[go: up one dir, main page]

WO2025166573A1 - Procédé et dispositif de communication et support d'enregistrement - Google Patents

Procédé et dispositif de communication et support d'enregistrement

Info

Publication number
WO2025166573A1
WO2025166573A1 PCT/CN2024/076448 CN2024076448W WO2025166573A1 WO 2025166573 A1 WO2025166573 A1 WO 2025166573A1 CN 2024076448 W CN2024076448 W CN 2024076448W WO 2025166573 A1 WO2025166573 A1 WO 2025166573A1
Authority
WO
WIPO (PCT)
Prior art keywords
information
model
indication information
function
terminal device
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/CN2024/076448
Other languages
English (en)
Chinese (zh)
Inventor
刘正宣
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Xiaomi Mobile Software Co Ltd
Original Assignee
Beijing Xiaomi Mobile Software Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Xiaomi Mobile Software Co Ltd filed Critical Beijing Xiaomi Mobile Software Co Ltd
Priority to CN202480024889.9A priority Critical patent/CN121002913A/zh
Priority to PCT/CN2024/076448 priority patent/WO2025166573A1/fr
Publication of WO2025166573A1 publication Critical patent/WO2025166573A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W8/00—Network data management
    • H04W8/22—Processing or transfer of terminal data, e.g. status or physical capabilities
    • H04W8/24—Transfer of terminal data

