User equipment side and base station side electronic equipment and method for wireless communication

Abstract

The invention provides electronic equipment and methods for a user equipment side and a base station side of wireless communication, and a computer readable storage medium. The user equipment side electronic equipment for wireless communication comprises a processing circuit configured to pair a first artificial intelligence model used for executing spatial domain beam prediction and a second artificial intelligence model used for executing time domain beam prediction; and multiplexing the beam measurement result between the first artificial intelligence model and the second artificial intelligence model in the case of successful pairing.

Description

Electronic device and method for user equipment side and base station side of wireless communication

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on April 3, 2023, with application number 202310354826.7 and invention name “Electronic device and method on user equipment side and base station side for wireless communication”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of wireless communications, and more specifically, to artificial intelligence (AI)-based beam management technology in wireless communications. More specifically, it relates to electronic devices and methods for user equipment (UE) of wireless communications, electronic devices and methods for base stations of wireless communications, and computer-readable storage media. Background Art

[0003] At the RAN#94e plenary meeting, Rel-18 added a new SID to introduce artificial intelligence / machine learning (AI / ML) into air interface access. AI-based beam management has become a major research direction. Using AI / ML models for beam management can replace traditional beam scanning processes and reduce overhead. Specifically, the AI / ML model can be trained using historical data and then used to predict beam-related information.

[0004] Recently, various companies have evaluated the performance of AI-based beam management solutions, discussing their impact on the physical layer, potential issues, and solutions. At the RAN#98e plenary meeting, it was clarified that RAN1's ​​current focus on AI-based beam management is limited to two functional types: time-domain beam prediction and spatial-domain beam prediction. Due to factors such as applicable environments, input and output set sizes, and input and output parameter properties, there are numerous AI models for each functional type. The relationship between AI model inputs and outputs is also under discussion in 3GPP.

[0005] Summary of the Invention

[0006] A brief overview of the present disclosure is provided below to provide a basic understanding of certain aspects of the present disclosure. It should be understood that this overview is not an exhaustive overview of the present disclosure. It is not intended to identify key or important aspects of the present disclosure, nor is it intended to limit the scope of the present disclosure. Its purpose is simply to present certain concepts in a simplified form as a prelude to the more detailed description discussed later.

[0007] According to one aspect of the present disclosure, an electronic device on a user equipment side for wireless communications is provided, including: a processing circuit configured to: pair a first artificial intelligence model for performing spatial domain beam prediction and a second artificial intelligence model for performing time domain beam prediction; and when the pairing is successful, multiplex beam measurement results between the first artificial intelligence model and the second artificial intelligence model.

[0008] According to another aspect of the present disclosure, a user equipment side method for wireless communication is provided, including: pairing a first artificial intelligence model for performing spatial domain beam prediction and a second artificial intelligence model for performing time domain beam prediction; and if the pairing is successful, multiplexing beam measurement results between the first artificial intelligence model and the second artificial intelligence model.

[0009] According to one aspect of the present disclosure, an electronic device on the base station side for wireless communication is provided, including: a processing circuit configured to: pair a first artificial intelligence model for performing spatial domain beam prediction and a second artificial intelligence model for performing time domain beam prediction; and when the pairing is successful, multiplex beam measurement results between the first artificial intelligence model and the second artificial intelligence model.

[0010] According to another aspect of the present disclosure, a base station-side method for wireless communication is provided, including: pairing a first artificial intelligence model for performing spatial domain beam prediction and a second artificial intelligence model for performing time domain beam prediction; and when the pairing is successful, multiplexing beam measurement results between the first artificial intelligence model and the second artificial intelligence model.

[0011] According to other aspects of the present disclosure, a computer program code and a computer program product for implementing the above-mentioned method for wireless communication, as well as a computer-readable storage medium having the computer program code for implementing the above-mentioned method for wireless communication recorded thereon, are also provided.

[0012] According to the electronic device and method of the embodiments of the present application, by pairing two functional types of AI models for beam prediction, beam measurement results can be reused, thereby further reducing beam measurement overhead.

[0013] These and other advantages of the present disclosure will become more apparent through the following detailed description of the preferred embodiments of the present disclosure in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to further illustrate the above and other advantages and features of the present disclosure, the following is a further detailed description of the specific embodiments of the present disclosure in conjunction with the accompanying drawings. The drawings, together with the detailed description below, are included in this specification and form a part of this specification. Elements with the same function and structure are represented by the same reference numerals. It should be understood that these drawings only depict typical examples of the present disclosure and should not be regarded as limiting the scope of the present disclosure. In the drawings:

[0015] FIG1 is a block diagram showing functional modules of an electronic device on a user equipment side for wireless communication according to an embodiment of the present application;

[0016] FIG2 shows a schematic diagram of AI-based beam management;

[0017] FIG3 is a schematic diagram showing an example of an information flow between a user equipment and a base station according to an embodiment of the present application;

[0018] FIG4 is a schematic diagram showing another example of an information flow between a user equipment and a base station according to an embodiment of the present application;

[0019] FIG5 is a schematic diagram showing another example of an information flow between a user equipment and a base station according to an embodiment of the present application;

[0020] FIG6 is a schematic diagram showing another example of an information flow between a user equipment and a base station according to an embodiment of the present application;

[0021] FIG7 is a schematic diagram showing another example of an information flow between a user equipment and a base station according to an embodiment of the present application;

[0022] FIG8 is a schematic diagram showing another example of an information flow between a user equipment and a base station according to an embodiment of the present application;

[0023] FIG9 is a schematic diagram showing another example of an information flow between a user equipment and a base station according to an embodiment of the present application;

[0024] FIG10 is a schematic diagram showing another example of an information flow between a user equipment and a base station according to an embodiment of the present application;

[0025] FIG11 is a schematic diagram showing another example of an information flow between a user equipment and a base station according to an embodiment of the present application;

[0026] 12 is a functional module block diagram showing an electronic device on a base station side for wireless communication according to another embodiment of the present application;

[0027] FIG13 is a schematic diagram showing another example of an information flow between a user equipment and a base station according to an embodiment of the present application;

[0028] FIG14 shows a flowchart of a method on a user equipment side for wireless communication according to an embodiment of the present application;

[0029] FIG15 shows a flowchart of a method at a base station side for wireless communication according to another embodiment of the present application;

[0030] FIG16 is a block diagram showing a first example of a schematic configuration of an eNB or gNB to which the technology of the present disclosure may be applied;

[0031] FIG17 is a block diagram illustrating a second example of a schematic configuration of an eNB or gNB to which the technology of the present disclosure may be applied;

[0032] FIG18 is a block diagram showing an example of a schematic configuration of a smartphone to which the technology of the present disclosure can be applied;

[0033] FIG19 is a block diagram showing an example of a schematic configuration of a car navigation device to which the technology of the present disclosure can be applied; and

[0034] FIG20 is a block diagram of an exemplary structure of a general-purpose personal computer in which the method and / or apparatus and / or system according to the embodiments of the present disclosure may be implemented. DETAILED DESCRIPTION

[0035] Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings. For the sake of clarity and conciseness, not all features of an actual implementation are described in this specification. However, it should be understood that in the process of developing any such actual implementation, many implementation-specific decisions must be made in order to achieve the developer's specific goals, such as compliance with system and business-related constraints, which may vary from implementation to implementation. In addition, it should be understood that although the development work may be very complex and time-consuming, it is a routine task for those skilled in the art who benefit from the contents of this disclosure.

[0036] It is also necessary to explain here that, in order to avoid obscuring the present disclosure due to unnecessary details, the accompanying drawings only show the device structure and / or processing steps that are closely related to the solution according to the present disclosure, while other details that are not closely related to the present disclosure are omitted.

[0037] <First embodiment>

[0038] As mentioned above, due to factors such as the applicable environment, the set size of input and output, and the different parameter attributes of input and output, there are many types of AI models used for beam prediction. When both the AI ​​model using spatial domain beam prediction and the AI ​​model using time domain beam prediction are working, the inputs of the two AI models may overlap, and repeated measurements will cause unnecessary measurement overhead. In this embodiment, in order to reduce measurement overhead, a framework structure is provided for performing beam management by pairing two functional types of AI models. The structure and operation of the electronic device that implements the framework structure will be described in detail below with reference to the accompanying drawings.

[0039] Figure 1 shows a functional module block diagram of an electronic device 100 on the user equipment side for wireless communication according to this embodiment. As shown in Figure 1, the electronic device 100 includes: a pairing unit 101, configured to pair a first AI model for performing spatial domain beam prediction and a second AI model for performing time domain beam prediction; and a multiplexing unit 102, configured to multiplex beam measurement results between the first AI model and the second AI model when the pairing is successful.

[0040] The pairing unit 101 and the multiplexing unit 102 can be implemented by one or more processing circuits, such as chips or processors. Furthermore, it should be understood that the various functional units in the electronic device shown in FIG1 are merely logical modules divided according to the specific functions they implement, and are not intended to limit specific implementations. The terms "first" and "second" are used only for differentiation and do not represent any order or priority.

[0041] The electronic device 100 may be located on the user equipment (UE) side, for example, and the UE may be various wireless communication terminals, or may be a communication terminal such as a mobile base station. The electronic device 100 may be implemented at the chip level, or may be implemented at the device level. For example, the electronic device 100 may work as the UE itself, and may also include external devices such as a memory and a transceiver (not shown). The memory may be used to store programs and related data information that the UE needs to execute to implement various functions. The transceiver may include one or more communication interfaces to support communication with different devices (for example, other UEs, base stations, core networks, etc.), and the implementation form of the transceiver is not specifically limited here.

[0042] For ease of understanding, Figure 2 shows a schematic diagram of AI-based beam management. In Figure 2, both spatial beam prediction and time domain beam prediction are shown. The numbers 1-6 in Figure 2 are used to represent different beams, and Nn, N-n+1, ​​..., N+1 are used to represent different moments. It should be noted that the quantities represented by these numbers and letters are schematic and not restrictive. In spatial beam prediction, in each measurement cycle, a measurement beam set, such as beams {1, 3, 6}, is measured, and the measured beam information is input into a pre-trained spatial beam prediction AI model (i.e., the first AI model, hereinafter also referred to as AI-S), thereby predicting the beam information of beams 2, 4, and 5. In time domain beam prediction, the beam information obtained at multiple historical moments (such as Nn, N-n+1, ​​..., N) can be collected as input to a pre-trained AI model for time domain beam prediction (i.e., the second AI model, also referred to as AI-T below), thereby predicting the beam information at time N+1.

[0043] In one example, when the input beam set for the first AI model includes the input beam set for the second AI model and the input parameter attributes of the first AI model include the input parameter attributes of the second AI model, the pairing is successful. The input beam set indicates which beam measurement results the AI ​​model requires as input; the input parameter attributes indicate the content properties of the input of the AI ​​model, for example, it can include at least one of the following: reference signal received power (RSRP), channel impulse response (CIR). The inclusion here can be understood as, for example, the latter is a subset of the former or the two are the same. For example, when the input beam set of the first AI model includes beams 1 to 6, and the beam input beam set of the second AI model includes 1, 3 and 5, and the input parameter attributes of both are RSRP, the pairing is successful.

[0044] In other words, at least some of the beam measurement results of the first AI model can be used for predictions in the second AI model, thereby achieving pairing. That is, the reused beam measurement results include at least a portion of the measurement results for the beams in the input beam set of the first AI model. If pairing is successful, it is only necessary to save the inputs of the first AI during the working cycle of the second AI and use these saved inputs for prediction, without the need for repeated measurements. In this way, the measurement results of a single measurement are shared between the two AI models, reducing measurement overhead.

[0045] After successful pairing, if the first AI model and / or the second AI model are switched, the pairing unit 101 is configured to re-perform pairing. This is because when the AI ​​model is switched, its model parameters may change, and the conditions for successful pairing may not be met at this time.

[0046] In summary, the electronic device 100 according to this embodiment can multiplex beam measurement results between the two functional types of AI models for beam prediction by pairing the two AI models, thereby further reducing the beam measurement overhead.

