • D2D Device to Device
  • M2M Machine to Machine to Machine
  • IoT Internet of Things
  • V2X Vehicle-to-Everything
  • V2X Vehicle-to-Everything
  • Figure 1B is a structural block diagram of a method of "CSI compression reporting and reconstruction based on bilateral model” in the above-mentioned background technology provided by an embodiment of the present disclosure, wherein in the method of "CSI compression reporting and reconstruction based on bilateral model", an encoder model is deployed on the terminal side, and a decoder model is deployed on the network device side, and the encoder model and the decoder model together constitute a feedback network model to realize CSI feedback.
  • the CSI feedback process may include: the encoder model deployed on the terminal side extracts features and compresses dimensions of the original CSI data to compress the original CSI data into codewords with a smaller data volume, and then transmits the codeword information to the network device through an uplink feedback link, wherein, since the codeword information transmitted by the terminal is the codeword compressed from the original CSI data, the feedback overhead can be reduced. And, After the network device receives the codeword information, it can reconstruct the CSI through the decoder model to output the CSI data of the original dimension.
  • a certain scale of data set is usually used to train the above feedback network model, so that the feedback network model can fully learn the CSI data feature distribution of the current training set, and then when facing CSI data with the same or similar feature distribution, it can minimize the difference between the CSI data reconstructed by the decoder model and the original CSI data input by the encoder model, and realize the compression, feedback and reconstruction of CSI with lower error.
  • a current training method for the feedback network model is: an offline model training method, that is, using a large-scale data set Data i collected in advance under a channel scenario Scenario i to complete offline training of the feedback network model, and deploying the feedback network model in terminals and network devices in the actual system.
  • the feedback network model parameters remain fixed.
  • the actual channel environment is complex and time-varying, and the terminal also has strong mobility. Therefore, there may be a variety of different channel scenarios in the actual communication system, the characteristic distribution of the channel data is unknown, and real-time channel characteristic changes may occur within a channel scenario.
  • the feedback network model is trained using an offline model training method based on deep learning, since the parameters of the feedback network model after training are fixed, the model obtained by training may be difficult to process CSI data with an unknown distribution that is different from the characteristic distribution of the training data set, that is, the problem of model-data mismatch is prone to occur, resulting in a decrease in the accuracy of CSI feedback (that is, the difference between the CSI data reconstructed by the decoder model and the original CSI data input by the encoder model is large), which in turn affects the overall performance of the system.
  • the indoor and outdoor feedback network models are trained independently using their respective data sets in the two channel scenarios, which are used to complete the CSI compression and reconstruction work in the indoor and outdoor channel scenarios, respectively.
  • the feedback network model is only trained for one channel scenario, and the trained feedback network model cannot be applied to CSI feedback in a variety of different channel scenarios. There will also be a mismatch between the model and the data distribution characteristics, which will cause a significant decrease in feedback accuracy, thereby affecting the work of the system.
  • FIG2A is an interactive schematic diagram of a model switching update method according to an embodiment of the present disclosure.
  • the embodiment of the present disclosure relates to a model switching update method, which is used in a communication system 100.
  • the first device is a terminal and the second device is a network device.
  • the method includes:
  • Step 2101 The first device determines model-related information corresponding to the second device.
  • one or more first models may be deployed on the first device, and one or more second models may be deployed on the second device, the first model and the second model correspond to each other, and optionally, the corresponding first model and the second model may be used to implement compression and reconstruction of CSI.
  • the first model when the first device is a terminal and the second device is a network device, the first model may be used to compress CSI, and the second model may be used to reconstruct CSI; for example, the first model may be an encoder model, and the second model may be a decoder model, but is not limited thereto; for example, if the first model #1 corresponds to the second model 1, then after determining the original CSI data, the first device may use the first model #1 to compress the original CSI data into codeword information and send it to the second device, and after the second device receives the codeword information, it may use the second model #1 to reconstruct the CSI data of the original dimension (i.e., data close to the original CSI data) based on the codeword information.
  • the first model can be used to reconstruct CSI
  • the second model can be used to compress CSI
  • the first model can be a decoder model
  • the second model can be an encoder model, but is not limited thereto.
  • the first model and the second model can be: Artificial Intelligence (AI) or Machine Learning (ML) models.
  • the first device is a terminal and the second device is a network device.
  • the model-related information may be used to reflect the model deployment and usage of the second device.
  • the model-related information may include at least one of the following:
  • the second model currently used by the second device may include, for example, a model number of the second model currently used by the second device;
  • At least one second model deployed by the second device may include a model number of the at least one second model deployed by the second device
  • the model structure corresponding to at least one second model deployed by the second device.
  • the model structure may be a model structure to which the second model belongs, such as: the second model belongs to a neural network model or an encoder model, etc.; and/or, in some embodiments, the model structure may be an internal model structure of the second model, such as: the number of convolutional layers of the second model, etc.
  • the method for the first device to determine the model-related information corresponding to the second device may include: the first device receiving the model-related information corresponding to the second device sent by the second device.
  • the first device may also send model-related information corresponding to the first device to the second device.
  • Step 2102 The first device determines a first parameter value.
  • the first parameter value may be used to determine whether a model switching update is required.
  • the method for the first device to determine the first parameter value may include at least one of the following:
  • the first device receives a first parameter value sent by the second device
  • the first device determines a first parameter value configured by itself
  • the first device determines a first parameter value based on a protocol agreement.
