Control method for ai / ML model in wireless communication system

Abstract

Provided in the present disclosure is a control method for an artificial intelligence / machine learning (AI / ML) model in a wireless communication system, which comprises: a node in the wireless communication system acquires AI / ML model region information, the node comprising at least one of a terminal or a base station; and the node determines model control for the AI / ML model on the basis of the AI / ML model region information. Further provided in the present disclosure is a wireless communication method for a terminal, which comprises: the terminal acquires from a first base station the AI / ML model region information, the AI / ML model region information being used for the terminal to determine the model control for the AI / ML model, and the terminal determines, on the basis of the AI / ML model region information, whether to execute the model control for the AI / ML model.

Description

Control Methods for AI / ML Models in Wireless Communication Systems Technical Field

[0001] The present disclosure relates to the field of communication systems, and more specifically, to a control method for an artificial intelligence / machine learning (AI / ML) model in a wireless communication system, a chip thereof, a computer-readable storage medium, and a computer program product thereof, a wireless communication method for user equipment (terminal), a chip thereof, a computer-readable storage medium, and a computer program product thereof, and a wireless communication method for a first base station, a chip thereof, a computer-readable storage medium, and a computer program product thereof. Background Art

[0002] Our society is undergoing a digital revolution. Over the past few years, artificial intelligence (AI) and machine learning (ML) methods have been widely adopted across various industries to drive innovation and improve process efficiency. However, in network design, with the dramatic increase in data volume and network complexity, traditional methods are no longer able to provide quick solutions in many cases. Therefore, AI / ML will become an indispensable technology to drive performance improvements in future wireless communication networks.

[0003] In wireless communication systems that use AI / ML over the air interface, the channel may deteriorate due to user equipment (terminal) mobility, potentially leading to handovers. When a terminal moves to a new serving base station (such as a gNB), the system can use traditional or AI / ML-based approaches in certain use cases. If an AI / ML-based approach is used, model control of the AI / ML model (e.g., model selection, switching, updating, etc.) may be required during the handover process to ensure service continuity. However, current wireless communication networks do not have the capability to support such model control.

[0004] It should be noted that the content described in this section does not constitute or should not be regarded as an admission of any prior art.

[0005] Summary of the Invention

[0006] In order to overcome at least one of the above technical problems, the present disclosure provides a control method for an artificial intelligence / machine learning (AI / ML) model in a wireless communication system, which can ensure the normal operation of the model control during the switching process, reduce signaling overhead, and reduce the delay in reporting dynamic information.

[0007] One aspect of the present disclosure provides a method for controlling an artificial intelligence / machine learning AI / ML model in a wireless communication system, including:

[0008] The wireless communication node obtains AI / ML model area information, and the wireless communication node determines model control of the AI / ML model based on the AI / ML model area information. The wireless communication node is a base station or a terminal.

[0009] According to one or more embodiments of the present disclosure, the model control of the AI / ML model includes at least one of the following:

[0010] AI / ML model activation, AI / ML model deactivation, AI / ML model selection, AI / ML model switching, AI / ML model rollback, AI / ML model update, AI / ML model training, AI / ML model retraining, or AI / ML model fine-tuning.

[0011] According to one or more embodiments of the present disclosure, the AI / ML model region information includes at least one of the following:

[0012] AI / ML model region identification ID,

[0013] location information,

[0014] Scene information,

[0015] Wireless communication node capability information,

[0016] and / or AI / ML model information.

[0017] According to one or more embodiments of the present disclosure, the location information includes at least one of the following:

[0018] Cell information, AI / ML model area identification ID, terminal location information, base station location information, scene information, control unit CU ID, data unit DU ID.

[0019] According to one or more embodiments of the present disclosure, the scene information includes at least one of the following:

[0020] Transmission environment information, speed information, angle spread information, delay spread information, signal-to-noise ratio, channel received power, and / or interference information.

[0021] According to one or more embodiments of the present disclosure, the wireless communication node capability information includes at least one of the following:

[0022] Computing capacity, storage capacity, AI / ML model support capacity, load conditions, and / or transmission capacity, AI / ML model support for AI / ML use cases and / or AI / ML model support for AI / ML features. Specifically, in some examples, for example, certain specific computing capacity, certain specific storage capacity, AI / ML model support for certain specific capabilities, certain specific load conditions, certain specific transmission capacity, AI / ML model support for certain specific use cases and / or support for certain specific features.

[0023] Wireless network capability node capability information; indicates the capabilities of a wireless network node, such as the model operation capabilities of the AI / ML models supported by the node, the node's hardware capability information (such as storage capacity, computing power, battery capacity, etc.), the functionality / features of the AI / ML models supported by the node, the data of the AI / ML models supported by the node, and the scenarios of the AI / ML models supported by the node.

[0024] According to one or more embodiments of the present disclosure, the AI / ML model information includes at least one of the following:

[0025] AI / ML model ID information and / or AI / ML model meta information.

[0026] According to one or more embodiments of the present disclosure, the AI / ML model region information is transmitted via at least one of the following:

[0027] Non-access stratum (NAS) messages, radio resource control (RRC) messages, medium access control element (MAC-CE) messages, downlink control information (DCI) messages, uplink channels, downlink channels, system messages, N2 messages, and / or Xn messages

[0028] According to one or more embodiments of the present disclosure, determining, by the wireless communication node, model control of the AI / ML model based on the AI / ML model region information includes at least one of the following:

[0029] If the location information of the wireless communication node belongs to the location information indicated in the AI / ML model area information, the wireless communication node does not perform model control of the AI / ML model;

[0030] If the location information of the wireless communication node does not belong to the location information indicated in the AI / ML model area information, the wireless communication node performs model control of the AI / ML model;

[0031] If the AI / ML model information of the wireless communication node belongs to the AI / ML model information indicated in the AI / ML model area information, the wireless communication node does not perform model control of the AI / ML model;

[0032] If the AI / ML model information of the wireless communication node does not belong to the AI / ML model information indicated in the AI / ML model area information, the wireless communication node performs model control of the AI / ML model.

[0033] According to one or more embodiments of the present disclosure, the model control of executing the AI / ML model includes at least one of the following:

[0034] The model control of executing the AI / ML model includes at least one of the following:

[0035] AI / ML model activation, AI / ML model deactivation, AI / ML model selection, AI / ML model switching, AI / ML model rollback, AI / ML model update, AI / ML model training, AI / ML model retraining, or AI / ML model fine-tuning, reporting of results of model control of an AI / ML model, and / or response of model control of an AI / ML model.

[0036] According to one or more embodiments of the present disclosure, when the wireless communication node is a terminal, the terminal obtains the AI / ML model area configuration information, and the terminal performs model control of the AI / ML model according to the AI / ML model area information.

[0037] According to one or more embodiments of the present disclosure, the terminal acquiring the AI / ML model area configuration information includes at least one of the following:

[0038] AI / ML model information, reason, indicator, terminal ID, and / or location information.

[0039] According to one or more embodiments of the present disclosure, when the wireless communication node is a base station, the base station obtains the AI / ML model area configuration information, and the base station performs model control of the AI / ML model according to the AI / ML model area information.

[0040] According to one or more embodiments of the present disclosure, the base station acquires the AI / ML model area configuration information including at least one of the following:

[0041] AI / ML model information, reason, indicator, base station ID, and / or location information.

[0042] According to one or more embodiments of the present disclosure, the AI / ML model region information request message and / or response message sent by the first wireless communication node to the second wireless communication node includes at least one of the following:

[0043] AI / ML model region identification ID,

[0044] location information,

[0045] Scene information,

[0046] Wireless communication node capability information, and / or

[0047] AI / ML model information.

[0048] According to one or more embodiments of the present disclosure, the wireless communication node sends the request message for the AI / ML model area information, including periodic transmission or event triggering.

[0049] According to one or more embodiments of the present disclosure, in the case of the periodic transmission, the AI / ML model region information is updated in response to a periodic configuration, wherein the periodic configuration includes a duration, a start time, and / or an end time of the update.

[0050] According to one or more embodiments of the present disclosure, when the method is event triggered, the AI / ML model region information is updated in response to the event triggered configuration.

[0051] According to one or more embodiments of the present disclosure, the periodic transmission and / or the event triggering corresponding periodic configuration and / or event triggering configuration are preconfigured, configured, and / or fixed.

[0052] According to one or more embodiments of the present disclosure, the AI / ML model region information request message and / or response message is carried by at least one of the following:

[0053] Non-access stratum NAS message, radio resource control RRC message, medium access control element MAC-CE message, downlink control information DCI message, uplink channel, downlink channel, system message, N2 message and / or Xn message.

[0054] One aspect of the present disclosure provides a wireless communication method for a terminal, including:

[0055] The terminal obtains AI / ML model area information from the first base station, where the AI / ML model area information is used by the terminal to determine model control of the AI / ML model.

[0056] The terminal determines whether to execute model control of the AI / ML model based on the AI / ML model area information.

[0057] According to one or more embodiments of the present disclosure, the present invention further includes:

[0058] The terminal obtains an AI / ML model area configuration message from the first base station.

[0059] According to one or more embodiments of the present disclosure, if the second base station is not in the AI / ML model area, the terminal determines to perform model control of the AI / ML model.

[0060] According to one or more embodiments of the present disclosure, executing model control of the AI / ML model includes updating the AI / ML model area information between the terminal and the first base station.