[0047] Below, we will describe in detail an embodiment in which the first AI model and the second AI model are deployed at different locations. It should be noted that this is not restrictive. In addition, the features and operations of the various embodiments herein can be combined with each other without contradiction, and such combination does not necessarily need to be explicitly stated.

[0048] <Second embodiment>

[0049] In this embodiment, the first AI model and the second AI model are both arranged on the UE side. The pairing unit 101 is configured to perform pairing based on the model parameters of the first AI model and the model parameters of the second AI model to determine whether the pairing is successful. The model parameters of the first AI model, for example, include one or more of the following: input parameter size, input and output parameter attributes. The input parameter size can refer to the number of measurement beams, or it can indicate information about the beam set to be measured. The input and output parameter attributes, for example, include at least one of the above-mentioned RSRP and CIR. The model parameters of the second AI model, for example, include one or more of the following: input time window length and output time window length, input parameter size, input and output parameter attributes, and the minimum number of measurements of historical data to be adopted. Among them, the input time window length refers to the size of the time range of the measurement beam information used for prediction, the output time window length refers to the size of the time range of the predicted beam information, the input parameter size refers to the number of measurement beams in each measurement or the information of the beam set to be measured each time, the input and output parameter attributes include, for example, at least one of the above-mentioned RSRP and CIR, and the minimum number of measurements of historical data to be used refers to the minimum number of groups of historical data on which each prediction is based, that is, the minimum number of measurements required within the input time window length.

[0050] As shown in the dotted box in Figure 1, the electronic device 100 also includes a communication unit 103. For example, the communication unit 103 is configured to send a message to the base station indicating whether the pairing is successful or unsuccessful. This message can, for example, occupy 1 bit. In the case of successful pairing, for example, the bit is assigned a value of 1 to notify the base station, such as the gNB, that the AI ​​model currently performing beam management includes two functional types, namely, the first AI model and the second AI model are successfully paired. Conversely, in the case of unsuccessful pairing, the bit is assigned a value of 0 to notify the base station that the pairing is unsuccessful. It should be understood that the corresponding relationship between the value of the 1 bit and its meaning here can be changed in the opposite direction and is not limited. Alternatively, the communication unit 103 can also send the indication message to the base station only when the pairing is successful.

[0051] When the pairing is successful, the UE side regards the first AI model and the second AI model as a whole, and the communication unit 103 can also send the model parameters or model index of the second AI to the base station to inform the base station which AI model will be used for prediction. The model parameters here include, for example, one or more of the following: input time window length and output time window length, input parameter size, input and output parameter attributes, etc. In addition, if the base station and the UE can reach a consensus on all AI models, the model parameters of each AI model can be replaced by a model index, thereby reducing signaling overhead. The above-mentioned 1-bit indication information and the model parameters or model index here can be transmitted through the control channel. The two can be included in the same signaling, or they can be sent separately using different signaling.

[0052] If pairing is unsuccessful, the pairing unit 101 may further be configured to determine whether to use one of the first AI model and the second AI model, or to use a non-AI beam management mechanism instead of the AI ​​model. If it is determined that one of the first AI model and the second AI model is to be used, the communication unit 103 may be configured to send the model parameters or model index of the AI ​​model to be used to the base station. If it is determined that the non-AI beam management mechanism is to be used, the communication unit 103 may not send any indication information to the base station. Similarly, the model parameters or model index may be transmitted via a control channel.

[0053] For ease of understanding, Figure 3 shows a schematic diagram of the information flow between the UE and the gNB according to this embodiment. First, the UE pairs the first AI model (AI-S) with the second AI model (AI-T). If the pairing is successful, it sends a message to the gNB indicating the pairing success, and may also send the model parameters or model index of the second AI model to the gNB. It should be noted that although two arrows are shown here to represent the signaling for transmission, this does not necessarily mean that two signalings are required to send the indication message and the model parameters or model index. They can also be sent in one signaling. This is just an example. Similar situations also exist in the various information flows illustrated below. For example, one arrow does not necessarily represent one signaling.

[0054] Subsequently, AI-S and AI-T are performed on the UE side, and the prediction results, or a portion thereof, are reported to the gNB, for example, via a data channel. Furthermore, the UE may also report measurement results to the gNB as needed. Figure 3 primarily illustrates the case of successful pairing. If pairing is unsuccessful, as shown in the dashed box in Figure 3, the UE may optionally send one of the following to the gNB: a message indicating unsuccessful pairing and the model parameters or model index of the AI-S or AI-T to be adopted, or the model parameters or model index of the AI-S or AI-T to be adopted; or no message is sent and a non-AI beam management mechanism is adopted.

[0055] After the pairing is successful, the first AI model can be kept silent within the output time window of the second AI model.

[0056] After pairing is successful, if the first AI model and / or the second AI model triggers the supervision mechanism, the communication unit 103 is configured to obtain the execution period of the supervision mechanism, and the pairing unit 101 is configured to keep the unsupervised AI model silent during the execution period. If the supervision is performed by the UE side, the communication unit 103 can locally obtain the execution period information. If the supervision is performed by the base station side, the communication unit 103 obtains the execution period information through specific signaling or can infer the parameter value of the execution period.

[0057] In addition, after pairing is successful, if pairing is re-executed due to switching of the first AI model and / or the second AI model, if pairing is unsuccessful, the communication unit 103 needs to notify the gNB of the message that pairing is unsuccessful.

[0058] <Third embodiment>

[0059] In this embodiment, the first AI model is deployed on one side of the UE and the base station, and the second AI model is deployed on the other side of the UE and the base station. The pairing unit 101 is configured to perform pairing by interacting with the base station via the communication unit 103. The pairing is initiated by the UE.

[0060] For example, pairing unit 101 may determine the AI ​​model to be used on the UE side, and communication unit 103 may send a pairing request to the base station, so that the base station determines the AI ​​model to be used on the base station side based on the pairing request, and receive a message from the base station indicating whether the pairing is successful or unsuccessful. Pairing unit 101 determines whether the pairing is successful based on the message. The pairing request may include model parameters or a model index of the AI ​​model to be used on the UE side.

[0061] If pairing is successful, communication unit 103 is further configured to receive, from the base station, model parameters or a model index of the AI ​​model to be used by the base station. After pairing is successful, multiplexing unit 102 multiplexes beam measurement results between the first AI model and the second AI model via communication unit 103. After pairing is successful, the first AI model may be muted during the output time window of the second AI model.

[0062] On the other hand, if pairing is unsuccessful, the communication unit 103 may be further configured to send a message to the base station indicating whether to select the first AI model or the second AI model. Alternatively, the communication unit 103 may not send any message and adopt a non-AI beam management mechanism, or send a message to the base station indicating that a non-AI beam management mechanism is to be adopted.

[0063] In the first example, the first AI model is deployed on the UE side, and the second AI model is deployed on the base station side. In this case, the pairing request may include the model parameters of the first AI model to be used by the UE, such as the input parameter size and input and output parameter attributes of the first AI model, or may include the model index of the first AI model. After receiving the pairing request, the base station selects the best second AI model that meets the requirements. If such a second AI model is found, the base station sends a message to the UE indicating successful pairing, such as a one-bit message, and distributes the model parameters or model index of the selected second AI model to the UE. The model parameters of the second AI model received from the base station may include, for example, at least the input and output time window lengths, and the minimum number of historical data measurements to be used. The UE can schedule the execution of the first AI model based on this minimum number of measurements.

[0064] In the event of unsuccessful pairing, if the UE wants to select the first AI model, the message sent by the communication unit 103 to the base station includes the model parameters or model index of the selected first AI model; if the UE wants to select the second AI model, the message sent by the communication unit 103 to the base station includes 1-bit notification information to inform the base station to use the second AI model to perform beam prediction.

[0065] For ease of understanding, Figures 4 and 5 illustrate the information flow between a UE and a gNB according to a first example. Figure 4 illustrates a successful pairing scenario, while Figure 5 illustrates an unsuccessful pairing scenario. The UE initiates pairing by first determining its intended AI model, namely, AI-S. It then sends a pairing request to the gNB, which may include the AI-S model parameters or model index. Upon receiving the pairing request, the gNB selects the optimal AI-T for pairing based on the AI-S and determines pairing is successful if such an AI-T is found. The gNB sends a successful pairing indication message to the UE, along with the AI-T model parameters or model index. After successful pairing, the UE executes AI-S and reports the AI-S prediction results and beam measurement results to the gNB, for example, via a data channel. The gNB stores the measurement results reported by the UE within the input time window as input to AI-T, thereby executing AI-T and indicating the optimal beam to the UE. The communication unit 103 on the UE side may report the beam measurement results within the input time window length of the AI-T to the base station in a single or multiple forms, so that the AI-T uses the beam measurement results to perform beam prediction.

[0066] 5 , if pairing is unsuccessful, the gNB sends an indication message indicating the pairing failure to the UE. The UE then selects AI-S or AI-T, and sends the AI-S model parameter or model index to the gNB if AI-S is selected, and sends 1-bit notification information to the gNB if AI-T is selected.

[0067] In a second example, the second AI model is deployed on the UE side, and the first AI model is deployed on the base station side. In this case, the pairing request may include model parameters of the second AI model to be used by the UE, such as the input and output time window lengths, input parameter sizes, input and output parameter attributes, etc., or may include a model index of the second AI model. After receiving the pairing request, the base station may select the best first AI model that meets the requirements. If such a first AI model is found, the base station sends a message to the UE indicating successful pairing, such as a one-bit message. Furthermore, if the input of the first AI model requires additional extended information, such as if the input parameter size of the first AI model is larger than that of the second AI model, the base station may also send this information to the UE. For example, the base station may send the model parameters or model index of the selected first AI model to the UE. The model parameters of the first AI model received from the base station may include, for example, the input parameter size of the first AI model.

[0068] In the event of unsuccessful pairing, if the UE wants to select the second AI model, the message sent by the communication unit 103 to the base station includes the model parameters or model index of the selected second AI model; if the UE wants to select the first AI model, the message sent by the communication unit 103 to the base station includes 1-bit notification information to inform the base station to use the first AI model to perform beam prediction.

[0069] For ease of understanding, Figures 6 and 7 illustrate the information flow between a UE and a gNB according to the second example. Figure 6 illustrates a successful pairing scenario, while Figure 7 illustrates an unsuccessful pairing scenario. The UE initiates pairing by first determining the second AI model to be used, namely AI-T, and then sending a pairing request to the gNB. This pairing request may include the AI-T model parameters or model index. Upon receiving the pairing request, the gNB selects the optimal AI-S to pair with it based on the AI-T and determines that pairing is successful if such an AI-S is found. The gNB sends an indication message to the UE indicating the pairing success and, if the AI-S input parameter size is larger, also sends the AI-S model parameters, such as the input parameter size or model index. After pairing is successful, the UE reports beam measurement results to the gNB, for example, via a data channel. The gNB uses these beam measurement results to perform AI-S and indicates the optimal beam to the UE. The UE stores the measurement results within the input time window as input to AI-T, thereby performing AI-T and reporting the AI-T prediction results to the gNB.

[0070] 7 , if pairing is unsuccessful, the gNB sends an indication message indicating the pairing failure to the UE. The UE then selects AI-S or AI-T, and when AI-T is selected, sends the model parameters or model index of AI-T to the gNB. When AI-S is selected, the UE sends 1-bit notification information to the gNB.

[0071] Furthermore, after successful pairing, if the first and second AI models trigger a supervision mechanism, communication unit 103 provides the base station with the execution period of the supervision mechanism if the supervision mechanism is executed by the UE; if the supervision mechanism is executed by the base station, communication unit 103 obtains the execution period of the supervision mechanism from the base station. Furthermore, if the base station's AI model triggers the supervision mechanism, pairing unit 101 is configured to silence the UE's AI model during the execution period.

[0072] Similarly, after pairing is successful, if switching of the first AI model and / or the second AI model occurs, the above pairing operation must be performed again between the UE and the base station.

