WO2025166573A1 - Communication method and device, and storage medium - Google Patents

Abstract

The present invention relates to a communication method and device, and a storage medium. The method comprises: determining a first AI model or a first AI function from among a plurality of AI models or AI functions on the basis of first information, the first information comprising information associated with parameters for training the first AI model or the first AI function. That is to say, a terminal device can perform model selection on the basis of first information, and does not need to monitor every model, thereby reducing the computing complexity of the terminal device, and the terminal device does not need to send monitoring data to a network device, thereby reducing the overhead of data transmission, improving network performance.

Description

Communication method, device and storage medium Technical Field

The present disclosure relates to the field of communication technology, and in particular to a communication method, device, and storage medium.

Background Art

Machine learning algorithms are currently one of the most important implementation methods of artificial intelligence (AI) technology. By applying machine learning to large amounts of training data, AI models can be generated, which can then be used to predict events. Multiple AI models with different parameter configurations can be trained in a single scenario, and the terminal device needs to select the best performing AI model from among these models for prediction.

Summary of the Invention

The embodiments of the present disclosure provide a communication method, a device, and a storage medium.

According to a first aspect of an embodiment of the present disclosure, a communication method is proposed, which is performed by a terminal device. The method includes:

A first AI model or a first AI function is determined from a plurality of AI models or AI functions based on first information, wherein the first information includes information associated with parameters for training the first AI model or the first AI function.

According to a second aspect of an embodiment of the present disclosure, a communication method is provided, which is performed by a network device. The method includes:

Sending fourth information to the terminal device, where the fourth information is used by the terminal device to determine a first AI model or a first AI function from a plurality of AI models or AI functions, the fourth information including information associated with the network device in the first information, and the first information including information associated with parameters for training the first AI model or the first AI function.

According to a third aspect of an embodiment of the present disclosure, a terminal device is provided, including:

A processing module is configured to determine a first AI model or a first AI function from a plurality of AI models or AI functions based on first information, wherein the first information includes information associated with parameters for training the first AI model or the first AI function.

According to a fourth aspect of an embodiment of the present disclosure, a network device is provided, including:

The transceiver module is configured to send fourth information to the terminal device, where the fourth information is used by the terminal device to determine a first AI model or a first AI function from a plurality of AI models or AI functions, the fourth information including information associated with the network device in the first information, and the first information including information associated with parameters for training the first AI model or the first AI function.

According to a fifth aspect of an embodiment of the present disclosure, a communication device is proposed, comprising: one or more processors; wherein the communication device is used to execute an optional implementation of the first aspect or the second aspect.

According to the sixth aspect of an embodiment of the present disclosure, a communication system is proposed, which may include: a terminal device and a network device; wherein the terminal device is configured to execute the method described in the optional implementation manner of the first aspect, and the network device is configured to execute the method described in the optional implementation manner of the second aspect.

According to a seventh aspect of an embodiment of the present disclosure, a storage medium is proposed, which stores instructions. When the instructions are executed on a communication device, the communication device executes the method described in the optional implementation of the first aspect or the second aspect.

The technical solutions provided by the embodiments of the present disclosure may include the following advantageous effects: a first AI model or a first AI function is determined from a plurality of AI models or AI functions based on first information, wherein the first information includes information associated with parameters for training the first AI model or the first AI function. In other words, a terminal device can select a model based on the first information, eliminating the need to monitor each model, thereby reducing the computational complexity of the terminal device and eliminating the need to send monitoring data to a network device, thereby reducing data transmission overhead and improving network performance.

It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following drawings required for describing the embodiments are introduced. The following drawings are merely some embodiments of the present disclosure and do not impose specific limitations on the protection scope of the present disclosure.

FIG1A is a schematic diagram showing the architecture of a communication system according to an embodiment of the present disclosure.

FIG1B is a schematic diagram showing an observation window and a prediction window according to an embodiment of the present disclosure.

FIG2A is an interactive schematic diagram illustrating a communication method according to an embodiment of the present disclosure.

FIG2B is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure.

FIG2C is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure.

FIG3A is a flow chart showing a communication method according to an embodiment of the present disclosure.

FIG3B is a flow chart showing a communication method according to an embodiment of the present disclosure.

FIG3C is a flow chart showing a communication method according to an embodiment of the present disclosure.

FIG3D is a flow chart illustrating a communication method according to an embodiment of the present disclosure.

FIG3E is a flow chart showing a communication method according to an embodiment of the present disclosure.

FIG3F is a flow chart illustrating a communication method according to an embodiment of the present disclosure.

FIG4A is a flow chart showing a communication method according to an embodiment of the present disclosure.

FIG4B is a flow chart showing a communication method according to an embodiment of the present disclosure.

FIG5 is an interactive schematic diagram illustrating a communication method according to an embodiment of the present disclosure.

FIG6A is a schematic structural diagram of a terminal device proposed in an embodiment of the present disclosure.

FIG6B is a schematic structural diagram of a network device proposed in an embodiment of the present disclosure.

FIG7A is a schematic structural diagram of a communication device proposed in an embodiment of the present disclosure.

FIG7B is a schematic diagram of the structure of the chip proposed in an embodiment of the present disclosure.

DETAILED DESCRIPTION

The embodiments of the present disclosure provide a communication method, a device, and a storage medium.

In a first aspect, an embodiment of the present disclosure provides a communication method, which is executed by a terminal device. The method includes:

A first AI model or a first AI function is determined from a plurality of AI models or AI functions based on first information, wherein the first information includes information associated with parameters for training the first AI model or the first AI function.

In the above embodiment, the terminal device can select a model based on the first information without having to monitor each model, thereby reducing the computational complexity of the terminal device and eliminating the need to send monitoring data to the network device, thereby reducing data transmission overhead and improving network performance.

In conjunction with some embodiments of the first aspect, in some embodiments, the first information includes at least one of the following:

first indication information, where the first indication information is used to indicate channel characteristics;

Second indication information, where the second indication information is used to indicate an additional condition corresponding to the terminal device;

third indication information, where the third indication information is used to indicate an additional condition corresponding to the network device;

The fourth indication information is used to indicate the parameters configured by the network device for the terminal device.

In the above embodiment, the terminal device can select a model according to a variety of indication information, thereby improving the flexibility of model selection.

In conjunction with some embodiments of the first aspect, in some embodiments, determining the first AI model or the first AI function from the plurality of AI models or AI functions according to the first information includes:

Determining, according to the first indication information, at least one second AI model or second AI function from the multiple AI models or AI functions;

Determine the first AI model or the first AI function from the at least one second AI model or second AI function according to fifth indication information, where the fifth indication information includes at least one of the following: the second indication information, the third indication information, and the fourth indication information.

In the above embodiment, the terminal device can select a model according to the first indication information and the fifth indication information, thereby improving the accuracy of the model selection.

In conjunction with some embodiments of the first aspect, in some embodiments, determining the first AI model or the first AI function from the plurality of AI models or AI functions according to the first information includes:

Determining, according to the third indication information, at least one third AI model or third AI function from the multiple AI models or AI functions;

The first AI model or the first AI function is determined from the at least one third AI model or third AI function according to sixth indication information, where the sixth indication information includes at least one of the following: the second indication information and the fourth indication information.

In the above embodiment, the terminal device can select a model according to the third indication information and the sixth indication information, thereby improving the accuracy of the model selection.

In conjunction with some embodiments of the first aspect, in some embodiments, the third AI model or the third AI function includes multiple third AI models or third AI functions, and determining the first AI model or the first AI function from the at least one third AI model or third AI function according to the sixth indication information includes:

determining, according to the second indication information, at least one fourth AI model or fourth AI function from a plurality of third AI models or third AI functions;

Determine, according to the fourth indication information, the first AI model or the first AI function from the at least one fourth AI model or fourth AI function.

In the above embodiment, the terminal device can select a model based on the third indication information, the second indication information, and the fourth indication information, thereby further improving the accuracy of the model selection.

In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

Receive fourth information sent by the network device, where the fourth information is used by the terminal device to determine a first AI model or a first AI function from a plurality of AI models or AI functions, and the fourth information includes information associated with the network device in the first information.

In the above embodiment, the network device may send the fourth information to the terminal device so that the terminal device selects a model according to the fourth information.

In conjunction with some embodiments of the first aspect, in some embodiments, receiving fourth information sent by the network device includes:

Reporting second information to the network device, where the second information is used by the network device to determine the fourth indication information;

Receive the fourth indication information sent by the network device.

In the above embodiment, the terminal device may send the second information to the network device to request the network device to send the fourth indication information.

In combination with some embodiments of the first aspect, in some embodiments, third information sent by the network device is received, and the third information is used to instruct the terminal device to report the second information.

In the above embodiment, the terminal device may send the second information to the network device after receiving the third information sent by the network, so that the information transmission method is more flexible.

In conjunction with some embodiments of the first aspect, in some embodiments, receiving third information sent by the network device includes:

The third information is received by the network device through the first signaling, where the first signaling includes at least one of the following: a radio resource control RRC message, a media access control control element MAC-CE, and downlink control information DCI.

In the above embodiment, the terminal device can receive the third information sent by the network device through any signaling in the first signaling, so that the transmission method of the third information is more flexible.

In conjunction with some embodiments of the first aspect, in some embodiments, reporting the second information to the network device includes:

The second information is reported to the network device through a second signaling, where the second signaling includes at least one of the following: RRC, MAC-CE, and uplink control information UCI.

In the above embodiment, the terminal device can report the second information through any signaling in the second signaling, so that the transmission method of the second information is more flexible.

In conjunction with some embodiments of the first aspect, in some embodiments, the second information includes at least one of the following:

the first indication information;

the second indication information;

Seventh indication information, where the seventh indication information is used to indicate a fifth AI model or a fifth AI function, where the fifth AI model or the fifth AI function includes an AI model or AI function that can be used by the terminal device.

In the above embodiment, the terminal device can report different indication information to the network device so that the network device can determine the fourth indication information, making the determination method of the fourth indication information more flexible and accurate.

In conjunction with some embodiments of the first aspect, in some embodiments, receiving fourth information sent by the network device includes:

Receive the third indication information sent by the network device.

In the above embodiment, the terminal device may receive the third indication information sent by the network device, so as to perform model selection according to the third indication information.

In combination with some embodiments of the first aspect, in some embodiments, each first indication information corresponds to at least one AI model or AI function.

In the above embodiment, different channel characteristics can correspond to at least one AI model or AI function, so that the most suitable AI model or AI function can be selected, thereby improving the accuracy of model prediction.