[0061] According to one or more embodiments of the present disclosure, the present invention further includes:

[0062] The terminal sends a report message for the AI / ML model area configuration message and a result of executing the model control of the AI / ML model to the first base station, and the first base station sends the obtained result of executing the model control of the AI / ML model to the second base station.

[0063] One aspect of the present disclosure provides a wireless communication method for a first base station, including:

[0064] The first base station obtains a model control reporting message of the AI / ML model from the terminal, where the model control reporting message of the AI / ML model includes a result of model control of the AI / ML model;

[0065] The first base station sends the cell switching request of the second base station and the result of the model control of the AI / ML model to the second base station.

[0066] One aspect of the present disclosure provides a wireless communication method for a first base station, including:

[0067] The first base station determines, according to the model control message of the AI / ML model fed back by the terminal, whether the second base station is in the AI / ML model area information;

[0068] If the second base station is not in the AI / ML model area information, the first base station performs model control of the AI / ML model.

[0069] According to one or more embodiments of the present disclosure, the model control of the AI / ML model includes updating the AI / ML model region information between the first base station and the second base station.

[0070] According to one or more embodiments of the present disclosure, the present invention further includes:

[0071] The first base station sends the cell switching request of the second base station and the result of the model control to the second base station.

[0072] According to one or more embodiments of the present disclosure, the present invention further includes:

[0073] If the second base station is in the AI / ML model area information, the first base station sends the AI / ML model information included in the AI / ML model area information and a cell switching request to the second base station to the second base station.

[0074] One aspect of the present disclosure provides a chip, including:

[0075] The processor is configured to call and execute the computer program stored in the memory, so that the device in which the chip is installed performs any one of the methods in the example embodiments.

[0076] One aspect of the present disclosure provides a chip, comprising: a processor configured to call and execute a computer program stored in a memory, so that a device in which the chip is installed executes a method of an embodiment of any aspect of the present disclosure.

[0077] One aspect of the present disclosure provides a computer-readable storage medium having a computer program stored therein, wherein the computer program enables a computer to execute a method according to any one of the embodiments of the present disclosure.

[0078] One aspect of the present disclosure provides a computer program product, comprising a computer program, wherein the computer program enables a computer to execute the method of any embodiment of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the embodiments of the present disclosure or related technologies, the following drawings will be briefly introduced in the embodiments. Obviously, the drawings are only some embodiments of the present disclosure, and ordinary technicians in this field can derive other drawings based on these drawings without inventive work.

[0080] FIG1 illustrates a signaling diagram of an AI / ML model region information update process according to some embodiments of the present disclosure.

[0081] FIG2 illustrates a signaling diagram of an AI / ML model region information update process according to some embodiments of the present disclosure.

[0082] FIG3 illustrates a signaling diagram of an AI / ML model region information update process according to some embodiments of the present disclosure.

[0083] FIG4 illustrates a signaling diagram of an AI / ML model region information update process according to some embodiments of the present disclosure.

[0084] FIG5 illustrates a signaling diagram of an AI / ML model region information update process according to some embodiments of the present disclosure.

[0085] FIG6 illustrates a signaling diagram of an AI / ML model region information update process according to some embodiments of the present disclosure.

[0086] FIG7 illustrates a signaling diagram of an AI / ML model region information update process according to some embodiments of the present disclosure.

[0087] FIG8 illustrates a signaling diagram of an AI / ML model region information update process according to some embodiments of the present disclosure.

[0088] FIG9 illustrates a signaling diagram of a fast model control procedure during a handover procedure according to some embodiments of the present disclosure.

[0089] FIG10 illustrates a signaling diagram of a fast model control procedure during a handover procedure according to some embodiments of the present disclosure.

[0090] FIG11 illustrates a signaling diagram of a fast model control procedure during a handover procedure according to some embodiments of the present disclosure.

[0091] 12 is a block diagram of an example system for wireless communications according to an embodiment of the disclosure. DETAILED DESCRIPTION

[0092] The embodiments of the present disclosure describe technical matters, structural features, objectives and effects in detail with reference to the accompanying drawings, as described below. Specifically, the terms in the embodiments of the present disclosure are only used to describe the purpose of specific embodiments, rather than to limit the present disclosure.

[0093] In this disclosure, "A or B" may mean "only A," "only B," or "both A and B."

[0094] In other words, in the present disclosure, "A or B" may be interpreted as "A and / or B." For example, in the present disclosure, "A, B or C" may mean "only A," "only B," "only C," or "any combination of A, B, and C."

[0095] As used in this disclosure, a slash ( / ) or a comma may mean "and / or". For example, "A / B" may mean "A and / or B". Thus, "A / B" may mean "only A", "only B", or "both A and B". For example, "A, B, C" may mean "A, B, or C".

[0096] In the present disclosure, “at least one of A and B” may mean “only A”, “only B”, or “both A and B”. In addition, in the present disclosure, the expression “at least one of A or B” or “at least one of A and / or B” may be interpreted as “at least one of A and B”.

[0097] In addition, in the present disclosure, “at least one of A, B, and C” may mean “only A,” “only B,” “only C,” or “any combination of A, B, and C.” In addition, “at least one of A, B, or C” or “at least one of A, B, and / or C” may mean “at least one of A, B, and C.”

[0098] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the described features. Throughout the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0099] In a wireless communication system that uses AI / ML over an air interface, the channel may become worse due to terminal mobility, which may cause handover to occur. When the terminal moves to a new serving base station (such as a gNB), the system may use traditional methods or AI / ML-based methods in certain use cases. If an AI / ML-based method is adopted, in order to ensure service continuity, model control of the AI / ML model may need to be performed during the handover process (for example, AI / ML model activation, AI / ML model deactivation, AI / ML model selection, AI / ML model switching, AI / ML model fallback, AI / ML model update, AI / ML model training, AI / ML model retraining, or AI / ML model fine-tuning, etc., which are not limited in this disclosure). However, current wireless communication networks do not have the ability to support such model control.

[0100] According to the progress of the protocol, whether the AI / ML model on the terminal side needs model control (also known as model operation, model management, etc.) is mainly based on the results of model monitoring. That is to say, in the life cycle management process of the AI / ML model, model control of the AI / ML model such as model activation, deactivation, selection, switching, fallback, update, training, and retraining is a decision made based on the monitoring results after the model starts monitoring. During the switching process, if the model monitoring solution is used to determine whether the current AI / ML model is suitable for the second base station, the second base station needs to configure model monitoring and monitoring reporting. However, this configuration process usually requires the terminal to access the second base station, that is, after the switching is completed, before the model monitoring judgment can be made. Moreover, to obtain accurate model monitoring results, it is necessary to perform model monitoring through multiple AI-based measurements and traditional measurements.

[0101] Furthermore, in different scenarios, for terminal-side models, when model monitoring is performed on the terminal and monitoring decisions are made on the network (e.g., but not limited to, base stations (e.g., gNBs)), the terminal needs to report monitoring results to assist the network in selecting a new model. The network then sends the newly selected model to the terminal. When model monitoring is performed on the terminal and monitoring decisions are also made on the terminal, the terminal needs to reselect a model based on the decision.

[0102] Therefore, for switching, if the model monitoring method is used, it needs to be performed after the terminal accesses the new second base station, and the entire process from model monitoring to model control will take a long time, causing a large delay to the entire system and failing to meet the requirements for service continuity.

[0103] Another optional solution that does not use model monitoring is that when a cell handover occurs, in order to adapt the terminal's AI / ML model to the new network side (such as the second base station), the model control of the AI ​​model can be performed directly. For example, the newly switched / selected / updated AI model can be directly transmitted to the terminal after being selected by the second base station, or the second base station configures the relevant information of the new model (such as the model identifier (ID), model metadata, etc.) and sends it to the terminal to assist the terminal side in selecting / switching the new AI model. However, this method will result in the need for direct interaction between the base station and the terminal for the AI ​​model or the required AI model information, resulting in a large signaling overhead. In addition, considering that the transmission and configuration of the new model are also carried out after the terminal accesses the new second base station, the entire system will face a large delay and cannot meet the requirements for service continuity.

[0104] Based on the progress of RAN1 and RAN2, to reduce handover latency issues caused by AI model control, some companies have proposed methods for exchanging model information during the handover preparation process. This method allows the terminal to quickly switch to the correct new AI / ML context after the handover, should a handover occur. While this method effectively reduces latency, the conditions for executing model control are unclear. Furthermore, the preparation phase involves a large amount of interaction, and if the current AI / ML model is still applicable, this results in wasted signaling overhead.

[0105] Based on the above discussion, the inventors have an insight that designing a simpler method to achieve rapid model control is urgently needed and has practical significance.

[0106] Therefore, the present disclosure proposes a control method for an artificial intelligence / machine learning (AI / ML) model in a wireless communication system. The method can be used to determine whether model control needs to be performed during a switching process, and can be used to assist nodes in, for example, quickly selecting / switching / updating one or more suitable AI / ML models when model control occurs. With the help of the method of the present disclosure, the delay caused by model monitoring and model control of AI / ML models can be effectively reduced; and the signaling interaction overhead when performing model control can be reduced.

[0107] In some embodiments, the terminal may be various user devices such as a user station, a mobile station, a mobile station, a wireless communication device, a user agent, etc. In addition, some examples of the terminal may be a mobile phone, a tablet computer, a laptop computer, a PDA, a mobile Internet device, a wearable device, a virtual reality device, an augmented reality device, etc., and this disclosure does not impose any limitation on this.