[0073] <Fourth embodiment>

[0074] In this embodiment, similar to the third embodiment, the first AI model is deployed on one side of the UE and the base station, and the second AI model is deployed on the other side of the UE and the base station. The pairing unit 101 is configured to perform pairing by interacting with the base station via the communication unit 103. Unlike the third embodiment, the base station initiates the pairing.

[0075] For example, the base station determines the AI ​​model to be used in pairing and includes the model parameters or model index of the determined AI model in a pairing request and sends it to the UE. The communication unit 103 on the UE side is configured to receive the pairing request from the base station. The pairing unit 101 is configured to determine the AI ​​model to be used by the UE in pairing based on the received pairing request, and the communication unit 103 is configured to send a message to the base station indicating whether the pairing is successful or unsuccessful.

[0076] If pairing is successful, communication unit 103 is further configured to send the model parameters or model index of the UE-side AI model to the base station. After pairing is successful, multiplexing unit 102 multiplexes the beam measurement results between the first AI model and the second AI model via communication unit 103. After pairing is successful, the first AI model may be kept silent during the output time window of the second AI model.

[0077] On the other hand, if pairing is unsuccessful, the communication unit 103 may be further configured to receive a message from the base station indicating whether to select the first AI model or the second AI model. Alternatively, the communication unit 103 may not receive any message and adopt a non-AI beam management mechanism, or may receive a message from the base station indicating that a non-AI beam management mechanism is to be adopted.

[0078] In the first example, the first AI model is deployed on the UE side and the second AI model is deployed on the base station side. In this case, the pairing request may include one or more of the following model parameters of the second AI model to be used by the base station side: input time window length and output time window length, input parameter size, input and output parameter attributes, and the minimum number of measurements of historical data to be used; or include the model index of the second AI model. After receiving the pairing request, the UE can select the best first AI model that meets the requirements. If such a first AI model can be found, the communication unit 103 sends a message indicating successful pairing to the base station, such as a 1-bit message, and also sends the model parameters of the first AI model, such as the input parameter size or model index, if the input parameter size of the first AI model is larger.

[0079] In the event of unsuccessful pairing, if the base station is to select the first AI model, the message received by the communication unit 103 from the base station includes 1-bit notification information to inform the UE to use the first AI model to perform beam prediction; if the base station is to select the second AI model, the message received by the communication unit 103 from the base station includes the model parameters or model index of the selected second AI model.

[0080] For ease of understanding, Figures 8 and 9 illustrate the information flow between the UE and the gNB according to the first example. Figure 8 illustrates a successful pairing scenario, while Figure 9 illustrates an unsuccessful pairing scenario. The gNB initiates pairing by first determining the second AI model to be used, namely AI-T, and then sending a pairing request to the UE. This pairing request may include the AI-T model parameters or model index. After receiving the pairing request, the UE selects the optimal AI-S to pair with it based on the AI-T and determines that pairing is successful if such an AI-S is found. The UE sends an indication message to the gNB indicating successful pairing and, if the AI-S input parameter size is larger, also sends the AI-S model parameters, such as the input parameter size or model index. After successful pairing, the UE executes AI-S and reports the AI-S prediction results and beam measurement results to the gNB, for example, via a data channel. The gNB stores the measurement results reported by the UE within the input time window as input to AI-T, thereby executing AI-T and indicating the optimal beam to the UE. The communication unit 103 on the UE side may report the beam measurement results within the input time window length of the AI-T to the base station in a single or multiple forms, so that the AI-T uses the beam measurement results to perform beam prediction.

[0081] 9 , if pairing is unsuccessful, the UE sends an indication message indicating pairing failure to the gNB, and the gNB then selects AI-S or AI-T. If AI-T is selected, the UE sends the model parameters or model index of AI-T to the UE, and if AI-S is selected, the gNB sends 1-bit notification information to the gNB.

[0082] In the second example, the second AI model is deployed on the UE side and the first AI model is deployed on the base station side. In this case, the pairing request may include the model parameters of the first AI model to be used by the base station side, such as the input parameter size and input and output parameter attributes of the first AI model, or include the model index of the first AI model. After receiving the pairing request, the UE can select the best second AI model that meets the requirements. If such a second AI model can be found, the communication unit 103 sends a message indicating successful pairing to the base station, such as a 1-bit message. In addition, the communication unit 103 also needs to send the model parameters or model index of the selected second AI model to the base station. The model parameters of the second AI model may, for example, include at least the input time window length and the output time window length.

[0083] In the event of unsuccessful pairing, if the base station is to select the first AI model, the message received by the communication unit 103 from the base station includes the model parameters or model index of the selected first AI model; if the base station is to select the second AI model, the message received by the communication unit 103 from the base station includes 1-bit notification information to inform the UE to use the second AI model to perform beam prediction.

[0084] For ease of understanding, Figures 10 and 11 illustrate the information flow between a UE and a gNB according to the second example. Figure 10 illustrates a successful pairing scenario, while Figure 11 illustrates an unsuccessful pairing scenario. The gNB initiates pairing by first determining the first AI model to be used, namely, AI-S. The gNB then sends a pairing request to the UE, which may include the model parameters or model index of the AI-S. Upon receiving the pairing request, the UE selects the optimal AI-T to pair with it based on the AI-S and determines that pairing is successful if such an AI-T is found. The UE sends an indication message to the gNB indicating successful pairing, along with the model parameters or model index of the AI-T. After successful pairing, the UE can report beam measurement results to the gNB, for example, via a data channel. The gNB uses these beam measurement results to perform AI-S and indicate the optimal beam to the UE. The UE stores the measurement results within the input time window as input to AI-T, which then performs AI-T and reports the predicted AI-T results to the gNB.

[0085] Referring to Figure 11, if pairing is unsuccessful, the UE sends an indication message indicating pairing failure to the gNB, and then the gNB selects AI-S or AI-T. When AI-S is selected, the gNB sends the model parameters or model index of AI-S to the UE, and when AI-T is selected, it sends 1-bit notification information to the UE.

[0086] Furthermore, after successful pairing, if the first and second AI models trigger a supervision mechanism, communication unit 103 provides the base station with the execution period of the supervision mechanism if the supervision mechanism is executed by the UE; if the supervision mechanism is executed by the base station, communication unit 103 obtains the execution period of the supervision mechanism from the base station. Furthermore, if the base station's AI model triggers the supervision mechanism, pairing unit 101 is configured to silence the UE's AI model during the execution period.

[0087] Similarly, after pairing is successful, if switching of the first AI model and / or the second AI model occurs, the above pairing operation must be performed again between the UE and the base station.

[0088] <Fifth embodiment>

[0089] From the above description of the framework structure of pairing based on the AI ​​model, it can be seen that according to an embodiment of the present application, an electronic device 200 on the base station side for wireless communication is also provided. As shown in Figure 12, the electronic device 200 includes: a pairing unit 201, configured to pair a first AI model for performing spatial domain beam prediction and a second AI model for performing time domain beam prediction; and a multiplexing unit 202, configured to multiplex beam measurement results between the first AI model and the second AI model when the pairing is successful.

[0090] The pairing unit 201 and the multiplexing unit 202 can be implemented by one or more processing circuits, such as chips or processors. Furthermore, it should be understood that the various functional units in the electronic device shown in FIG12 are merely logical modules divided according to the specific functions they implement, and are not intended to limit specific implementations. The terms "first" and "second" are used only for differentiation and do not represent any order or priority.

[0091] The electronic device 200 can be set on the base station side or the wireless transceiver node side. It should also be noted that the electronic device 200 can be implemented at the chip level, or it can also be implemented at the device level. For example, the electronic device 200 can work as the base station itself, and can also include external devices such as memory, transceiver (not shown in the figure), etc. The memory can be used to store programs and related data information that need to be executed by the base station to implement various functions. The transceiver may include one or more communication interfaces to support communication with different devices (for example, other base stations, UE, etc.), and the implementation form of the transceiver is not specifically limited here.

[0092] The AI-based beam management described above with reference to FIG. 2 in the first embodiment also applies to this embodiment. Furthermore, the model parameters of the AI ​​model, definitions of model parameters, and other aspects, pairing rules, and signaling procedures compatible with this embodiment described in the first to fourth embodiments also apply to this embodiment and subsequent embodiments and will be appropriately simplified or omitted in the following description.

[0093] In one example, pairing is successful when the input beam set for the first AI model includes the input beam set for the second AI model, and the input parameter attributes of the first AI model include the input parameter attributes of the second AI model. The input beam set indicates which beam measurement results the AI ​​model requires as input; the input parameter attributes indicate the content nature of the AI ​​model input, for example, they may include at least one of RSRP and CIR. The inclusion here can be understood as, for example, the latter being a subset of the former.

[0094] In other words, at least some of the beamforming measurement results of the first AI model can be used for prediction by the second AI model, thereby enabling pairing. This enables the sharing of measurement results between the two AI models, reducing measurement overhead.

[0095] After successful pairing, if the first AI model and / or the second AI model are switched, the pairing unit 101 is configured to re-perform pairing. This is because when the AI ​​model is switched, its model parameters may change, and the conditions for successful pairing may not be met at this time.

[0096] In summary, the electronic device 200 according to this embodiment can multiplex beam measurement results between the two functional types of AI models for beam prediction by pairing the two AI models, thereby further reducing the beam measurement overhead.

[0097] Hereinafter, an embodiment in which the first AI model and the second AI model are respectively deployed at different locations will be described from the perspective of a base station. It should be noted that this is not restrictive.

[0098] <Sixth embodiment>

[0099] In this embodiment, the first AI model and the second AI model are both deployed on the base station side. The pairing unit 201 is configured to perform pairing based on the model parameters of the first AI model and the model parameters of the second AI model to determine whether the pairing is successful. The model parameters of the first AI model include, for example, one or more of the following: input parameter size, input and output parameter attributes. The model parameters of the second AI model include, for example, one or more of the following: input time window length and output time window length, input parameter size, input and output parameter attributes, and the minimum number of measurements of historical data to be used.

[0100] As shown in the dashed box in Figure 12, the electronic device 100 also includes a communication unit 203. For example, if pairing is successful, the communication unit 203 is configured to send the input time window length (T1) and output time window length (T2) of the second AI model to the UE. The UE measures the beam within the input time window length and reports the measurement results. The base station saves the beam measurement results within the input time window length as input to the second AI model.

[0101] To facilitate understanding, Figure 13 illustrates the information flow between the UE and the gNB according to this embodiment. First, the gNB pairs the first AI model (AI-S) with the second AI model (AI-T). If the pairing is successful, the gNB sends T1 and T2 of the second AI model to the UE. After the pairing is successful, the UE sends the beam measurement results within T1 to the gNB, for example, via a data channel.

[0102] In the above example, the base station does not send an indication message to the UE indicating whether the pairing is successful. However, this is not restrictive, and the base station may also send the indication message to the UE.

[0103] After the pairing is successful, the first AI model can be kept silent within the output time window of the second AI model.

[0104] After successful pairing, if the first AI model and / or the second AI model triggers a supervision mechanism, if the supervision mechanism is executed by the base station, the communication unit 203 is configured to provide the execution period of the supervision mechanism to the UE; if the supervision mechanism is executed by the UE, the communication unit 203 is configured to obtain the execution period of the supervision mechanism from the UE. The pairing unit 201 is configured to keep the unsupervised AI model silent during the execution period.

[0105] <Seventh embodiment>

[0106] In this embodiment, the first AI model is deployed on one side of the UE and the base station, and the second AI model is deployed on the other side of the UE and the base station. The pairing unit 201 is configured to interact with the UE via the communication unit 203 to perform pairing. The pairing is initiated by the UE.

[0107] For example, the UE determines the AI ​​model to be used on the UE side and sends a pairing request to the base station. The pairing request includes the model parameters or model index of the AI ​​model to be used on the UE side. Communication unit 203 receives the pairing request from the UE. Pairing unit 201 determines the AI ​​model to be used on the base station side based on the pairing request. Communication unit 203 sends a message to the UE indicating whether the pairing is successful or unsuccessful.