Definitions

  • the present disclosure relates to the field of communication technology, and in particular to a communication method, device, and storage medium.
  • Machine learning algorithms are currently one of the most important implementation methods of artificial intelligence (AI) technology. By applying machine learning to large amounts of training data, AI models can be generated, which can then be used to predict events. Multiple AI models with different parameter configurations can be trained in a single scenario, and the terminal device needs to select the best performing AI model from among these models for prediction.
  • AI artificial intelligence
  • the embodiments of the present disclosure provide a communication method, a device, and a storage medium.
  • a communication method which is performed by a terminal device.
  • the method includes:
  • a first AI model or a first AI function is determined from a plurality of AI models or AI functions based on first information, wherein the first information includes information associated with parameters for training the first AI model or the first AI function.
  • a communication method is provided, which is performed by a network device.
  • the method includes:
  • Sending fourth information to the terminal device where the fourth information is used by the terminal device to determine a first AI model or a first AI function from a plurality of AI models or AI functions, the fourth information including information associated with the network device in the first information, and the first information including information associated with parameters for training the first AI model or the first AI function.
  • a terminal device including:
  • a processing module is configured to determine a first AI model or a first AI function from a plurality of AI models or AI functions based on first information, wherein the first information includes information associated with parameters for training the first AI model or the first AI function.
  • a network device including:
  • the transceiver module is configured to send fourth information to the terminal device, where the fourth information is used by the terminal device to determine a first AI model or a first AI function from a plurality of AI models or AI functions, the fourth information including information associated with the network device in the first information, and the first information including information associated with parameters for training the first AI model or the first AI function.
  • a communication system which may include: a terminal device and a network device; wherein the terminal device is configured to execute the method described in the optional implementation manner of the first aspect, and the network device is configured to execute the method described in the optional implementation manner of the second aspect.
  • a storage medium which stores instructions.
  • the communication device executes the method described in the optional implementation of the first aspect or the second aspect.
  • a first AI model or a first AI function is determined from a plurality of AI models or AI functions based on first information, wherein the first information includes information associated with parameters for training the first AI model or the first AI function.
  • a terminal device can select a model based on the first information, eliminating the need to monitor each model, thereby reducing the computational complexity of the terminal device and eliminating the need to send monitoring data to a network device, thereby reducing data transmission overhead and improving network performance.
  • FIG1A is a schematic diagram showing the architecture of a communication system according to an embodiment of the present disclosure.
  • FIG1B is a schematic diagram showing an observation window and a prediction window according to an embodiment of the present disclosure.
  • FIG2C is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure.
  • FIG3D is a flow chart illustrating a communication method according to an embodiment of the present disclosure.
  • FIG3F is a flow chart illustrating a communication method according to an embodiment of the present disclosure.
  • FIG4A is a flow chart showing a communication method according to an embodiment of the present disclosure.
  • FIG4B is a flow chart showing a communication method according to an embodiment of the present disclosure.
  • FIG5 is an interactive schematic diagram illustrating a communication method according to an embodiment of the present disclosure.
  • FIG6A is a schematic structural diagram of a terminal device proposed in an embodiment of the present disclosure.
  • FIG6B is a schematic structural diagram of a network device proposed in an embodiment of the present disclosure.
  • FIG7A is a schematic structural diagram of a communication device proposed in an embodiment of the present disclosure.
  • a first AI model or a first AI function is determined from a plurality of AI models or AI functions based on first information, wherein the first information includes information associated with parameters for training the first AI model or the first AI function.
  • the first information includes at least one of the following:
  • first indication information where the first indication information is used to indicate channel characteristics
  • the fourth indication information is used to indicate the parameters configured by the network device for the terminal device.
  • the terminal device can select a model according to a variety of indication information, thereby improving the flexibility of model selection.
  • the fifth indication information includes at least one of the following: the second indication information, the third indication information, and the fourth indication information.
  • the terminal device can select a model according to the first indication information and the fifth indication information, thereby improving the accuracy of the model selection.
  • determining the first AI model or the first AI function from the plurality of AI models or AI functions according to the first information includes:
  • the terminal device can select a model according to the third indication information and the sixth indication information, thereby improving the accuracy of the model selection.
  • the terminal device can select a model based on the third indication information, the second indication information, and the fourth indication information, thereby further improving the accuracy of the model selection.
  • the method further includes:
  • the network device may send the fourth information to the terminal device so that the terminal device selects a model according to the fourth information.
  • receiving third information sent by the network device includes:
  • Seventh indication information where the seventh indication information is used to indicate a fifth AI model or a fifth AI function, where the fifth AI model or the fifth AI function includes an AI model or AI function that can be used by the terminal device.
  • the terminal device can report different indication information to the network device so that the network device can determine the fourth indication information, making the determination method of the fourth indication information more flexible and accurate.
  • receiving fourth information sent by the network device includes:
  • the terminal device may receive the third indication information sent by the network device, so as to perform model selection according to the third indication information.
  • different channel characteristics can correspond to at least one AI model or AI function, so that the most suitable AI model or AI function can be selected, thereby improving the accuracy of model prediction.
  • an embodiment of the present disclosure provides a communication method, which is performed by a network device.
  • the method includes:
  • Sending fourth information to the terminal device where the fourth information is used by the terminal device to determine a first AI model or a first AI function from a plurality of AI models or AI functions, the fourth information including information associated with the network device in the first information, and the first information including information associated with parameters for training the first AI model or the first AI function.
  • sending the fourth information to the terminal device includes:
  • the method further includes:
  • the third information is sent to the terminal device via a first signaling, where the first signaling includes at least one of the following: a radio resource control RRC message, a media access control control element MAC-CE, and downlink control information DCI.
  • the first signaling includes at least one of the following: a radio resource control RRC message, a media access control control element MAC-CE, and downlink control information DCI.
  • receiving the second information reported by the terminal device includes:
  • the second signaling includes at least one of the following: RRC, MAC-CE, uplink control information UCI.
  • Seventh indication information where the seventh indication information is used to indicate a fifth AI model or a fifth AI function, where the fifth AI model or the fifth AI function includes an AI model or AI function that can be used by the terminal device.