[0108] In some embodiments, a base station may include or may be referred to as a base transceiver station, a wireless base station, an access point, a wireless transceiver, a Node B, an eNode B (eNB), a next-generation Node B, or a giga-Node B (any of which may be referred to as a gNB), a Home Node B, a Home eNode B, or other appropriate terms. A wireless communication system may include different types of base stations (e.g., macro cell base stations or small cell base stations, etc.). A terminal may communicate with various base stations and / or network devices, including macro eNBs, small cell eNBs, gNBs, relay base stations, etc.

[0109] Provided is an AI / ML model-specific area information that can be used to assist nodes (such as terminals, gNBs, etc.) in performing AI / ML model control. An AI / ML model can be used for one or more locations in a specific AI / ML area. In some embodiments, the AI / ML model area information may include at least one of the following: an AI / ML model area identifier ID, location information, scenario information, wireless communication node capability information, and / or AI / ML model information. In some embodiments, the AI / ML model area information may be configured to nodes, such as terminals and / or base stations, via RRC messages, NAS messages, N2 messages, Xn messages, DCI, MAC CE, system messages, uplink channels, downlink channels, and the like.

[0110] Some of the terms that appear in the embodiments of this aspect are explained as follows:

[0111] Model control: also known as model operation, model management, etc., refers to some model operations in lifecycle management (LCM), such as model selection, model switching, model update, model activation, model deactivation (model deactivation), model rollback, model retention (fine-tuning), model training, model retraining, etc. This disclosure does not impose any restrictions on this. On-device (end) model: can refer to the AI / ML model located on the end (end), including the case of on-device model and the case of the end part of the two-sided model;

[0112] AI / ML model area information: refers to an AI-specific area. In this disclosure, it may also be interchangeably referred to as an AI / ML model-specific area. The AI / ML model area information corresponds to or is associated with an AI / ML model.

[0113] AI / ML model region indicator: used to identify AI-specific regions;

[0114] AI-specific area code of AI-specific area: an AI-specific area code that can be used to indicate an AI-specific area;

[0115] AI / ML model information: information that can be used to indicate the AI / ML model, such as model ID, model meta-information, etc. In addition, when not specifically stated, AI / ML model information can be information about one AI / ML model or multiple AI / ML models; Location information: cell information, AI / ML model area identification ID, terminal location information, base station location information, scenario information, control unit CU ID, data unit DU ID, etc.; Among them,

[0116] According to one or more embodiments of the present disclosure, the wireless communication node capability information includes at least one of the following:

[0117] Computing capacity, storage capacity, AI / ML model support capacity, load conditions, and / or transmission capacity, AI / ML model support for AI / ML use cases and / or AI / ML model support for AI / ML features. Specifically, in some examples, for example, certain specific computing capacity, certain specific storage capacity, AI / ML model support for certain specific capabilities, certain specific load conditions, certain specific transmission capacity, AI / ML model support for certain specific use cases and / or support for certain specific features.

[0118] Wireless network capability node capability information; indicates the capabilities of a wireless network node, such as the model operation capabilities of the AI / ML models supported by the node, the node's hardware capability information (such as storage capacity, computing power, battery capacity, etc.), the functionality / features of the AI / ML models supported by the node, the data of the AI / ML models supported by the node, and the scenarios of the AI / ML models supported by the node.

[0119] Base station information: may indicate cell ID, transmission reception point (TRP) ID, central unit (CU) ID and / or distribution unit (DU) ID, etc.;

[0120] Scenario information: can transmit one or more parameters reflecting channel characteristics, such as environmental information, speed information, angle spread information, delay spread information, signal-to-noise ratio, channel received power, and / or interference information;

[0121] Terminal location information: may include, for example, terminal ID, cell ID associated with the terminal, physical location area of ​​the terminal, longitude, latitude, altitude, etc.

[0122] Model ID: used to identify the AI / ML model, including the global model ID and / or local model ID, where:

[0123] The global AI / ML model ID is used to identify the AI / ML model, which can be a globally unique ID, a PLMN-specific unique ID, an operator-specific unique ID, or an AI / ML management platform-specific unique ID.

[0124] The local AI / ML model ID is used to identify the AI / ML model used in the network and has a certain mapping relationship with the global ID.

[0125] The logical ID can be a globally unique ID, a PLMN-specific unique ID, an operator-specific unique ID, a cell-specific ID, a link-specific ID, a tracking area (TA)-specific ID, a CU-specific ID, a DU-specific ID, a user plane function (UPF)-specific ID, an access and mobility management function (AMF)-specific ID, a radio resource control (RRC)-specific ID, or a network slice-specific ID.

[0126] The term "cell-specific" indicates that each AI / ML model has a unique ID within a specific cell. Similarly, "link-specific," "TA-specific," "CU-specific," "DU-specific," "UPF-specific," "AMF-specific," and "network slice-specific" refer to the unique ID of an AI / ML model within a specific context within the network.

[0127] The model ID may also be indicated in an implicit manner, such as RRC ID, DCI ID, etc.

[0128] Model meta-information: Information used to describe AI / ML models, which may include model functionality / features, model version information, model format information, supplier information, computational complexity, model complexity, model performance, applicable conditions, etc.

[0129] Model functionality / features: Indicates the functionality of a model, e.g., the model is used for CSI compression, beam prediction, positioning, etc. The indication should be at the sub-use case level or even finer level.

[0130] Model version information: indicates the version information of the AI / ML model.

[0131] Model format information: indicates the format information of the AI / ML model, such as open formats (such as ONNX) and private formats.

[0132] Vendor Information: Indicates the information of the AI / ML model training vendor.

[0133] Computational complexity: used to indicate the computational complexity of model training, such as FLOPs (floating-point operations) and the level of pre-processing / post-processing.

[0134] Model complexity: used to indicate the complexity of model training, including the number of real-valued model parameters, the number of real values, etc.

[0135] Model performance: used to indicate the performance of the model, which can be model accuracy, model bias, model variance, etc.

[0136] Applicable conditions: used to indicate the applicable conditions of AI / ML models or functionality, such as scenarios, configurations or sites, and internal terminal capabilities.

[0137] The scenario information includes at least one of the following: transmission environment information, speed information, angle spread information, delay spread information, signal-to-noise ratio, channel received power, and / or interference information, wherein,

[0138] Transmission environment information: used to describe the channel transmission environment in which the current wireless communication node is located, such as indoor / urban / urban suburban / rural / tunnel, etc., or channel classification information, such as classification information based on UMA / UMi / InH / RMA, etc.

[0139] Speed ​​information: used to describe the current moving speed of the wireless communication node. The moving speed can be measured by the terminal or the base station.

[0140] Angle extension information: used to describe the angle extension of the channel output by the current wireless communication node, that is, the real-time or statistical information of the channel angle extension, or other information related to the angle extension.

[0141] Delay spread information: used to describe the delay spread of the channel sent by the current wireless communication node, that is, the real-time or statistical information of the channel delay spread, or other information related to the delay spread.

[0142] According to one or more embodiments of the present disclosure, the wireless communication node capability information includes at least one of the following:

[0143] Computing capacity, storage capacity, AI / ML model support capacity, load conditions, and / or transmission capacity, AI / ML model support for AI / ML use cases and / or AI / ML model support for AI / ML features. Specifically, in some examples, for example, certain specific computing capacity, certain specific storage capacity, AI / ML model support for certain specific capabilities, certain specific load conditions, certain specific transmission capacity, AI / ML model support for certain specific use cases and / or support for certain specific features.

[0144] Wireless network capability node capability information; indicates the capabilities of a wireless network node, such as the model operation capabilities of the AI / ML models supported by the node, the node's hardware capability information (such as storage capacity, computing power, battery capacity, etc.), the functionality / features of the AI / ML models supported by the node, the data of the AI / ML models supported by the node, and the scenarios of the AI / ML models supported by the node.

[0145] According to one or more embodiments of the present disclosure, the AI / ML model information includes at least one of the following:

[0146] AI / ML model ID information and / or AI / ML model meta information.

[0147] As used in this disclosure, the term "model control" may also be referred to as model operation, model management, etc.

[0148] As used in this disclosure, when referring to the expression "not to initiate", it may refer to prohibiting execution, not executing, giving up execution, etc.

[0149] AI / ML model regional information configuration

[0150] AI-specific area information is introduced to assist nodes (such as terminals, accounting, etc.) to correctly perform AI / ML model control, and / or to help nodes, for example, select / switch / update one or more appropriate AI / ML models when model control occurs.

[0151] Regarding the AI / ML model regional information proposed in this disclosure, the following issues and related methods are listed below:

[0152] AI / ML model region information can be constructed in the following ways.

[0153] The composition of AI / ML model area information includes at least one of the following: AI / ML model area identification ID, location information, scene information, wireless communication node capability information, and / or AI / ML model information. Several possible options are as follows

[0154] Option 1: AI / ML model region information can consist of single or multiple location information;

[0155] Option 2: AI / ML model region information can consist of single or multiple location information and some AI / ML model information;

[0156] Option 3: The AI / ML model region information can consist of single or multiple location information, some AI / ML model information, and some scene information.

[0157] A mapping relationship exists between any two combinations of the AI / ML model area identification ID, location information, scenario information, wireless communication node capability information, and / or AI / ML model information. The mapping relationship can be pre-configured or configured through, for example, RRC messages, NAS messages, DCI messages, etc., or can be fixed in specific circumstances. In one implementation, the AI / ML model information includes at least one of the following: AI / ML model ID information, and / or AI / ML model meta-information.