[0108] If pairing is successful, communication unit 203 is further configured to send the model parameters or model index of the AI ​​model to be used by the base station side to the UE. After pairing is successful, multiplexing unit 202 multiplexes the beam measurement results between the first AI model and the second AI model through communication unit 203. After pairing is successful, the first AI model can be kept silent during the output time window of the second AI model.

[0109] On the other hand, if pairing is unsuccessful, the communication unit 203 may be further configured to receive a message from the UE indicating whether to select the first AI model or the second AI model. Alternatively, the communication unit 203 may not receive any message and adopt a non-AI beam management mechanism, or may receive a message from the UE indicating that a non-AI beam management mechanism is to be adopted.

[0110] In the first example, the first AI model is deployed on the UE side, and the second AI model is deployed on the base station side. In this case, the pairing request may include the model parameters of the first AI model to be used by the UE, such as the input parameter size and input and output parameter attributes of the first AI model, or may include the model index of the first AI model. After receiving the pairing request, the base station selects the best second AI model that meets the requirements. If such a second AI model is found, the communication unit 203 on the base station sends a message indicating successful pairing to the UE, such as a one-bit message, and sends the model parameters or model index of the selected second AI model to the UE. The model parameters of the second AI model sent may include, for example, at least the input time window length and the output time window length, and the minimum number of historical data measurements to be used. The UE can determine the number of executions of the first AI model based on this minimum number of measurements.

[0111] In the event that pairing is unsuccessful, if the UE is to select the first AI model, the message received by the communication unit 203 includes the model parameters or model index of the selected first AI model; if the UE is to select the second AI model, the message received by the communication unit 203 includes 1-bit notification information to inform the base station to use the second AI model to perform beam prediction.

[0112] After successful pairing, the UE performs spatial beam prediction using the first AI model. The communication unit 203 is further configured to receive beam measurement results within the input time window length of the second AI model from the UE in a single or multiple times, so that the second AI model uses the beam measurement results to perform beam prediction. The communication unit 203 can receive the beam measurement results and the prediction results of the first AI model through a data channel.

[0113] For schematic diagrams of examples of the information flow between the UE and the gNB in ​​the first example, please refer to Figures 4 and 5, which are not repeated here.

[0114] In a second example, the second AI model is deployed on the UE side, and the first AI model is deployed on the base station side. In this case, the pairing request may include model parameters of the second AI model to be used by the UE, such as the input and output time window lengths, input parameter sizes, input and output parameter attributes, etc., or may include a model index of the second AI model. After receiving the pairing request, the base station may select the best first AI model that meets the requirements. If such a first AI model is found, the communication unit 203 on the base station side sends a message to the UE indicating successful pairing, such as a one-bit message. In addition, if the input of the first AI model requires additional extended information, such as if the input parameter size of the first AI model is larger than that of the second AI model, the communication unit 203 may also send this information to the UE. For example, the communication unit 203 may send the model parameters or model index of the first AI model to be used by the base station to the UE. The model parameters of the first AI model, for example, include the input parameter size of the first AI model.

[0115] In the event that pairing is unsuccessful, if the UE is to select the second AI model, the message received by the communication unit 203 from the UE includes the model parameters or model index of the selected second AI model; if the UE is to select the first AI model, the message received by the communication unit 103 from the UE includes 1-bit notification information to inform the base station to use the first AI model to perform beam prediction.

[0116] For schematic diagrams of examples of the information flow between the UE and the gNB in ​​the second example, please refer to Figures 6 and 7, which are not repeated here.

[0117] Furthermore, after successful pairing, if the first and second AI models trigger a supervision mechanism, communication unit 203 obtains the execution period of the supervision mechanism from the UE if the supervision mechanism is executed by the UE; if the supervision mechanism is executed by the base station, communication unit 203 provides the execution period of the supervision mechanism to the UE. Furthermore, if the UE-side AI model triggers the supervision mechanism, pairing unit 201 is configured to silence the base station-side AI model during the execution period.

[0118] Similarly, after pairing is successful, if switching of the first AI model and / or the second AI model occurs, the above pairing operation must be performed again between the UE and the base station.

[0119] <Eighth Embodiment>

[0120] In this embodiment, similar to the seventh embodiment, the first AI model is deployed on one side of the UE and the base station, and the second AI model is deployed on the other side of the UE and the base station. The pairing unit 201 is configured to perform pairing by interacting with the UE via the communication unit 203. Unlike the seventh embodiment, the base station initiates the pairing.

[0121] For example, the base station determines the AI ​​model to be used by the base station for pairing. The communication unit 203 includes the model parameters or model index of the determined AI model in the pairing request and sends it to the UE, so that the UE determines the AI ​​model to be used on the UE side based on the model parameters or model index. The communication unit 203 is also configured to receive a message from the UE indicating whether the pairing is successful or unsuccessful.

[0122] If pairing is successful, communication unit 203 is further configured to receive model parameters or a model index of the UE-side AI model from the UE. After pairing is successful, multiplexing unit 202 multiplexes beam measurement results between the first AI model and the second AI model via communication unit 203. After pairing is successful, the first AI model may be muted within the output time window of the second AI model.

[0123] On the other hand, if pairing is unsuccessful, the communication unit 203 is further configured to send a message to the UE indicating whether to select the first AI model or the second AI model. Alternatively, the communication unit 203 may not send any message and adopt a non-AI beam management mechanism, or send a message to the UE indicating that a non-AI beam management mechanism is to be adopted.

[0124] In the first example, the first AI model is deployed on the UE side and the second AI model is deployed on the base station side. In this case, the pairing request may include one or more of the following model parameters of the second AI model to be used by the base station side: input time window length and output time window length, input parameter size, input and output parameter attributes, and the minimum number of measurements of historical data to be used; or include the model index of the second AI model. After receiving the pairing request, the UE can select the best first AI model that meets the requirements. If such a first AI model can be found, the UE sends a message indicating successful pairing to the base station, such as a 1-bit message. Accordingly, the communication unit 203 receives the message indicating successful pairing from the UE. In addition, if the input parameter size of the first AI model is larger, the communication unit 203 also receives the model parameters of the first AI model, such as the input parameter size or model index, from the UE.

[0125] In the event of unsuccessful pairing, if the base station selects the first AI model, the message sent by the communication unit 203 to the UE includes 1-bit notification information to inform the UE that the first AI model is to be used to perform beam prediction; if the base station selects the second AI model, the message sent by the communication unit 203 to the UE includes the model parameters or model index of the selected second AI model.

[0126] After successful pairing, the UE performs spatial beam prediction using the first AI model. The communication unit 203 is further configured to receive beam measurement results within the input time window length of the second AI model from the UE in a single or multiple times, so that the second AI model uses the beam measurement results to perform beam prediction. The communication unit 203 can receive the beam measurement results and the prediction results of the first AI model through a data channel.

[0127] For schematic diagrams of examples of the information flow between the UE and the gNB in ​​the first example, please refer to Figures 8 and 9, which are not repeated here.

[0128] In the second example, the second AI model is deployed on the UE side, and the first AI model is deployed on the base station side. In this case, the pairing request may include the model parameters of the first AI model to be used by the base station side, such as the input parameter size and input and output parameter attributes of the first AI model, or include the model index of the first AI model. After receiving the pairing request, the UE can select the best second AI model that meets the requirements. If such a second AI model can be found, the UE sends a message indicating successful pairing to the base station, such as a 1-bit message. Accordingly, the communication unit 203 receives the message indicating successful pairing from the UE. In addition, the communication unit 203 also receives the model parameters or model index of the second AI model selected by the UE from the UE. The model parameters of the second AI model may, for example, include at least the input time window length and the output time window length.

[0129] In the event of unsuccessful pairing, if the base station selects the first AI model, the message sent by the communication unit 203 to the UE includes the model parameters or model index of the selected first AI model; if the base station selects the second AI model, the message sent by the communication unit 203 to the UE includes 1-bit notification information to inform the UE to use the second AI model to perform beam prediction.

[0130] For schematic diagrams of examples of the information flow between the UE and the gNB in ​​the second example, please refer to Figures 10 and 11, which are not repeated here.

[0131] Furthermore, in this embodiment, after successful pairing, if the first and second AI models trigger a supervision mechanism, if the supervision mechanism is executed by the UE, communication unit 203 obtains the execution period of the supervision mechanism from the UE; if the supervision mechanism is executed by the base station, communication unit 203 provides the execution period of the supervision mechanism to the UE. Furthermore, if the UE-side AI model triggers the supervision mechanism, pairing unit 201 is configured to silence the base station-side AI model during the execution period.

[0132] Similarly, after pairing is successful, if switching of the first AI model and / or the second AI model occurs, the above pairing operation must be performed again between the UE and the base station.

[0133] Ninth embodiment

[0134] In the process of describing the electronic device for wireless communication in the above embodiments, it is obvious that some processes or methods are also disclosed. Below, an overview of these methods is given without repeating some of the details discussed above, but it should be noted that although these methods are disclosed in the process of describing the electronic device for wireless communication, these methods do not necessarily use the components described or are not necessarily performed by those components. For example, the embodiments of the electronic device for wireless communication can be partially or completely implemented using hardware and / or firmware, and the methods for wireless communication discussed below can be completely implemented by computer-executable programs, although these methods can also use the hardware and / or firmware of the electronic device for wireless communication.

[0135] Figure 14 shows a flowchart of a method for wireless communication on a user equipment side according to an embodiment of the present application, the method comprising: pairing a first AI model for performing spatial domain beam prediction with a second AI model for performing time domain beam prediction (S11); and if the pairing is successful, multiplexing beam measurement results between the first AI model and the second AI model (S12). This method can be performed on the UE side, for example.

[0136] For example, pairing is successful when the input beam set for the first AI model includes the input beam set for the second AI model, and the input parameter attributes of the first AI model include the input parameter attributes of the second AI model. The input parameter attributes include at least one of the following: reference signal received power and channel impulse response. The multiplexed beam measurement results, for example, include at least a portion of the measurement results for the beams in the input beam set of the first AI model.

[0137] After pairing is successful, if switching of the first AI model and / or the second AI model occurs, pairing is performed again.

[0138] According to the layout location of the first AI model and the second AI model and the initiator of the pairing, it can be divided into four scenarios: in the first scenario, the first AI model and the second AI model are both deployed on the UE side; in the second scenario, the first AI model is deployed on one side between the UE side and the base station side, and the second AI model is deployed on the other side between the UE side and the base station side, and the pairing is initiated by the UE; in the third scenario, the first AI model is deployed on one side between the UE side and the base station side, and the second AI model is deployed on the other side between the UE side and the base station side, and the pairing is initiated by the base station; in the fourth scenario, the first AI model and the second AI model are both deployed on the base station side.

[0139] In the first scenario, in step S11, pairing is performed based on the model parameters of the first AI model and the model parameters of the second AI model to determine whether the pairing is successful. Although not shown in the figure, the above method also includes the following steps: sending a message to the base station indicating whether the pairing is successful or unsuccessful. In the case of successful pairing, the method also includes sending the model parameters or model index of the second AI model to the base station. The model parameters of the second AI model sent include, for example, one or more of the following: the input time window length and the output time window length of the second AI model, the input parameter size, and the input and output parameter attributes. In the case of unsuccessful pairing, the method also includes: determining to use one of the first AI model and the second AI model, and sending the model parameters or model index of the AI ​​model to be used to the base station.

[0140] After the pairing is successful, when the first AI model and / or the second AI model triggers the supervision mechanism, the method further includes obtaining an execution cycle of the supervision mechanism and keeping the unsupervised AI model silent during the execution cycle.

[0141] The above method in the first scenario corresponds to the electronic device 100 in the second embodiment. The relevant specific details can be referred to the second embodiment and will not be repeated here.

[0142] In the second scenario, in step S11, pairing is performed by interacting with the base station. For example, step S11 includes: determining an AI model to be used on the UE side; sending a pairing request to the base station, so that the base station determines a second AI model to be used on the base station side based on the pairing request; and receiving a message from the base station indicating whether the pairing is successful or unsuccessful, and determining whether the pairing is successful based on the message.