  • the second device when the model-related information of the second device is sent by the second device to the first device, and when the first parameter value is sent by the second device to the first device, the second device may use the same message to simultaneously send the first parameter value and the model-related information of the second device to the first device; or, the first parameter value and the model-related information of the second device may also be included in different messages, such as the second device may first send the first parameter value and then send the model-related information of the second device; or, the second device may first send the model-related information of the second device and then send the first parameter value.
  • Step 2103 The first device performs performance monitoring on the first model currently used by the first device and/or the second model currently used by the second device.
  • the first device may determine real-time CSI data, and the CSI data may be obtained by the first device (i.e., the terminal) by measuring a pilot signal sent by the second device (i.e., the network device) and estimating the channel. Also, the first device may use the determined CSI data to perform performance monitoring on the first model currently used by the first device and/or the second model currently used by the second device to obtain a performance monitoring result.
  • the performance monitoring result may be used to reflect the CSI feedback accuracy of the first model and the second model.
  • the feedback accuracy may be understood, for example, as: the error value between the CSI data reconstructed by the second model and the original CSI data input to the first model.
  • Step 2104 The performance monitoring result meets the first preset condition, and the first device determines that a model switching update is required.
  • the first preset condition may include: the performance monitoring result is less than the first parameter value.
  • the first parameter value may be an accuracy threshold value; when the performance monitoring result corresponding to the first model currently used by the first device and/or the second model currently used by the second device meets the first preset condition, it means that the performance monitoring result corresponding to the first model currently used by the first device and/or the second model currently used by the second device is low, that is, it means that the CSI feedback accuracy of the first model currently used by the first device and/or the second model currently used by the second device is low, and at this time, it is considered that a model switching update is required.
  • Step 2105 The first device determines at least one of a target first model, a first model update parameter, a target second model, and a second model update parameter.
  • the above-mentioned first target model may be a target model to be switched by the first device
  • the second target model may be a target model to be switched by the second device.
  • the above-mentioned first model update parameters can be used to update the model parameters of the first model deployed on the first device.
  • the first model update parameters can be used to update the model parameters of the first model currently used by the first device, or can be used to update the model parameters of the target first model;
  • the above-mentioned second model update parameters can be used to update the model parameters of the second model deployed on the second device.
  • the second model update parameters can be used to update the model parameters of the second model currently used by the second device, or can be used to update the model parameters of the target second model.
  • the method for the first device to determine at least one of the target first model, the first model update parameter, the target second model, and the second model update parameter may include:
  • Step a The first model currently used by the first device is the first model uniquely deployed by the first device, and/or the second model currently used by the second device is the second model uniquely deployed by the second device, and sample data is determined.
  • the sample data may be: CSI data obtained by the first device measuring a pilot signal and estimating a channel within a first preset time period.
  • the first preset time period may be pre-set, for example, a period of time before a current time period.
  • Step b training the first model currently used by the first device and/or the second model currently used by the second device based on the sample data until the performance monitoring result of the trained first model and/or the trained second model meets the second preset condition, and then stopping the training;
  • the performance monitoring result of the trained first model and/or the trained second model may be completed.
  • the model's feedback accuracy on the training data set is obtained directly during the model training process.
  • Step 1 The first device deploys at least two first models, and/or the second device deploys at least two second models, and at least one model group is determined, where the model group includes the first model and the second model corresponding to each other;
  • Step 2 monitoring the performance of the first model and/or the second model in each model group
  • Step 3 If the performance monitoring result of at least one model group meets the second preset condition, the first model in the model group with the highest performance monitoring result is determined as the target first model, and the second model in the model group with the highest performance monitoring result is determined as the target second model.
  • Step 1 The first device deploys at least two first models, and/or the second device deploys at least two second models, and at least one model group is determined, where the model group includes the first model and the second model corresponding to each other;
  • Step 2 Monitor the performance of each model group
  • Step 3 If the performance monitoring results of each model group do not meet the second preset condition, the first model in the model group with the highest performance monitoring result is determined as the target first model, and the second model in the model group with the highest performance monitoring result is determined as the target second model;
  • Step 5 Train the target first model and the target second model based on the sample data until the performance monitoring results of the trained first model and/or the trained second model meet the second preset condition, and then stop the training;
  • Step six determine the model parameters of the trained target first model as first model update parameters, and/or determine the model parameters of the trained target second model as second model update parameters.
  • Step 2106 The first device performs a model switching update and sends an indication message to the second device.
  • the indication information may be used to indicate a target second model and/or a second model update parameter.
  • the method for the first device to perform model switching update and send indication information to the second device may include at least one of the following:
  • the first device updates the model parameters of the first model currently in use to first model update parameters
  • the first device sends indication information to the second device, and the indication information may include the second model update parameter and/or the first indication; the first indication is used to instruct the second device to update the model parameters of the second model currently in use to the second model update parameters.
  • the first device may quantize the second model update parameters, and the second model update parameters included in the indication information may be the quantized second model update parameters.
  • the first device when the first device deploys at least two first models, and/or the second device deploys at least two second models, and the performance monitoring result of at least one model group (that is, the model group includes the first model and the second model corresponding to each other) meets the second preset condition, the first device performs a model switching update and sends an indication information to the second device, including at least one of the following:
  • the first device deactivates the currently used first model and switches from the currently used first model to the target first model
  • the first device sends indication information to the second device, where the indication information may include a second indication and/or relevant information of a target second model; optionally, the relevant information of the target second model may be: a model number of the target second model.
  • the second indication may be used to indicate at least one of the following:
  • the second device deactivates the second model currently in use
  • the second device switches from the currently used second model to the target second model.
