WO2024046206A1 - Receiving method, device and readable storage medium - Google Patents

Abstract

The present application relates to the field of communications. Disclosed are a receiving method, a device and a readable storage medium. The receiving method in embodiments of the present application comprises: a first device receives target information sent by a second device, the target information comprising any one of the following: target configuration information, first capability information or second capability information, wherein the target configuration information is used for indicating a first number, and the first number is the number of sending beams of the second device; and the first device selects, according to the target information, an artificial intelligence (AI) model corresponding to the target information, the first capability information being used for indicating a second number, the second number being the number of receiving beams of the second device, the second capability information being used for indicating the number of repetitions of a first resource, and the first resource being a reference signal resource associated with the input and/or output of the AI model.

Description

Receiving methods, equipment and readable storage media

Cross-references to related applications

This application claims priority to Chinese Patent Application No. 202211049310.3 filed in China on August 30, 2022, the entire content of which is incorporated herein by reference.

Technical field

This application belongs to the field of communication technology, and specifically relates to a receiving method, equipment and a readable storage medium.

Background technique

Currently, both User Equipment (UE) and network-side equipment can use Artificial Intelligence (AI) models to predict the Reference Signal Receiving Power (RSRP) of beam pairs. For example, some beams can be used The RSRP of the pair is taken as input, so that the RSRP results of all beam pairs can be output through the AI model to achieve prediction of the RSRP of the beam pair, where the beam pair includes a transmit beam and a receive beam.

However, the above-mentioned AI model is obtained through training. However, due to the uncertainty of the training position and inference position of the AI model, the AI model may be obtained through UE training, or the AI model obtained by network side device training, and the trained AI The inference location of the model may be on the network side or on the UE side, depending on the usage method and deployment location of the AI model. Therefore, there may be an AI model that needs to be transmitted from one side to the device on the other side, and there may be the number of inputs and outputs of the AI model. It is related to the deployment side device and/or training side device of the AI model, and since after the AI model training is completed, the number of input data and output data of the AI model, as well as the type of the AI model are determined, if there is no interaction of additional information, it may lead to The model deployment side cannot obtain a sufficient number of model input parameters for model inference, or cannot obtain a sufficient number of model input and output parameters for model training, etc., resulting in AI model performance degradation or unusability. At the same time, if the AI model deployment side has a larger number of When multiple models are used for different scenarios/configurations, if the wrong model is selected on the model deployment side, the performance of the AI model will drop sharply or even become unusable.

Contents of the invention

Embodiments of the present application provide a receiving method, device and readable storage medium, which can improve the performance degradation of AI models.

In a first aspect, a receiving method is provided, which method includes: a first device receiving target information sent by a second device; the target information includes any of the following: target configuration information, first capability information, or second capability information; wherein , the target configuration information is used to indicate the first quantity, and the first quantity is the number of transmission beams of the second device; the first device selects the artificial intelligence AI model corresponding to the target information according to the target information; the first capability information is used to indicate the Two quantities, the second quantity is the number of receiving beams of the second device; the second capability information is used to indicate the number of repetitions of the first resource, and the first resource is a reference signal resource associated with the input and/or output of the AI model.

In a second aspect, a receiving device is provided. The device includes: a receiving module and a selecting module; a receiving module configured to receive target information sent by the second device; the target information includes any of the following: target configuration information, first capability information or second capability information; wherein the target configuration information is used to indicate the first quantity, and the first quantity is the number of transmit beams of the second device; the selection module is used to select the target information corresponding to the target information received by the receiving module. artificial intelligence AI model; the first capability information is used to indicate the second quantity, and the second quantity is the number of receiving beams of the second device; the second capability information is used to indicate the number of repetitions of the first resource, and the first resource is related to the AI Reference signal resources associated with the model's inputs and/or outputs.

In a third aspect, a device is provided. The terminal includes a processor and a memory, and the memory stores the A program or instruction running on the processor, which when executed by the processor, implements the steps of the method described in the first aspect.

In a fourth aspect, a device is provided, including a processor and a communication interface, wherein the processor is configured to receive target information sent by the second device, and select an artificial intelligence AI model corresponding to the target information according to the target information. .

In a fifth aspect, a readable storage medium is provided. Programs or instructions are stored on the readable storage medium. When the programs or instructions are executed by a processor, the steps of the method described in the first aspect are implemented.

In a sixth aspect, a chip is provided. The chip includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the method described in the first aspect. .

In a seventh aspect, a computer program/program product is provided, the computer program/program product is stored in a storage medium, and the computer program/program product is executed by at least one processor to implement the method described in the first aspect. Method steps.