In a second aspect, an embodiment of the present disclosure provides a communication method, which is performed by a network device. The method includes:

Sending fourth information to the terminal device, where the fourth information is used by the terminal device to determine a first AI model or a first AI function from a plurality of AI models or AI functions, the fourth information including information associated with the network device in the first information, and the first information including information associated with parameters for training the first AI model or the first AI function.

In conjunction with some embodiments of the second aspect, in some embodiments, sending the fourth information to the terminal device includes:

receiving second information reported by the terminal device;

Determining fourth indication information according to the second information, where the fourth indication information is used to indicate parameters configured by the network device for the terminal device;

Send the fourth indication information to the terminal device.

In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes:

Send third information to the terminal device, where the third information is used to instruct the terminal device to report the second information.

In conjunction with some embodiments of the second aspect, in some embodiments, sending the third information to the terminal device includes:

The third information is sent to the terminal device via a first signaling, where the first signaling includes at least one of the following: a radio resource control RRC message, a media access control control element MAC-CE, and downlink control information DCI.

In conjunction with some embodiments of the second aspect, in some embodiments, receiving the second information reported by the terminal device includes:

Receive the second information reported by the terminal device through the second signaling, where the second signaling includes at least one of the following: RRC, MAC-CE, uplink control information UCI.

In conjunction with some embodiments of the second aspect, in some embodiments, the second information includes at least one of the following:

first indication information, where the first indication information is used to indicate channel characteristics;

Second indication information, where the second indication information is used to indicate an additional condition corresponding to the terminal device;

Seventh indication information, where the seventh indication information is used to indicate a fifth AI model or a fifth AI function, where the fifth AI model or the fifth AI function includes an AI model or AI function that can be used by the terminal device.

In conjunction with some embodiments of the second aspect, in some embodiments, sending the fourth information to the terminal device includes:

Send third indication information to the terminal device, where the third indication information is used to indicate additional conditions corresponding to the network device.

In a third aspect, an embodiment of the present disclosure provides a communication method, the method comprising:

The network device sends fourth information to the terminal device, where the fourth information includes information associated with the network device in the first information, where the first information includes information associated with parameters for training the first AI model or the first AI function;

The terminal device determines a first AI model or a first AI function from a plurality of AI models or AI functions according to the first information.

In a fourth aspect, an embodiment of the present disclosure proposes a terminal device, which may include at least one of a transceiver module and a processing module; wherein the terminal device can be used to execute the optional implementation method of the first aspect.

In a fifth aspect, an embodiment of the present disclosure proposes a network device, which may include at least one of a transceiver module and a processing module; wherein the network device can be used to execute the optional implementation method of the second aspect.

In a sixth aspect, an embodiment of the present disclosure proposes a terminal device, which may include: one or more processors; wherein the terminal device can be used to execute the optional implementation method of the first aspect.

In a seventh aspect, an embodiment of the present disclosure proposes a network device, which may include: one or more processors; wherein, the network device can be used to execute the optional implementation method of the second aspect.

In an eighth aspect, an embodiment of the present disclosure proposes a communication device, which may include: one or more processors; wherein the communication device can be used to execute an optional implementation of the first aspect or the second aspect.

In the ninth aspect, an embodiment of the present disclosure proposes a communication system, which may include: a terminal device and a network device; wherein, the terminal device is configured to execute the method described in the optional implementation manner of the first aspect, and the network device is configured to execute the method described in the optional implementation manner of the second aspect.

In a tenth aspect, an embodiment of the present disclosure proposes a storage medium storing instructions, which, when executed on a communication device, enables the communication device to execute the method described in the optional implementation of the first aspect or the second aspect.

In an eleventh aspect, an embodiment of the present disclosure proposes a program product, which, when executed by a communication device, enables the communication device to execute the method described in the optional implementation manner of the first aspect or the second aspect.

In a twelfth aspect, an embodiment of the present disclosure proposes a computer program, which, when executed on a computer, enables the computer to execute the method described in the optional implementation of the first or second aspect.

In a thirteenth aspect, an embodiment of the present disclosure provides a chip or a chip system, wherein the chip or chip system includes a processing circuit configured to execute the method described in the optional implementation of the first aspect or the second aspect.

It is understandable that the above-mentioned terminal devices, network devices, communication devices, communication systems, storage media, program products, computer programs, chips, or chip systems can all be used to perform the methods proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding methods and will not be repeated here.

The present disclosure provides a communication method, device, and storage medium. In some embodiments, the terms communication method and information processing method are interchangeable; the terms communication device and information processing device are interchangeable; and the terms communication system and information processing system are interchangeable.

The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and/or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular, such as "a", "an", "the", "above", "said", "the", "the", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun following the article may be understood as a singular expression or a plural expression.

In some embodiments, "plurality" may refer to two or more.

In some embodiments, the terms "at least one of", "one or more", "a plurality of", "multiple", etc. can be used interchangeably.

In some embodiments, descriptions such as "at least one of A and B," "A and/or B," "A in one case, B in another case," or "in response to one case A, in response to another case B" may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); and in some embodiments, A and B (both A and B are executed). The above is also applicable when there are more branches such as A, B, and C.

In some embodiments, "A or B" and other descriptions may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The above is also applicable when there are more branches such as A, B, C, etc.

The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, quantity or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for another example, if the description object is "information", then the "first information" and the "second information" can be the same information or different information, and their contents can be the same or different.

In some embodiments, “including A,” “comprising A,” “used to indicate A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

In some embodiments, terms such as "in response to...", "in response to determining...", "in the case of...", "at the time of...", "when...", "if...", "if...", etc. can be used interchangeably.

In some embodiments, terms such as "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not less than", and "above" can be replaced with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", and "below" can be replaced with each other.

In some embodiments, devices and the like can be interpreted as physical or virtual, and their names are not limited to those described in the embodiments. Terms such as "device," "equipment," "device," "circuit," "network element," "node," "function," "unit," "section," "system," "network," "chip," "chip system," "entity," and "subject" can be used interchangeably.

In some embodiments, "network" can be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).

In some embodiments, “Access Network Device (AN Device)” also includes “Radio Access Network Device (RAN Device)”, “Base Station (BS)”, “Radio Base Station (Radio Base Station)”, “Fixed Station (Fixed Station)”, “Node (Node)”, “Access Point (Access Point)”, “Transmission Point (TP)”, “Reception Point (RP)”, “Transmission and/or Reception Point (Transmission /Reception Point (TRP)", "Panel", "Antenna Panel", "Antenna Array", "Cell", "Macro Cell", "Small Cell", "Femto Cell", "Pico Cell", "Sector", "Cell Group", "Serving Cell", "Carrier", "Component Carrier", and "Bandwidth Part (BWP)" are terms that can be used interchangeably.

In some embodiments, the terms "terminal", "terminal device", "user equipment (UE)", "user terminal (User Terminal)", "mobile station (MS)", "mobile terminal (MT)", subscriber station (Subscriber Station), mobile unit (Mobile Unit), subscriber unit (Subscriber Unit), wireless unit (Wireless Unit), remote unit (Remote Unit), mobile device (Mobile Device), wireless device (Wireless Device), wireless communication device (Wireless Communication Device), remote device (Remote Device), mobile subscriber station (Mobile Subscriber Station), access terminal (Access Terminal), mobile terminal (Mobile Terminal), wireless terminal (Wireless Terminal), remote terminal (Remote Terminal), handset (Handset), user agent (User Agent), mobile client (Mobile Client), client (Client) and the like can be used interchangeably.

In some embodiments, the access network device, the core network device, or the network device can be replaced by a terminal. For example, the various embodiments of the present disclosure can also be applied to a structure in which the communication between the access network device, the core network device, or the network device and the terminal is replaced by communication between multiple terminals (for example, device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, it is also possible to set the structure in which the terminal has all or part of the functions of the access network device. In addition, terms such as "uplink" and "downlink" can also be replaced by terms corresponding to communication between terminals (for example, "side"). For example, uplink channels, downlink channels, etc. can be replaced by side channels or direct channels, and uplinks, downlinks, etc. can be replaced by side links or direct links.

In some embodiments, the terminal may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, the core network device, or the network device may have a structure that has all or part of the functions of the terminal.

In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

In some embodiments, data, information, etc. may be obtained with the user's consent.

In addition, each element, each row, or each column in the table of the embodiment of the present disclosure can be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns can also be implemented as an independent embodiment.

FIG1A is a schematic diagram illustrating the architecture of a communication system according to an embodiment of the present disclosure. As shown in FIG1A , the communication system 100 may include a terminal device 101 and a network device 102.

In some embodiments, the terminal device 101 may include at least one of a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in smart grid, a wireless terminal device in transportation safety, a wireless terminal device in smart city, and a wireless terminal device in smart home, but is not limited to these.

In some embodiments, the network device 102 may include at least one of an access network device and a core network device.

In some embodiments, the access network device may be a node or device that accesses the terminal device to the wireless network. The access network device may include an evolved Node B (eNB), a next generation evolved Node B (ng-eNB), a next generation Node B (gNB), a node B (NB), a home node B (HNB), a home evolved node B (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, and at least one of an access node in a Wi-Fi system, but is not limited thereto.

In some embodiments, the technical solution of the present disclosure may be applicable to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure may become internal interfaces of Open RAN, and the processes and information interactions between these internal interfaces may be implemented through software or programs.

In some embodiments, the access network device may be composed of a centralized unit (CU) and a distributed unit (DU), where the CU may also be called a control unit (Control Unit). The CU-DU structure may be used to split the protocol layer of the access network device, with some functions of the protocol layer being centrally controlled by the CU, and the remaining part or all of the functions of the protocol layer being distributed in the DU, which is centrally controlled by the CU, but is not limited to this.

In some embodiments, the core network device may be a single device, or may be multiple devices or a group of devices. The core network may include at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC).

It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.

The following embodiments of the present disclosure may be applied to the communication system 100 shown in FIG1A , or a portion thereof, but are not limited thereto. The entities shown in FIG1A are examples. The communication system may include all or part of the entities shown in FIG1A , or may include other entities outside of FIG1A . The number and form of the entities are arbitrary. The entities may be physical or virtual. The connection relationship between the entities is an example. The entities may be connected or disconnected. The connection may be in any manner, whether direct or indirect, and may be wired or wireless.

The embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G New Radio (NR), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New Radio Access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) network, Device-to-Device (D2D) system, Machine-to-Machine (M2M) system, Internet of Things (IoT) system, Vehicle-to-Everything (V2X), systems using other communication methods, and next-generation systems based on these. In addition, multiple systems can also be combined (for example, For example, a combination of LTE or LTE-A and 5G) applications.

In some embodiments of the present disclosure, for terminal devices moving at medium to high speeds, using the traditional 3rd Generation Partnership Project (3GPP) Rel-15/16/17 Type II codebook to feed back Channel Status Information (CSI) can degrade system performance due to the rapid changes in channel information in the time domain. To address this issue, the 3GPP Rel-18 standardization introduced an additional codebook, Rel-18 Type II.

In some embodiments, the Rel-18 Type II codebook uses a traditional autoregressive or linear minimum mean squares error (LMMSE) algorithm to predict future downlink channel information based on historical downlink channel information estimated by the terminal. The precoding information corresponding to the future time is then calculated based on the predicted downlink channel information. In medium- and high-speed mobility scenarios, the Rel-18 Type II codebook can significantly improve system performance compared to the Rel-16/17 Type II codebook.

In some embodiments, with the development of AI technology, AI has been widely applied to the physical layer of wireless communications. The aforementioned traditional CSI prediction algorithm can also predict future channel information through AI model reasoning. Current simulation evaluations show that AI-based prediction performance outperforms traditional non-AI algorithms.

In some embodiments, when using AI or non-AI prediction algorithms, CSI at multiple historical moments is required. Downlink channel information is estimated based on Channel Status Information-Reference Signals (CSI-RS) sent at multiple historical moments. This range (the time range corresponding to multiple historical moments) is called the observation window. The range (the time range corresponding to multiple future moments) within which CSI at multiple future moments is predicted is called the prediction window.

Figure 1B is a schematic diagram of an observation window and a prediction window according to an embodiment of the present disclosure. As shown in Figure 1B, three observation windows and prediction windows under different parameter configurations are listed. Wherein, N represents the N moments at which CSI-RS is transmitted within the observation window, for example, N = 4/5/8/10; M represents the interval at which CSI-RS is transmitted within the observation window, for example, M = D = 2.5/4/5 slots; K represents the K moments at which CSI is predicted within the prediction window, for example, K = 1/3/4 slots; D represents the interval between adjacent moments at which CSI is predicted within the prediction window, for example, D = 1/2.5/4/5/8 slots; and the length of the prediction window, w _d = K·D.

It should be noted that the observation window and prediction window in FIG1B are for illustration only, and the values of the four parameters N, M, K, and D can be arbitrarily combined, which is not limited in the embodiment of the present disclosure.

In some embodiments, the AI function or AI model on the UE side can realize function or model identification between the UE and the network (Network, NW) through corresponding function identification or model identification methods.

In some embodiments, function identification can be achieved by the UE reporting its supported functions to the NW through capability reporting. A function supported by the UE may include one or more artificial intelligence/machine learning (AI/ML) models. Model IDs can also be used in life cycle management (LCM) based on function identification.

In some embodiments, model identification can be achieved by NW identifying the models supported by UE through Model ID, and UE can indicate the supported models to NW through Model ID.

In some embodiments, model identification must be completed before model inference. UE-side model identification includes the following types:

(1) Identify NW and UE side models offline

During the offline model identification process, the corresponding model can be assigned a corresponding Model ID.

(2) Model recognition is achieved through air interface signaling, which includes the following two methods:

Method 1: The UE actively initiates model identification, and the NW can assist in completing the remaining steps of model identification.

During model identification, the corresponding model can be assigned a corresponding Model ID.

Method 2: The NW actively initiates model identification, and the UE can assist in completing the remaining steps of model identification.

During model identification, the corresponding model can be assigned a corresponding Model ID.

In some embodiments, after completing the relevant function identification or model identification, for an AI model on the UE side, additional conditional information can be used to ensure that the UE side model used for reasoning is consistent with the trained model. The additional conditional information can be conditional information on the NW side or conditional information on the UE side. However, when the additional conditional information corresponds to multiple AI models, the AI model used for reasoning cannot be uniquely determined based on the additional conditional information. For example, in a channel scenario of a certain urban macro cell (UMa), multiple AI models corresponding to different N, M, D, and K parameter configurations can be trained. Therefore, it is necessary to select one model from multiple models for reasoning.

In some embodiments, based on model performance monitoring, a model with better performance can be selected from multiple models for inference. Performance monitoring of the CSI prediction model on the UE side includes the following three methods:

Method 1: UE-side performance monitoring:

The UE calculates the performance criteria of the AI model based on the measured channel information to monitor the model performance.

Method 2: NW-side performance monitoring:

The UE reports the measured channel information to the NW, allowing the NW to calculate the performance criteria of the AI model to monitor the model performance.

Method 3: Hybrid monitoring on the UE side and the NW side:

UE calculates the performance criteria of the AI model based on the measured channel information and reports the performance criteria calculation results to the NW so that the NW can monitor the performance of the AI model. Test model performance.

In some embodiments, AI/ML model performance monitoring based on the UE side and/or NW side is a method for selecting a model, but this method may result in high computational complexity of the UE or high data transmission overhead (a method of hybrid monitoring on the UE side and the NW side). In particular, when there are many AI models on the UE side, the UE needs to traverse and monitor each AI model and calculate the performance indicators of each AI model. The computational complexity of the UE will increase exponentially with the number of AI models on the UE side. Therefore, how the UE selects an AI model or AI function becomes an urgent problem to be solved.

FIG2A is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure. The method may be executed by the above-mentioned communication system. As shown in FIG2A , the method may include:

Step S2101: The network device sends third information to the terminal device.

In some embodiments, the terminal device may receive the third message. For example, the terminal device may receive the third message sent by the network device. For another example, the terminal device may also receive the third message sent by another entity.

In some embodiments, the third information can be used to instruct the terminal device to report the second information.

In some embodiments, the second information may be used by the network device to determine the fourth indication information.

In some embodiments, the fourth indication information can be used to indicate the parameters configured by the network device for the terminal device.

In some embodiments, the parameters configured by the network device for the terminal device may include one or more of N, M, K, and D, wherein the definitions of N, M, K, and D can refer to the description of Figure 1B and will not be repeated here.

It should be noted that the parameters configured by the above network device for the terminal device are for illustration only and are not limited to these in the embodiments of the present disclosure.

In some embodiments, the second information may include at least one of the following:

First instruction information;

Second instruction information;

The seventh indication information may be used to indicate a fifth AI model or a fifth AI function, where the fifth AI model or the fifth AI function includes an AI model or AI function that can be used by the terminal device.

In some embodiments, the first indication information may be used to indicate channel characteristics. For example, the first indication information may be time-domain channel properties (TDCP).

In some embodiments, the second indication information can be used to indicate additional conditions corresponding to the terminal device.

In some embodiments, the additional conditions corresponding to the terminal device may be hardware parameters such as memory and power.

In some embodiments, the first indication information and the second indication information may be a value or a range of value intervals.

For example, the first indication information may be an amplitude value or an amplitude range of TDCP, for example, the first indication information may be TDCP of 0.5, or TDCP of 0.26 to 0.5. The second indication information may be a power value of 20%.

In some embodiments, the fifth AI model may be an AI model that can be used at the current moment among multiple AI models supported by the terminal device, and the fifth AI function may be an AI function that can be used at the current moment among multiple AI functions supported by the terminal device.

In some embodiments, an AI function may include one or more AI models.

In some embodiments, the seventh indication information may include a fifth AI model and a fifth AI function, and the seventh indication information may include a model identifier of the fifth AI model or a function identifier of the fifth AI function. Different AI models correspond to different model identifiers, and different AI functions correspond to different function identifiers.

In some embodiments, the AI model may be an AI model for CSI prediction.

In some embodiments, the AI function may be an AI function for CSI prediction.

In some embodiments, the network device may send the third information to the terminal device via first signaling.

In some embodiments, the first signaling may include at least one of the following: Radio Resource Control (RRC) message, Medium Access Control Control Element (MAC-CE), and Downlink Control Information (DCI).

For example, the network device may send the third information to the terminal device via an RRC message, triggering or instructing the terminal device to report the second information. For another example, the network device may send the third information to the terminal device via a MAC-CE, triggering or instructing the terminal device to report the second information. For another example, the network device may send the third information to the terminal device via a DCI, triggering or instructing the terminal device to report the second information.

Step S2102: The terminal device reports the second information to the network device.

In some embodiments, the network device may receive the second message. For example, the network device may receive the second information reported by the terminal device. For another example, the network device may also receive the second information reported by another entity.

In some embodiments, the terminal device may report the second information to the network device through a second signaling, and the second signaling may include at least one of the following: RRC, MAC-CE, and uplink control information (Uplink Control Information, UCI).

For example, the terminal device may report the second information to the network device via an RRC message. For another example, the terminal device may report the second information to the network device via MAC-CE. For another example, the terminal device may report the second information to the network device via UCI.

Step S2103: The network device determines fourth indication information according to the second information.

In some embodiments, the network device may determine the fourth indication information based on the first indication information. The value or value range of TDCP determines at least one of the parameters N, M, K, and D.

In some embodiments, the network device may determine the fourth indication information based on the second indication information. For example, the network device may determine at least one of the parameters N, M, K, and D based on the remaining memory or remaining power of the terminal device.

In some embodiments, the network device may determine the fourth indication information based on the seventh indication information. For example, the network device may determine at least one of the parameters N, M, K, and D based on the fifth AI model or the fifth AI function.

In some embodiments, the network device may also determine the fourth indication information according to multiple of the first indication information, the second indication information, and the seventh indication information.

Step S2104: The network device sends fourth indication information to the terminal device.

In some embodiments, the terminal device may receive the fourth indication information. For example, the terminal device may receive the fourth indication information sent by the network device. For another example, the terminal device may also receive the fourth indication information sent by another entity.

Step S2105: The network device sends third indication information to the terminal device.

In some embodiments, the third indication information may be used to indicate additional conditions corresponding to the network device.

In some embodiments, the additional conditions corresponding to the network device may be channel scenarios, data sets, etc. related to training AI models or AI functions.

Step S2106: The terminal device determines at least one third AI model or third AI function from the multiple AI models or AI functions according to the third indication information.

In some embodiments, multiple AI models or AI functions may be AI models or AI functions supported by the terminal device.

In some embodiments, the AI model supported by the terminal device may be an AI model identified through model recognition. The specific method of model recognition can refer to the above description and will not be repeated here.