  • the network device sends fourth information to the terminal device, where the fourth information includes information associated with the network device in the first information, where the first information includes information associated with parameters for training the first AI model or the first AI function;
  • the terminal device determines a first AI model or a first AI function from a plurality of AI models or AI functions according to the first information.
  • an embodiment of the present disclosure proposes a storage medium storing instructions, which, when executed on a communication device, enables the communication device to execute the method described in the optional implementation of the first aspect or the second aspect.
  • the present disclosure provides a communication method, device, and storage medium.
  • the terms communication method and information processing method are interchangeable; the terms communication device and information processing device are interchangeable; and the terms communication system and information processing system are interchangeable.
  • the terms "at least one of”, “one or more”, “a plurality of”, “multiple”, etc. can be used interchangeably.
  • descriptions such as “at least one of A and B,” “A and/or B,” “A in one case, B in another case,” or “in response to one case A, in response to another case B” may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); and in some embodiments, A and B (both A and B are executed). The above is also applicable when there are more branches such as A, B, and C.
  • 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 “first device” as an example, the number of "devices" can be one or more.
  • the objects modified by different prefixes can be the same or different.
  • terms such as “in response to", “in response to determining", “in the case of", “at the time of", “when!, “if", “if", etc. can be used interchangeably.
  • devices and the like can be interpreted as physical or virtual, and their names are not limited to those described in the embodiments.
  • Terms such as “device,” “equipment,” “device,” “circuit,” “network element,” “node,” “function,” “unit,” “section,” “system,” “network,” “chip,” “chip system,” “entity,” and “subject” can be used interchangeably.
  • network can be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).
  • the terminal device 101 may include at least one of a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer, 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, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in smart grid, a wireless terminal device in transportation safety, a wireless terminal device in smart city, and a wireless terminal device in 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, 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, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in smart
  • the technical solution of the present disclosure may be applicable to the Open RAN architecture.
  • the interfaces between or within the 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.
  • LTE Long Term Evolution
  • LTE-A LTE-Advanced
  • LTE-B LTE-Beyond
  • SUPER 3G IMT-Advanced
  • 4G 4th generation mobile communication system
  • 5G 5th generation mobile communication system
  • 5G 5G New Radio
  • FAA New Radio Access Technology
  • RAT New Radio
  • NR New Radio Access
  • NX New Radio Access
  • FX Future generation radio access
  • GSM Global System for Mobile communications
  • GSM registered trademark
  • CDMA2000 Ultra Mobile Broadband
  • 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), Public Land Mobile Network (PLMN) network
  • D2D Device-to-Device
  • M2M Machine-to-Machine
  • IoT Vehicle-to-Everything
  • the Rel-18 Type II codebook uses a traditional autoregressive or linear minimum mean squares error (LMMSE) algorithm to predict future downlink channel information based on historical downlink channel information estimated by the terminal. The precoding information corresponding to the future time is then calculated based on the predicted downlink channel information.
  • LMMSE linear minimum mean squares error
  • the Rel-18 Type II codebook can significantly improve system performance compared to the Rel-16/17 Type II codebook.
  • AI has been widely applied to the physical layer of wireless communications.
  • the aforementioned traditional CSI prediction algorithm can also predict future channel information through AI model reasoning.
  • Current simulation evaluations show that AI-based prediction performance outperforms traditional non-AI algorithms.
  • CSI at multiple historical moments is required.
  • Downlink channel information is estimated based on Channel Status Information-Reference Signals (CSI-RS) sent at multiple historical moments.
  • This range (the time range corresponding to multiple historical moments) is called the observation window.
  • the range (the time range corresponding to multiple future moments) within which CSI at multiple future moments is predicted is called the prediction window.
  • Figure 1B is a schematic diagram of an observation window and a prediction window according to an embodiment of the present disclosure. As shown in Figure 1B, three observation windows and prediction windows under different parameter configurations are listed.
  • the length of the prediction window, w d K ⁇ D.
  • observation window and prediction window in FIG1B are for illustration only, and the values of the four parameters N, M, K, and D can be arbitrarily combined, which is not limited in the embodiment of the present disclosure.
  • the AI function or AI model on the UE side can realize function or model identification between the UE and the network (Network, NW) through corresponding function identification or model identification methods.
  • function identification can be achieved by the UE reporting its supported functions to the NW through capability reporting.
  • a function supported by the UE may include one or more artificial intelligence/machine learning (AI/ML) models.
  • Model IDs can also be used in life cycle management (LCM) based on function identification.
  • model identification can be achieved by NW identifying the models supported by UE through Model ID, and UE can indicate the supported models to NW through Model ID.
  • model identification must be completed before model inference.
  • UE-side model identification includes the following types:
  • the corresponding model can be assigned a corresponding Model ID.
  • Model recognition is achieved through air interface signaling, which includes the following two methods:
  • Method 2 The NW actively initiates model identification, and the UE can assist in completing the remaining steps of model identification.
  • the corresponding model can be assigned a corresponding Model ID.
  • Performance monitoring of the CSI prediction model on the UE side includes the following three methods:
  • the UE calculates the performance criteria of the AI model based on the measured channel information to monitor the model performance.
  • the UE reports the measured channel information to the NW, allowing the NW to calculate the performance criteria of the AI model to monitor the model performance.
  • the UE calculates the performance criteria of the AI model based on the measured channel information and reports the performance criteria calculation results to the NW so that the NW can monitor the performance of the AI model. Test model performance.
  • AI/ML model performance monitoring based on the UE side and/or NW side is a method for selecting a model, but this method may result in high computational complexity of the UE or high data transmission overhead (a method of hybrid monitoring on the UE side and the NW side).
  • the UE needs to traverse and monitor each AI model and calculate the performance indicators of each AI model.
  • the computational complexity of the UE will increase exponentially with the number of AI models on the UE side. Therefore, how the UE selects an AI model or AI function becomes an urgent problem to be solved.
  • FIG2A is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure.
  • the method may be executed by the above-mentioned communication system. As shown in FIG2A , the method may include:
  • Step S2101 The network device sends third information to the terminal device.
  • the terminal device may receive the third message.
  • the terminal device may receive the third message sent by the network device.
  • the terminal device may also receive the third message sent by another entity.
  • the second information may be used by the network device to determine the fourth indication information.
  • the fourth indication information can be used to indicate the parameters configured by the network device for the terminal device.
  • the parameters configured by the network device for the terminal device may include one or more of N, M, K, and D, wherein the definitions of N, M, K, and D can refer to the description of Figure 1B and will not be repeated here.
  • the second information may include at least one of the following:
  • the seventh indication information may be used to indicate a fifth AI model or a fifth AI function, where the fifth AI model or the fifth AI function includes an AI model or AI function that can be used by the terminal device.