  • the first device when the first device deploys at least two first models, and/or the second device deploys at least two first models, when the performance monitoring results of each group (i.e., the model group includes the first model and the second model corresponding to each other) do not meet the second preset condition, the first device performs model switching update and sends indication information to the second device, including at least one of the following:
  • the first device deactivates the currently used first model, switches from the currently used first model to the target first model, and updates the model parameters of the target first model to the first model update parameters;
  • the first device sends indication information to the second device, where the indication information may include at least one of relevant information of the target second model, a second model update parameter, and a third indication, where the third indication is used to indicate at least one of the following:
  • the second device deactivates the second model currently in use
  • the second device switches from the currently used second model to the target second model
  • the second device updates the model parameters of the target second model to second model update parameters.
  • Step 2107 The second device performs model switching update based on the indication information.
  • the above-mentioned method for model switching update based on the indication information may include: the second device updates the model parameters of the second model currently used to the second model update parameters.
  • the above-mentioned method for model switching update based on the indication information may include: the second device deactivates the currently used second model and switches from the currently used second model to the target second model.
  • the above-mentioned method for model switching update based on the indication information may include: the second device deactivates the currently used second model, switches from the currently used second model to the target second model, and updates the model parameters of the target second model to the second model update parameters.
  • Step 2108 The second device sends a confirmation message.
  • the confirmation information may be used to indicate that the second device has completed the model switching update.
  • Step 2109 The first device uses the first model after switching and updating to perform CSI compression.
  • the first device may perform CSI compression using the target first model determined above.
  • Step 2110 The second device uses the second model after switching and updating to perform CSI reconstruction.
  • the second device may use the target second model determined above to perform CSI reconstruction.
  • the model switching update method involved in the embodiment of the present disclosure may include at least one of steps S2101 to S2110.
  • step S2101 may be implemented as an independent embodiment
  • step S2102 may be implemented as an independent embodiment
  • step S2103 may be implemented as an independent embodiment, but is not limited thereto.
  • each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementation modes or other examples.
  • FIG2B is an interactive schematic diagram of a model switching update method according to an embodiment of the present disclosure.
  • the embodiment of the present disclosure relates to a model switching update method, which is used in a communication system 100.
  • the first device is a network device and the second device is a terminal.
  • Step 2201 The first device determines model-related information corresponding to the second device.
  • Step 2202 The first device determines a first parameter value
  • Step 2203 The first device sends a first request to the second device, where the first request is used to request CSI data.
  • the first request may be used to request real-time CSI data on the second device side.
  • the CSI data may be obtained by the second device (i.e., terminal) by measuring a pilot signal sent by the first device (i.e., network device) and estimating the channel.
  • Step 2204 The second device sends CSI data to the first device.
  • the second device sends the real-time CSI data of the second device side to the second device.
  • the second device may quantize the real-time CSI data and send the quantized CSI data to the first device.
  • Step 2205 The first device performs performance monitoring on the first model currently used by the first device and/or the second model currently used by the second device.
  • Step 2206 The performance monitoring result meets the first preset condition, and the first device determines that a model switching update is required.
  • Step 2207 The first device determines the target first model, the first model update parameter, the target second model, and the second model update parameter. At least one.
  • the method by which the first device determines at least one of the target first model, the first model update parameter, the target second model, and the second model update parameter is consistent with the method of step 2105 above, except that, when the first device is a network device, when determining the sample data for training the first model and/or the second model, the first device does not directly determine the CSI data measured and estimated within the first preset time period as in step 2105 above, but the first device (i.e., the network device) needs to send a second request to the second device (i.e., the terminal), and the second request is used to request the sample data; thereafter, the first device receives the sample data sent by the second device.
  • the second device after the second device receives the second request and determines the sample data, it can quantize the sample data and send the quantized sample data to the first device. Other steps are consistent with step 2105 above.
  • Step 2208 The first device performs a model switching update and sends an indication message to the second device.
  • Step 2209 The second device performs model switching update based on the indication information.
  • Step 2210 The second device sends a confirmation message.
  • Step 2211 The second device uses the second model after switching and updating to perform CSI compression.
  • Step 2212 The first device uses the first model after switching and updating to perform CSI reconstruction.
  • steps 2201 - 2212 please refer to the above embodiment description.
  • the model switching update method involved in the embodiment of the present disclosure may include at least one of steps S2201 to S2212.
  • step S2201 may be implemented as an independent embodiment
  • step S2202 may be implemented as an independent embodiment, but is not limited thereto.
  • each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementation modes or other examples.
  • FIG3A is an interactive schematic diagram of a model switching update method according to an embodiment of the present disclosure.
  • the embodiment of the present disclosure relates to a model switching update method, which is used for a first device, the first device being a terminal, and the method includes:
  • Step 3101 The first device determines model-related information corresponding to the second device.
  • Step 3102 The first device determines a first parameter value
  • Step 3103 The first device performs performance monitoring on the first model currently used by the first device and/or the second model currently used by the second device;
  • Step 3104 The performance monitoring result meets the first preset condition, and the first device determines that a model switching update is required.
  • Step 3105 The first device determines at least one of a target first model, a first model update parameter, a target second model, and a second model update parameter.
  • Step 3106 The first device performs a model switching update and sends an indication message to the second device.
  • Step 3107 The first device receives confirmation information sent by the second device.
  • Step 3108 The first device uses the first model after switching and updating to perform CSI compression.
  • steps 3101 - 3108 please refer to the above embodiment description.
  • the model switching update method involved in the embodiment of the present disclosure may include at least one of steps S3101 to S3108.
  • step S3101 may be implemented as an independent embodiment
  • step S3102 may be implemented as an independent embodiment, but is not limited thereto.