[0143] The pairing request, for example, includes the model parameters of the AI ​​model to be used on the UE. If the first AI model is deployed on the UE, the pairing request includes the input parameter size and input / output parameter attributes of the first AI model. If the second AI model is deployed on the UE, the pairing request may include the input and output time window lengths, input parameter size, and input / output parameter attributes of the second AI model. Alternatively, the pairing request may also include the model index of the AI ​​model to be used on the UE.

[0144] If the pairing is successful, the method further includes receiving, from the base station, model parameters of the AI ​​model to be used by the base station. Where the first AI model is deployed on the base station side, the received model parameters include the input parameter size of the first AI model. Where the second AI model is deployed on the base station side, the received model parameters include at least: the input time window length and the output time window length, and the minimum number of measurements of historical data to be used. Alternatively, a model index of the AI ​​model to be used by the base station may also be received from the base station.

[0145] If pairing is unsuccessful, the method further includes sending a message to the base station indicating whether to select the first AI model or the second AI model. If the first AI model is deployed on the UE side, if the first AI model is to be selected, the message includes model parameters or a model index of the selected first AI model; if the second AI model is to be selected, the message includes one bit of notification information. If the second AI model is deployed on the UE side, if the second AI model is to be selected, the message includes model parameters or a model index of the selected second AI model; if the first AI model is to be selected, the message includes one bit of notification information.

[0146] The above method in the second scenario corresponds to the electronic device 100 in the third embodiment. The relevant specific details can be referred to the third embodiment and will not be repeated here.

[0147] In the third scenario, step S11, for example, includes: receiving a pairing request from a base station, the pairing request including model parameters or a model index of an AI model to be used in pairing determined by the base station; determining, based on the received model parameters or model index, the AI ​​model to be used by the UE in pairing; and sending a message to the base station indicating whether the pairing is successful or unsuccessful.

[0148] When the first AI model is arranged on the UE side, the received model parameters include one or more of the following model parameters of the second AI model: input time window length and output time window length, input parameter size, input and output parameter attributes, and the minimum number of measurements of historical data to be adopted; when the second AI model is arranged on the UE side, the received model parameters include at least the input parameter size and input and output parameter attributes of the first AI model.

[0149] If the pairing is successful, the method further includes sending the model parameters or model index of the AI ​​model determined by the UE to be used to the base station. If the first AI model is deployed on the UE side, the input parameter size of the first AI model can be sent to the base station; and if the second AI model is deployed on the UE side, the input time window length and output time window length of the second AI model can be sent to the base station.

[0150] If pairing is unsuccessful, the method further includes receiving a message from the base station indicating whether to select the first AI model or the second AI model. If the first AI model is deployed on the UE side and the first AI model is to be selected, the message includes one bit of notification information; if the first AI model is deployed on the UE side and the second AI model is to be selected, the message includes model parameters or a model index of the selected second AI model; if the second AI model is deployed on the UE side and the first AI model is to be selected, the message includes model parameters or a model index of the selected first AI model; and if the second AI model is deployed on the UE side and the second AI model is to be selected, the message includes one bit of notification information.

[0151] The above method in the third scenario corresponds to the electronic device 100 in the fourth embodiment. The relevant specific details can be referred to the fourth embodiment and will not be repeated here.

[0152] In addition, in the second scenario and the third scenario, when the first AI model is arranged on the UE side and the second AI model is arranged on the base station side, after the pairing is successful, the above method also includes reporting the beam measurement results within the input time window length of the second AI model to the base station in a single or multiple forms, so that the second AI model uses the beam measurement results to perform beam prediction. For example, the beam measurement results and the prediction results of the first AI model can be reported through the data channel. After the pairing is successful, when the first AI model and / or the second AI model triggers the supervision mechanism, if the supervision mechanism is executed by the UE, the execution period of the supervision mechanism is provided to the base station, and if the supervision mechanism is executed by the base station, the execution period of the supervision mechanism is obtained from the base station. Wherein, when the AI ​​model on the base station side triggers the supervision mechanism, the method also includes making the AI ​​model on the UE side remain silent during the execution period.

[0153] Figure 15 shows a flowchart of a base station-side method for wireless communication according to another embodiment of the present application, the method comprising: pairing a first AI model for performing spatial-domain beam prediction with a second AI model for performing time-domain beam prediction (S21); and if the pairing is successful, multiplexing beam measurement results between the first AI model and the second AI model (S22). This method can be performed, for example, on the base station side.

[0154] Similarly, for example, pairing is successful when the input beam set for the first AI model includes the input beam set for the second AI model, and the input parameter attributes of the first AI model include the input parameter attributes of the second AI model. The input parameter attributes include at least one of the following: reference signal received power and channel impulse response. The multiplexed beam measurement results, for example, include at least a portion of the measurement results for the beams in the input beam set of the first AI model.

[0155] After the pairing is successful, if the first AI model and / or the second AI model is switched, the pairing is re-executed. For details, please refer to the fifth embodiment and will not be repeated here.

[0156] The present method is still described respectively using the first to fourth scenarios defined above as examples.

[0157] In the fourth scenario, in step S21 , pairing is performed based on the model parameters of the first AI model and the model parameters of the second AI model to determine whether the pairing is successful.

[0158] If the pairing is successful, the method further includes sending the input time window length and the output time window length of the second AI model to the UE. After the pairing is successful, if the first AI model and / or the second AI model triggers the supervision mechanism, if the supervision mechanism is executed by the base station, the execution period of the supervision mechanism is provided to the UE; if the supervision mechanism is executed by the UE, the execution period of the supervision mechanism is obtained from the UE.

[0159] The above method in the fourth scenario corresponds to the electronic device 200 in the sixth embodiment. The relevant specific details can be referred to the sixth embodiment and will not be repeated here.

[0160] In the second scenario, pairing is performed by interacting with the UE in step S21. For example, step S21 may include: receiving a pairing request from the UE, the pairing request including model parameters or a model index of an AI model to be used on the UE side as determined by the UE; determining the AI ​​model to be used on the base station side based on the pairing request; and sending a message to the UE indicating whether the pairing is successful or unsuccessful.

[0161] When the first AI model is deployed on the UE side, the pairing request may include the input parameter size and input and output parameter attributes of the first AI model. When the second AI model is deployed on the UE side, the pairing request may include the input time window length and output time window length, input parameter size, and input and output parameter attributes of the second AI model.

[0162] If the pairing is successful, the method further includes sending the model parameters of the AI ​​model to be used on the base station side to the UE. Wherein, when the first AI model is deployed on the base station side, the model parameters sent include the input parameter size of the first AI model. When the second AI model is deployed on the base station side, the model parameters sent include at least: the input time window length and the output time window length, and the minimum number of measurements of historical data to be used. Alternatively, the method may further include sending the model index of the AI ​​model to be used on the base station side to the UE.

[0163] If pairing is unsuccessful, the method further includes receiving a message from the UE indicating whether to select the first AI model or the second AI model. If the first AI model is deployed on the UE side and the first AI model is to be selected, the message includes model parameters or a model index of the selected first AI model; if the first AI model is deployed on the UE side and the second AI model is to be selected, the message includes one bit of notification information; if the second AI model is deployed on the UE side and the first AI model is to be selected, the message includes one bit of notification information; and if the second AI model is deployed on the UE side and the second AI model is to be selected, the message includes model parameters or a model index of the selected second AI model.

[0164] The above method in the second scenario corresponds to the electronic device 200 in the seventh embodiment. The relevant specific details can be referred to the seventh embodiment and will not be repeated here.

[0165] In the third scenario, pairing is performed by interacting with the UE in step S21. For example, step S21 may include: determining an AI model to be used by the base station for pairing; including model parameters or a model index of the determined AI model in a pairing request and sending it to the UE, so that the UE determines the AI ​​model to be used on the UE side based on the model parameters or the model index; and receiving a message from the UE indicating whether the pairing is successful or unsuccessful.

[0166] When the first AI model is deployed on the UE side, the model parameters sent include one or more of the following model parameters of the second AI model: input time window length and output time window length, input parameter size, input and output parameter attributes, and the minimum number of measurements of historical data to be used; when the second AI model is deployed on the UE side, the model parameters sent include at least the input parameter size and input and output parameter attributes of the first AI model.

[0167] If pairing is successful, the method further includes receiving, from the UE, model parameters or a model index of the AI ​​model to be used by the UE. If the first AI model is deployed on the UE side, the received model parameters may include the input parameter size of the first AI model; and if the second AI model is deployed on the UE side, the received model parameters may include the input time window length and output time window length of the second AI model.

[0168] If pairing is unsuccessful, the method further includes sending a message to the UE indicating whether to select the first AI model or the second AI model. If the first AI model is deployed on the UE side and the first AI model is to be selected, the message includes one bit of notification information; if the first AI model is deployed on the UE side and the second AI model is to be selected, the message includes model parameters or a model index of the selected second AI model; if the second AI model is deployed on the UE side and the first AI model is to be selected, the message includes model parameters or a model index of the selected first AI model; and if the second AI model is deployed on the UE side and the second AI model is to be selected, the message includes one bit of notification information.

[0169] The above method in the third scenario corresponds to the electronic device 200 in the eighth embodiment. The relevant specific details can be referred to the eighth embodiment and will not be repeated here.

[0170] Furthermore, in the second and third scenarios, when the first AI model is deployed on the UE side and the second AI model is deployed on the base station side, after pairing is successful, the method further includes receiving beam measurement results within the input time window length of the second AI model from the UE in a single or multiple manner, so that the second AI model performs beam prediction using the beam measurement results. For example, the beam measurement results and the prediction results of the first AI model can be received via a data channel.

[0171] After pairing is successful, if the first AI model and / or the second AI model triggers a supervision mechanism, if the supervision mechanism is executed by the UE, the execution period of the supervision mechanism is obtained from the UE; if the supervision mechanism is executed by the base station, the execution period of the supervision mechanism is provided to the UE. In the case where the AI ​​model on the UE side triggers the supervision mechanism, the method further includes silencing the AI ​​model on the base station side during the execution period.

[0172] Note that the above methods can be used in combination or individually.

[0173] The technology of the present disclosure can be applied to various products.

[0174] For example, the electronic device 100 can be implemented as various user devices. The user device can be implemented as a mobile terminal (such as a smart phone, a tablet personal computer (PC), a notebook PC, a portable game terminal, a portable / dongle-type mobile router, and a digital camera) or a vehicle-mounted terminal (such as a car navigation device). The user device can also be implemented as a terminal that performs machine-to-machine (M2M) communication (also known as a machine type communication (MTC) terminal). In addition, the user device can be a wireless communication module (such as an integrated circuit module including a single chip) installed on each of the above-mentioned terminals.

[0175] The electronic device 200 can also be implemented as various base stations. The base station can be implemented as any type of evolved Node B (eNB) or gNB (5G base station). eNBs include, for example, macro eNBs and small eNBs. Small eNBs can be eNBs that cover cells smaller than macro cells, such as pico eNBs, micro eNBs, and home (femto) eNBs. Similar situations can also be encountered for gNBs. Alternatively, the base station can be implemented as any other type of base station, such as a NodeB and a base transceiver station (BTS). The base station may include: a main body (also referred to as a base station device) configured to control wireless communications; and one or more remote radio heads (RRHs) located at a different place from the main body. In addition, various types of user equipment can work as a base station by temporarily or semi-permanently performing base station functions.

[0176] [Application examples for base stations]

[0177] (First application example)

[0178] FIG16 is a block diagram illustrating a first example of a schematic configuration of an eNB or gNB to which the techniques of this disclosure can be applied. Note that the following description uses an eNB as an example, but is equally applicable to a gNB. An eNB 800 includes one or more antennas 810 and a base station device 820. The base station device 820 and each antenna 810 can be connected to each other via an RF cable.

[0179] Each of the antennas 810 includes a single or multiple antenna elements (such as multiple antenna elements included in a multiple-input multiple-output (MIMO) antenna) and is used for base station device 820 to transmit and receive wireless signals. As shown in FIG16 , eNB 800 may include multiple antennas 810. For example, multiple antennas 810 may be compatible with multiple frequency bands used by eNB 800. Although FIG16 shows an example in which eNB 800 includes multiple antennas 810, eNB 800 may also include a single antenna 810.