  • the second indication information can be used to indicate additional conditions corresponding to the terminal device.
  • the additional conditions corresponding to the terminal device may be hardware parameters such as memory and power.
  • the first indication information may be an amplitude value or an amplitude range of TDCP, for example, the first indication information may be TDCP of 0.5, or TDCP of 0.26 to 0.5.
  • the second indication information may be a power value of 20%.
  • the fifth AI model may be an AI model that can be used at the current moment among multiple AI models supported by the terminal device
  • the fifth AI function may be an AI function that can be used at the current moment among multiple AI functions supported by the terminal device.
  • the seventh indication information may include a fifth AI model and a fifth AI function
  • the seventh indication information may include a model identifier of the fifth AI model or a function identifier of the fifth AI function.
  • Different AI models correspond to different model identifiers
  • different AI functions correspond to different function identifiers.
  • the AI model may be an AI model for CSI prediction.
  • the network device may send the third information to the terminal device via first signaling.
  • the first signaling may include at least one of the following: Radio Resource Control (RRC) message, Medium Access Control Control Element (MAC-CE), and Downlink Control Information (DCI).
  • RRC Radio Resource Control
  • MAC-CE Medium Access Control Control Element
  • DCI Downlink Control Information
  • Step S2102 The terminal device reports the second information to the network device.
  • the network device may receive the second message.
  • the network device may receive the second information reported by the terminal device.
  • the network device may also receive the second information reported by another entity.
  • the terminal device may report the second information to the network device through a second signaling, and the second signaling may include at least one of the following: RRC, MAC-CE, and uplink control information (Uplink Control Information, UCI).
  • RRC Radio Resource Control
  • MAC-CE Radio Resource Control Control
  • UCI Uplink Control Information
  • the terminal device may report the second information to the network device via an RRC message.
  • the terminal device may report the second information to the network device via MAC-CE.
  • the terminal device may report the second information to the network device via UCI.
  • the network device may determine the fourth indication information based on the first indication information.
  • the value or value range of TDCP determines at least one of the parameters N, M, K, and D.
  • the network device may determine the fourth indication information based on the second indication information. For example, the network device may determine at least one of the parameters N, M, K, and D based on the remaining memory or remaining power of the terminal device.
  • the network device may also determine the fourth indication information according to multiple of the first indication information, the second indication information, and the seventh indication information.
  • Step S2104 The network device sends fourth indication information to the terminal device.
  • the terminal device may receive the fourth indication information.
  • the terminal device may receive the fourth indication information sent by the network device.
  • the terminal device may also receive the fourth indication information sent by another entity.
  • Step S2105 The network device sends third indication information to the terminal device.
  • the third indication information may be used to indicate additional conditions corresponding to the network device.
  • the additional conditions corresponding to the network device may be channel scenarios, data sets, etc. related to training AI models or AI functions.
  • Step S2106 The terminal device determines at least one third AI model or third AI function from the multiple AI models or AI functions according to the third indication information.
  • multiple AI models or AI functions may be AI models or AI functions supported by the terminal device.
  • the AI model supported by the terminal device may be an AI model identified through model recognition.
  • the specific method of model recognition can refer to the above description and will not be repeated here.
  • the AI function supported by the terminal device may be an AI function identified through function.
  • the specific method of function identification can refer to the above description and will not be repeated here.
  • multiple AI models or AI functions may be AI models or AI functions deployed on the terminal device side.
  • each third indication information may correspond to at least one AI model or AI function.
  • the additional condition corresponding to the network device is the channel scenario.
  • Table 1 shows the AI models under different channel scenarios. As shown in Table 1, multiple AI models include A0, B0, C0, A1, B1, C1, A2, B2, and C2. Each channel scenario corresponds to three AI models, among which the AI models corresponding to the UMa scenario include A0, B0, and C0, the AI models corresponding to the UMi scenario include A1, B1, and C1, and the AI models corresponding to the indoor scenario include A2, B2, and C2. If the third indication information is UMa, the terminal device can determine that A0, B0, and C0 are the third AI model from A0, B0, C0, A1, B1, C1, A2, B2, and C2.
  • Step S2107 The terminal device determines the first AI model or the first AI function from at least one third AI model or third AI function according to the fourth indication information.
  • the terminal device may determine an AI model that matches the fourth indication information from at least one third AI model as the first AI model.
  • the terminal device may determine an AI function matching the fourth indication information from at least one third AI function as the first AI function.
  • step S2106 can be omitted, and the third AI model or the third AI function can be used as the first AI model or the first AI function.
  • the third AI model can be used as the first AI model
  • the third AI function can be used as the first AI function.
  • the terminal device can select a model based on the third indication information and the fourth indication information, without the need to monitor each model, thereby reducing the computational complexity of the terminal device and eliminating the need to send monitoring data to the network device, thereby reducing the data transmission overhead and improving network performance.
  • step S2101 can be implemented as an independent embodiment
  • step S2102 can be implemented as an independent embodiment
  • step S2103 can be implemented as an independent embodiment
  • step S2104 can be implemented as an independent embodiment
  • step S2105 can be implemented as an independent embodiment
  • step S2101 + step S2102 can be implemented as an independent embodiment
  • step S2102 + step S2103 can be implemented as an independent embodiment
  • step S2103 + step S2104 can be implemented as an independent embodiment
  • step S2106 + step S2107 can be implemented as an independent embodiment
  • step S2104 + step S2105 + step S2106 + step S2107 can be implemented as independent embodiments, but the present invention is not limited thereto.
  • the above steps S2101 to S2107 can be executed in a swapped order or simultaneously.
  • the above steps S2101 to S2107 are all optional steps.
  • FIG2B is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure.
  • the method may be executed by the above-mentioned communication system. As shown in FIG2B , the method may include:
  • Step S2201 The network device sends third information to the terminal device.
  • step S2201 can refer to the optional implementation of step S2101 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.
  • step S3102 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.
  • the terminal device can obtain the fourth indication information from the upper layer(s).
  • the terminal device may perform processing to obtain the fourth indication information.
  • step S3104 can refer to the optional implementation of step S2105 in FIG2A and other related parts in the embodiment involved in FIG2A , which will not be described in detail here.
  • the terminal device may obtain third indication information specified by the protocol.
  • the terminal device can obtain third indication information from upper layer(s).
  • the terminal device may perform processing to obtain the third indication information.
  • step S3106 can refer to the optional implementation of step S2107 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.
  • Step S3201 Obtain third information.
  • step S3201 can be found in step S2101 of FIG. 2A , the optional implementation of step S3101 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be repeated here.
  • Step S3202 Report the second information.
  • step S3202 can be found in step S2102 of FIG. 2A , the optional implementation of step S3102 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be described in detail here.
  • Step S3203 Obtain fourth indication information.
  • step S3204 can be found in step S2105 of FIG. 2A , the optional implementation of step S3104 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be described in detail here.