In some embodiments, the AI function supported by the terminal device may be an AI function identified through function. The specific method of function identification can refer to the above description and will not be repeated here.

In some embodiments, multiple AI models or AI functions may be AI models or AI functions deployed on the terminal device side.

In some embodiments, each third indication information may correspond to at least one AI model or AI function.

For example, the additional condition corresponding to the network device is the channel scenario. Table 1 shows the AI models under different channel scenarios. As shown in Table 1, multiple AI models include A0, B0, C0, A1, B1, C1, A2, B2, and C2. Each channel scenario corresponds to three AI models, among which the AI models corresponding to the UMa scenario include A0, B0, and C0, the AI models corresponding to the UMi scenario include A1, B1, and C1, and the AI models corresponding to the indoor scenario include A2, B2, and C2. If the third indication information is UMa, the terminal device can determine that A0, B0, and C0 are the third AI model from A0, B0, C0, A1, B1, C1, A2, B2, and C2.

Table 1

Step S2107: The terminal device determines the first AI model or the first AI function from at least one third AI model or third AI function according to the fourth indication information.

In some embodiments, the terminal device may determine an AI model that matches the fourth indication information from at least one third AI model as the first AI model.

In some embodiments, the terminal device may determine an AI function matching the fourth indication information from at least one third AI function as the first AI function.

For example, if the parameter N corresponding to A0 is 4, the parameter N corresponding to B0 is 5, the parameter N corresponding to C0 is 8, and the fourth indication information is N=5, then B0 can be used as the first AI model.

In some embodiments, if the third AI model or the third AI function includes one, step S2106 can be omitted, and the third AI model or the third AI function can be used as the first AI model or the first AI function. For example, the third AI model can be used as the first AI model, and the third AI function can be used as the first AI function.

By using the above method, the terminal device can select a model based on the third indication information and the fourth indication information, without the need to monitor each model, thereby reducing the computational complexity of the terminal device and eliminating the need to send monitoring data to the network device, thereby reducing the data transmission overhead and improving network performance.

The method involved in the embodiments of the present disclosure may include at least one of the above-mentioned steps S2101 to S2107. For example, step S2101 can be implemented as an independent embodiment, step S2102 can be implemented as an independent embodiment, step S2103 can be implemented as an independent embodiment, step S2104 can be implemented as an independent embodiment, step S2105 can be implemented as an independent embodiment, step S2101 + step S2102 can be implemented as an independent embodiment, step S2102 + step S2103 can be implemented as an independent embodiment, step S2103 + step S2104 can be implemented as an independent embodiment, step S2106 + step S2107 can be implemented as an independent embodiment, and step S2104 + step S2105 + step S2106 + step S2107 can be implemented as independent embodiments, but the present invention is not limited thereto.

In some embodiments, the above steps S2101 to S2107 can be executed in a swapped order or simultaneously.

In some embodiments, the above steps S2101 to S2107 are all optional steps.

In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2A .

FIG2B is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure. The method may be executed by the above-mentioned communication system. As shown in FIG2B , the method may include:

Step S2201: The network device sends third information to the terminal device.

The optional implementation of step S2201 can refer to the optional implementation of step S2101 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

Step S2202: The terminal device reports the second information to the network device.

The optional implementation of step S2202 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

Step S2203: The network device determines fourth indication information according to the second information.

The optional implementation of step S2203 can refer to the optional implementation of step S2103 in FIG2A and other related parts in the embodiment involved in FIG2A , which will not be described in detail here.

Step S2204: The network device sends fourth indication information to the terminal device.

The optional implementation of step S2204 can refer to the optional implementation of step S2104 in FIG. 2A and other related parts in the embodiment involved in FIG. 2A , which will not be described in detail here.

Step S2205: The network device sends third indication information to the terminal device.

The optional implementation of step S2205 can refer to the optional implementation of step S2105 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

Step S2206: The terminal device determines multiple third AI models or third AI functions from the multiple AI models or AI functions according to the third indication information.

The optional implementation of step S2206 can refer to the optional implementation of step S2106 in FIG2A and other related parts in the embodiment involved in FIG2A , which will not be described in detail here.

Step S2207: The terminal device determines at least one fourth AI model or fourth AI function from multiple third AI models or third AI functions according to the second indication information.

In some embodiments, the terminal device may determine an AI model that matches the second indication information from at least one third AI model as the first AI model.

In some embodiments, the terminal device may determine an AI function that matches the second indication information from at least one third AI function as the first AI function.

For example, if the second indication information indicates that the remaining memory of the terminal device is a0, the third AI model includes A0, B0, and C0, the remaining memory corresponding to A0 is a1, the remaining memory corresponding to B0 is a2, and the remaining memory corresponding to C0 is a3, a1<a2<a3, a1<a0<a2, then the terminal device can select B0 and C0 from A0, B0, and C0 as the fourth AI model.

Step S2208: The terminal device determines the first AI model or the first AI function from at least one fourth AI model or fourth AI function according to the fourth indication information.

The optional implementation of step S2208 can refer to the optional implementation of step S2107 in FIG2A and other related parts in the embodiment involved in FIG2A , which will not be described in detail here.

In some embodiments, if the fourth AI model or the fourth AI function includes one, step S2208 can be omitted, and the fourth AI model or the fourth AI function can be used as the first AI model or the first AI function. For example, the fourth AI model can be used as the first AI model, and the fourth AI function can be used as the first AI function.

By using the above method, the terminal device can select a model based on the third indication information and the fourth indication information, without the need to monitor each model, thereby reducing the computational complexity of the terminal device and eliminating the need to send monitoring data to the network device, thereby reducing the data transmission overhead and improving network performance.

The method involved in the embodiments of the present disclosure may include at least one of the above-mentioned steps S2201 to S2208. For example, step S2201 can be implemented as an independent embodiment, step S2202 can be implemented as an independent embodiment, step S2203 can be implemented as an independent embodiment, step S2204 can be implemented as an independent embodiment, step S2205 can be implemented as an independent embodiment, step S2201 + step S2202 can be implemented as an independent embodiment, step S2202 + step S2203 can be implemented as an independent embodiment, step S2203 + step S2204 can be implemented as an independent embodiment, step S2206 + step S2207 + step S2208 can be implemented as an independent embodiment, and step S2204 + step S2205 + step S2206 + step S2207 + step S2208 can be implemented as independent embodiments, but the present invention is not limited thereto.

In some embodiments, the above steps S2201 to S2208 can be executed in a swapped order or simultaneously.

In some embodiments, the above steps S2201 to S2208 are all optional steps.

FIG2C is an interactive diagram of a communication method according to an embodiment of the present disclosure. The method can be executed by the above-mentioned communication system. As shown in FIG2C , the method may include:

Step S2301: The network device sends fourth information to the terminal device.

In some embodiments, the terminal device may receive the fourth message. For example, the terminal device may receive the fourth message sent by the network device. For another example, the terminal device may also receive the fourth message sent by another entity.

In some embodiments, the fourth information can be used by the terminal device to determine a first AI model or a first AI function from a plurality of AI models or AI functions.

In some embodiments, the fourth information may include information in the first information associated with the network device.

In some embodiments, the first information may include information associated with parameters for training the first AI model or the first AI function.

In some embodiments, the fourth information may be used to request the terminal device to perform model selection.

For example, a first AI model or a first AI function is determined from a plurality of AI models or AI functions.

In some embodiments, the fourth information may be used to trigger the terminal device to select a model.

In some embodiments, the fourth information may be used to instruct the terminal device to select a model.

In some embodiments, the name of the fourth information is not limited, for example, it can be "model selection request information", "model selection indication information", "function selection request information", "function selection indication information", etc.

In some embodiments, the first information may include at least one of the following:

first indication information, where the first indication information may be used to indicate channel characteristics;

Second indication information, where the second indication information may be used to indicate an additional condition corresponding to the terminal device;

third indication information, where the third indication information may be used to indicate an additional condition corresponding to the network device;

The fourth indication information can be used to indicate the parameters configured by the network device for the terminal device.

In some embodiments, the first information may be a value or a value range.

For example, the third indication information may be UMa, and the fourth indication information may be N=4, M=4 slots, K=4 slots, and D=8 slots.

In some embodiments, the fourth information may include at least one of the following:

Third instruction information;

Fourth instruction information.

In some embodiments, the third indication information and the fourth indication information can be sent independently. For example, the network device can send the third indication information to the terminal device at a first moment and send the fourth indication information to the terminal device at a second moment, and the first moment is different from the second moment.

It should be noted that the manner in which the network device sends the fourth indication information to the terminal device can refer to the above steps S2101 to S2104, which will not be repeated here. Similarly, the manner in which the third indication information is sent can refer to the manner in which the fourth indication information is sent, which will not be repeated here.

In some embodiments, the network device may send the third indication information and the fourth indication information to the terminal device simultaneously.

Step S2302: The terminal device determines at least one second AI model or second AI function from multiple AI models or AI functions based on the first indication information.

In some embodiments, each first indication information may correspond to at least one AI model or AI function.

In some embodiments, a first indication information may correspond to an AI model or an AI function.

In some embodiments, one first indication information may correspond to multiple AI models or AI functions.

For example, the first indication information is used to indicate TDCP. Table 2 shows the correspondence between an AI model and a TDCP amplitude value. As shown in Table 2, multiple AI models include A3, B3, A4, B4, A5, B5, A6, and B6. The TDCP value range corresponding to A3 and B3 is 0 to 0.25, the TDCP value range corresponding to A4 and B4 is 0.26 to 0.5, the TDCP value range corresponding to A5 and B5 is 0.51 to 0.75, and the TDCP value range corresponding to A6 and B6 is 0.76 to 1. The TDCP value range is the normalized result.

Table 2

If the first indication information is 0.27, the terminal device can use A4 and B4 as the second AI model; if the first indication information is 0.59-0.75, A5 and B5 can be used as the second AI model.

It should be noted that if the first indication information corresponds to an AI model or AI function, step S2302 can be omitted.

Step S2303: The terminal device determines the first AI model or the first AI function from at least one second AI model or second AI function according to the fifth indication information.

In some embodiments, the fifth indication information may include at least one of the following: the second indication information, the third indication information, and the fourth indication information.

In some embodiments, the terminal device can determine from at least one second AI model or second AI function according to the second indication information. First AI model or first AI function.

In some embodiments, the specific method of determining the first AI model or the first AI function from at least one second AI model or the second AI function based on the second indication information can refer to the implementation method of step S2207 and will not be repeated here.