[0180] The base station device 820 includes a controller 821 , a memory 822 , a network interface 823 , and a wireless communication interface 825 .

[0181] The controller 821 may be, for example, a CPU or a DSP, and operates various functions of the higher layers of the base station device 820. For example, the controller 821 generates data packets based on the data in the signal processed by the wireless communication interface 825, and transmits the generated packets via the network interface 823. The controller 821 may bundle data from multiple baseband processors to generate bundled packets, and transmit the generated bundled packets. The controller 821 may have logic functions for performing the following controls: the control may be radio resource control, radio bearer control, mobility management, admission control, and scheduling. The control may be performed in conjunction with a nearby eNB or core network node. The memory 822 includes RAM and ROM, and stores programs executed by the controller 821 and various types of control data (such as a terminal list, transmission power data, and scheduling data).

[0182] The network interface 823 is a communication interface for connecting the base station device 820 to the core network 824. The controller 821 can communicate with the core network node or another eNB via the network interface 823. In this case, the eNB 800 and the core network node or other eNBs can be connected to each other through a logical interface (such as an S1 interface and an X2 interface). The network interface 823 can also be a wired communication interface or a wireless communication interface for a wireless backhaul line. If the network interface 823 is a wireless communication interface, the network interface 823 can use a higher frequency band for wireless communication than the frequency band used by the wireless communication interface 825.

[0183] The wireless communication interface 825 supports any cellular communication scheme, such as Long Term Evolution (LTE) and LTE-Advanced, and provides wireless connectivity to terminals located in the cell of the eNB 800 via the antenna 810. The wireless communication interface 825 may typically include, for example, a baseband (BB) processor 826 and RF circuitry 827. The BB processor 826 can perform various signal processing functions, such as encoding / decoding, modulation / demodulation, and multiplexing / demultiplexing, and performs various types of signal processing for layers such as Layer 1 (L1), Medium Access Control (MAC), Radio Link Control (RLC), and Packet Data Convergence Protocol (PDCP). In place of the controller 821, the BB processor 826 may have some or all of the aforementioned logical functions. The BB processor 826 may be a memory that stores communication control programs, or a module including a processor configured to execute programs and associated circuitry. Program updates can modify the functionality of the BB processor 826. This module may be a card or blade inserted into a slot in the base station device 820. Alternatively, the module may be a chip mounted on the card or blade. Meanwhile, the RF circuit 827 may include, for example, a mixer, a filter, and an amplifier, and transmit and receive wireless signals via the antenna 810 .

[0184] As shown in FIG16 , the wireless communication interface 825 may include multiple BB processors 826. For example, multiple BB processors 826 may be compatible with multiple frequency bands used by the eNB 800. As shown in FIG16 , the wireless communication interface 825 may include multiple RF circuits 827. For example, multiple RF circuits 827 may be compatible with multiple antenna elements. Although FIG16 illustrates an example in which the wireless communication interface 825 includes multiple BB processors 826 and multiple RF circuits 827, the wireless communication interface 825 may also include a single BB processor 826 or a single RF circuit 827.

[0185] In the eNB 800 shown in FIG16 , the communication unit 203 and transceiver of the electronic device 200 may be implemented by the wireless communication interface 825. At least a portion of the functionality may also be implemented by the controller 821. For example, the controller 821 may execute the functions of the pairing unit 201, the multiplexing unit 202, and the communication unit 203 to pair two AI models of different functional types used for beam prediction, thereby multiplexing beam measurement results and further reducing beam measurement overhead.

[0186] (Second application example)

[0187] FIG17 is a block diagram illustrating a second example of a schematic configuration of an eNB or gNB to which the techniques of this disclosure can be applied. Note that similarly, the following description uses an eNB as an example, but is equally applicable to a gNB. An eNB 830 includes one or more antennas 840, a base station device 850, and an RRH 860. The RRH 860 and each antenna 840 can be connected to each other via an RF cable. The base station device 850 and the RRH 860 can be connected to each other via a high-speed line such as an optical fiber cable.

[0188] Each of the antennas 840 includes a single or multiple antenna elements (such as multiple antenna elements included in a MIMO antenna) and is used for RRH 860 to transmit and receive wireless signals. As shown in FIG17 , eNB 830 may include multiple antennas 840. For example, multiple antennas 840 may be compatible with multiple frequency bands used by eNB 830. Although FIG17 shows an example in which eNB 830 includes multiple antennas 840, eNB 830 may also include a single antenna 840.

[0189] Base station device 850 includes a controller 851, a memory 852, a network interface 853, a wireless communication interface 855, and a connection interface 857. Controller 851, memory 852, and network interface 853 are the same as controller 821, memory 822, and network interface 823 described with reference to FIG.

[0190] The wireless communication interface 855 supports any cellular communication scheme (such as LTE and LTE-Advanced) and provides wireless communication to terminals located in the sector corresponding to the RRH 860 via the RRH 860 and the antenna 840. The wireless communication interface 855 may generally include, for example, a BB processor 856. The BB processor 856 is the same as the BB processor 826 described with reference to FIG. 16, except that the BB processor 856 is connected to the RF circuit 864 of the RRH 860 via the connection interface 857. As shown in FIG. 17, the wireless communication interface 855 may include multiple BB processors 856. For example, the multiple BB processors 856 may be compatible with multiple frequency bands used by the eNB 830. Although FIG. 17 shows an example in which the wireless communication interface 855 includes multiple BB processors 856, the wireless communication interface 855 may also include a single BB processor 856.

[0191] The connection interface 857 is an interface for connecting the base station device 850 (wireless communication interface 855) to the RRH 860. The connection interface 857 may also be a communication module for connecting the base station device 850 (wireless communication interface 855) to the RRH 860 for communication in the high-speed line.

[0192] The RRH 860 includes a connection interface 861 and a wireless communication interface 863 .

[0193] The connection interface 861 is an interface for connecting the RRH 860 (wireless communication interface 863) to the base station device 850. The connection interface 861 may also be a communication module for communication in the above-mentioned high-speed line.

[0194] The wireless communication interface 863 transmits and receives wireless signals via the antenna 840. The wireless communication interface 863 may generally include, for example, an RF circuit 864. The RF circuit 864 may include, for example, a mixer, a filter, and an amplifier, and transmits and receives wireless signals via the antenna 840. As shown in FIG17 , the wireless communication interface 863 may include multiple RF circuits 864. For example, multiple RF circuits 864 may support multiple antenna elements. Although FIG17 shows an example in which the wireless communication interface 863 includes multiple RF circuits 864, the wireless communication interface 863 may also include a single RF circuit 864.

[0195] In the eNB 830 shown in FIG17 , the communication unit 203 and transceiver of the electronic device 200 may be implemented by the wireless communication interface 855 and / or the wireless communication interface 863. At least a portion of the functionality may also be implemented by the controller 851. For example, the controller 851 may execute the functions of the pairing unit 201, the multiplexing unit 202, and the communication unit 203 to pair two functional types of AI models used for beam prediction, thereby multiplexing beam measurement results and further reducing beam measurement overhead.

[0196] [Application examples on user devices]

[0197] (First application example)

[0198] 18 is a block diagram showing an example of a schematic configuration of a smartphone 900 to which the technology of the present disclosure can be applied. The smartphone 900 includes a processor 901, a memory 902, a storage device 903, an external connection interface 904, a camera 906, a sensor 907, a microphone 908, an input device 909, a display device 910, a speaker 911, a wireless communication interface 912, one or more antenna switches 915, one or more antennas 916, a bus 917, a battery 918, and an auxiliary controller 919.

[0199] The processor 901 may be, for example, a CPU or a system on a chip (SoC), and controls the functions of the application layer and other layers of the smartphone 900. The memory 902 includes RAM and ROM, and stores data and programs executed by the processor 901. The storage device 903 may include storage media such as semiconductor memories and hard disks. The external connection interface 904 is an interface for connecting external devices (such as memory cards and universal serial bus (USB) devices) to the smartphone 900.

[0200] The camera 906 includes an image sensor such as a charge coupled device (CCD) and a complementary metal oxide semiconductor (CMOS) and generates a captured image. The sensor 907 may include a group of sensors such as a measurement sensor, a gyroscope sensor, a geomagnetic sensor, and an acceleration sensor. The microphone 908 converts the sound input to the smartphone 900 into an audio signal. The input device 909 includes, for example, a touch sensor, a keypad, a keyboard, a button, or a switch configured to detect a touch on the screen of the display device 910, and receives an operation or information input from the user. The display device 910 includes a screen such as a liquid crystal display (LCD) and an organic light emitting diode (OLED) display and displays an output image of the smartphone 900. The speaker 911 converts the audio signal output from the smartphone 900 into sound.

[0201] The wireless communication interface 912 supports any cellular communication scheme (such as LTE and LTE-Advanced) and performs wireless communications. The wireless communication interface 912 may typically include, for example, a BB processor 913 and an RF circuit 914. The BB processor 913 may perform, for example, encoding / decoding, modulation / demodulation, and multiplexing / demultiplexing, and may also perform various types of signal processing for wireless communications. Meanwhile, the RF circuit 914 may include, for example, mixers, filters, and amplifiers, and transmit and receive wireless signals via an antenna 916. Note that while the figure shows a scenario where one RF link is connected to one antenna, this is merely illustrative, and also encompasses scenarios where one RF link is connected to multiple antennas via multiple phase shifters. The wireless communication interface 912 may be a chip module on which the BB processor 913 and the RF circuit 914 are integrated. As shown in FIG18 , the wireless communication interface 912 may include multiple BB processors 913 and multiple RF circuits 914. While FIG18 illustrates an example in which the wireless communication interface 912 includes multiple BB processors 913 and multiple RF circuits 914, the wireless communication interface 912 may also include a single BB processor 913 or a single RF circuit 914.

[0202] In addition, in addition to the cellular communication scheme, the wireless communication interface 912 can support other types of wireless communication schemes, such as a short-range wireless communication scheme, a near-field communication scheme, and a wireless local area network (LAN) scheme. In this case, the wireless communication interface 912 may include a BB processor 913 and an RF circuit 914 for each wireless communication scheme.

[0203] Each of the antenna switches 915 switches a connection destination of the antenna 916 between a plurality of circuits (eg, circuits for different wireless communication schemes) included in the wireless communication interface 912 .

[0204] Each of the antennas 916 includes a single or multiple antenna elements (such as multiple antenna elements included in a MIMO antenna) and is used for transmitting and receiving wireless signals via the wireless communication interface 912. As shown in FIG18 , the smartphone 900 may include multiple antennas 916. Although FIG18 shows an example in which the smartphone 900 includes multiple antennas 916, the smartphone 900 may also include a single antenna 916.

[0205] In addition, the smartphone 900 may include an antenna 916 for each wireless communication scheme. In this case, the antenna switch 915 may be omitted from the configuration of the smartphone 900.

[0206] The bus 917 connects the processor 901, the memory 902, the storage device 903, the external connection interface 904, the camera 906, the sensor 907, the microphone 908, the input device 909, the display device 910, the speaker 911, the wireless communication interface 912, and the auxiliary controller 919. The battery 918 supplies power to the various blocks of the smartphone 900 shown in FIG18 via feeders, which are partially shown as dotted lines in the figure. The auxiliary controller 919 operates the minimum necessary functions of the smartphone 900, for example, in sleep mode.

[0207] In the smartphone 900 shown in FIG18 , the communication unit 103 and transceiver of the electronic device 100 may be implemented by the wireless communication interface 912 . At least a portion of the functionality may also be implemented by the processor 901 or the auxiliary controller 919 . For example, the processor 901 or the auxiliary controller 919 may execute the functions of the pairing unit 101 , the multiplexing unit 102 , and the communication unit 103 to pair two functional types of AI models used for beam prediction, thereby multiplexing beam measurement results and further reducing beam measurement overhead.