  • Step S3205 Determine multiple third AI models or third AI functions from the multiple AI models or AI functions according to the third indication information.
  • step S3205 can be found in step S2106 of FIG. 2A , the optional implementation of step S3105 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be described in detail here.
  • Step S3207 Determine the first AI model or the first AI function from at least one fourth AI model or fourth AI function according to the fourth indication information.
  • the above steps S3201 to S3207 can be executed in a swapped order or simultaneously.
  • the above steps S3201 to S3207 are all optional steps.
  • FIG3C is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3C , the embodiment of the present disclosure relates to a communication method, which can be executed by a terminal device. The method may include:
  • Step S3301 Obtain third information.
  • step S3301 can be found in step S2101 of FIG. 2A , the optional implementation of step S3101 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be repeated here.
  • Step S3302 Report the second information.
  • step S3302 can be found in step S2102 of FIG. 2A , the optional implementation of step S3102 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be repeated here.
  • Step S3303 Obtain fourth indication information.
  • step S3303 can be found in step S2104 of FIG. 2A , the optional implementation of step S3103 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be described in detail here.
  • Step S3304 Obtain third indication information.
  • step S3304 can be found in step S2105 of FIG. 2A , the optional implementation of step S3104 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be described in detail here.
  • Step S3305 Determine multiple third AI models or third AI functions from the multiple AI models or AI functions according to the third indication information.
  • step S3305 can be found in step S2106 of FIG. 2A , the optional implementation of step S3105 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be described in detail here.
  • Step S3306 Determine a first AI model or a first AI function from at least one third AI model or a third AI function according to the sixth indication information.
  • step S3306 can refer to the optional implementation of step S2107 in Figure 2A, steps S2207 to S2208 in Figure 2B, and other related parts in the embodiments involved in Figures 2A and 2B, which will not be repeated here.
  • the sixth indication information may include at least one of the following: the second indication information and the fourth indication information.
  • the terminal device may determine the first AI model or the first AI function from at least one third AI model or third AI function based on the second indication information.
  • the terminal device may determine the first AI model or the first AI function from at least one third AI model or third AI function based on the fourth indication information.
  • the terminal device may determine the first AI model or the first AI function from at least one third AI model or third AI function based on the second indication information and the fourth indication information.
  • the terminal device may first determine at least one eighth AI model or eighth AI function from at least one third AI model or third AI function based on the second indication information, and then determine the first AI model or first AI function from at least one eighth AI model or eighth AI function based on the fourth indication information.
  • the eighth AI model or eighth AI function determined according to the second indication information includes one, then the eighth AI model or eighth AI function can be used as the first AI model or first AI function.
  • the terminal device may first determine at least one ninth AI model or ninth AI function from at least one third AI model or third AI function according to the fourth indication information, and then determine the first AI model or first AI function from at least one ninth AI model or ninth AI function according to the second indication information.
  • the ninth AI model or ninth AI function determined according to the fourth indication information includes one, then the ninth AI model or ninth AI function can be used as the first AI model or first AI function.
  • step S3301 can be implemented as an independent embodiment
  • step S3302 can be implemented as an independent embodiment
  • step S3303 can be implemented as an independent embodiment
  • step S3304 can be implemented as an independent embodiment
  • step S3302 + step S3303 can be implemented as an independent embodiment
  • step S3305 + step S3306 can be implemented as an independent embodiment
  • step S3303 + step S3304 + step S3305 + step S3306 can be implemented as independent embodiments, but the present invention is not limited thereto.
  • the above steps S3301 to S3306 can be executed in a swapped order or simultaneously.
  • the above steps S3301 to S3306 are all optional steps.
  • FIG3D is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3D , the embodiment of the present disclosure relates to a communication method, which can be executed by a terminal device. The method may include:
  • Step S3401 Obtain fourth information.
  • step S3401 can refer to the optional implementation of step S2301 in Figure 2C and other related parts in the embodiment involved in Figure 2C, which will not be repeated here.
  • the terminal device may receive the fourth information sent by the network device, but is not limited thereto.
  • the terminal device may also receive the fourth information sent by other entities.
  • the terminal device may obtain fourth information specified by the protocol.
  • the terminal device can obtain the fourth information from the upper layer(s).
  • the terminal device may perform processing to obtain the fourth information.
  • step S3401 may be omitted, and the terminal device may autonomously implement the function indicated by the fourth information, or the above function may be default or by default.
  • Step S3402 Determine at least one second AI model or second AI function from multiple AI models or AI functions based on the first indication information.
  • step S3402 can refer to the optional implementation of step S2302 in Figure 2C and other related parts in the embodiment involved in Figure 2C, which will not be repeated here.
  • Step S3403 Determine a first AI model or a first AI function from at least one second AI model or a second AI function according to the fifth indication information.
  • step S3403 can refer to the optional implementation of step S2303 in Figure 2C and other related parts in the embodiment involved in Figure 2C, which will not be repeated here.
  • step S3401 can be implemented as an independent embodiment
  • step S3402 can be implemented as an independent embodiment
  • step S3403 can be implemented as an independent embodiment
  • step S3402 + step S3403 can be implemented as independent embodiments, but the present invention is not limited thereto.
  • the above steps S3401 to S3403 can be executed in a swapped order or simultaneously.
  • the above steps S3401 to S3403 are all optional steps.
  • FIG3E is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3E , the embodiment of the present disclosure relates to a communication method, which can be executed by a terminal device. The method may include:
  • step S3501 can refer to the optional implementation of step S2301 in Figure 2C and other related parts in the embodiment involved in Figure 2C, which will not be repeated here.
  • Step S3502 Determine a first AI model or a first AI function from multiple AI models or AI functions based on the first information.
  • step S3502 For optional implementations of step S3502, reference may be made to steps S2106 to S2107 of FIG. 2A , steps S2206 to S2208 of FIG. 2B , steps S2302 to S2303 of FIG. 2C , steps S3105 to S3106 of FIG. 3A , steps S3205 to S3207 of FIG. 3B , steps S3305 to S3306 of FIG. 3C , and optional implementations of steps S3402 to S3403 of FIG. 3D , as well as other related parts in the embodiments involved in FIG. 2A , FIG. 2B , FIG. 2C , FIG. 3A , FIG. 3B , FIG. 3C , and FIG. 3D , which will not be repeated here.
  • the above steps S3501 to S3502 can be executed in an interchangeable order or simultaneously.
  • the above steps S3501 to S3502 are all optional steps.
  • FIG3F is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3F , the embodiment of the present disclosure relates to a communication method, which can be executed by a terminal device. The method may include:
  • Step S3601 Determine a first AI model or a first AI function from multiple AI models or AI functions based on first information.
  • step S3601 For optional implementations of step S3601, reference may be made to steps S2106 to S2107 of FIG. 2A , steps S2206 to S2208 of FIG. 2B , steps S2302 to S2303 of FIG. 2C , steps S3105 to S3106 of FIG. 3A , steps S3205 to S3207 of FIG. 3B , steps S3305 to S3306 of FIG. 3C , and optional implementations of steps S3402 to S3403 of FIG. 3D , as well as other related parts in the embodiments involved in FIG. 2A , FIG. 2B , FIG. 2C , FIG. 3A , FIG. 3B , FIG. 3C , and FIG. 3D , which will not be repeated here.