  • each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementation modes or other examples.
  • FIG3B is an interactive schematic diagram of a model switching update method according to an embodiment of the present disclosure.
  • the embodiment of the present disclosure relates to a model switching update method for a first device, the first device being a network device, and the method includes:
  • Step 3201 The first device determines model-related information corresponding to the second device.
  • Step 3202 The first device determines a first parameter value
  • Step 3203 The first device sends a first request to the second device.
  • Step 3204 The first device receives CSI data sent by the second device.
  • Step 3205 The first device performs performance monitoring on the first model currently used by the first device and/or the second model currently used by the second device.
  • Step 3208 The first device performs a model switching update and sends an indication message to the second device.
  • Step 3209 The first device receives confirmation information sent by the second device.
  • Step 3210 The first device uses the first model after switching and updating to perform CSI reconstruction.
  • steps 3201 - 3210 please refer to the above embodiment description.
  • the model switching update method involved in the embodiment of the present disclosure may include at least one of steps S3201 to S3210.
  • step S3201 may be implemented as an independent embodiment
  • step S3202 may be implemented as an independent embodiment, but is not limited thereto.
  • FIG3C is an interactive schematic diagram of a model switching update method according to an embodiment of the present disclosure.
  • the embodiment of the present disclosure relates to a model switching update method, which is used for a first device, and the method includes:
  • Step 3302 The performance monitoring result meets the first preset condition, and it is determined that a model switching update is required;
  • Step 3303 The first device determines at least one of a target first model, a first model update parameter, a target second model, and a second model update parameter.
  • Step 3304 The first device performs a model switching update and sends indication information to the second device, where the indication information indicates a target second model and/or second model update parameters.
  • the first model and the second model correspond to each other, and the first model and the second model are used to implement compression and reconstruction of channel state information CSI;
  • the model-related information includes at least one of the following:
  • the method further includes:
  • the first device determines a first parameter value, where the first parameter value is used to determine whether a model switching update is required;
  • the first preset condition includes: the performance monitoring result is less than the first parameter value.
  • the first device determines the first parameter value, including at least one of the following:
  • the first device receives the first parameter value sent by the second device
  • the first device determines the first parameter value configured by itself
  • the first device determines the first parameter value based on a protocol agreement.
  • the first device determines at least one of a target first model, a first model update parameter, a target second model, and a second model update parameter, including:
  • the first model currently used by the first device is the first model uniquely deployed by the first device, and/or the second model currently used by the second device is the second model uniquely deployed by the second device, and sample data is determined;
  • the model parameters of the trained first model are determined as the first model update parameters, and/or the model parameters of the trained second model are determined as the second model update parameters.
  • the first device performs model switching update and sends indication information to the second device, including at least one of the following:
  • the first device updates the model parameters of the first model currently in use to the first model update parameters
  • the first device sends indication information to the second device, where the indication information includes a second model update parameter and/or a first indication;
  • An instruction is used to instruct the second device to update the model parameters of the second model currently in use to the second model update parameters.
  • the first device determines at least one of a target first model, a first model update parameter, a target second model, and a second model update parameter, including:
  • the first device deploys at least two first models, and/or the second device deploys at least two second models, and at least one model group is determined, wherein the model group includes the first model and the second model corresponding to each other;
  • the first model in the model group with the highest performance monitoring result is determined as the target first model, and the second model in the model group with the highest performance monitoring result is determined as the target second model.
  • the first device performs model switching update and sends indication information to the second device, including at least one of the following:
  • the first device deactivates the currently used first model and switches from the currently used first model to the target first model
  • the first device sends indication information to the second device, where the indication information includes a second indication and/or relevant information of a target second model; the second indication is used to indicate at least one of the following:
  • the second device deactivates the second model currently in use
  • the second device switches from the currently used second model to the target second model.
  • the first device determines at least one of a target first model, a first model update parameter, a target second model, and a second model update parameter, including:
  • the first device deploys at least two first models, and/or the second device deploys at least two second models, and at least one model group is determined, wherein the model group includes the first model and the second model corresponding to each other;
  • the first model in the model group with the highest performance monitoring result is determined as the target first model, and the second model in the model group with the highest performance monitoring result is determined as the target second model;
  • the model parameters of the trained target first model are determined as the first model update parameters, and/or the model parameters of the trained target second model are determined as the second model update parameters.
  • the first device performs model switching update and sends indication information to the second device, including at least one of the following:
  • the first device deactivates the currently used first model, switches from the currently used first model to the target first model, and updates the model parameters of the target first model to the first model update parameters;
  • the first device sends indication information to the second device, where the indication information includes at least one of relevant information of the target second model, a second model update parameter, and a third indication, where the third indication is used to indicate at least one of the following:
  • the second device deactivates the second model currently in use
  • the second device switches from the currently used second model to the target second model
  • the second device updates the model parameters of the target second model to the second model update parameters.
  • the second preset condition includes: the performance monitoring result is greater than or equal to a first parameter value.
  • the method further includes:
  • the first device is a terminal
  • the second device is a network device
  • the first model is used to compress the CSI
  • the second model is used to reconstruct the CSI.
  • the first device performs performance monitoring on a first model currently used by the first device and/or a second model currently used by the second device, including:
  • the first device uses the CSI data to monitor the performance of a first model currently used by the first device and/or a second model currently used by the second device.
  • the method further includes:
  • the first model after switching update is used for CSI compression.
  • the first device is a network device, and the second device is a terminal;
  • the second model is used to compress the CSI
  • the first model is used to reconstruct the CSI.