[0208] (Second application example)

[0209] 19 is a block diagram showing an example of a schematic configuration of a car navigation device 920 to which the technology of the present disclosure can be applied. The car navigation device 920 includes a processor 921, a memory 922, a global positioning system (GPS) module 924, a sensor 925, a data interface 926, a content player 927, a storage medium interface 928, an input device 929, a display device 930, a speaker 931, a wireless communication interface 933, one or more antenna switches 936, one or more antennas 937, and a battery 938.

[0210] The processor 921 may be, for example, a CPU or an SoC, and controls a navigation function and other functions of the car navigation apparatus 920. The memory 922 includes a RAM and a ROM, and stores data and programs executed by the processor 921.

[0211] The GPS module 924 measures the position (such as latitude, longitude, and altitude) of the car navigation device 920 using GPS signals received from GPS satellites. The sensor 925 may include a group of sensors such as a gyroscope sensor, a geomagnetic sensor, and an air pressure sensor. The data interface 926 is connected to, for example, the in-vehicle network 941 via an unillustrated terminal and acquires data generated by the vehicle (such as vehicle speed data).

[0212] The content player 927 reproduces content stored in a storage medium (such as a CD or DVD) inserted into the storage medium interface 928. The input device 929 includes, for example, a touch sensor, button, or switch configured to detect a touch on the screen of the display device 930, and receives an operation or information input from the user. The display device 930 includes a screen such as an LCD or OLED display and displays an image of a navigation function or reproduced content. The speaker 931 outputs the sound of the navigation function or the reproduced content.

[0213] The wireless communication interface 933 supports any cellular communication scheme (such as LTE and LTE-Advanced) and performs wireless communication. The wireless communication interface 933 may generally include, for example, a BB processor 934 and an RF circuit 935. The BB processor 934 may perform, for example, encoding / decoding, modulation / demodulation, and multiplexing / demultiplexing, and perform various types of signal processing for wireless communication. Meanwhile, the RF circuit 935 may include, for example, a mixer, a filter, and an amplifier, and transmit and receive wireless signals via an antenna 937. The wireless communication interface 933 may also be a chip module on which the BB processor 934 and the RF circuit 935 are integrated. As shown in Figure 19, the wireless communication interface 933 may include multiple BB processors 934 and multiple RF circuits 935. Although Figure 19 shows an example in which the wireless communication interface 933 includes multiple BB processors 934 and multiple RF circuits 935, the wireless communication interface 933 may also include a single BB processor 934 or a single RF circuit 935.

[0214] In addition, in addition to the cellular communication scheme, the wireless communication interface 933 can support other types of wireless communication schemes, such as a short-range wireless communication scheme, a near field communication scheme, and a wireless LAN scheme. In this case, for each wireless communication scheme, the wireless communication interface 933 can include a BB processor 934 and an RF circuit 935.

[0215] Each of the antenna switches 936 switches a connection destination of the antenna 937 between a plurality of circuits included in the wireless communication interface 933 , such as circuits for different wireless communication schemes.

[0216] Each of the antennas 937 includes a single or multiple antenna elements (such as multiple antenna elements included in a MIMO antenna) and is used for transmitting and receiving wireless signals via the wireless communication interface 933. As shown in FIG19, the car navigation device 920 may include multiple antennas 937. Although FIG19 shows an example in which the car navigation device 920 includes multiple antennas 937, the car navigation device 920 may also include a single antenna 937.

[0217] Furthermore, the car navigation device 920 may include an antenna 937 for each wireless communication scheme. In this case, the antenna switch 936 may be omitted from the configuration of the car navigation device 920.

[0218] The battery 938 supplies power to the respective blocks of the car navigation device 920 shown in Fig. 19 via a feeder line, which is partially shown as a dotted line in the figure. The battery 938 accumulates the power supplied from the vehicle.

[0219] In the car navigation device 920 shown in FIG19 , the communication unit 103 and transceiver of the electronic device 100 can be implemented by the wireless communication interface 933 . At least a portion of the functionality can also be implemented by the processor 921 . For example, the processor 921 can execute the functions of the pairing unit 101 , the multiplexing unit 102 , and the communication unit 103 to pair two functional types of AI models used for beam prediction, thereby multiplexing beam measurement results and further reducing beam measurement overhead.

[0220] The technology of the present disclosure can also be implemented as an in-vehicle system (or vehicle) 940 including a car navigation device 920, an in-vehicle network 941, and one or more blocks of a vehicle module 942. The vehicle module 942 generates vehicle data (such as vehicle speed, engine speed, and fault information) and outputs the generated data to the in-vehicle network 941.

[0221] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be pointed out that for those skilled in the art, it is understandable that all or any steps or components of the methods and devices of the present disclosure can be implemented in any computing device (including a processor, storage medium, etc.) or a network of computing devices in the form of hardware, firmware, software, or a combination thereof. This can be achieved by those skilled in the art using their basic circuit design knowledge or basic programming skills after reading the description of the present disclosure.

[0222] Furthermore, the present disclosure also provides a program product storing machine-readable instruction codes. When the instruction codes are read and executed by a machine, the method according to the embodiment of the present disclosure can be executed.

[0223] Accordingly, the storage medium for carrying the program product storing the machine-readable instruction code is also included in the disclosure of the present invention, including but not limited to a floppy disk, an optical disk, a magneto-optical disk, a memory card, a memory stick, and the like.

[0224] When the present disclosure is implemented through software or firmware, the programs constituting the software are installed from a storage medium or a network to a computer with a dedicated hardware structure (such as the general-purpose computer 2000 shown in Figure 20). When various programs are installed on the computer, it can perform various functions, etc.

[0225] In FIG20 , a central processing unit (CPU) 2001 executes various processes according to a program stored in a read-only memory (ROM) 2002 or a program loaded from a storage section 2008 to a random access memory (RAM) 2003. In the RAM 2003, data required when the CPU 2001 executes various processes, etc., is also stored as needed. The CPU 2001, the ROM 2002, and the RAM 2003 are connected to each other via a bus 2004. An input / output interface 2005 is also connected to the bus 2004.

[0226] The following components are connected to the input / output interface 2005: an input section 2006 (including a keyboard, a mouse, etc.), an output section 2007 (including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.), a storage section 2008 (including a hard disk, etc.), and a communication section 2009 (including a network interface card such as a LAN card, a modem, etc.). The communication section 2009 performs communication processing via a network such as the Internet. A drive 2010 may also be connected to the input / output interface 2005 as needed. A removable medium 2011 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is installed in the drive 2010 as needed, so that a computer program read therefrom is installed in the storage section 2008 as needed.

[0227] In the case where the above-described series of processing is realized by software, a program constituting the software is installed from a network such as the Internet or a storage medium such as the removable medium 2011 .

[0228] It should be understood by those skilled in the art that such storage media is not limited to the removable medium 2011 shown in FIG. 20 , which stores the program and is distributed separately from the device to provide the program to the user. Examples of the removable medium 2011 include magnetic disks (including floppy disks (registered trademark)), optical disks (including compact disk read-only memories (CD-ROMs) and digital versatile disks (DVDs)), magneto-optical disks (including minidiscs (MDs) (registered trademark)), and semiconductor memories. Alternatively, the storage medium may be a ROM 2002, a hard disk included in the storage section 2008, or the like, in which the program is stored and distributed to the user together with the device containing them.

[0229] It should also be noted that in the apparatus, method, and system of the present disclosure, each component or step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure. Furthermore, the steps of performing the above series of processes can naturally be performed in chronological order according to the order of description, but do not necessarily need to be performed in chronological order. Certain steps can be performed in parallel or independently of each other.

[0230] Finally, it should be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. Furthermore, in the absence of further limitations, an element defined by the phrase "comprises a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0231] Although the embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, it should be understood that the embodiments described above are merely illustrative of the present disclosure and are not intended to limit the present disclosure. Those skilled in the art will appreciate that various modifications and variations can be made to the above embodiments without departing from the spirit and scope of the present disclosure. Therefore, the scope of the present disclosure is solely defined by the appended claims and their equivalents.

Claims

1. An electronic device on a user equipment side for wireless communication, comprising: The processing circuit is configured to: Pairing a first artificial intelligence model for performing spatial domain beam prediction and a second artificial intelligence model for performing temporal domain beam prediction; as well as In case the pairing is successful, the beam measurement results are multiplexed between the first artificial intelligence model and the second artificial intelligence model.

2. The electronic device according to claim 1, wherein: Pairing is successful if the input beam set for the first artificial intelligence model contains the input beam set for the second artificial intelligence model and the input parameter attributes of the first artificial intelligence model contain the input parameter attributes of the second artificial intelligence model.

3. The electronic device according to claim 2, wherein: The input parameter attributes include at least one of the following: reference signal received power, channel impulse response.

4. The electronic device according to claim 2, wherein: The beam measurements include at least a portion of measurements for beams in an input beam set for the first artificial intelligence model.

5. The electronic device according to claim 1, wherein: The first artificial intelligence model and the second artificial intelligence model are both arranged on the user device side, and the processing circuit is configured to perform pairing based on model parameters of the first artificial intelligence model and model parameters of the second artificial intelligence model to determine whether the pairing is successful.

6. The electronic device according to claim 5, wherein: The processing circuit is further configured to send a message to the base station indicating whether the pairing is successful or unsuccessful.

7. The electronic device according to claim 6, wherein: When the pairing is successful, the processing circuit is further configured to send the model parameters or model index of the second artificial intelligence model to the base station.

8. The electronic device according to claim 7, wherein: The model parameters of the second artificial intelligence model include one or more of the following: input time window length and output time window length of the second artificial intelligence model, input parameter size, and input and output parameter attributes.

9. The electronic device according to claim 6, wherein: In the event that pairing is unsuccessful, the processing circuit is further configured to determine to use one of the first artificial intelligence model and the second artificial intelligence model, and to send a model parameter or a model index of the artificial intelligence model to be used to the base station.

10. The electronic device according to claim 5, wherein: After the pairing is successful, when the first artificial intelligence model and / or the second artificial intelligence model triggers the supervision mechanism, the processing circuit is configured to obtain the execution cycle of the supervision mechanism and keep the unsupervised artificial intelligence model silent during the execution cycle.

11. The electronic device according to claim 1, wherein after pairing is successful, when switching of the first artificial intelligence model and / or the second artificial intelligence model occurs, the processing circuit is configured to re-execute pairing.

12. The electronic device according to claim 1, wherein: The first artificial intelligence model is arranged on one side of the user equipment side and the base station side, the second artificial intelligence model is arranged on the other side of the user equipment side and the base station side, and the processing circuit is configured to perform pairing by interacting with the base station.

13. The electronic device according to claim 12, wherein: The processing circuit is configured to perform pairing as follows: Determining an artificial intelligence model to be used on the user device side; Sending a pairing request to the base station, so that the base station determines an artificial intelligence model to be used on the base station side based on the pairing request; as well as A message indicating whether the pairing is successful or unsuccessful is received from the base station to determine whether the pairing is successful based on the message.

14. The electronic device according to claim 13, wherein: The pairing request includes model parameters or model index of the artificial intelligence model to be used by the user equipment side, Wherein, in the case where the first artificial intelligence model is arranged on the user device side, the pairing request includes the input parameter size and input and output parameter attributes of the first artificial intelligence model, and In which, when the second artificial intelligence model is arranged on the user device side, the pairing request includes the input time window length and output time window length, input parameter size, and input and output parameter attributes of the second artificial intelligence model.

15. The electronic device according to claim 13, wherein: The pairing request includes a model index of the artificial intelligence model to be used on the user device side.

16. The electronic device according to claim 13, wherein: In the case where the pairing is successful, the processing circuit is further configured to receive, from the base station, model parameters of the artificial intelligence model to be used by the base station, Wherein, in the case where the first artificial intelligence model is arranged at the base station side, the received model parameters include the input parameter size of the first artificial intelligence model, and Wherein, when the second artificial intelligence model is arranged on the base station side, the received model parameters include at least: input time window length and output time window length, and the minimum number of measurements of historical data to be adopted.