In some embodiments, the terminal device may determine the first AI model or the first AI function from at least one second AI model or second AI function based on the third indication information.

In some embodiments, the specific method of determining the first AI model or the first AI function from at least one second AI model or the second AI function based on the third indication information can refer to the implementation method of step S2105 and will not be repeated here.

In some embodiments, the terminal device may determine the first AI model or the first AI function from at least one second AI model or second AI function based on the fourth indication information.

In some embodiments, the specific method of determining the first AI model or the first AI function from at least one second AI model or the second AI function according to the fourth indication information can refer to the implementation method of step S2106 and is not repeated here.

In some embodiments, the terminal device may determine the first AI model or the first AI function from at least one second AI model or second AI function based on the second indication information and the fourth indication information.

In one implementation, the terminal device may first determine at least one sixth AI model or sixth AI function from at least one second AI model or second AI function based on the second indication information, and then determine the first AI model or first AI function from at least one sixth AI model or sixth AI function based on the fourth indication information.

It should be noted that if the sixth AI model or sixth AI function determined according to the second indication information includes one, the sixth AI model or sixth AI function can be used as the first AI model or first AI function.

In another implementation, the terminal device may first determine at least one seventh AI model or seventh AI function from at least one second AI model or second AI function according to the fourth indication information, and then select the first AI model or first AI function from the at least one seventh AI model or seventh AI function according to the second indication information.

It should also be noted that if the seventh AI model or seventh AI function determined according to the fourth indication information includes one, then the seventh AI model or seventh AI function can be used as the first AI model or first AI function.

By using the above method, the terminal device can select a model based on the first indication information and the fifth indication information, without the need to monitor each model, thereby reducing the computational complexity of the terminal device and eliminating the need to send monitoring data to the network device, thereby reducing the data transmission overhead and improving network performance.

The method involved in the embodiments of the present disclosure may include at least one of steps S2301 to S2303. For example, step S2301 may be implemented as an independent embodiment, step S2302 may be implemented as an independent embodiment, step S2303 may be implemented as an independent embodiment, and step S2302 + step S2303 may be implemented as independent embodiments, but the present invention is not limited thereto.

In some embodiments, the above steps S2301 to S2303 can be executed in a swapped order or simultaneously.

In some embodiments, the above steps S2301 to S2303 are all optional steps.

In some embodiments, the names of information, etc. are not limited to the names described in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codeword", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

In some embodiments, "obtain", "get", "get", "receive", "transmit", "bidirectional transmission", "send and/or receive" can be interchangeable, and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining by self-processing, autonomous implementation, etc.

In some embodiments, terms such as "send", "transmit", "report", "download", "transmit", "bidirectional transmission", "send and/or receive" can be used interchangeably.

In some embodiments, terms such as "certain", "preset", "preset", "setting", "indicated", "a certain", "any", and "first" can be interchangeable. "Specific A", "preset A", "preset A", "setting A", "indicated A", "a certain A", "any A", and "first A" can be interpreted as A pre-specified in a protocol, etc., or as A obtained through setting, configuration, or indication, etc., or as specific A, a certain A, any A, or first A, etc., but not limited to this.

FIG3A is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3A , the embodiment of the present disclosure relates to a communication method, which can be executed by a terminal device. The method may include:

Step S3101: Obtain third information.

The optional implementation of step S3101 can refer to the optional implementation of step S2101 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

In some embodiments, the terminal device may receive the third information sent by the network device, but is not limited thereto. The terminal device may also receive the third information sent by other entities.

In some embodiments, the terminal device may obtain third information specified by the protocol.

In some embodiments, the terminal device can obtain third information from upper layer(s).

In some embodiments, the terminal device may perform processing to obtain the third information.

In some embodiments, step S3101 may be omitted, and the terminal device may autonomously implement the function indicated by the third information, or the above function may be default or by default.

Step S3102: Report the second information.

The optional implementation of step S3102 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

Step S3103: Obtain fourth indication information.

The optional implementation of step S3103 can refer to the optional implementation of step S2104 in FIG2A and other related parts in the embodiment involved in FIG2A , which will not be described in detail here.

In some embodiments, the terminal device may receive the fourth indication information sent by the network device, but is not limited thereto. The terminal device may also receive the fourth indication information sent by other entities.

In some embodiments, the terminal device may obtain fourth indication information specified by the protocol.

In some embodiments, the terminal device can obtain the fourth indication information from the upper layer(s).

In some embodiments, the terminal device may perform processing to obtain the fourth indication information.

In some embodiments, step S3103 may be omitted, and the terminal device may autonomously implement the function indicated by the fourth indication information, or the above function may be default or acquiescent.

Step S3104: Obtain third indication information.

The optional implementation of step S3104 can refer to the optional implementation of step S2105 in FIG2A and other related parts in the embodiment involved in FIG2A , which will not be described in detail here.

In some embodiments, the terminal device may receive the third indication information sent by the network device, but is not limited thereto. The terminal device may also receive the third indication information sent by other entities.

In some embodiments, the terminal device may obtain third indication information specified by the protocol.

In some embodiments, the terminal device can obtain third indication information from upper layer(s).

In some embodiments, the terminal device may perform processing to obtain the third indication information.

In some embodiments, step S3104 may be omitted, and the terminal device may autonomously implement the function indicated by the third indication information, or the above function may be default or acquiescent.

Step S3105: Determine at least one third AI model or third AI function from the plurality of AI models or AI functions according to the third indication information.

The optional implementation of step S3105 can refer to the optional implementation of step S2106 in FIG2A and other related parts in the embodiment involved in FIG2A , which will not be described in detail here.

Step S3106: Determine a first AI model or a first AI function from at least one third AI model or a third AI function according to the fourth indication information.

The optional implementation of step S3106 can refer to the optional implementation of step S2107 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

The method involved in the embodiments of the present disclosure may include at least one of the above steps S3101 to S3106. For example, step S3101 can be implemented as an independent embodiment, step S3102 can be implemented as an independent embodiment, step S3103 can be implemented as an independent embodiment, step S3104 can be implemented as an independent embodiment, step S3101 + step S3102 can be implemented as an independent embodiment, step S3105 + step S3106 can be implemented as an independent embodiment, and step S3103 + step S3104 + step S3105 + step S3106 can be implemented as independent embodiments, but the present invention is not limited thereto.

In some embodiments, the above steps S3101 to S3106 can be executed in a swapped order or simultaneously.

In some embodiments, the above steps S3101 to S3106 are all optional steps.

FIG3B is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3B , the embodiment of the present disclosure relates to a communication method, which can be executed by a terminal device. The method may include:

Step S3201: Obtain third information.

The optional implementation of step S3201 can be found in step S2101 of FIG. 2A , the optional implementation of step S3101 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be repeated here.

Step S3202: Report the second information.

The optional implementation of step S3202 can be found in step S2102 of FIG. 2A , the optional implementation of step S3102 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be described in detail here.

Step S3203: Obtain fourth indication information.

The optional implementation of step S3203 can be found in step S2104 of FIG. 2A , the optional implementation of step S3103 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be described in detail here.

Step S3204: Obtain third indication information.

The optional implementation of step S3204 can be found in step S2105 of FIG. 2A , the optional implementation of step S3104 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be described in detail here.

Step S3205: Determine multiple third AI models or third AI functions from the multiple AI models or AI functions according to the third indication information.

The optional implementation of step S3205 can be found in step S2106 of FIG. 2A , the optional implementation of step S3105 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be described in detail here.

Step S3206: Determine at least one fourth AI model or fourth AI function from the plurality of third AI models or third AI functions according to the second indication information.

The optional implementation of step S3206 can refer to the optional implementation of step S2207 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

Step S3207: Determine the first AI model or the first AI function from at least one fourth AI model or fourth AI function according to the fourth indication information.

The optional implementation of step S3207 can refer to the optional implementation of step S2208 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

The method involved in the embodiments of the present disclosure may include at least one of the above-mentioned steps S3201 to S3207. For example, step S3201 can be implemented as an independent embodiment, step S3202 can be implemented as an independent embodiment, step S3203 can be implemented as an independent embodiment, step S3204 can be implemented as an independent embodiment, step S3201 + step S3202 can be implemented as an independent embodiment, step S3202 + step S3203 can be implemented as an independent embodiment, step S3205 + step S3206 + step S3207 can be implemented as an independent embodiment, and step S3203 + step S3204 + step S3205 + step S3206 + step S3207 can be implemented as an independent embodiment, but the present invention is not limited thereto.

In some embodiments, the above steps S3201 to S3207 can be executed in a swapped order or simultaneously.

In some embodiments, the above steps S3201 to S3207 are all optional steps.

FIG3C is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3C , the embodiment of the present disclosure relates to a communication method, which can be executed by a terminal device. The method may include:

Step S3301: Obtain third information.

The optional implementation of step S3301 can be found in step S2101 of FIG. 2A , the optional implementation of step S3101 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be repeated here.

Step S3302: Report the second information.

The optional implementation of step S3302 can be found in step S2102 of FIG. 2A , the optional implementation of step S3102 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be repeated here.

Step S3303: Obtain fourth indication information.

The optional implementation of step S3303 can be found in step S2104 of FIG. 2A , the optional implementation of step S3103 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be described in detail here.

Step S3304: Obtain third indication information.

The optional implementation of step S3304 can be found in step S2105 of FIG. 2A , the optional implementation of step S3104 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be described in detail here.

Step S3305: Determine multiple third AI models or third AI functions from the multiple AI models or AI functions according to the third indication information.

The optional implementation of step S3305 can be found in step S2106 of FIG. 2A , the optional implementation of step S3105 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be described in detail here.

Step S3306: Determine a first AI model or a first AI function from at least one third AI model or a third AI function according to the sixth indication information.

The optional implementation of step S3306 can refer to the optional implementation of step S2107 in Figure 2A, steps S2207 to S2208 in Figure 2B, and other related parts in the embodiments involved in Figures 2A and 2B, which will not be repeated here.

In some embodiments, the sixth indication information may include at least one of the following: the second indication information and the fourth indication information.

In some embodiments, the terminal device may determine the first AI model or the first AI function from at least one third AI model or third AI function based on the second indication information.

In some embodiments, the terminal device may determine the first AI model or the first AI function from at least one third AI model or third AI function based on the fourth indication information.

In some embodiments, the terminal device may determine the first AI model or the first AI function from at least one third AI model or third AI function based on the second indication information and the fourth indication information.