[0158] The mapping between AI / ML model region information and AI / ML model information can be a one-to-one, one-to-many, or many-to-one relationship. Note that any one or more elements in the AI / ML model region information can be mapped to any one or more elements in the AI / ML model information, and this disclosure does not impose any limitations on this.

[0159] In some examples, an AI / ML model region indicator can be used to indicate the mapping between location information and AI / ML model information. Table 1 shows one possible mapping relationship between AI / ML model region information and AI / ML model information, where the location information is a cell ID and the model information is a model ID. Table 2 shows another possible mapping relationship between AI / ML model region information and AI / ML model information, where the location information is a cell ID and the model information is model features and / or functionality, and model format.

[0160] Table 1: Mapping relationship between AI / ML model region information and AI / ML model information

[0161] Table 2: Mapping relationship between AI / ML model region information and AI / ML model information

[0162] In some examples, a possible mapping relationship between AI / ML model region information and AI / ML model information is shown in Table 3 below, where the location information is the cell ID and the model information is the model ID:

[0163] Table 3: Mapping relationship between AI / ML model region information and AI / ML model information

[0164] In some examples, a possible mapping relationship between AI / ML model region information and AI / ML model information is shown in Table 4 below, where the location information is the cell ID and the model information is the model ID and AI features:

[0165] Table 4: Mapping relationship between AI / ML model region information and AI / ML model information

[0166] In some examples, to indicate a possible mapping relationship between AI / ML model region information and AI / ML model information, an index may be introduced, as shown in Table 5 below, where the location information is the cell ID and the model information is the model ID:

[0167] Table 5: Mapping relationship between AI / ML model region information and AI / ML model information

[0168] It should be understood that the examples of several different ways to construct AI / ML model region information described above are provided for illustrative purposes only and are not intended to be limiting. After reading the above examples, those skilled in the art will readily conceive of other suitable additions, deletions, modifications, variations, etc., all of which fall within the scope of this disclosure.

[0169] There are several different options for configuring AI / ML model region information:

[0170] A terminal (such as a UE) in an RRC active, inactive, or idle state can be configured with AI / ML model area information or an AI / ML model area information list. This can be configured using the following options:

[0171] Option 1: The core network element (CN, such as AMF) sends AI / ML model area information or AI / ML model area information list to the terminal through, for example, NAS messages. This disclosure does not impose any restrictions on the type of message used, and does not exclude the use of any other suitable message other than NAS messages;

[0172] Option 2: The base station (e.g., gNB) sends the AI / ML model area information or the AI / ML model area information list to the terminal via, for example, an RRC message. The RRC message includes, for example, an RRC reconfiguration message, an RRC release message, or other dedicated RRC messages. This disclosure does not impose any restrictions on the type of message used and does not exclude the use of any other suitable message other than the RRC message.

[0173] Option 3: The base station (e.g., gNB) sends AI / ML model area information or an AI / ML model area information list to the terminal via a system information message, including SIB1 or other newly defined SIBs. This disclosure does not impose any restrictions on the type of message used and does not exclude the use of any other suitable message other than the system information message.

[0174] Option 4: The base station (e.g., gNB) sends the AI / ML model area information or the AI / ML model area information list to the terminal through, for example, a MAC CE message. This disclosure does not impose any restrictions on the type of message used, and does not exclude the use of any other suitable message other than the MAC CE message.

[0175] Option 5: The base station (e.g., gNB) sends the AI / ML model area information or the AI / ML model area information list to the terminal through, for example, a DCI message. This disclosure does not impose any restrictions on the type of message used, and does not exclude the use of any other suitable message other than the DCI message.

[0176] A base station (e.g., gNB) can be configured with AI / ML model region information or a list of AI / ML model region information. This can be configured using the following options:

[0177] Option 1: The core network element (such as AMF) sends AI / ML model area information or AI / ML model area information list to the base station (such as gNB) through the N2 message. This disclosure does not impose any restrictions on the type of message used and does not exclude the use of any other suitable message other than the N2 message;

[0178] Option 2: A base station (e.g., a gNB) sends AI / ML model area information or an AI / ML model area information list to another base station (e.g., a gNB) via an Xn message. This disclosure does not impose any restrictions on the type of message used and does not exclude the use of any other suitable message besides the Xn message.

[0179] It should be understood that the examples of several different ways to configure AI / ML model region information described above are provided for illustrative purposes only and are not intended to be limiting in any way. After reading the above examples, those skilled in the art will readily conceive of other suitable additions, deletions, modifications, variations, etc., all of which fall within the scope of this disclosure.

[0180] Regarding AI / ML model regional information updates, at least three alternatives can be considered:

[0181] Alternative 1: Periodic update request. The AI / ML model area information update period can be configured through, for example, RRC messages, NAS messages, N2 messages or system messages, Xn messages, MAC CE, DCI, etc. The update period configuration content includes the duration, start time and end time of at least one of the following update periods, for example, 5 minutes to 720 minutes.

[0182] Alternative 2: Event-triggered update request. Any of the following events may trigger an update of AI / ML model region information:

[0183] Event 1: The model corresponding to the AI / ML model region information is not in the configured AI / ML model region information. This triggers an AI / ML model region information update. For example, if the model changes (including model selection, model switching, and model update), the corresponding AI / ML model region information is not in the configured AI / ML model region information.

[0184] Event 2: The location information of the candidate base station and / or cell is not in the configured AI / ML model area information, which will trigger an AI / ML model area information update.

[0185] Event 3: When the terminal's cell reselection process selects a base station and / or cell that does not belong to the configured AI / ML model area information, it will trigger the AI / ML model area information update.

[0186] The following describes a possible process for updating AI / ML model region information.

[0187] FIG1 illustrates a signaling diagram of an AI / ML model region information update process according to some embodiments of the present disclosure.

[0188] Figure 1 illustrates the terminal-triggered AI / ML model region information update process. This process can be triggered by an event or a period.

[0189] According to some embodiments, a method for updating AI / ML model region information is provided. The following are the steps / signaling of the method:

[0190] Exemplarily, step 1 may include a terminal (e.g., a terminal) sending a request message for updating AI / ML model area information to a base station (e.g., a gNB). The message includes at least the following information:

[0191] -Model information: used to indicate the current AI model on the terminal side, which can be an activated AI model or an inactivated AI model.

[0192] -Reason: Indicates the reason for updating the AI / ML model region information. In this example, the reason is that the current AI model and AI / ML model region information on the terminal are not included in the configured AI / ML model region information list.

[0193] -Terminal ID: used to identify a specific terminal.

[0194] For example, the message may be a dedicated message for requesting an AI / ML model region information update. Once a base station (e.g., a gNB) receives the message, it knows that the message is for requesting an AI / ML model region information update. Alternatively, the message may be a traditional message that includes an indicator indicating that the message is for requesting an AI / ML model region information update. Furthermore, the message may also be a traditional message that includes a cause indicating the reason for initiating the dedicated message.

[0195] Exemplarily, step 2 may include the terminal receiving updated AI / ML model area information. More specifically, the updated AI / ML model area information may include an AI / ML model area indicator, an AI / ML model area indicator list, AI model information, location information, or a mapping rule / relationship between AI / ML model area information and model information. By receiving this information, the terminal can update its AI / ML model area information and obtain the latest AI model information and related location information and mapping rules. In this way, the terminal can better adapt to the current network environment and needs and provide higher quality services. Therefore, the embodiments of the present disclosure provide an effective AI / ML model area information update method that can improve network performance and user experience.

[0196] It should be noted that the above embodiments are merely examples and do not limit the scope of the present invention. Various modifications and variations can be made to the embodiments without departing from the spirit and scope of the present invention.

[0197] FIG2 illustrates a signaling diagram of an AI / ML model region information update process according to some embodiments of the present disclosure.

[0198] Figure 2 shows the terminal-triggered AI / ML model region information update process. This process can be triggered by an event or periodically.

[0199] In some cases, if the first base station (for example, the serving base station s-gNB when the base station is a gNB) does not have a model corresponding to the requested AI / ML model area information, the first base station may forward the request to other base stations (for example, the second base station), as shown in Figure 2.

[0200] According to some embodiments, a method for updating AI / ML model region information is provided. The following are the steps / signaling of the method:

[0201] Exemplarily, step 1 may include the terminal sending a request message for updating AI / ML model area information to the first base station, the information of which is the same as step 1 in the example shown in Figure 1.

[0202] Exemplarily, step 2 may include the first base station forwarding the request message received in step 1 to (e.g., multiple) other base stations. The other base stations will send updated AI / ML model area information. More specifically, the updated AI / ML model area information may include an AI / ML model area identification ID, location information, scenario information, wireless communication node capability information, and / or AI / ML model information.

[0203] Exemplarily, step 3 may include the terminal receiving a response message regarding the AI / ML model area information update request, wherein the information is the same as the information sent by other base stations to the first base station in step 2.

[0204] Through the above example process, the terminal can request an update of AI / ML model region information from a base station, such as the first base station and then other base stations. By receiving the updated AI / ML model region information, the terminal can ensure that it has the most relevant AI model, improve performance, and adapt to changing network conditions. Therefore, the embodiments of the present disclosure provide an efficient terminal-triggered AI / ML model region information update process that can improve network performance and user experience.

[0205] It should be noted that the above embodiments are merely examples and do not limit the scope of the present invention. Various modifications and variations can be made to the embodiments without departing from the spirit and scope of the present invention.