17. The electronic device according to claim 13, wherein: In the event that pairing is successful, the processing circuit is further configured to receive, from the base station, a model index of the artificial intelligence model to be used by the base station.

18. The electronic device according to claim 13, wherein: In the event that pairing is unsuccessful, the processing circuit is further configured to send a message to the base station indicating whether the first artificial intelligence model or the second artificial intelligence model is to be selected.

19. The electronic device according to claim 18, wherein: In the case where the first artificial intelligence model is arranged on the user device side and the first artificial intelligence model is to be selected, the message includes a model parameter or a model index of the selected first artificial intelligence model; In the case where the first artificial intelligence model is arranged on the user device side and the second artificial intelligence model is to be selected, the message includes one bit of notification information; In the case where the second artificial intelligence model is arranged on the user device side and the first artificial intelligence model is to be selected, the message includes one bit of notification information; as well as In the case where the second artificial intelligence model is arranged on the user equipment side and the second artificial intelligence model is to be selected, the message includes a model parameter or a model index of the selected second artificial intelligence model.

20. The electronic device according to claim 12, wherein: The processing circuit is configured to perform pairing as follows: receiving a pairing request from the base station, the pairing request including a model parameter or a model index of an artificial intelligence model determined by the base station to be used in pairing; Determining, according to the received model parameters or model index, an artificial intelligence model to be used by the user device in pairing; and A message indicating successful or unsuccessful pairing is sent to the base station.

21. The electronic device according to claim 20, wherein: In the case where the first artificial intelligence model is arranged at the user equipment side, the received model parameters include one or more of the following model parameters of the second artificial intelligence model: input time window length and output time window length, input parameter size, input and output parameter attributes, and a minimum number of measurements of historical data to be adopted; In the case where the second artificial intelligence model is arranged on the user equipment side, the received model parameters include at least input parameter size and input and output parameter attributes of the first artificial intelligence model.

22. The electronic device according to claim 20, wherein: If the pairing is successful, In the case where the first artificial intelligence model is arranged at the user equipment side, the processing circuit is further configured to send an input parameter size of the first artificial intelligence model to the base station; as well as In the case where the second artificial intelligence model is arranged at the user equipment side, the processing circuit is further configured to send an input time window length and an output time window length of the second artificial intelligence model to the base station.

23. The electronic device according to claim 20, wherein: In the event that pairing is unsuccessful, the processing circuit is further configured to receive a message from the base station indicating whether the first artificial intelligence model or the second artificial intelligence model is to be selected.

24. The electronic device according to claim 23, wherein: In the case where the first artificial intelligence model is arranged on the user device side and the first artificial intelligence model is to be selected, the message includes one bit of notification information; In the case where the first artificial intelligence model is arranged on the user device side and the second artificial intelligence model is to be selected, the message includes a model parameter or a model index of the selected second artificial intelligence model; In the case where the second artificial intelligence model is arranged on the user device side and the first artificial intelligence model is to be selected, the message includes a model parameter or a model index of the selected first artificial intelligence model; as well as In the case where the second artificial intelligence model is arranged on the user equipment side and the second artificial intelligence model is to be selected, the message includes one bit of notification information.

25. The electronic device according to claim 12, wherein: In the case that the first artificial intelligence model is arranged on the user equipment side and the second artificial intelligence model is arranged on the base station side, after pairing is successful, the processing circuit is also configured to report the beam measurement results within the input time window length of the second artificial intelligence model to the base station in a single or multiple form, so that the second artificial intelligence model uses the beam measurement results to perform beam prediction.

26. The electronic device according to claim 25, wherein: The processing circuit is configured to report the beam measurement result and the prediction result of the first artificial intelligence model through a data channel.

27. The electronic device according to claim 12, wherein: After pairing is successful, In the case where the first artificial intelligence model and / or the second artificial intelligence model triggers a supervision mechanism, if the supervision mechanism is executed by the user equipment, the processing circuit is configured to provide the base station with an execution period of the supervision mechanism, and if the supervision mechanism is executed by the base station, the processing circuit is configured to obtain the execution period of the supervision mechanism from the base station, and In which, when the artificial intelligence model on the base station side triggers the supervision mechanism, the processing circuit is also configured to keep the artificial intelligence model on the user equipment side silent during the execution cycle.

28. An electronic device at a base station side for wireless communication, comprising: The processing circuit is configured to: Pairing a first artificial intelligence model for performing spatial domain beam prediction and a second artificial intelligence model for performing temporal domain beam prediction; as well as In case the pairing is successful, the beam measurement results are multiplexed between the first artificial intelligence model and the second artificial intelligence model.

29. The electronic device according to claim 20, wherein: The first artificial intelligence model and the second artificial intelligence model are both arranged on the base station side, and the processing circuit is configured to perform pairing based on model parameters of the first artificial intelligence model and model parameters of the second artificial intelligence model to determine whether the pairing is successful.

30. The electronic device according to claim 29, wherein: When pairing is successful, the processing circuit is configured to send the input time window length and the output time window length of the second artificial intelligence model to the user device.

31. The electronic device according to claim 29, wherein: After successful pairing, when the first artificial intelligence model and / or the second artificial intelligence model triggers a supervision mechanism, if the supervision mechanism is executed by the base station, the processing circuit is configured to provide the execution cycle of the supervision mechanism to the user equipment; if the supervision mechanism is executed by the user equipment, the processing circuit is configured to obtain the execution cycle of the supervision mechanism from the user equipment.

32. The electronic device according to claim 29, wherein: After pairing is successful, in the event of a switch between the first artificial intelligence model and / or the second artificial intelligence model, the processing circuit is configured to re-execute pairing.

33. The electronic device according to claim 28, wherein: The first artificial intelligence model is arranged on one side of the user equipment side and the base station side, the second artificial intelligence model is arranged on the other side of the user equipment side and the base station side, and the processing circuit is configured to perform pairing by interacting with the user equipment.

34. The electronic device according to claim 33, wherein: The processing circuit is configured to perform pairing as follows: Receiving a pairing request from the user device, the pairing request including a model parameter or a model index of an artificial intelligence model to be used by the user device side determined by the user device; Determining an artificial intelligence model to be used on the base station side based on the pairing request; and A message indicating successful or unsuccessful pairing is sent to the user equipment.

35. The electronic device according to claim 34, wherein: In the case where the first artificial intelligence model is arranged on the user device side, the pairing request includes the input parameter size and input and output parameter attributes of the first artificial intelligence model, and In the case where the second artificial intelligence model is arranged on the user device side, the configuration The request includes the input time window length and output time window length, input parameter size, and input and output parameter attributes of the second artificial intelligence model.

36. The electronic device according to claim 34, wherein: In the case where the pairing is successful, the processing circuit is further configured to send the model parameters of the artificial intelligence model to be used by the base station side to the user equipment, Wherein, in the case where the first artificial intelligence model is arranged at the base station side, the model parameters sent include the input parameter size of the first artificial intelligence model, and Wherein, when the second artificial intelligence model is arranged on the base station side, the model parameters sent include at least: input time window length and output time window length, and the minimum number of measurements of historical data to be adopted.

37. The electronic device according to claim 34, wherein: In the event that pairing is successful, the processing circuit is also configured to send a model index of the artificial intelligence model to be used by the base station side to the user equipment.

38. The electronic device according to claim 34, wherein: In the event that pairing is unsuccessful, the processing circuit is further configured to receive a message from the user device indicating whether the first artificial intelligence model or the second artificial intelligence model is to be selected.

39. The electronic device according to claim 38, wherein: In the case where the first artificial intelligence model is arranged on the user device side and the first artificial intelligence model is to be selected, the message includes a model parameter or a model index of the selected first artificial intelligence model; In the case where the first artificial intelligence model is arranged on the user device side and the second artificial intelligence model is to be selected, the message includes one bit of notification information; In the case where the second artificial intelligence model is arranged on the user device side and the first artificial intelligence model is to be selected, the message includes one bit of notification information; as well as In the case where the second artificial intelligence model is arranged on the user equipment side and the second artificial intelligence model is to be selected, the message includes a model parameter or a model index of the selected second artificial intelligence model.

40. The electronic device according to claim 33, wherein: The processing circuit is configured to perform pairing as follows: Determining an artificial intelligence model to be used by the base station side in pairing; Including the determined model parameters or model index of the artificial intelligence model in a pairing request and sending it to the user device, so that the user device determines the artificial intelligence model to be used on the user device side based on the model parameters or model index; and A message is received from the user equipment indicating successful or unsuccessful pairing.

41. The electronic device according to claim 40, wherein: In the case where the first artificial intelligence model is arranged at the user equipment side, the model parameters sent include one or more of the following model parameters of the second artificial intelligence model: input time window length and output time window length, input parameter size, input and output parameter attributes, and a minimum number of measurements of historical data to be adopted; In the case where the second artificial intelligence model is arranged on the user equipment side, the transmitted model parameters include at least the input parameter size and input and output parameter attributes of the first artificial intelligence model.

42. The electronic device according to claim 40, wherein: If the pairing is successful, In the case where the first artificial intelligence model is arranged at the user device side, the processing circuit is further configured to receive an input parameter size of the first artificial intelligence model from the user device; as well as In the case where the second artificial intelligence model is arranged on the user device side, the processing circuit is further configured to receive an input time window length and an output time window length of the second artificial intelligence model from the user device.

43. The electronic device according to claim 40, wherein: In the event that pairing is unsuccessful, the processing circuit is further configured to send a message to the user device indicating whether the first artificial intelligence model or the second artificial intelligence model is to be selected.

44. The electronic device according to claim 43, wherein: In the case where the first artificial intelligence model is arranged on the user device side and the first artificial intelligence model is to be selected, the message includes one bit of notification information; In the case where the first artificial intelligence model is arranged on the user device side and the second artificial intelligence model is to be selected, the message includes a model parameter or a model index of the selected second artificial intelligence model; In the case where the second artificial intelligence model is arranged on the user device side and the first artificial intelligence model is to be selected, the message includes a model parameter or a model index of the selected first artificial intelligence model; as well as In the case where the second artificial intelligence model is arranged on the user equipment side and the second artificial intelligence model is to be selected, the message includes one bit of notification information.

45. The electronic device according to claim 33, wherein: In the case that the first artificial intelligence model is arranged on the user equipment side and the second artificial intelligence model is arranged on the base station side, after the pairing is successful, the processing circuit is also configured to receive the beam measurement results within the input time window length of the second artificial intelligence model from the user equipment in a single or multiple form, so that the second artificial intelligence model uses the beam measurement results to perform beam prediction.

46. ​​The electronic device according to claim 45, wherein: The processing circuit is configured to receive the beam measurement results and the prediction results of the first artificial intelligence model through a data channel.

47. The electronic device according to claim 33, wherein: After pairing is successful, In the case where the first artificial intelligence model and / or the second artificial intelligence model triggers a supervision mechanism, if the supervision mechanism is executed by the user equipment, the processing circuit is configured to obtain an execution period of the supervision mechanism from the user equipment, and if the supervision mechanism is executed by the base station, the processing circuit is configured to provide the execution period of the supervision mechanism to the user equipment, and Wherein, when the artificial intelligence model on the user equipment side triggers the supervision mechanism, the processing circuit is also configured to keep the artificial intelligence model on the base station side silent during the execution cycle.

48. A method for a user equipment side of wireless communication, comprising: Pairing a first artificial intelligence model for performing spatial domain beam prediction and a second artificial intelligence model for performing temporal domain beam prediction; as well as In case the pairing is successful, the beam measurement results are multiplexed between the first artificial intelligence model and the second artificial intelligence model.

49. A method for a base station side of wireless communication, comprising: A first artificial intelligence model for performing spatial domain beam prediction and a first artificial intelligence model for performing temporal domain beam prediction Pairing with a predicted second AI model; as well as In case the pairing is successful, the beam measurement results are multiplexed between the first artificial intelligence model and the second artificial intelligence model.

50. A computer-readable storage medium having computer-executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the method according to claim 48 or 49.

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