In one implementation, the terminal device may first determine at least one eighth AI model or eighth AI function from at least one third AI model or third AI function based on the second indication information, and then determine the first AI model or first AI function from at least one eighth AI model or eighth AI function based on the fourth indication information.

It should be noted that if the eighth AI model or eighth AI function determined according to the second indication information includes one, then the eighth AI model or eighth AI function can be used as the first AI model or first AI function.

In another implementation, the terminal device may first determine at least one ninth AI model or ninth AI function from at least one third AI model or third AI function according to the fourth indication information, and then determine the first AI model or first AI function from at least one ninth AI model or ninth AI function according to the second indication information.

It should also be noted that if the ninth AI model or ninth AI function determined according to the fourth indication information includes one, then the ninth AI model or ninth AI function can be used as the first AI model or first AI function.

The method involved in the embodiments of the present disclosure may include at least one of the above steps S3301 to S3306. For example, step S3301 can be implemented as an independent embodiment, step S3302 can be implemented as an independent embodiment, step S3303 can be implemented as an independent embodiment, step S3304 can be implemented as an independent embodiment, step S3302 + step S3303 can be implemented as an independent embodiment, step S3305 + step S3306 can be implemented as an independent embodiment, and step S3303 + step S3304 + step S3305 + step S3306 can be implemented as independent embodiments, but the present invention is not limited thereto.

In some embodiments, the above steps S3301 to S3306 can be executed in a swapped order or simultaneously.

In some embodiments, the above steps S3301 to S3306 are all optional steps.

FIG3D is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3D , the embodiment of the present disclosure relates to a communication method, which can be executed by a terminal device. The method may include:

Step S3401: Obtain fourth information.

The optional implementation of step S3401 can refer to the optional implementation of step S2301 in Figure 2C and other related parts in the embodiment involved in Figure 2C, which will not be repeated here.

In some embodiments, the terminal device may receive the fourth information sent by the network device, but is not limited thereto. The terminal device may also receive the fourth information sent by other entities.

In some embodiments, the terminal device may obtain fourth information specified by the protocol.

In some embodiments, the terminal device can obtain the fourth information from the upper layer(s).

In some embodiments, the terminal device may perform processing to obtain the fourth information.

In some embodiments, step S3401 may be omitted, and the terminal device may autonomously implement the function indicated by the fourth information, or the above function may be default or by default.

Step S3402: Determine at least one second AI model or second AI function from multiple AI models or AI functions based on the first indication information.

The optional implementation of step S3402 can refer to the optional implementation of step S2302 in Figure 2C and other related parts in the embodiment involved in Figure 2C, which will not be repeated here.

Step S3403: Determine a first AI model or a first AI function from at least one second AI model or a second AI function according to the fifth indication information.

The optional implementation of step S3403 can refer to the optional implementation of step S2303 in Figure 2C and other related parts in the embodiment involved in Figure 2C, which will not be repeated here.

The method involved in the embodiments of the present disclosure may include at least one of the above steps S3401 to S3403. For example, step S3401 can be implemented as an independent embodiment, step S3402 can be implemented as an independent embodiment, step S3403 can be implemented as an independent embodiment, and step S3402 + step S3403 can be implemented as independent embodiments, but the present invention is not limited thereto.

In some embodiments, the above steps S3401 to S3403 can be executed in a swapped order or simultaneously.

In some embodiments, the above steps S3401 to S3403 are all optional steps.

FIG3E is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3E , the embodiment of the present disclosure relates to a communication method, which can be executed by a terminal device. The method may include:

Step S3501: Obtain fourth information.

The optional implementation of step S3501 can refer to the optional implementation of step S2301 in Figure 2C and other related parts in the embodiment involved in Figure 2C, which will not be repeated here.

Step S3502: Determine a first AI model or a first AI function from multiple AI models or AI functions based on the first information.

For optional implementations of step S3502, reference may be made to steps S2106 to S2107 of FIG. 2A , steps S2206 to S2208 of FIG. 2B , steps S2302 to S2303 of FIG. 2C , steps S3105 to S3106 of FIG. 3A , steps S3205 to S3207 of FIG. 3B , steps S3305 to S3306 of FIG. 3C , and optional implementations of steps S3402 to S3403 of FIG. 3D , as well as other related parts in the embodiments involved in FIG. 2A , FIG. 2B , FIG. 2C , FIG. 3A , FIG. 3B , FIG. 3C , and FIG. 3D , which will not be repeated here.

In some embodiments, the above steps S3501 to S3502 can be executed in an interchangeable order or simultaneously.

In some embodiments, the above steps S3501 to S3502 are all optional steps.

FIG3F is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3F , the embodiment of the present disclosure relates to a communication method, which can be executed by a terminal device. The method may include:

Step S3601: Determine a first AI model or a first AI function from multiple AI models or AI functions based on first information.

For optional implementations of step S3601, reference may be made to steps S2106 to S2107 of FIG. 2A , steps S2206 to S2208 of FIG. 2B , steps S2302 to S2303 of FIG. 2C , steps S3105 to S3106 of FIG. 3A , steps S3205 to S3207 of FIG. 3B , steps S3305 to S3306 of FIG. 3C , and optional implementations of steps S3402 to S3403 of FIG. 3D , as well as other related parts in the embodiments involved in FIG. 2A , FIG. 2B , FIG. 2C , FIG. 3A , FIG. 3B , FIG. 3C , and FIG. 3D , which will not be repeated here.

In some embodiments, the terminal device may determine a first AI model or a first AI function from a plurality of AI models or AI functions based on the first indication information.

In some embodiments, the terminal device may determine a first AI model or a first AI function from a plurality of AI models or AI functions based on the second indication information.

In some embodiments, the terminal device may determine a first AI model or a first AI function from a plurality of AI models or AI functions based on the third indication information.

In some embodiments, the terminal device may determine a first AI model or a first AI function from a plurality of AI models or AI functions based on the fourth indication information.

It should be noted that the terminal device can determine the first AI model or the first AI function from multiple AI models or AI functions based on any one or more indication information in the first information, and the embodiments of the present disclosure are not limited to this.

In some embodiments, the first information includes at least one of the following:

first indication information, where the first indication information is used to indicate channel characteristics;

Second indication information, where the second indication information is used to indicate an additional condition corresponding to the terminal device;

third indication information, where the third indication information is used to indicate an additional condition corresponding to the network device;

The fourth indication information is used to indicate the parameters configured by the network device for the terminal device.

In some embodiments, determining the first AI model or the first AI function from the plurality of AI models or AI functions according to the first information includes:

Determining, according to the first indication information, at least one second AI model or second AI function from the multiple AI models or AI functions;

Determine the first AI model or the first AI function from the at least one second AI model or second AI function according to fifth indication information, where the fifth indication information includes at least one of the following: the second indication information, the third indication information, and the fourth indication information.

In some embodiments, determining the first AI model or the first AI function from the plurality of AI models or AI functions according to the first information includes:

Determining, according to the third indication information, at least one third AI model or third AI function from the multiple AI models or AI functions;

The first AI model or the first AI function is determined from the at least one third AI model or third AI function according to sixth indication information, where the sixth indication information includes at least one of the following: the second indication information and the fourth indication information.

In some embodiments, determining, according to the sixth indication information, the first AI model or the first AI function from the at least one third AI model or third AI function includes:

Determine, according to the fourth indication information, the first AI model or the first AI function from the at least one third AI model or third AI function.

In some embodiments, the third AI model or the third AI function includes a plurality of third AI models or third AI functions, and determining, according to the sixth indication information, the first AI model or the first AI function from the at least one third AI model or third AI function includes:

determining, according to the second indication information, at least one fourth AI model or fourth AI function from a plurality of third AI models or third AI functions;

Determine, according to the fourth indication information, the first AI model or the first AI function from the at least one fourth AI model or fourth AI function.

In some embodiments, the method further comprises:

Reporting second information to the network device, where the second information is used by the network device to determine the fourth indication information;

Receive the fourth indication information sent by the network device.

In some embodiments, the method further comprises:

Receive third information sent by the network device, where the third information is used to instruct the terminal device to report the second information.

In some embodiments, the receiving third information sent by the network device includes:

The third information is received by the network device through the first signaling, where the first signaling includes at least one of the following: a radio resource control RRC message, a media access control control element MAC-CE, and downlink control information DCI.

In some embodiments, reporting the second information to the network device includes:

The second information is reported to the network device through a second signaling, where the second signaling includes at least one of the following: RRC, MAC-CE, and uplink control information UCI.

In some embodiments, the second information includes at least one of the following:

the first indication information;

the second indication information;

Seventh indication information, where the seventh indication information is used to indicate a fifth AI model or a fifth AI function, where the fifth AI model or the fifth AI function includes an AI model or AI function that can be used by the terminal device.

In some embodiments, the method further comprises:

Receive the third indication information sent by the network device.

In some embodiments, each first indication information corresponds to at least one AI model or AI function.

FIG4A is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4A , the embodiment of the present disclosure relates to a communication method, which can be performed by a network device. The method may include:

Step S4101: Send the third information.

The optional implementation of step S4101 can refer to the optional implementation of step S2101 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

In some embodiments, the network device may send the third information to the terminal device, but is not limited thereto. The network device may also send the third information to other entities.

Step S4102: Obtain second information.

The optional implementation of step S4102 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

Step S4103: Determine fourth indication information according to the second information.

The optional implementation of step S4103 can refer to the optional implementation of step S2103 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

Step S4104: Send the fourth indication information.

The optional implementation of step S4104 can refer to the optional implementation of step S2104 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

Step S4105: Send the third indication information.

The optional implementation of step S4105 can refer to the optional implementation of step S2105 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

In some embodiments, the network device may send the fourth indication information to the terminal device, but is not limited thereto. The network device may also send the fourth indication information to other entities.

The method involved in the embodiments of the present disclosure may include at least one of the above steps S4101 to S4105. For example, step S4101 can be implemented as an independent embodiment, step S4102 can be implemented as an independent embodiment, step S4103 can be implemented as an independent embodiment, step S4104 can be implemented as an independent embodiment, step S4105 can be implemented as an independent embodiment, step S4101 + step S4102 can be implemented as an independent embodiment, step S4102 + step S4103 can be implemented as an independent embodiment, and step S4103 + step S4104 can be implemented as an independent embodiment, but the present invention is not limited thereto.

In some embodiments, the above steps S4101 to S4105 can be executed in a swapped order or simultaneously.

In some embodiments, the above steps S4101 to S4105 are all optional steps.