[0206] FIG3 illustrates a signaling diagram of an AI / ML model region information update process according to some embodiments of the present disclosure.

[0207] Figure 3 illustrates the AI / ML model area information update process triggered by a base station (e.g., gNB). This process can be triggered by an event or periodically.

[0208] In some cases, if the first base station (s-gNB) does not have AI / ML model area information (or AI / ML model area information list) corresponding to other candidate gNBs, the first base station triggers the AI / ML model area information update process.

[0209] According to some embodiments, a method for updating AI / ML model region information is provided. The following are the steps / signaling of the method:

[0210] Exemplarily, step 1 may include the first base station triggering an AI / ML model region information update process. Specifically, the first base station may send a request message for AI / ML model region information update to other base stations, where the message may include model information and a reason.

[0211] Exemplarily, step 2 may include the other base stations sending a response message regarding the AI / ML model region information update request to the first base station B. By way of example and not limitation, the response message may indicate that the AI / ML model region information update request is granted and include the updated AI / ML model region information.

[0212] Through the above example process, a base station (e.g., a gNB) can trigger an update of the AI / ML model region information to ensure the validity of the configured AI / ML model region information (list), for example, including the AI / ML model region information (list) corresponding to other candidate base stations. Therefore, embodiments of the present disclosure provide an AI / ML model region information update process triggered by a base station (e.g., a gNB), which can improve network performance and efficiency.

[0213] It should be noted that the above embodiments are merely examples and do not limit the scope of the present invention. Various modifications and variations can be made to the embodiments without departing from the spirit and scope of the present invention.

[0214] FIG4 illustrates a signaling diagram of an AI / ML model region information update process according to some embodiments of the present disclosure.

[0215] Figure 4 shows the terminal-triggered AI / ML model region information update process. This process can be triggered by an event or periodically.

[0216] According to some embodiments, a method for updating AI / ML model region information is provided. The following are the steps / signaling of the method:

[0217] Exemplarily, step 1 may include the terminal sending a request message for updating AI / ML model area information to a base station (e.g., a gNB), where the message includes at least the following:

[0218] Location information: used to indicate the location information of base stations (such as gNBs), including candidate base stations.

[0219] Reason: Indicates the reason for the AI / ML model area information update. In this case, the reason could be that the candidate base station's cell is not in the configured AI / ML model area information, or that a model (re)selection was performed and the new resident cell does not belong to the configured AI / ML model area information.

[0220] Terminal ID: used to indicate the identity of the terminal.

[0221] This message can be a dedicated message for requesting an AI / ML model area information update. Once a base station (e.g., a gNB) receives this message, it knows that it is requesting an AI / ML model area information update. Alternatively, this message can be a legacy message that includes an indicator indicating that the message is for an AI-TA update request. Alternatively, this message can be a legacy message that includes a reason indicating why a dedicated message is being initiated.

[0222] Exemplarily, step 2 may include the terminal receiving updated AI / ML model area information. More specifically, the updated AI / ML model area information may include any one of the following: an AI / ML model area indicator, an AI / ML model area indicator list, AI model information, location information, or a mapping rule / relationship between AI / ML model area information and model information.

[0223] Through the above example process, the terminal is able to send an AI / ML model area information update request to a base station (such as a gNB) and receive updated AI / ML model area information to improve network performance and efficiency.

[0224] It should be noted that the above embodiments are merely examples and do not limit the scope of the present invention. Various modifications and variations can be made to the embodiments without departing from the spirit and scope of the present invention.

[0225] FIG5 illustrates a signaling diagram of an AI / ML model region information update process according to some embodiments of the present disclosure.

[0226] Figure 5 shows the terminal-triggered AI / ML model region information update process. This process can be triggered by an event or periodically.

[0227] According to some embodiments, a method for updating AI / ML model region information is provided. The following are the steps / signaling of the method:

[0228] Exemplarily, step 1 may include the terminal sending a request message for updating AI / ML model area information to a base station (e.g., a gNB), where the message includes at least one of the following: location information, reason, and terminal identification. More specifically, for event 2 above, information about the candidate base stations needs to be included in the message to indicate the requested AI / ML model area information (list).

[0229] Exemplarily, step 2 may include the first base station forwarding the request message in step 1 to (e.g., multiple) other base stations, and the other base stations sending updated AI / ML model area information. More specifically, the updated AI / ML model area information includes at least one of the following: an AI / ML model area indicator, an AI / ML model area indicator list, AI model information, location information, or a mapping rule / relationship between AI / ML model area information and model information.

[0230] Exemplarily, step 3 may include the terminal receiving updated AI / ML model area information from the first base station. More specifically, the updated AI / ML model area information includes at least one of the following: an AI / ML model area indicator, an AI / ML model area indicator list, AI model information, location information, or a mapping rule / relationship between AI / ML model area information and model information.

[0231] Through the above example process, the terminal can send an AI / ML model area information update request to a base station, such as the first base station and then other base stations, and receive updated AI / ML model area information from the first base station to improve network performance and efficiency.

[0232] It should be noted that the above steps are merely examples and do not limit the scope of the present invention. Various modifications and variations can be made to the steps without departing from the spirit and scope of the present invention.

[0233] FIG6 illustrates a signaling diagram of an AI / ML model region information update process according to some embodiments of the present disclosure.

[0234] Figure 6 illustrates the AI / ML model area information update process triggered by a base station (e.g., gNB). This process can be triggered by an event or periodically.

[0235] According to some embodiments, a method for updating AI / ML model region information is provided. The following are the steps / signaling of the method:

[0236] Exemplarily, step 1 may include the first base station sending a request message for updating AI / ML model area information to other base stations, where the message includes at least one of the following: location information, reason, terminal identification, etc. More specifically, for event 2, the cell information of the candidate base station needs to be included in the message to indicate the requested AI / ML model area information (list).

[0237] Exemplarily, step 2 may include other base stations responding to the message in step 1, and the other base stations sending updated AI / ML model area information. More specifically, the updated AI / ML model area information may be an AI / ML model area indicator, an AI / ML model area indicator list, AI model information, location information, or a mapping rule / relationship between AI / ML model area information and model information.

[0238] Through the above example process, the first base station is able to send AI / ML model area information update requests to other base stations and receive updated AI / ML model area information from other base stations to improve network performance and efficiency.

[0239] It should be noted that the above steps are merely examples and do not limit the scope of the present invention. Various modifications and variations can be made to the steps without departing from the spirit and scope of the present invention.

[0240] FIG7 illustrates a signaling diagram of an AI / ML model region information update process according to some embodiments of the present disclosure.

[0241] Figure 7 shows the terminal-triggered AI / ML model region information update process. This process can be triggered by an event or periodically.

[0242] In some cases, the terminal triggers the AI / ML model area information update process and sends a request message to the base station (such as the gNB). The base station (such as the gNB) forwards the message to the CN node, and the CN responds to the terminal by updating the AI / ML model area information. The following is an example of a cell reselection. The AI / ML model area information update may occur during the registration process. The AI / ML model area information is sent to the terminal by the AMF.

[0243] According to some embodiments, a method for updating AI / ML model region information is provided. The following are the steps / signaling of the method:

[0244] Exemplarily, step 1 may include the terminal sending a request message for updating AI / ML model area information to a base station (e.g., a gNB) via a registration request message. The message includes at least one of the following: location information, reason, terminal identifier, AI model information, etc. More specifically, for event 1, the AI ​​model information needs to be included in the message; for event 2, the cell information of the candidate base station needs to be included in the message to indicate the requested AI / ML model area information (list).

[0245] Exemplarily, step 2 may include a base station (e.g., gNB) forwarding the AI / ML model area information update request message to the AMF via, for example, an N2 message.

[0246] Exemplarily, step 3 may include if the terminal's registration request is accepted, the terminal receiving updated AI / ML model area information through a registration accept message from the AMF.

[0247] Through the above example process, the terminal can trigger the AI / ML model area information update process and receive the updated AI / ML model area information from the AMF to improve network performance and efficiency.

[0248] It should be noted that the above steps are merely examples and do not limit the scope of the present invention. Various modifications and variations can be made to the steps without departing from the spirit and scope of the present invention.

[0249] FIG8 illustrates a signaling diagram of an AI / ML model region information update process according to some embodiments of the present disclosure.

[0250] Figure 8 illustrates the AI / ML model area information update process triggered by a base station (e.g., gNB). This process can be triggered by an event or periodically.

[0251] In some cases, the base station (such as gNB) triggers the AI / ML model area information update process, and it will send a request message to the CN node (such as AMF, Network Data Analysis Function (NWDAF) etc.).

[0252] According to some embodiments, a method for updating AI / ML model region information is provided. The following are the steps / signaling of the method:

[0253] Exemplarily, step 1 may include a base station (e.g., gNB) sending a request message for updating AI / ML model area information to the AMF via, for example, an N2 message. The request message includes at least one of the following: location information, reason, terminal identifier, AI model information, etc. More specifically, for event 1, the AI ​​model information needs to be included in the message; for event 2, the cell information of the candidate base station needs to be included in the message to indicate the requested AI / ML model area information (list).

[0254] Exemplarily, step 2 may include the base station receiving updated AI / ML model area information from the AMF via, for example, an N2 message.

[0255] Through the above example process, the base station (such as gNB) can send a request message for updating the AI / ML model area information to the AMF through the N2 message, and can receive the updated AI / ML model area information from the AMF through the N2 message to achieve more efficient network communication and optimization.