  • the first device performs performance monitoring on a first model currently used by the first device and/or a second model currently used by the second device, including:
  • the first device sends a first request to the second device, where the first request is used to request CSI data;
  • the first device receives the CSI data sent by the second device
  • the first device uses the CSI data to monitor the performance of a first model currently used by the first device and/or a second model currently used by the second device.
  • determining the sample data includes:
  • the first device sends a second request to the second device, where the second request is used to request the sample data;
  • the first device receives the sample data sent by the second device.
  • the method further includes:
  • the CSI reconstruction is performed using the first model after switching updates.
  • the CSI data is obtained by the terminal through measuring a pilot signal sent by a network device and estimating a channel.
  • the sample data is: CSI data measured and estimated by the terminal within a first preset time period.
  • the performance monitoring result is used to reflect the CSI feedback accuracy of the first model and the second model.
  • steps 3301 - 3304 please refer to the above embodiment description.
  • the model switching update method involved in the embodiment of the present disclosure may include at least one of steps S3301 to S3310.
  • step S3301 may be implemented as an independent embodiment
  • step S3302 may be implemented as an independent embodiment, but is not limited thereto.
  • step 3306 may be an optional step and may be performed optionally, for example, may be performed or not performed.
  • each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementation modes or other examples.
  • FIG4A is an interactive schematic diagram of a model switching update method according to an embodiment of the present disclosure.
  • the embodiment of the present disclosure relates to a model switching update method, which is used for a second device 102, where the second device is a network device, and the method includes:
  • Step 4101 The second device sends model-related information corresponding to the second device to the first device.
  • Step 4102 The second device sends a first parameter value to the first device.
  • Step 4103 The second device receives the indication information sent by the first device.
  • Step 4104 The second device performs model switching update based on the indication information.
  • Step 4105 The second device sends a confirmation message to the first device.
  • Step 4106 The second device uses the second model after switching and updating to perform CSI reconstruction.
  • steps 4101 - 4106 please refer to the above embodiment description.
  • each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementation modes or other examples.
  • FIG4B is an interactive schematic diagram of a model switching update method according to an embodiment of the present disclosure.
  • the embodiment of the present disclosure relates to a model switching update method, which is used for a second device 102, where the second device is a terminal, and the method includes:
  • Step 4201 The second device sends model-related information corresponding to the second device to the first device.
  • Step 4203 The second device receives the first request sent by the first device.
  • Step 4204 The second device sends CSI data to the first device.
  • Step 4205 The second device receives the indication information sent by the first device.
  • Step 4206 The second device performs model switching update based on the indication information.
  • Step 4207 The second device sends a confirmation message to the first device.
  • Step 4208 The second device uses the second model after switching and updating to perform CSI compression.
  • steps 4201-4208 please refer to the above embodiment description.
  • the model switching update method involved in the embodiment of the present disclosure may include at least one of steps S4201 to S4206.
  • Step S4201 can be implemented as an independent embodiment
  • step S4202 can be implemented as an independent embodiment, but are not limited thereto.
  • each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementation modes or other examples.
  • FIG4C is an interactive schematic diagram of a model switching update method according to an embodiment of the present disclosure.
  • the embodiment of the present disclosure relates to a model switching update method, which is used for the second device 102, and the method includes:
  • Step 4301 The second device receives indication information sent by the first device.
  • Step 4302 The second device performs model switching update based on the indication information.
  • the indication information indicates the target second model and/or the second model update parameter.
  • the target second model is the target model to be switched by the second device.
  • a first model is deployed on the first device, and a second model is deployed on the second device; the first model and the second model correspond to each other, and the first model and the second model are used to realize compression and reconstruction of CSI.
  • the method before the second device receives the indication information sent by the first device, the method further includes:
  • model-related information corresponding to the second device to the first device includes at least one of the following:
  • the model structure corresponding to at least one second model deployed by the second device.
  • the method before the second device receives the indication information sent by the first device, the method further includes:
  • a first parameter value is sent to the first device, where the first parameter value is used to determine whether a model switching update is required.
  • the indication information includes a second model update parameter and/or a first indication; the first indication is used to instruct the second device to update a model parameter of a second model currently in use to the second model update parameter;
  • the performing model switching update based on the indication information includes:
  • the second device updates the model parameters of the second model currently in use to the second model update parameters.
  • the indication information includes the second indication and/or relevant information of the target second model; the second indication is used to indicate at least one of the following: the second device deactivates the currently used second model, the second device switches from the currently used second model to the target second model;
  • the performing model switching update based on the indication information includes:
  • the second device deactivates the currently used second model and switches from the currently used second model to the target second model.
  • the indication information includes at least one of relevant information of the target second model, a second model update parameter, and a third indication, wherein the third indication is used to indicate at least one of the following: the second device deactivates the currently used second model, the second device switches from the currently used second model to the target second model, and the second device updates the model parameters of the target second model to the second model update parameters;
  • the performing model switching update based on the indication information includes:
  • the second device deactivates the currently used second model, switches from the currently used second model to the target second model, and updates the model parameters of the target second model to the second model update parameters.
  • the method further includes:
  • the first device is a terminal
  • the second device is a network device
  • the first model is used to compress the CSI
  • the second model is used to reconstruct the CSI.
  • the method further includes:
  • the CSI reconstruction is performed using the second model after switching updates.
  • the first device is a network device, and the second device is a terminal;
  • the second model is used to compress the CSI
  • the first model is used to reconstruct the CSI.
  • the method further includes:
  • the second model after switching and updating is used for CSI compression.
  • steps 4301-4302 please refer to the above embodiment description.