  • the terminal device may determine a first AI model or a first AI function from a plurality of AI models or AI functions based on the second indication information.
  • the terminal device may determine a first AI model or a first AI function from a plurality of AI models or AI functions based on the third indication information.
  • the terminal device may determine a first AI model or a first AI function from a plurality of AI models or AI functions based on the fourth indication information.
  • the terminal device can determine the first AI model or the first AI function from multiple AI models or AI functions based on any one or more indication information in the first information, and the embodiments of the present disclosure are not limited to this.
  • the first information includes at least one of the following:
  • first indication information where the first indication information is used to indicate channel characteristics
  • Second indication information where the second indication information is used to indicate an additional condition corresponding to the terminal device
  • the fourth indication information is used to indicate the parameters configured by the network device for the terminal device.
  • determining the first AI model or the first AI function from the plurality of AI models or AI functions according to the first information includes:
  • the first AI model or the first AI function is determined from the at least one third AI model or third AI function according to sixth indication information, where the sixth indication information includes at least one of the following: the second indication information and the fourth indication information.
  • determining, according to the sixth indication information, the first AI model or the first AI function from the at least one third AI model or third AI function includes:
  • the third AI model or the third AI function includes a plurality of third AI models or third AI functions, and determining, according to the sixth indication information, the first AI model or the first AI function from the at least one third AI model or third AI function includes:
  • the receiving third information sent by the network device includes:
  • the third information is received by the network device through the first signaling, where the first signaling includes at least one of the following: a radio resource control RRC message, a media access control control element MAC-CE, and downlink control information DCI.
  • the first signaling includes at least one of the following: a radio resource control RRC message, a media access control control element MAC-CE, and downlink control information DCI.
  • the second information is reported to the network device through a second signaling, where the second signaling includes at least one of the following: RRC, MAC-CE, and uplink control information UCI.
  • the second information includes at least one of the following:
  • Seventh indication information where the seventh indication information is used to indicate a fifth AI model or a fifth AI function, where the fifth AI model or the fifth AI function includes an AI model or AI function that can be used by the terminal device.
  • the method further comprises:
  • each first indication information corresponds to at least one AI model or AI function.
  • FIG4A is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4A , the embodiment of the present disclosure relates to a communication method, which can be performed by a network device. The method may include:
  • Step S4101 Send the third information.
  • step S4101 can refer to the optional implementation of step S2101 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.
  • the network device may send the third information to the terminal device, but is not limited thereto.
  • the network device may also send the third information to other entities.
  • Step S4102 Obtain second information.
  • step S4102 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.
  • Step S4103 Determine fourth indication information according to the second information.
  • step S4103 can refer to the optional implementation of step S2103 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.
  • Step S4104 Send the fourth indication information.
  • step S4104 can refer to the optional implementation of step S2104 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.
  • Step S4105 Send the third indication information.
  • step S4105 can refer to the optional implementation of step S2105 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.
  • the network device may send the fourth indication information to the terminal device, but is not limited thereto.
  • the network device may also send the fourth indication information to other entities.
  • the above steps S4101 to S4105 are all optional steps.
  • the fourth information includes at least one of the following:
  • the fourth indication information is used to indicate the parameters configured by the network device for the terminal device.
  • sending the fourth information to the terminal device includes:
  • the method further comprises:
  • sending the third information to the terminal device includes:
  • the receiving the second information reported by the terminal device includes:
  • the second signaling includes at least one of the following: RRC, MAC-CE, and uplink control information UCI.
  • the second information includes at least one of the following:
  • first indication information where the first indication information is used to indicate channel characteristics
  • Second indication information where the second indication information is used to indicate an additional condition corresponding to the terminal device
  • Seventh indication information where the seventh indication information is used to indicate a fifth AI model or a fifth AI function, where the fifth AI model or the fifth AI function includes an AI model or AI function that can be used by the terminal device.
  • FIG5 is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG5 , the embodiment of the present disclosure relates to a communication method, which can be executed by a communication system. The method may include:
  • Step S5101 The network device sends fourth information.
  • step S5101 can be found in the optional implementation of step S2301 in Figure 2C, step S3401 in Figure 3D, step S4201 in Figure 4B, and other related parts in the embodiments involved in Figures 2C, 3D, and 4B, which will not be repeated here.
  • Step S5102 The terminal device determines a first AI model or a first AI function from multiple AI models or AI functions based on the first information.
  • step S5102 For optional implementations of step S5102, reference may be made to steps S2106 to S2107 of FIG. 2A , steps S2206 to S2208 of FIG. 2B , steps S2302 to S2303 of FIG. 2C , steps S3105 to S3106 of FIG. 3A , steps S3205 to S3207 of FIG. 3B , steps S3305 to S3306 of FIG. 3C , and optional implementations of steps S3402 to S3403 of FIG. 3D , as well as other related parts in the embodiments involved in FIG. 2A , FIG. 2B , FIG. 2C , FIG. 3A , FIG. 3B , FIG. 3C , and FIG. 3D , which will not be repeated here.
  • the NW can send signaling to query the UE's ability to support the AI function for AI CSI prediction.
  • the UE reports the AI function supported by the UE to the NW based on the received signaling, thus completing function identification.
  • model identification has been completed between the UE and the NW through a model identification method. Due to changes in the UE's own hardware conditions or channel environment, the UE can select an appropriate AI model based on relevant parameter information associated with the trained AI model or additional condition information on the UE/NW side to reduce the number of candidate AI models, thereby reducing the computational complexity of the UE's model selection or reducing the overhead of the UE's feedback monitoring data.
  • the first parameter information in Example 2 and Example 1 may correspond to a value or a value range.
  • Example 4 The UE proactively reports one or more of the following information to the NW through signaling such as RRC/MAC-CE/UCI, or the NW triggers or instructs the UE to report one or more of the following information through signaling such as RRC/MAC-CE/DCI:
  • the NW may also send second parameter information to the UE, that is, additional condition information indicating the NW side.
  • Example 6 The UE may select a corresponding AI function or model for inference based on the received configuration information related to the selected AI model or the received second parameter information.
  • the UE can complete the collection of data for training the CSI prediction AI model based on the received CSI-RS resources. Due to UE mobility, different movement speeds will result in different time-varying channel characteristics, that is, different TDCPs. Accordingly, the values of the parameters N, M, K, and D may also vary, which will cause the UE to train different models based on these parameter configurations and collected data. Therefore, the trained AI model can be associated with at least one value corresponding to the TDCP.
  • TDCP measurements are obtained by the UE based on the Tracking Reference Signals (TRS) resources configured by the gNB. Based on the received TRS, the UE can calculate the correlation of the time domain channel at different time intervals.