[0256] It should be noted that the above steps are merely examples and do not limit the scope of the present invention. Various modifications and variations can be made to the steps without departing from the spirit and scope of the present invention.

[0257] The terminal determines the process of fast model control during the switching process

[0258] FIG9 illustrates a signaling diagram of a fast model control procedure during a handover procedure according to some embodiments of the present disclosure.

[0259] As shown in Figure 9, fast model control during a potential switching process is demonstrated. In this example, model control (e.g., model selection, model switching, model update, etc.) is located on the terminal side.

[0260] Exemplarily, step 0: The terminal receives a configuration message about AI / ML model area information, which is used to help the terminal quickly determine model control during the handover process. The message may include an AI / ML model area indicator, an AI / ML model area indicator list, AI model information, location information, or a mapping rule / relationship between AI / ML model area information and model information, and the message may be transmitted in, for example, an RRC message, system information, MAC CE, DCI, NAS, etc.

[0261] Exemplarily, step 1: the terminal receives a traditional measurement configuration, where the measurement configuration is provided via dedicated signaling (eg, RRCReconfiguration or RRCResume, etc.).

[0262] Exemplarily, step 2: the terminal checks the configured AI / ML model area information. If the candidate second base station (such as Target gNB, T-gNB) is within the configured AI / ML model area information list, there is no need to perform model control, and then the terminal (such as the terminal) performs step 4; if the candidate second base station (such as Target gNB, T-gNB) is not within the configured AI / ML model area information list, the terminal will perform step 3 model control, and then the terminal performs steps 3' and 4. The details of step 3' are described above.

[0263] Exemplarily, step 4: based on the inspection result of step 2, the terminal sends a measurement reporting message to the first base station, where the message may carry the model control result, and the message includes at least one of the following: model information, indication or reason.

[0264] Indication: Used to indicate whether the current in-progress model has changed.

[0265] Model information: used to indicate the current AI / ML model.

[0266] Reason: Used to indicate why the AI / ML model was or was not changed.

[0267] Exemplarily, step 5: the first base station initiates a handover (HO) decision.

[0268] Exemplarily, step 6: the first base station sends a switching request message to the second base station, which may carry model information, and the model information represents an AI / ML model applicable to the second base station. This step can perform model identification in advance between the terminal and the second base station.

[0269] Exemplarily, step 7: the second base station performs admission control.

[0270] Exemplarily, step 8: the second base station sends a confirmation message about the handover request to the original base station, which carries information about the second base station and resource information of the terminal.

[0271] Exemplarily, in step 9: the first base station sends a message with a handover command (such as an RRCReconfiguration message, etc.) to the terminal, which carries information for accessing the second base station, such as a cell ID.

[0272] It should be noted that step 3' can occur before or after step 4. In some examples, if step 3' is sent after step 4, steps 4 and 6 do not carry AI model information.

[0273] It should also be noted that if step 3 occurs and a new AI model is selected / switched / updated, it does not mean that the new model is activated immediately. Instead, it can be activated through the RRC reconfiguration indication in step 9. Specifically, step 9 carries activation / deactivation configuration information, including at least one of the following: activation / deactivation start time, offset, duration, and end time of the indicated model. Optionally, step 9 only carries model activation / deactivation indication information. When the terminal receives this indication information, it automatically activates / deactivates the model.

[0274] It should be noted that the above steps are merely examples and do not limit the scope of the present invention. Various modifications and variations can be made to the steps without departing from the spirit and scope of the present invention.

[0275] The order of the described steps (signaling / boxes) is not intended to be construed as a limitation, and any number of the described steps (signaling / boxes) may be skipped or combined in any order to implement a method or an alternative method.

[0276] The process by which a base station (e.g., gNB) determines fast model control during handover

[0277] FIG10 illustrates a signaling diagram of a fast model control procedure during a handover procedure according to some embodiments of the present disclosure.

[0278] As shown in Figure 10, fast model control during a potential switching process is demonstrated. In this example, model control (e.g., model selection, model switching, model update, etc.) is located on the terminal side.

[0279] Exemplarily, step 1: the terminal receives a traditional measurement configuration, which is provided via dedicated signaling (such as RRCReconfiguration or RRCResume).

[0280] Exemplarily, step 2: the terminal sends a measurement reporting message to the first base station (such as Source gNB, S-gNB) according to the measurement configuration in step 1.

[0281] Exemplarily, step 3: based on the reporting message in step 2, the first base station initiates a handover (eg, handover, HO) decision.

[0282] Exemplarily, in step 4, the first base station checks the configured AI / ML model area information list. If the candidate second base station (such as Target gNB, T-gNB) is within the configured AI / ML model area information list, no model control is required. At this point, the first base station sends the AI / ML model information to the second base station and may include it in the handover request message of step 8. If the candidate second base station is not within the configured AI / ML model area information list, the first base station executes steps 5, 6, and 7.

[0283] Exemplarily, step 5: The terminal receives a message for model control, which is used to indicate that the current AI / ML model of the terminal needs to be controlled. The message includes at least one of the following:

[0284] Location information: used to indicate the current location information of the second base station;

[0285] AI / ML model area information: used to indicate the AI / ML model area information corresponding to the second base station, such as an AI / ML model area indicator, an AI / ML model area indicator list, AI model information, location information, or a mapping rule / relationship between AI / ML model area information and model information; can also be used to help the terminal select / switch AI / ML models;

[0286] Indicator: used to indicate that model controls need to be implemented and / or that the current AI / ML model is no longer applicable;

[0287] Reason: Used to indicate why the AI / ML model was or was not changed.

[0288] Exemplarily, step 6: the terminal performs model control according to step 5.

[0289] Exemplarily, step 7: the terminal sends a message with a model control result to the first base station, such as AI / ML model information; or an indicator for indicating that the AI / ML model has been changed;

[0290] Exemplarily, step 8: The first base station sends a handover request message to the second base station. The message may carry AI / ML model information, where the AI / ML model information indicates an AI model applicable to the second base station. This step allows for pre-model identification between the terminal and the second base station.

[0291] Exemplarily, step 9: the second base station performs admission model control.

[0292] Exemplarily, step 10: the second base station sends a confirmation message regarding the handover request to the first base station, which carries information about the second base station and resource information of the terminal.

[0293] Exemplarily, step 11: the first base station sends a message with a handover command (such as an RRCReconfiguration message, etc.) to the terminal, which carries information for accessing the second base station, such as a cell ID.

[0294] It should be noted that in some examples, steps 4-7 can be performed simultaneously with steps 3, 8, and 9-11.

[0295] It should also be noted that in some examples, after step 4, the first base station checks the configured AI / ML model area information list. If the candidate second base station is within the configured AI / ML model area information list, it indicates that the terminal-side AI / ML is applicable to the second base station, and there is no need to perform model control. At this time, the first base station may also send an indication message to the terminal, which is used to indicate to the terminal that the terminal-side AI / ML is applicable to the second base station and it is not necessary to perform model control, or the first base station may also send a traditional message to the terminal, which includes an indicator, which is used to indicate to the terminal that the terminal-side AI / ML is applicable to the second base station and it is not necessary to perform model control.

[0296] It should be noted that the above steps are merely examples and do not limit the scope of the present invention. Various modifications and variations can be made to the steps without departing from the spirit and scope of the present invention.

[0297] The order of the described steps (signaling / boxes) is not intended to be construed as a limitation, and any number of the described steps (signaling / boxes) may be skipped or combined in any order to implement a method or an alternative method.

[0298] FIG11 illustrates a signaling diagram of a fast model control procedure during a handover procedure according to some embodiments of the present disclosure.

[0299] Figure 11 shows fast model control during a potential handover process. In this example, model control (e.g., model selection, model switching, model update, etc.) is located at the base station (e.g., gNB).

[0300] Exemplarily, step 1: the terminal receives a traditional measurement configuration, where the traditional measurement configuration is provided via dedicated signaling (eg, RRCReconfiguration, RRCResume, etc.).

[0301] Exemplarily, step 2: the terminal sends a measurement report message to the first base station (such as Source gNB, S-gNB) according to the measurement configuration in step 1.

[0302] Exemplarily, step 3: based on the report message in step 2, the first base station initiates a handover HO decision.

[0303] Exemplarily, in step 4: the first base station checks the configured AI / ML model area information list. If the candidate second base station (such as Target gNB, T-gNB) is within the configured AI / ML model area information list, no model control is required. The first base station executes step 6. At this time, the first base station can send the AI / ML model information to the second base station and carry it in the handover request message of step 6. If the candidate second base station is not within the configured AI / ML model area information list, the first base station executes steps 4' and 5.

[0304] Exemplarily, step 4': Alternatively, the first base station initiates an AI / ML model area information update process, the specific details of which are shown in Example 2.

[0305] Exemplarily, step 5: the terminal performs model control according to step 4', such as selecting / switching one or more suitable AIML models.

[0306] Exemplarily, step 6: The first base station sends a handover request message to the second base station. The message may carry model information, where the model information indicates an AI / ML model applicable to the second base station. This step can be performed in advance between the terminal and the second base station to perform model identification.

[0307] Exemplarily, step 7: the second base station performs admission model control.

[0308] Exemplarily, step 8: the second base station sends a confirmation message regarding the handover request to the first base station, which carries information about the second base station and resource information of the terminal.

[0309] Exemplarily, in step 9: the first base station sends a message with a handover command (such as an RRCReconfiguration message, etc.) to the terminal, which carries information for accessing the second base station, such as a cell ID.