  • the model switching update method involved in the embodiment of the present disclosure may include at least one of steps S4301 to S4302.
  • step Step S4301 can be implemented as an independent embodiment
  • step S4302 can be implemented as an independent embodiment, but are not limited thereto.
  • each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementation modes or other examples.
  • FIG5A is a flow chart of a model switching update method according to an embodiment of the present disclosure. As shown in FIG5A , the present disclosure embodiment relates to a model switching update method for a communication system, and the method includes at least one of the following:
  • Step 5101 The first device performs performance monitoring on a first model currently used by the first device and/or a second model currently used by a second device;
  • Step 5102 The performance monitoring result meets the first preset condition, and the first device determines that a model switching update is required;
  • Step 5103 The first device determines at least one of a target first model, a first model update parameter, a target second model, and a second model update parameter.
  • Step 5104 The first device performs a model switching update and sends indication information to the second device, where the indication information indicates the target second model and/or the second model update parameters.
  • Step 5105 The second device receives the indication information sent by the first device.
  • Step 5106 The second device performs a model switching update based on the indication information.
  • steps 5101 to 5106 may refer to the description of the above embodiment.
  • the above method may include the method of the above-mentioned communication system side, terminal side, network terminal side, etc., which will not be repeated here.
  • the model switching update method involved in the embodiment of the present disclosure may include at least one of steps S5101 to S5107.
  • step S5101 may be implemented as an independent embodiment
  • step S5102 may be implemented as an independent embodiment, but is not limited thereto.
  • each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementation modes or other examples.
  • FIG5B is a flow chart of a model switching update method according to an embodiment of the present disclosure. As shown in FIG5B , the method includes:
  • the confirmation information includes: an Indicator complete confirmation indication that the network device has completed the model switching or updating.
  • model switching and update operations which can include two methods:
  • the terminal performs model switching and updating operations based on the relevant parameters for AI/ML model switching and updating issued by the network device.
  • the terminal autonomously switches and updates the model according to the relevant parameters of the AI/ML model switching and updating configured by itself.
  • the specific operations of model switching and updating are:
  • Model 1 , Model 2 , ..., Model n deployed by the terminal if only one AI/ML model currently in use corresponding to Model current is deployed, first complete a certain amount of real-time sample data collection, use the real-time sample data set data to update the model parameters based on the current model parameters, until the feedback accuracy is higher than the accuracy threshold ⁇ , obtain the updated encoder model ⁇ ′ en and decoder model ⁇ ′ de , replace the current encoder model parameters with ⁇ ′ en , generate a model update instruction Indicator update , and quantize the decoder model parameters ⁇ ′ de ;
  • Model 1 , Model 2 , ..., Model n Make judgments based on the model numbers Model 1 , Model 2 , ..., Model n deployed by the terminal. If multiple groups of AI/ML models are deployed, use real-time test samples to calculate the feedback accuracy of each group of models.
  • the confirmation information includes: an Indicator complete confirmation indication that the network device has completed the model switching or updating.
  • deep neural network models are used to compress downlink CSI on the UE side and reconstruct it on the BS side.
  • the distribution characteristics of the real-time data that the model needs to process are significantly different from the distribution characteristics of the model training data, the model and data mismatch problem occurs, resulting in a significant decrease in the accuracy of CSI feedback, which in turn affects the overall performance of the large-scale MIMO system.
  • the UE can directly obtain real-time downlink CSI data through downlink channel estimation. Therefore, the real-time feedback network model performance test, switching and update on the UE side do not require the transmission of CSI training data sets from UE to BS, which helps to reduce data transmission overhead.
  • the present disclosure is dedicated to overcoming the significant performance degradation problem caused by the mismatch between the model and the data in the offline training mode, so as to improve the practical value of the channel state information feedback network based on deep learning in the actual system.
  • the present disclosure designs a terminal-triggered CSI feedback network switching and update scheme, and completes the process design of CSI feedback network switching and updating between the specific terminal and the network equipment.
  • the scheme proposed in the present disclosure can ensure the feedback accuracy of the CSI feedback network in the actual time-varying channel environment with low communication overhead and model training overhead, fully improve the adaptive ability of the feedback network model, effectively guarantee the performance of the CSI feedback network model, and improve the practicality of the feedback network model.
  • FIG5C is a flow chart of a model switching update method according to an embodiment of the present disclosure. As shown in FIG5C , the method includes:
  • Model 1 a) According to the model numbers Model 1 , Model 2 , ..., Model n deployed by the network device, if only one AI/ML model currently in use corresponding to Model current is deployed,
  • Model 1 , Model 2 , ..., Model n Make judgments based on the model numbers Model 1 , Model 2 , ..., Model n deployed by the network device. If multiple groups of AI/ML models are deployed, use real-time test samples to calculate the feedback accuracy of each group of models.
  • the confirmation information includes: Indicator complete
  • Receive model switching, update request information and related parameters sent by the network device, the information and parameters include: deactivate the current CSI feedback
  • the received model parameters are dequantized and the encoder model parameters are replaced with ⁇ ′ en .
  • the confirmation information includes: an Indicator complete confirmation indication that the terminal has completed the model switching or updating.
  • deep neural network models are used to compress downlink CSI on the UE side and reconstruct it on the BS side.
  • the distribution characteristics of the real-time data that the model needs to process are significantly different from the distribution characteristics of the model training data, the model and data mismatch problem occurs, causing a significant decrease in the accuracy of CSI feedback, which in turn affects the overall performance of the large-scale MIMO system.
  • the real-time computing resources of UE are relatively limited, while BS can better provide hardware support for the training and updating of deep neural network models.