FIG4B is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4B , the embodiment of the present disclosure relates to a communication method, which can be performed by a network device. The method may include:

Step S4201: Send the fourth information.

The optional implementation of step S4201 can refer to the optional implementation of step S2301 in Figure 2C and other related parts in the embodiment involved in Figure 2C, which will not be repeated here.

In some embodiments, the network device may send the fourth information to the terminal device, but is not limited thereto. The network device may also send the fourth information to other entities.

In some embodiments, the fourth information includes at least one of the following:

third indication information, where the third indication information is used to indicate an additional condition corresponding to the network device;

The fourth indication information is used to indicate the parameters configured by the network device for the terminal device.

In some embodiments, sending the fourth information to the terminal device includes:

receiving second information reported by the terminal device;

determining the fourth indication information according to the second information;

Send the fourth indication information to the terminal device.

In some embodiments, the method further comprises:

Send third information to the terminal device, where the third information is used to instruct the terminal device to report the second information.

In some embodiments, sending the third information to the terminal device includes:

The third information is sent to the terminal device via a first signaling, where the first signaling includes at least one of the following: a radio resource control RRC message, a media access control control element MAC-CE, and downlink control information DCI.

In some embodiments, the receiving the second information reported by the terminal device includes:

Receive the second information reported by the terminal device through the second signaling, where the second signaling includes at least one of the following: RRC, MAC-CE, and uplink control information UCI.

In some embodiments, the second information includes at least one of the following:

first indication information, where the first indication information is used to indicate channel characteristics;

Second indication information, where the second indication information is used to indicate an additional condition corresponding to the terminal device;

Seventh indication information, where the seventh indication information is used to indicate a fifth AI model or a fifth AI function, where the fifth AI model or the fifth AI function includes an AI model or AI function that can be used by the terminal device.

FIG5 is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG5 , the embodiment of the present disclosure relates to a communication method, which can be executed by a communication system. The method may include:

Step S5101: The network device sends fourth information.

The optional implementation of step S5101 can be found in the optional implementation of step S2301 in Figure 2C, step S3401 in Figure 3D, step S4201 in Figure 4B, and other related parts in the embodiments involved in Figures 2C, 3D, and 4B, which will not be repeated here.

Step S5102: The terminal device determines a first AI model or a first AI function from multiple AI models or AI functions based on the first information.

For optional implementations of step S5102, reference may be made to steps S2106 to S2107 of FIG. 2A , steps S2206 to S2208 of FIG. 2B , steps S2302 to S2303 of FIG. 2C , steps S3105 to S3106 of FIG. 3A , steps S3205 to S3207 of FIG. 3B , steps S3305 to S3306 of FIG. 3C , and optional implementations of steps S3402 to S3403 of FIG. 3D , as well as other related parts in the embodiments involved in FIG. 2A , FIG. 2B , FIG. 2C , FIG. 3A , FIG. 3B , FIG. 3C , and FIG. 3D , which will not be repeated here.

In some embodiments, the above method may include the method described in the embodiments of the above communication system, terminal equipment, network equipment, etc., which will not be repeated here.

In some embodiments, the NW can send signaling to query the UE's ability to support the AI function for AI CSI prediction. The UE reports the AI function supported by the UE to the NW based on the received signaling, thus completing function identification. Alternatively, model identification has been completed between the UE and the NW through a model identification method. Due to changes in the UE's own hardware conditions or channel environment, the UE can select an appropriate AI model based on relevant parameter information associated with the trained AI model or additional condition information on the UE/NW side to reduce the number of candidate AI models, thereby reducing the computational complexity of the UE's model selection or reducing the overhead of the UE's feedback monitoring data.

In some embodiments, the AI function or model selection method for CSI prediction may include the following embodiments:

Example 1: The trained AI model is associated with at least first parameter information, where the first parameter information characterizes channel characteristics. For example, the first parameter information may be TDCP.

The first parameter information in Example 2 and Example 1 may correspond to a value or a value range.

In Example 3, the trained AI model is also associated with at least second parameter information, which may be additional condition information on the UE side or the NW side, or parameter information configured by the network for the terminal. The additional condition information on the UE side may be related hardware information such as the UE's memory or power, and the additional condition information on the NW side may be channel scenarios, data sets, etc. related to the trained AI model.

Example 4: The UE proactively reports one or more of the following information to the NW through signaling such as RRC/MAC-CE/UCI, or the NW triggers or instructs the UE to report one or more of the following information through signaling such as RRC/MAC-CE/DCI:

First parameter information calculated by the UE according to the pilot information configured by the network;

Second parameter information, i.e., additional condition information on the UE side;

AI functions or models available on the UE side.

In embodiment 5, the NW reconfigures configuration information related to the AI model to be selected based on one or more information received in embodiment 4.

Optionally, the NW may also send second parameter information to the UE, that is, additional condition information indicating the NW side.

Example 6: The UE may select a corresponding AI function or model for inference based on the received configuration information related to the selected AI model or the received second parameter information.

The following two examples illustrate the model selection method:

Example 1:

If the gNB is configured with the parameters N, M, K, and D and the corresponding CSI-RS resources, the UE can complete the collection of data for training the CSI prediction AI model based on the received CSI-RS resources. Due to UE mobility, different movement speeds will result in different time-varying channel characteristics, that is, different TDCPs. Accordingly, the values of the parameters N, M, K, and D may also vary, which will cause the UE to train different models based on these parameter configurations and collected data. Therefore, the trained AI model can be associated with at least one value corresponding to the TDCP.

TDCP measurements are obtained by the UE based on the Tracking Reference Signals (TRS) resources configured by the gNB. Based on the received TRS, the UE can calculate the correlation of the time domain channel at different time intervals. The correlation of the time domain channel includes the amplitude value and phase, where the amplitude value is the normalized result and the value range is between 0 and 1. The trained AI model has a certain degree of generalization, and it is not necessary to have each TDCP amplitude value correspond to an AI model, that is, a certain TDCP amplitude range corresponds to an AI model. For example, the UE trained four models A, B, C, and D, and the correspondence between each model and the TDCP amplitude value range is shown in Table 3.

Table 3

Optionally, if multiple CSI prediction AI models correspond to the same TDCP amplitude value, the UE or gNB can select the corresponding AI model based on one or more of the configuration parameter information N, M, K, and D.

Example 2:

If the gNB is configured with the parameters N, M, K, and D and the corresponding CSI-RS resources, the UE can complete the collection of AI models for training CSI prediction based on the received CSI-RS resources, thereby training AI models corresponding to different parameter configurations. As shown in Example 1, different trained AI models can also be associated with different TDCP amplitude values or amplitude ranges.

The collected dataset can be a dataset for a specific channel scenario, including UMa, Urban Microcell (UMi), indoor, and rural scenarios. The channel scenario is conditional information on the gNB side, and different channel scenarios can correspond to different AI models. For example, Table 1 shows the different AI models corresponding to different channel scenarios. Each channel scenario includes three AI models. If the UE does not know the current channel scenario, it will need to select from nine models, significantly increasing the UE's computational complexity. The channel scenario is a condition on the gNB side, and the gNB can send this condition information to the UE via RRC signaling. For example, the RRC signaling can indicate that the current channel scenario is the UMa scenario. In this way, the UE can select one of A0, B0, and C0 corresponding to the UMa scenario, significantly reducing the UE's computational complexity.

If a UE's hardware capabilities, such as battery life, degrade during AI model inference, causing it to lose some of the AI models' processing power, the UE will no longer be able to use them. In this case, the UE can proactively report available AI models to the gNB. Alternatively, the gNB can send signaling to query the UE for supported AI models, prompting the UE to report available AI models. Changes in the UE's processing capabilities or hardware capabilities constitute additional conditional information on the UE side. Only after receiving the additional conditional information or available AI models reported by the UE will the gNB configure the corresponding N, M, K, or D parameters based on the received information or the UE's reported AI model. Finally, the UE can select an AI model from a limited set of one or more AI models based on the reconfigured parameters from the gNB and the UE's additional conditional information. This avoids the UE having to select candidate models from a larger number of AI models, thereby reducing the complexity of the UE's model selection process.

In some embodiments of the present disclosure, a communication system is provided, which may include a terminal device and a network device, wherein the terminal device can execute the communication method executed by the terminal device in the aforementioned embodiment of the present disclosure; the network device can execute the communication method executed by the network device in the aforementioned embodiment of the present disclosure.

The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, etc.) in any of the above methods.

It should be understood that the division of the various units or modules in the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. In addition, the units or modules in the device can be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and instructions are stored in the memory. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits. The above-mentioned hardware circuits can be understood as one or more processors. For example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), and the functions of some or all of the above units or modules are realized by designing the logical relationship of the components in the circuit. For another example, in another implementation, the above-mentioned hardware circuit can be implemented by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by a configuration file, thereby realizing the functions of some or all of the above units or modules. All units or modules of the above devices can be implemented in the form of software called by the processor, or in the form of hardware circuits, or in part by software called by the processor, and the rest by hardware circuits.

In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationship of hardware circuits, and the logical relationship of the above hardware circuits is fixed. Or reconfigurable, for example, a processor is a hardware circuit implemented as an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc.

Figure 6A is a structural diagram of a terminal device proposed in an embodiment of the present disclosure. As shown in Figure 6A, the terminal device 101 may include at least one of a processing module 6101, a transceiver module 6102, etc. In some embodiments, the processing module 6101 is configured to determine a first AI model or a first AI function from a plurality of AI models or AI functions based on first information, wherein the first information includes information associated with parameters for training the first AI model or the first AI function. Optionally, the transceiver module 6102 can be used to execute at least one of the communication steps such as sending and/or receiving (for example, step S2101, but not limited thereto) performed by the terminal device 101 in any of the above methods, which will not be repeated here. Optionally, the processing module 6101 is used to execute at least one of the other steps (for example, step S2105, but not limited thereto) performed by the terminal device 101 in any of the above methods, which will not be repeated here.

In some embodiments, the transceiver module may include a transmitting module and/or a receiving module, and the transmitting module and the receiving module may be separate or integrated. Optionally, the transceiver module may be interchangeable with the transceiver.

In some embodiments, the processing module can be a single module or can include multiple submodules. Optionally, the multiple submodules respectively execute all or part of the steps required to be executed by the processing module. Optionally, the processing module can be interchangeable with the processor.