[0310] Note that selecting / switching / updating a new AI model in step 5 does not automatically activate the new model. Activation can be indicated via a switch request message in step 7. Specifically, step 7 carries activation / deactivation configuration information, including the activation / deactivation start time, offset, duration, and end time of the model. Optionally, only the model activation / deactivation instruction is carried. Upon receiving this instruction, the model is automatically activated / deactivated.

[0311] It should be noted that the model information can be transmitted through the message of step 9 (such as RRCReconfiguration message, etc.), or can be sent through a dedicated RRC message, MAC CE or DCI after step 4.

[0312] It should be noted that the above steps are merely examples and do not limit the scope of the present invention. Various modifications and variations can be made to the steps without departing from the spirit and scope of the present invention.

[0313] The order of the described steps (signaling / boxes) is not intended to be construed as a limitation, and any number of the described steps (signaling / boxes) may be skipped or combined in any order to implement a method or an alternative method.

[0314] The present disclosure describes examples of communication between terminals and network element components in a network architecture in the above embodiments, which are mainly for illustrative purposes and not restrictive.

[0315] The order of the steps (signaling / boxes) described is not intended to be interpreted as limiting, and any number of the steps (signaling / boxes) described can be skipped or combined in any order to implement a method or alternative method. Typically, any of the components, modules, methods, and operations described herein can be implemented using software, firmware, hardware (e.g., fixed logic circuitry), manual processing, or any combination thereof. Some operations of the example methods can be described in the general context of executable instructions stored on a computer-readable memory locally and / or remotely on a computer processing system, and implementation methods can include software applications, programs, functions, and the like. Alternatively or in addition, any function described herein can be performed, at least in part, by one or more hardware logic components, such as, but not limited to, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), an application specific standard product (ASSP), a system on a chip (SoC), a complex programmable logic device (CPLD), and the like.

[0316] In addition, the signaling described in the embodiments of the present disclosure can be implemented in any manner known in the art. For example, the signaling can be explicit and / or implicit. In addition, the steps (signaling / frames) shown are for illustrative purposes only and are not intended to limit the present application.

[0317] Various aspects of the present invention may be understood from the following enumerated exemplary embodiments:

[0318] According to an example embodiment, a method for controlling an artificial intelligence / machine learning AI / ML model in a wireless communication system is provided, including:

[0319] The wireless communication node obtains AI / ML model area information, and the wireless communication node determines model control of the AI / ML model based on the AI / ML model area information. The wireless communication node is a base station or a terminal.

[0320] According to example embodiments, the model control of the AI / ML model includes at least one of the following:

[0321] AI / ML model activation, AI / ML model deactivation, AI / ML model selection, AI / ML model switching, AI / ML model rollback, AI / ML model update, AI / ML model training, AI / ML model retraining, or AI / ML model fine-tuning.

[0322] According to an example embodiment, the AI / ML model region information includes at least one of the following:

[0323] AI / ML model region identification ID,

[0324] location information,

[0325] Scene information,

[0326] and / or AI / ML model information.

[0327] According to an example embodiment, the location information includes at least one of:

[0328] Cell information, AI / ML model area identification ID, terminal location information, base station location information, scene information, control unit CU ID, data unit DU ID.

[0329] According to an example embodiment, the scene information includes at least one of the following:

[0330] Transmission environment information, speed information, angle spread information, delay spread information, signal-to-noise ratio, channel received power, and / or interference information.

[0331] According to one or more embodiments of the present disclosure, the wireless communication node capability information includes at least one of the following:

[0332] Computing capacity, storage capacity, AI / ML model support capacity, load conditions, and / or transmission capacity, AI / ML model support for AI / ML use cases and / or AI / ML model support for AI / ML features. Specifically, in some examples, for example, certain specific computing capacity, certain specific storage capacity, AI / ML model support for certain specific capabilities, certain specific load conditions, certain specific transmission capacity, AI / ML model support for certain specific use cases and / or support for certain specific features.

[0333] Wireless network capability node capability information; indicates the capabilities of a wireless network node, such as the model operation capabilities of the AI / ML models supported by the node, the node's hardware capability information (such as storage capacity, computing power, battery capacity, etc.), the functionality / features of the AI / ML models supported by the node, the data of the AI / ML models supported by the node, and the scenarios of the AI / ML models supported by the node.

[0334] According to one or more embodiments of the present disclosure, the AI / ML model information includes at least one of the following:

[0335] AI / ML model ID information and / or AI / ML model meta information.

[0336] According to an example embodiment, the AI / ML model information includes at least one of the following:

[0337] AI / ML model ID information and / or AI / ML model meta information.

[0338] According to an example embodiment, the AI / ML model region information is transmitted via at least one of the following:

[0339] Non-access stratum NAS message, radio resource control RRC message, medium access control element MAC-CE message, downlink control information DCI message, uplink channel, downlink channel, system message, N2 message and / or Xn message.

[0340] According to an example embodiment, determining, by the wireless communication node, model control of the AI / ML model based on the AI / ML model region information includes at least one of the following:

[0341] If the location information of the wireless communication node belongs to the location information indicated in the AI / ML model area information, the wireless communication node does not perform model control of the AI / ML model;

[0342] If the location information of the wireless communication node does not belong to the location information indicated in the AI / ML model area information, the wireless communication node performs model control of the AI / ML model;

[0343] If the AI / ML model information of the wireless communication node belongs to the AI / ML model information indicated in the AI / ML model area information, the wireless communication node does not perform model control of the AI / ML model;

[0344] If the AI / ML model information of the wireless communication node does not belong to the AI / ML model information indicated in the AI / ML model area information, the wireless communication node performs model control of the AI / ML model;

[0345] According to an example embodiment, the model control of executing the AI / ML model includes at least one of the following:

[0346] AI / ML model activation, AI / ML model deactivation, AI / ML model selection, AI / ML model switching, AI / ML model rollback, AI / ML model update, AI / ML model training, AI / ML model retraining, or AI / ML model fine-tuning, reporting of results of model control of an AI / ML model, and / or response of model control of an AI / ML model.

[0347] According to an example embodiment, when the wireless communication node is a terminal, the terminal acquires the AI / ML model area configuration information, and the terminal performs model control of the AI / ML model according to the AI / ML model area information.

[0348] According to an example embodiment, the terminal acquiring the AI / ML model area configuration information includes at least one of the following:

[0349] AI / ML model information, reason, indicator, terminal ID, and / or location information.

[0350] According to an example embodiment, when the wireless communication node is a base station, the base station acquires the AI / ML model area configuration information, and the base station performs model control of the AI / ML model according to the AI / ML model area information.

[0351] According to an example embodiment, the base station acquiring the AI / ML model area configuration information includes at least one of the following:

[0352] AI / ML model information, reason, indicator, base station ID, and / or location information.

[0353] According to an example embodiment, the AI / ML model region information request message and / or response message sent by the first wireless communication node to the second wireless communication node includes at least one of the following:

[0354] AI / ML model region identification ID,

[0355] location information,

[0356] Scene information,

[0357] Wireless communication node capability information, and / or

[0358] AI / ML model information.

[0359] According to an example embodiment, the wireless communication node sends the request message for the AI / ML model region information, including periodic transmission or event triggering.

[0360] According to an example embodiment, in the case of the periodic transmission, the AI / ML model region information is updated in response to a periodic configuration, wherein the periodic configuration includes a duration, a start time, and / or an end time of the update.

[0361] According to an example embodiment, in a case where the manner is event triggering, the AI / ML model region information is updated in response to an event triggering configuration.

[0362] According to an example embodiment, the periodic transmission and / or the event triggering corresponding periodic configuration and / or event triggering configuration are preconfigured, and / or configured, and / or fixed.

[0363] According to an example embodiment, the AI / ML model region information request message and / or response message is carried by at least one of the following:

[0364] Non-access stratum NAS message, radio resource control RRC message, medium access control element MAC-CE message, downlink control information DCI message, uplink channel, downlink channel, system message, N2 message and / or Xn message.

[0365] According to an example embodiment, there is provided a wireless communication method for a terminal, including:

[0366] The terminal obtains AI / ML model area information from the first base station, where the AI / ML model area information is used by the terminal to determine model control of the AI / ML model.

[0367] The terminal determines whether to execute model control of the AI / ML model based on the AI / ML model area information.

[0368] According to an example embodiment, further comprising:

[0369] The terminal obtains an AI / ML model area configuration message from the first base station.

[0370] According to an example embodiment, if the second base station is not in the AI / ML model area, the terminal determines to perform model control of the AI / ML model.

[0371] According to an example embodiment, the performing model control of the AI / ML model includes updating the AI / ML model region information between the terminal and the first base station.

[0372] According to an example embodiment, further comprising:

[0373] The terminal sends a report message for the AI / ML model area configuration message and a result of executing the model control of the AI / ML model to the first base station, and the first base station sends the obtained result of executing the model control of the AI / ML model to the second base station.

[0374] According to an example embodiment, there is provided a wireless communication method for a first base station, comprising:

[0375] The first base station obtains a model control reporting message of the AI / ML model from the terminal, where the model control reporting message of the AI / ML model includes a result of model control of the AI / ML model;

[0376] The first base station sends the cell switching request of the second base station and the result of the model control of the AI / ML model to the second base station.