  • the present disclosure is dedicated to overcoming the significant performance degradation problem caused by the mismatch between the model and the data in the offline training mode, so as to improve the practical value of the channel state information feedback network based on deep learning in the actual system.
  • the present disclosure designs a CSI feedback network switching and updating scheme triggered by a network device, and completes the process design of CSI feedback network switching and updating between the specific terminal and the network device.
  • the scheme proposed in the present disclosure can ensure the feedback accuracy of the CSI feedback network in the actual time-varying channel environment with low communication overhead and model training overhead, fully improve the adaptive ability of the feedback network model, effectively guarantee the performance of the CSI feedback network model, and improve the practicality of the feedback network model.
  • the embodiments of the present disclosure also propose a device for implementing any of the above methods, for example, a device is proposed, the above device includes a unit or module for implementing each step performed by the terminal in any of the above methods.
  • another device is also proposed, including a unit or module for implementing each step performed by a network device in any of the above methods.
  • the network device is, for example, an access network device, a core network function node, a core network device, etc.
  • the division of the units or modules in the above device is only a division of logical functions, which can be fully or partially integrated into one physical entity or physically separated in actual implementation.
  • the units or modules in the device can be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and instructions are stored in the memory.
  • the processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory inside the device or a memory outside the device.
  • CPU central processing unit
  • microprocessor a microprocessor
  • the units or modules in the device may be implemented in the form of hardware circuits, and the functions of some or all of the units or modules may be implemented by designing the hardware circuits.
  • the hardware circuits may be understood as one or more processors; for example, in one implementation, the hardware circuits are application-specific integrated circuits (ASICs), and the functions of some or all of the above units or modules may be implemented by designing the logical relationship of the components in the circuits; for another example, in another implementation, the hardware circuits may be implemented by programmable logic devices (PLDs), and Field Programmable Gate Arrays (FPGAs) may be used as an example, which may include a large number of logic gate circuits, and the connection relationship between the logic gate circuits may be configured by configuring the configuration files, thereby implementing the functions of some or all of the above units or modules. All units or modules of the above devices may be implemented in the form of software called by the processor, or in the form of hardware circuits, or in the form of software called by the processor, and the remaining part may be implemented in
  • the processor is a circuit with signal processing capability.
  • the processor may be a circuit with instruction reading and execution capability, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which may be understood as a microprocessor), or a digital signal processor (DSP); in another implementation, the processor may implement certain functions through the logical relationship of a hardware circuit, and the logical relationship of the above hardware circuit may be fixed or reconfigurable, such as a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA.
  • ASIC application-specific integrated circuit
  • PLD programmable logic device
  • the process of the processor loading a configuration document to implement the hardware circuit configuration may be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules.
  • it may also be a hardware circuit designed for artificial intelligence, which may be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DLPU), or a computer programmable logic device (CLP). Learning Processing Unit (DPU), etc.
  • NPU neural network processing unit
  • TPU tensor processing unit
  • DLPU deep learning processing unit
  • CLP computer programmable logic device
  • DPU learning Processing Unit
  • FIG6A is a schematic diagram of the structure of the first device proposed in an embodiment of the present disclosure. As shown in FIG6A, it includes: a processing module, which is used to perform performance monitoring on the first model currently used by the first device and/or the second model currently used by the second device; the first model corresponds to the second model, and the first model and the second model are used to realize the compression and reconstruction of the channel state information CSI; the processing module is also used to determine that the model switching update is required when the performance monitoring result meets the first preset condition; the processing module is also used to determine at least one of the target first model, the first model update parameter, the target second model, and the second model update parameter; a sending module is used to perform the model switching update and send indication information to the second device, and the indication information indicates the target second model and/or the second model update parameter.
  • a processing module which is used to perform performance monitoring on the first model currently used by the first device and/or the second model currently used by the second device
  • the first model corresponds to the second model
  • the above-mentioned processing module is used to perform the steps related to "processing” performed in any of the above methods
  • the above-mentioned sending module is used to perform the steps related to "sending” performed in any of the above methods.
  • the above-mentioned first device may also include a receiving module, and the above-mentioned receiving module is used to perform the steps related to "receiving" performed in any of the above methods, which will not be repeated here.
  • Figure 6B is a schematic diagram of the structure of the network device proposed in the embodiment of the present disclosure. As shown in Figure 6B, it includes: a receiving module, which is used for the second device to receive the indication information sent by the first device, and the indication information indicates the target second model and/or the second model update parameter; a processing module, which is used for the second device to perform model switching update based on the indication information.
  • the above-mentioned receiving module is used to execute the steps related to "receiving" executed in any of the above methods
  • the above-mentioned processing module is used to execute the steps related to "processing" executed in any of the above methods.
  • the above-mentioned first device may also include a sending module, and the above-mentioned sending module is used to execute the steps related to "sending" executed in any of the above methods, which will not be repeated here.
  • FIG7A is a schematic diagram of the structure of a communication device 7100 proposed in an embodiment of the present disclosure.
  • the communication device 7100 may be a network device (e.g., an access network device, a core network device, etc.), or a terminal (e.g., a user device, etc.), or a chip, a chip system, or a processor that supports a network device to implement any of the above methods, or a chip, a chip system, or a processor that supports a terminal to implement any of the above methods.
  • the communication device 7100 may be used to implement the method described in the above method embodiment, and the details may refer to the description in the above method embodiment.
  • the communication device 7100 includes one or more processors 7101.
  • the processor 7101 may be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit.