  • the correlation of the time domain channel includes the amplitude value and phase, where the amplitude value is the normalized result and the value range is between 0 and 1.
  • the trained AI model has a certain degree of generalization, and it is not necessary to have each TDCP amplitude value correspond to an AI model, that is, a certain TDCP amplitude range corresponds to an AI model. For example, the UE trained four models A, B, C, and D, and the correspondence between each model and the TDCP amplitude value range is shown in Table 3.
  • the UE or gNB can select the corresponding AI model based on one or more of the configuration parameter information N, M, K, and D.
  • the collected dataset can be a dataset for a specific channel scenario, including UMa, Urban Microcell (UMi), indoor, and rural scenarios.
  • the channel scenario is conditional information on the gNB side, and different channel scenarios can correspond to different AI models.
  • Table 1 shows the different AI models corresponding to different channel scenarios.
  • Each channel scenario includes three AI models. If the UE does not know the current channel scenario, it will need to select from nine models, significantly increasing the UE's computational complexity.
  • the channel scenario is a condition on the gNB side, and the gNB can send this condition information to the UE via RRC signaling.
  • the RRC signaling can indicate that the current channel scenario is the UMa scenario. In this way, the UE can select one of A0, B0, and C0 corresponding to the UMa scenario, significantly reducing the UE's computational complexity.
  • the UE can proactively report available AI models to the gNB.
  • the gNB can send signaling to query the UE for supported AI models, prompting the UE to report available AI models. Changes in the UE's processing capabilities or hardware capabilities constitute additional conditional information on the UE side. Only after receiving the additional conditional information or available AI models reported by the UE will the gNB configure the corresponding N, M, K, or D parameters based on the received information or the UE's reported AI model.
  • the UE can select an AI model from a limited set of one or more AI models based on the reconfigured parameters from the gNB and the UE's additional conditional information. This avoids the UE having to select candidate models from a larger number of AI models, thereby reducing the complexity of the UE's model selection process.
  • a communication system which may include a terminal device and a network device, wherein the terminal device can execute the communication method executed by the terminal device in the aforementioned embodiment of the present disclosure; the network device can execute the communication method executed by the network device in the aforementioned embodiment of the present disclosure.
  • the embodiments of the present disclosure further provide an apparatus for implementing any of the above methods.
  • an apparatus comprising units or modules for implementing each step performed by a terminal in any of the above methods.
  • another apparatus comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, etc.) in any of the above methods.
  • a network device e.g., an access network device, a core network function node, a core network device, etc.
  • the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits.
  • the above-mentioned hardware circuits can be understood as one or more processors.
  • the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), and the functions of some or all of the above units or modules are realized by designing the logical relationship of the components in the circuit.
  • ASIC application-specific integrated circuit
  • the above-mentioned hardware circuit can be implemented by a programmable logic device (PLD).
  • PLD programmable logic device
  • FPGA field programmable gate array
  • it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by a configuration file, thereby realizing the functions of some or all of the above units or modules.
  • All units or modules of the above devices can be implemented in the form of software called by the processor, or in the form of hardware circuits, or in part by software called by the processor, and the rest by hardware circuits.
  • the processor is a circuit with signal processing capabilities.
  • the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP).
  • the processor can implement certain functions through the logical relationship of hardware circuits, and the logical relationship of the above hardware circuits is fixed.
  • reconfigurable for example, a processor is a hardware circuit implemented as 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 can 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 can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc.
  • NPU neural network processing unit
  • TPU tensor processing unit
  • DPU deep learning processing unit
  • Figure 6A is a structural diagram of a terminal device proposed in an embodiment of the present disclosure.
  • the terminal device 101 may include at least one of a processing module 6101, a transceiver module 6102, etc.
  • the processing module 6101 is configured to determine a first AI model or a first AI function from a plurality of AI models or AI functions based on first information, wherein the first information includes information associated with parameters for training the first AI model or the first AI function.
  • the transceiver module 6102 can be used to execute at least one of the communication steps such as sending and/or receiving (for example, step S2101, but not limited thereto) performed by the terminal device 101 in any of the above methods, which will not be repeated here.
  • the processing module 6101 is used to execute at least one of the other steps (for example, step S2105, but not limited thereto) performed by the terminal device 101 in any of the above methods, which will not be repeated here.
  • the transceiver module may include a transmitting module and/or a receiving module, and the transmitting module and the receiving module may be separate or integrated.
  • the transceiver module may be interchangeable with the transceiver.
  • the processing module can be a single module or can include multiple submodules.
  • the multiple submodules respectively execute all or part of the steps required to be executed by the processing module.
  • the processing module can be interchangeable with the processor.
  • Figure 6B is a structural diagram of a network device proposed in an embodiment of the present disclosure.
  • the network device 102 may include: at least one of a transceiver module 6201, a processing module 6202, etc.
  • the transceiver module 6201 is configured to send fourth information to the terminal device, and the fourth information is used by the terminal device to determine the first AI model or the first AI function from a plurality of AI models or AI functions.
  • the fourth information includes information associated with the network device in the first information, and the first information includes information associated with the parameters for training the first AI model or the first AI function.
  • the transceiver module 6201 can be used to perform at least one of the communication steps such as sending and/or receiving (for example, step S2101, but not limited to this) performed by the network device 102 in any of the above methods, which will not be repeated here.
  • the transceiver module may include a transmitting module and/or a receiving module, and the transmitting module and the receiving module may be separate or integrated.
  • the transceiver module may be interchangeable with the transceiver.
  • FIG. 7A is a schematic diagram of the structure of a communication device 7100 proposed in an embodiment of the present disclosure.
  • Communication device 7100 can be a network device (e.g., an access network device, a core network device, etc.), a terminal (e.g., a user device, etc.), a chip, a chip system, or a processor that supports a first 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.
  • Communication device 7100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.
  • 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 communication protocols and communication data
  • the central processing unit may be used to control a communication device (e.g., a base station, a baseband chip, an IoT device, an IoT device chip, a DU or CU, etc.), execute programs, and process program data.
  • the communication device 7100 is used to perform any of the above methods.
  • the communication device 7100 further includes one or more memories 7102 for storing instructions.
  • the memories 7102 may be located outside the communication device 7100.
  • the communication device 7100 may include one or more interface circuits.
  • the interface circuits are connected to the memory 7102 and may be used to receive signals from the memory 7102 or other devices, or to send signals to the memory 7102 or other devices.
  • the interface circuits may 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 first device or an IoT device, 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 chip 7200 includes one or more processors 7201 , and the chip 7200 is configured to execute any of the above methods.