[0377] According to an example embodiment, there is provided a wireless communication method for a first base station, comprising:

[0378] The first base station determines, according to the model control message of the AI / ML model fed back by the terminal, whether the second base station is in the AI / ML model area information;

[0379] If the second base station is not in the AI / ML model area information, the first base station performs model control of the AI / ML model.

[0380] According to an example embodiment, the model control of the AI / ML model includes updating the AI / ML model region information between the first base station and the second base station.

[0381] According to an example embodiment, the method further includes: the first base station sending the cell handover request of the second base station and a result of the model control to the second base station.

[0382] According to an example embodiment, the method further includes: if the second base station is in the AI / ML model area information, the first base station sending the AI / ML model information included in the AI / ML model area information and a cell switching request to the second base station to the second base station.

[0383] According to example embodiments, there is provided a chip including: a processor configured to call and execute a computer program stored in a memory, so that a device in which the chip is installed performs any one of the methods of example embodiments.

[0384] FIG12 is a block diagram of an example system 700 for wireless communication according to an embodiment of the present disclosure. The embodiments described herein can be implemented into a system using any appropriately configured hardware and / or software. FIG12 illustrates system 700, including radio frequency (RF) circuitry 710, baseband circuitry 720, processing unit 730, memory / storage 740, display 750, camera 760, sensor 770, and input / output (I / O) interface 780, coupled to one another as shown.

[0385] The processing unit 730 may include circuits, such as, but not limited to, one or more single-core or multi-core processors. The processor may include any combination of general-purpose processors and special-purpose processors, such as a graphics processor and an application processor. The processor may be coupled to a memory / storage and configured to execute instructions stored in the memory / storage to enable various applications and / or operating systems running on the system. The RF circuit 710, baseband circuit 720, processing unit 730, memory / storage 740, display 750, camera 760, sensor 770, and I / O interface 780 are well-known components of the system 700, such as, but not limited to, laptop computing devices, tablet computing devices, netbooks, ultrabooks, smartphones, etc. In addition, instructions as a software product may be stored in a computer-readable storage medium. The software product in the computer is stored in a storage medium and includes multiple commands for a computing device (such as a personal computer, server, or network device) to execute all or some of the steps disclosed in the embodiments of the present disclosure. The storage medium includes a USB disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a floppy disk, or other types of media capable of storing program codes.

[0386] The embodiments of the present disclosure are a combination of techniques / processes that may be employed in 3GPP specifications to create a final product.

[0387] While the present disclosure has been described in connection with what is considered to be the most practical and preferred embodiment, it is to be understood that the disclosure is not limited to the disclosed embodiment, but is intended to cover various arrangements embodied within the broadest interpretation of the appended claims.

Claims

1. A control method for an artificial intelligence / machine learning AI / ML model in a wireless communication system, comprising: The wireless communication node obtains AI / ML model area information, and the wireless communication node determines model control of the AI / ML model based on the AI / ML model area information. The wireless communication node is a base station or a terminal.

2. The method according to claim 1, wherein: Model control of the AI / ML model includes at least one of the following: AI / ML model activation, AI / ML model deactivation, AI / ML model selection, AI / ML model switching, AI / ML model rollback, AI / ML model update, AI / ML model training, AI / ML model retraining, or AI / ML model fine-tuning.

3. The method according to claim 1, wherein: The AI / ML model region information includes at least one of the following: AI / ML model region identification ID, Location information, Scene information, Wireless communication node capability information, and / or AI / ML model information.

4. The method according to claim 3, wherein: The location information includes at least one of the following: Cell information, AI / ML model area identification ID, terminal location information, base station location information, scene information, control unit CU ID, data unit DU ID.

5. The method according to claim 3, wherein: The scene information includes at least one of the following: Transmission environment information, speed information, angle spread information, delay spread information, signal-to-noise ratio, channel received power, and / or interference information.

6. The method according to claim 3, wherein: The wireless communication node capability information includes at least one of the following: Computing power, storage capacity, AI / ML model support capabilities, load conditions, transmission capacity, AI / ML model support, AI / ML usage scenarios, and / or AI / ML model support for AI / ML features.

7. The method according to claim 3, wherein: The AI / ML model information includes at least one of the following: AI / ML model ID information, and / or AI / ML model meta information.

8. The method according to claim 1, wherein: The AI / ML model region information is transmitted via at least one of the following: Non-access stratum NAS message, radio resource control RRC message, medium access control element MAC-CE message, downlink control information DCI message, uplink channel, downlink channel, system message, N2 message and / or Xn message.

9. The method according to claim 3, wherein: Determining, by the wireless communication node, model control of the AI / ML model based on the AI / ML model region information includes at least one of the following: If the location information of the wireless communication node belongs to the location information indicated in the AI / ML model area information, the wireless communication node does not perform model control of the AI / ML model; If the location information of the wireless communication node does not belong to the location information indicated in the AI / ML model area information, the wireless communication node performs model control of the AI / ML model; If the AI / ML model information of the wireless communication node belongs to the AI / ML model information indicated in the AI / ML model area information, the wireless communication node does not perform model control of the AI / ML model; If the AI / ML model information of the wireless communication node does not belong to the AI / ML model information indicated in the AI / ML model area information, the wireless communication node performs model control of the AI / ML model.

10. The method according to claim 9, wherein: The model control of executing the AI / ML model includes at least one of the following: AI / ML model activation, AI / ML model deactivation, AI / ML model selection, AI / ML model switching, AI / ML model rollback, AI / ML model update, AI / ML model training, AI / ML model retraining, or AI / ML model fine-tuning, reporting of results of model control of the AI / ML model, and / or response of model control of the AI / ML model.

11. The method according to claim 10, wherein: When the wireless communication node is a terminal, the terminal obtains the AI / ML model area configuration information, and the terminal performs model control of the AI / ML model according to the AI / ML model area information.

12. The method according to claim 11, wherein: The terminal acquiring the AI / ML model area configuration information includes at least one of the following: AI / ML model information, reasons, indicators, terminal ID, and / or location information.

13. The method according to claim 10, wherein: When the wireless communication node is a base station, the base station obtains the AI / ML model area configuration information, and the base station performs model control of the AI / ML model according to the AI / ML model area information.

14. The method according to claim 13, wherein: The base station acquiring the AI / ML model area configuration information includes at least one of the following items: AI / ML model information, reasons, indicators, base station ID, and / or location information.

15. The method according to claim 1, wherein: The AI / ML model area information request message and / or response message sent by the first wireless communication node to the second wireless communication node includes at least one of the following: AI / ML model region ID, Location information, Scene information, Wireless communication node capability information, and / or AI / ML model information.

16. The method according to claim 15, wherein: The wireless communication node sends a request message for the AI / ML model area information, including periodic sending or event triggering.

17. The method according to claim 16, wherein: In the case of the periodic transmission, the AI / ML model region information is updated in response to a periodic configuration, wherein the periodic configuration includes a duration, a start time, and / or an end time of the update.

18. The method according to claim 16, wherein: In the case where the method is event triggered, the AI / ML model area information is updated in response to the event triggered configuration.

19. The method according to claim 16, wherein: The periodic transmission and / or the event triggering corresponding periodic configuration and / or event triggering configuration are preconfigured and / or configured and / or fixed.

20. According to the method of claim 15, the AI / ML model region information request message and / or response message is carried by at least one of the following: Non-access stratum NAS message, radio resource control RRC message, medium access control element MAC-CE message, downlink control information DCI message, uplink channel, downlink channel, system message, N2 message and / or Xn message.

21. A wireless communication method for a terminal, comprising: The terminal acquires AI / ML model area information from the first base station, where the AI / ML model area information is used by the terminal to determine model control of the AI / ML model, The terminal determines whether to execute model control of the AI / ML model based on the AI / ML model area information.

22. The method according to claim 21, further comprising: The terminal obtains an AI / ML model area configuration message from the first base station.

23. The method according to claim 22, wherein: If the second base station is not in the AI / ML model area, the terminal determines to perform model control of the AI / ML model.

24. The method according to claim 23, wherein: The executing model control of the AI / ML model includes updating the AI / ML model area information between the terminal and the first base station.

25. The method of claim 21, further comprising: The terminal sends a report message for the AI / ML model area configuration message and a result of executing the model control of the AI / ML model to the first base station, and the first base station sends the obtained result of executing the model control of the AI / ML model to the second base station.

26. A terminal, configured to execute the method according to any one of claims 21 to 25.

27. A wireless communication method for a first base station, comprising: The first base station obtains a model control reporting message of the AI / ML model from the terminal, where the model control reporting message of the AI / ML model includes a result of the model control of the AI / ML model; The first base station sends the cell switching request of the second base station and the result of the model control of the AI / ML model to the second base station.

28. A wireless communication method for a first base station, comprising: The first base station determines, according to a model control message of the AI / ML model fed back by the terminal, whether the second base station is in the AI / ML model area information; If the second base station is not in the AI / ML model area information, the first base station performs model control of the AI / ML model.

29. The method according to claim 28, wherein: Model control of the AI / ML model includes updating the AI / ML model region information between the first base station and the second base station.

30. The method of claim 29, further comprising: The first base station sends the cell switching request of the second base station and the result of the model control to the second base station.

31. The method of claim 28, further comprising: If the second base station is in the AI / ML model area information, the first base station sends the AI / ML model information contained in the AI / ML model area information and a cell switching request to the second base station to the second base station.

32. A chip, comprising: A processor configured to call and run a computer program stored in a memory so that a device in which the chip is installed performs the method according to any one of claims 1 to 31.

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