  • the baseband processor may be used to process the communication protocol and the communication data
  • the central processing unit may be used to control the communication device (such as a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute a program, and process the data of the program.
  • the processor 7101 is used to call instructions so that the communication device 7100 executes any of the above methods.
  • the communication device 7100 further includes one or more memories 7102 for storing instructions.
  • the memory 7102 may also be outside the communication device 7100.
  • the transceiver may include a receiver and a transmitter, and the receiver and the transmitter may be separate or integrated.
  • the terms such as transceiver, transceiver unit, transceiver, transceiver circuit, etc. may be replaced with each other, the terms such as transmitter, transmission unit, transmitter, transmission circuit, etc. may be replaced with each other, and the terms such as receiver, receiving unit, receiver, receiving circuit, etc. may be replaced with each other.
  • the communication device 7100 further includes one or more interface circuits 7104, which are connected to the memory 7102.
  • the interface circuit 7104 can be used to receive signals from the memory 7102 or other devices, and can be used to send signals to the memory 7102 or other devices.
  • the interface circuit 7104 can read instructions stored in the memory 7102 and send the instructions to the processor 7101.
  • the communication device 7100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 7100 described in the present disclosure is not limited thereto, and the structure of the communication device 7100 may not be limited by FIG. 7a.
  • the communication device may be an independent device or may be part of a larger device.
  • the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, a vehicle-mounted device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.
  • FIG. 7B is a schematic diagram of the structure of a chip 7200 provided in an embodiment of the present disclosure.
  • the communication device 7100 may be a chip or a chip system
  • the chip 7200 includes one or more processors 7201, and the processor 7201 is used to call instructions so that the chip 7200 executes any of the above methods.
  • the chip 7200 further includes one or more interface circuits 7202, which are connected to the memory 7203.
  • the interface circuit 7202 can be used to receive signals from the memory 7203 or other devices, and the interface circuit 7202 can be used to send signals to the memory 7203 or other devices.
  • the interface circuit 7202 can read instructions stored in the memory 7203 and send the instructions to the processor 7201.
  • the terms such as interface circuit, interface, transceiver pin, and transceiver can be replaced with each other.
  • the chip 7200 further includes one or more memories 7203 for storing instructions.
  • the memory 7203 may be outside the chip 7200.
  • the present disclosure also proposes a storage medium, on which instructions are stored, and when the instructions are executed on the communication device 7100, the communication device 7100 executes any of the above methods.
  • the storage medium is an electronic storage medium.
  • the storage medium is a computer-readable storage medium, but is not limited to this, and it can also be a storage medium readable by other devices.
  • the storage medium can be a non-transitory storage medium, but is not limited to this, and it can also be a temporary storage medium.
  • the present disclosure also proposes a program product, which, when executed by the communication device 7100, enables the communication device 7100 to execute any of the above methods.
  • the program product is a computer program product.
  • the present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to execute any one of the above methods.
  • the available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a high-density digital video disc (DVD)), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.
  • a magnetic medium e.g., a floppy disk, a hard disk, a magnetic tape
  • an optical medium e.g., a high-density digital video disc (DVD)
  • DVD high-density digital video disc
  • SSD solid state disk

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

Abstract

La présente divulgation concerne un procédé et un appareil de mise à jour de commutation de modèle. Le procédé comprend : la réalisation, par un premier dispositif, d'une surveillance de performance sur un premier modèle actuellement utilisé par le premier dispositif et/ou un second modèle actuellement utilisé par un second dispositif ; si un résultat de surveillance de performance satisfait une première condition prédéfinie, la détermination qu'une mise à jour de commutation de modèle doit être réalisée ; la détermination, par le premier dispositif, d'au moins l'un d'un premier modèle cible, d'un premier paramètre de mise à jour de modèle, d'un second modèle cible et d'un second paramètre de mise à jour de modèle ; la réalisation, par le premier dispositif, d'une mise à jour de commutation de modèle et l'envoi des informations d'indication au second dispositif, les informations d'indication indiquant le second modèle cible et/ou le second paramètre de mise à jour de modèle. Selon le procédé de la présente divulgation, des modèles utilisés en temps réel par le premier dispositif et/ou le second dispositif peuvent correspondre à un scénario de canal actuel ou à des caractéristiques de distribution de données, et la précision de rétroaction de CSI est améliorée.
PCT/CN2023/107598 2023-07-14 2023-07-14 Procédé et appareil de mise à jour de commutation de modèle Pending WO2025015484A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114844785A (zh) * 2021-02-01 2022-08-02 大唐移动通信设备有限公司 通信系统中的模型更新方法、装置及存储介质
WO2023015499A1 (fr) * 2021-08-11 2023-02-16 Oppo广东移动通信有限公司 Procédé et dispositif de communication sans fil
CN116033456A (zh) * 2022-12-20 2023-04-28 京信网络系统股份有限公司 压缩模型更新方法、装置、系统和存储介质
WO2023092249A1 (fr) * 2021-11-23 2023-06-01 Zte Corporation Systèmes et procédés de gestion de modèle

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114844785A (zh) * 2021-02-01 2022-08-02 大唐移动通信设备有限公司 通信系统中的模型更新方法、装置及存储介质
WO2023015499A1 (fr) * 2021-08-11 2023-02-16 Oppo广东移动通信有限公司 Procédé et dispositif de communication sans fil
WO2023092249A1 (fr) * 2021-11-23 2023-06-01 Zte Corporation Systèmes et procédés de gestion de modèle
CN116033456A (zh) * 2022-12-20 2023-04-28 京信网络系统股份有限公司 压缩模型更新方法、装置、系统和存储介质

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