Figure 6B is a structural diagram of a network device proposed in an embodiment of the present disclosure. As shown in Figure 6B, the network device 102 may include: at least one of a transceiver module 6201, a processing module 6202, etc. In some embodiments, the transceiver module 6201 is configured to send fourth information to the terminal device, and the fourth information is used by the terminal device to determine the first AI model or the first AI function from a plurality of AI models or AI functions. The fourth information includes information associated with the network device in the first information, and the first information includes information associated with the parameters for training the first AI model or the first AI function. Optionally, the transceiver module 6201 can be used to perform at least one of the communication steps such as sending and/or receiving (for example, step S2101, but not limited to this) performed by the network device 102 in any of the above methods, which will not be repeated here.

In some embodiments, the transceiver module may include a transmitting module and/or a receiving module, and the transmitting module and the receiving module may be separate or integrated. Optionally, the transceiver module may be interchangeable with the transceiver.

Figure 7A is a schematic diagram of the structure of a communication device 7100 proposed in an embodiment of the present disclosure. Communication device 7100 can be a network device (e.g., an access network device, a core network device, etc.), a terminal (e.g., a user device, etc.), a chip, a chip system, or a processor that supports a first device to implement any of the above methods, or a chip, a chip system, or a processor that supports a terminal to implement any of the above methods. Communication device 7100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

As shown in FIG7A , the communication device 7100 includes one or more processors 7101. The processor 7101 may be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor may be used to process communication protocols and communication data, and the central processing unit may be used to control a communication device (e.g., a base station, a baseband chip, an IoT device, an IoT device chip, a DU or CU, etc.), execute programs, and process program data. The communication device 7100 is used to perform any of the above methods.

In some embodiments, the communication device 7100 further includes one or more memories 7102 for storing instructions. Optionally, all or part of the memories 7102 may be located outside the communication device 7100.

In some embodiments, the communication device 7100 further includes one or more transceivers 7103. When the communication device 7100 includes one or more transceivers 7103, the transceiver 7103 performs at least one of the communication steps such as sending and/or receiving in the above method (for example, step S2101 and step S2102, but not limited thereto), and the processor 7101 performs at least one of the other steps (for example, step S2105 and step S2106, but not limited thereto).

In some embodiments, a transceiver may include a receiver and/or a transmitter. The receiver and transmitter may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, and transceiver circuit may be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit may be used interchangeably.

In some embodiments, the communication device 7100 may include one or more interface circuits. Optionally, the interface circuits are connected to the memory 7102 and may be used to receive signals from the memory 7102 or other devices, or to send signals to the memory 7102 or other devices. For example, the interface circuits may read instructions stored in the memory 7102 and send the instructions to the processor 7101.

The communication device 7100 described in the above embodiments may be a first device or an IoT device, but the scope of the communication device 7100 described in the present disclosure is not limited thereto, and the structure of the communication device 7100 may not be limited by FIG. 7A . The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data or programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, an IoT device, an intelligent IoT device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a first device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

7B is a schematic diagram of the structure of a chip 7200 proposed in an embodiment of the present disclosure. If the communication device 7100 can be a chip or a chip system, please refer to the schematic diagram of the structure of the chip 7200 shown in FIG7B , but the present disclosure is not limited thereto.

The chip 7200 includes one or more processors 7201 , and the chip 7200 is configured to execute any of the above methods.

In some embodiments, the chip 7200 further includes one or more interface circuits 7203. Optionally, the interface circuit 7203 is connected to the memory 7202. The interface circuit 7203 can be used to receive signals from the memory 7202 or other devices, and can be used to send signals to the memory 7202 or other devices. For example, the interface circuit 7203 can read instructions stored in the memory 7202 and send the instructions to the processor 7201.

In some embodiments, the interface circuit 7203 executes at least one of the communication steps such as sending and/or receiving in the above method (for example, step S2101, step S2102, but not limited to these), and the processor 7201 executes at least one of the other steps (for example, step S2105, step S2106, but not limited to these).

In some embodiments, terms such as interface circuit, interface, transceiver pin, and transceiver may be used interchangeably.

In some embodiments, the chip 7200 further includes one or more memories 7202 for storing instructions. Alternatively, all or part of the memory 7202 may be external to the chip 7200.

The embodiments of the present disclosure further provide a storage medium having instructions stored thereon. When the instructions are executed on the communication device 7100, the communication device 7100 executes any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto, and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto, and may also be a temporary storage medium.

The present disclosure also provides a program product, which, when executed by the communication device 7100, enables the communication device 7100 to perform any of the above methods. Optionally, the program product may be a computer program product.

The embodiments of the present disclosure also provide a computer program, which, when executed on a computer, enables the computer to execute any one of the above methods.

Claims (25)

A communication method, characterized in that it is executed by a terminal device, the method comprising: A first AI model or a first AI function is determined from a plurality of AI models or AI functions based on first information, wherein the first information includes information associated with parameters for training the first AI model or the first AI function. The method according to claim 1, wherein the first information includes at least one of the following: first indication information, where the first indication information is used to indicate channel characteristics; Second indication information, where the second indication information is used to indicate an additional condition corresponding to the terminal device; third indication information, where the third indication information is used to indicate an additional condition corresponding to the network device; The fourth indication information is used to indicate the parameters configured by the network device for the terminal device. The method according to claim 2, wherein determining the first AI model or the first AI function from the plurality of AI models or AI functions based on the first information comprises: Determining, according to the first indication information, at least one second AI model or second AI function from the multiple AI models or AI functions; Determine the first AI model or the first AI function from the at least one second AI model or second AI function according to fifth indication information, where the fifth indication information includes at least one of the following: the second indication information, the third indication information, and the fourth indication information. The method according to claim 2, wherein determining the first AI model or the first AI function from the plurality of AI models or AI functions based on the first information comprises: Determining, according to the third indication information, at least one third AI model or third AI function from the multiple AI models or AI functions; The first AI model or the first AI function is determined from the at least one third AI model or third AI function according to sixth indication information, where the sixth indication information includes at least one of the following: the second indication information and the fourth indication information. The method according to claim 4, wherein the third AI model or the third AI function includes a plurality of third AI models or third AI functions, and determining the first AI model or the first AI function from the at least one third AI model or third AI function according to the sixth indication information comprises: determining, according to the second indication information, at least one fourth AI model or fourth AI function from a plurality of third AI models or third AI functions; Determine, according to the fourth indication information, the first AI model or the first AI function from the at least one fourth AI model or fourth AI function. The method according to any one of claims 2 to 5, further comprising: Receive fourth information sent by the network device, where the fourth information is used by the terminal device to determine the first AI model or the first AI function from the multiple AI models or AI functions, and the fourth information includes information associated with the network device in the first information. The method according to claim 6, wherein receiving the fourth information sent by the network device comprises: Reporting second information to the network device, where the second information is used by the network device to determine the fourth indication information; Receive the fourth indication information sent by the network device. The method according to claim 7, further comprising: Receive third information sent by the network device, where the third information is used to instruct the terminal device to report the second information. The method according to claim 8, wherein receiving the third information sent by the network device comprises: The third information is received by the network device through the first signaling, where the first signaling includes at least one of the following: a radio resource control RRC message, a media access control control element MAC-CE, and downlink control information DCI. The method according to any one of claims 7 to 9, wherein reporting the second information to the network device comprises: The second information is reported to the network device through a second signaling, where the second signaling includes at least one of the following: RRC, MAC-CE, and uplink control information UCI. The method according to any one of claims 7 to 10, wherein the second information includes at least one of the following: the first indication information; the second indication information; Seventh indication information, where the seventh indication information is used to indicate a fifth AI model or a fifth AI function, where the fifth AI model or the fifth AI function includes an AI model or AI function that can be used by the terminal device. The method according to any one of claims 6 to 11, wherein receiving the fourth information sent by the network device comprises: Receive the third indication information sent by the network device. The method according to any one of claims 2 to 12, wherein each first indication information corresponds to at least one AI model or AI function. A communication method, characterized in that it is executed by a network device, the method comprising: Sending fourth information to the terminal device, where the fourth information is used by the terminal device to determine a first AI model or a first AI function from a plurality of AI models or AI functions, the fourth information including information associated with the network device in the first information, and the first information including information associated with parameters for training the first AI model or the first AI function. The method according to claim 14, wherein sending the fourth information to the terminal device comprises: receiving second information reported by the terminal device; Determining fourth indication information according to the second information, where the fourth indication information is used to indicate parameters configured by the network device for the terminal device; Send the fourth indication information to the terminal device. The method according to claim 15, further comprising: Send third information to the terminal device, where the third information is used to instruct the terminal device to report the second information. The method according to claim 16, wherein sending the third information to the terminal device comprises: The third information is sent to the terminal device via a first signaling, where the first signaling includes at least one of the following: a radio resource control RRC message, a media access control control element MAC-CE, and downlink control information DCI. The method according to any one of claims 15 to 17, wherein receiving the second information reported by the terminal device comprises: Receive the second information reported by the terminal device through the second signaling, where the second signaling includes at least one of the following: RRC, MAC-CE, and uplink control information UCI. The method according to any one of claims 15 to 18, wherein the second information includes at least one of the following: first indication information, where the first indication information is used to indicate channel characteristics; Second indication information, where the second indication information is used to indicate an additional condition corresponding to the terminal device; Seventh indication information, where the seventh indication information is used to indicate a fifth AI model or a fifth AI function, where the fifth AI model or the fifth AI function includes an AI model or AI function that can be used by the terminal device. The method according to any one of claims 14 to 19, wherein sending the fourth information to the terminal device comprises: Send third indication information to the terminal device, where the third indication information is used to indicate additional conditions corresponding to the network device. A terminal device, comprising: A processing module is configured to determine a first AI model or a first AI function from a plurality of AI models or AI functions based on first information, wherein the first information includes information associated with parameters for training the first AI model or the first AI function. A network device, comprising: The transceiver module is configured to send fourth information to the terminal device, where the fourth information is used by the terminal device to determine a first AI model or a first AI function from a plurality of AI models or AI functions, the fourth information including information associated with the network device in the first information, and the first information including information associated with parameters for training the first AI model or the first AI function. A communication device, comprising: one or more processors; The communication device is used to execute the communication method according to any one of claims 1 to 13 or claims 14 to 20. A communication system, characterized in that the communication system includes a terminal device and a network device, wherein the terminal device is configured to implement the communication method described in any one of claims 1 to 13, and the network device is configured to implement the communication method described in any one of claims 14 to 20. A storage medium storing instructions, characterized in that when the instructions are executed on a communication device, the communication device executes the communication method according to any one of claims 1 to 13 or claims 14 to 20.