Landscapes

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

Abstract

La présente invention concerne un procédé et un dispositif de communication et un support d'enregistrement. Le procédé consiste à : déterminer un premier modèle d'IA ou une première fonction d'IA parmi une pluralité de modèles d'IA ou de fonctions d'IA sur la base de premières informations, les premières informations comprenant des informations associées à des paramètres pour entraîner le premier modèle d'IA ou la première fonction d'IA. En d'autres termes, un dispositif terminal peut effectuer une sélection de modèle sur la base de premières informations, et n'a pas besoin de surveiller chaque modèle, ce qui permet de réduire la complexité de calcul du dispositif terminal, et le dispositif terminal n'a pas besoin d'envoyer des données de surveillance à un dispositif de réseau, ce qui permet de réduire le surdébit de transmission de données, et d'améliorer les performances du réseau.
PCT/CN2024/076448 2024-02-06 2024-02-06 Procédé et dispositif de communication et support d'enregistrement Pending WO2025166573A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CN202480024889.9A CN121002913A (zh) 2024-02-06 2024-02-06 通信方法、设备和存储介质
PCT/CN2024/076448 WO2025166573A1 (fr) 2024-02-06 2024-02-06 Procédé et dispositif de communication et support d'enregistrement

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2024/076448 WO2025166573A1 (fr) 2024-02-06 2024-02-06 Procédé et dispositif de communication et support d'enregistrement

Publications (1)

Publication Number Publication Date
WO2025166573A1 true WO2025166573A1 (fr) 2025-08-14

Family

ID=96698787

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2024/076448 Pending WO2025166573A1 (fr) 2024-02-06 2024-02-06 Procédé et dispositif de communication et support d'enregistrement

Country Status (2)

Country Link
CN (1) CN121002913A (fr)
WO (1) WO2025166573A1 (fr)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023036268A1 (fr) * 2021-09-10 2023-03-16 华为技术有限公司 Procédé et appareil de communication
CN116471609A (zh) * 2022-01-07 2023-07-21 索尼集团公司 人工智能模型的管理和分发
CN116889009A (zh) * 2023-05-15 2023-10-13 北京小米移动软件有限公司 通信方法及装置、通信设备、通信系统
CN116963092A (zh) * 2022-04-15 2023-10-27 维沃移动通信有限公司 模型确定方法、信息传输方法、装置及相关设备
CN117397323A (zh) * 2023-08-31 2024-01-12 北京小米移动软件有限公司 模型选择方法、节点及系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023036268A1 (fr) * 2021-09-10 2023-03-16 华为技术有限公司 Procédé et appareil de communication
CN116471609A (zh) * 2022-01-07 2023-07-21 索尼集团公司 人工智能模型的管理和分发
CN116963092A (zh) * 2022-04-15 2023-10-27 维沃移动通信有限公司 模型确定方法、信息传输方法、装置及相关设备
CN116889009A (zh) * 2023-05-15 2023-10-13 北京小米移动软件有限公司 通信方法及装置、通信设备、通信系统
CN117397323A (zh) * 2023-08-31 2024-01-12 北京小米移动软件有限公司 模型选择方法、节点及系统

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
HUAWEI, HISILICON: "KI #1, Sol #5: Update to clarify the parameters for ML model discovery", 3GPP DRAFT; S2-2008646, 3RD GENERATION PARTNERSHIP PROJECT (3GPP), MOBILE COMPETENCE CENTRE ; 650, ROUTE DES LUCIOLES ; F-06921 SOPHIA-ANTIPOLIS CEDEX ; FRANCE, vol. SA WG2, no. Elbonia; 20201116 - 20201120, 9 November 2020 (2020-11-09), Mobile Competence Centre ; 650, route des Lucioles ; F-06921 Sophia-Antipolis Cedex ; France , XP051952684 *

Also Published As

Publication number Publication date
CN121002913A (zh) 2025-11-21

Similar Documents

Publication Publication Date Title
CN117121455A (zh) 模型测试方法、设备和存储介质
WO2025025065A1 (fr) Procédé de traitement de communication, terminal, dispositif, système de communication et support de stockage
CN117296362A (zh) 通信方法、网络设备、终端、通信系统及存储介质
WO2024234272A1 (fr) Procédé et appareil de communication, dispositif de communication et système de communication
WO2025147963A1 (fr) Procédé et appareil de surveillance de modèle, et support de stockage
WO2025175580A1 (fr) Procédé et appareil de communication, et support de stockage
CN117337607A (zh) 频段切换方法、终端、网络设备以及存储介质
WO2025166573A1 (fr) Procédé et dispositif de communication et support d'enregistrement
WO2025160849A1 (fr) Procédé de communication, dispositif, système de communication et support de stockage
WO2025208452A1 (fr) Procédé et appareil de communication, et support de stockage
WO2025081490A1 (fr) Procédé de communication, dispositif et support de stockage
WO2025081439A1 (fr) Procédé de communication, dispositif et support de stockage
WO2025118252A1 (fr) Procédé de communication, dispositif et support de stockage
WO2025184919A1 (fr) Procédé de communication, terminal, dispositif de réseau, système de communication et support de stockage
WO2025184912A1 (fr) Procédé et appareil de mesure, et support de stockage
WO2025179597A1 (fr) Procédé de communication, terminal, dispositif de réseau, système de communication et support de stockage
WO2025231591A1 (fr) Procédés de communication, terminal, dispositif réseau, appareils de communication, système de communication et support
WO2025102381A1 (fr) Procédé de traitement d'opération, appareil et support de stockage
WO2025208529A1 (fr) Procédé et appareil de communication, support de stockage
WO2025208384A1 (fr) Procédé de surveillance, et appareil et support de stockage
WO2025245705A1 (fr) Procédé et dispositif de communication, et support de stockage
WO2025166790A1 (fr) Procédés de communication, dispositifs, et support de stockage
WO2025043569A1 (fr) Procédé de communication, terminal, dispositif de réseau et support de stockage
WO2025179599A1 (fr) Procédé de communication, terminal, dispositif de réseau, système de communication et support de stockage
WO2025217919A1 (fr) Procédé de communication, terminal, dispositif de réseau, système de communication, et support de stockage

Legal Events

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

Ref document number: 24922816

Country of ref document: EP

Kind code of ref document: A1