In this embodiment of the present application, the first device receives target information sent by the second device; the target information includes any of the following: target configuration information, first capability information, or second capability information; where the target configuration information is used to indicate the third A quantity, the first quantity is the number of transmission beams of the second device; the first device selects an artificial intelligence AI model corresponding to the target information according to the target information; the first capability information is used to indicate the second quantity, and the second quantity is the The number of receiving beams of the second device; the second capability information is used to indicate the number of repetitions of the first resource, and the first resource is a reference signal resource associated with the input and/or output of the AI model. Since the first device can receive the target information sent by the second device, that is, receive the number of transmit beams, the number of receive beams, and the reference signal resources associated with the input and/or output of the AI model, and the first device can receive the target information according to the received Target information, select an applicable AI model corresponding to the target information; therefore, it can be ensured that through interactive interaction before AI training, the model deployment end can obtain a sufficient number of AI model input parameters, or obtain all The required number of AI model input parameters allows the first device to use and process the AI model normally, thus avoiding the shortage of resources for beam training or beam inference during AI model training or inference, or The wrong AI model is chosen; therefore, not only can the AI model be used normally, but the performance of the AI model can also be improved.

Description of drawings

Figure 1 is a schematic architectural diagram of a communication system provided by an embodiment of the present application;

Figure 2 is a schematic diagram of the composition of an AI neural network provided by an embodiment of the present application;

Figure 3 is a schematic diagram of the structure of a neuron provided by an embodiment of the present application;

Figure 4 is a schematic structural diagram of a feedback report provided by an embodiment of the present application;

Figure 5 is a schematic structural diagram of a feedback report of a group-based beam report provided by an embodiment of the present application;

Figure 6 is a schematic diagram of beam prediction using the AI method provided by the embodiment of the present application;

Figure 7 is a schematic diagram of using an AI method to enhance beam prediction performance provided by an embodiment of the present application;

Figure 8 is a schematic diagram of using an AI method to improve enhanced beam prediction performance provided by an embodiment of the present application;

Figure 9 is a flow chart of a receiving method provided by an embodiment of the present application;

Figure 10 is an interaction diagram of a receiving method provided by an embodiment of the present application;

Figure 11 is a schematic structural diagram of a receiving device provided by an embodiment of the present application;

Figure 12 is a schematic diagram of the hardware structure of a communication device provided by an embodiment of the present application;

Figure 13 is a schematic diagram of the hardware structure of a UE provided by an embodiment of the present application;

Figure 14 is a schematic diagram of the hardware structure of a network-side device provided by an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art fall within the scope of protection of this application.

The terms "first", "second", etc. in the description and claims of this application are used to distinguish similar objects and are not used to describe a specific order or sequence. It is to be understood that the terms so used are interchangeable under appropriate circumstances so that the embodiments of the present application can be practiced in sequences other than those illustrated or described herein, and that "first" and "second" are distinguished objects It is usually one type, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "and/or" in the description and claims indicates at least one of the connected objects, and the character "/" generally indicates that the related objects are in an "or" relationship.

It is worth pointing out that the technology described in the embodiments of this application is not limited to Long Term Evolution (LTE)/LTE Evolution (LTE-Advanced, LTE-A) systems, and can also be used in other wireless communication systems, such as code Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access, OFDMA), Single-carrier Frequency Division Multiple Access (SC-FDMA) and other systems. The terms "system" and "network" in the embodiments of this application are often used interchangeably, and the described technology can be used not only for the above-mentioned systems and radio technologies, but also for other systems and radio technologies. The following description describes a New Radio (NR) system for example purposes, and NR terminology is used in much of the following description, but these techniques can also be applied to applications other than NR system applications, such as 6th ^generation Generation, 6G) communication system.

Figure 1 shows a block diagram of a wireless communication system to which embodiments of the present application are applicable. The wireless communication system includes a terminal 11 and a network side device 12. Among them, the terminal 11 can be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), or a notebook computer, a personal digital assistant (Personal Digital Assistant, PDA), a handheld computer, a netbook, or a super mobile personal computer. (ultra-mobile personal computer, UMPC), mobile Internet device (Mobile Internet Device, MID), augmented reality (augmented reality, AR)/virtual reality (VR) equipment, robots, wearable devices (Wearable Device) , vehicle-mounted equipment (VUE), pedestrian terminal (PUE), smart home (home equipment with wireless communication functions, such as refrigerators, TVs, washing machines or furniture, etc.), game consoles, personal computers (personal computers, PC), teller machines or self-service Terminal devices such as mobile phones, wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart anklets, etc.), Smart wristbands, smart clothing, etc. It should be noted that the embodiment of the present application does not limit the specific type of the terminal 11. The network side equipment 12 may include access network equipment or core network equipment, where the access network equipment 12 may also be called wireless access network equipment, radio access network (Radio Access Network, RAN), radio access network function or Wireless access network unit. The access network device 12 may include a base station, a WLAN access point or a WiFi node, etc. The base station may be called a Node B, an evolved Node B (eNB), an access point, a Base Transceiver Station (BTS), a radio Base station, radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), Home B-Node, Home Evolved B-Node, Transmitting Receiving Point (TRP) or belonging to Some other appropriate terminology in the field, as long as the same technical effect is achieved, the base station is not limited to specific technical terms. It should be noted that in the embodiment of this application, only the base station in the NR system is used as an example for introduction. Define the specific type of base station.

Currently, AI has been widely used in many fields. There are many ways to implement AI networks, such as neural networks, decision trees, support vector machines, Bayesian classifiers, etc.

Figure 2 shows a schematic diagram of the composition of an AI neural network. As shown in Figure 2, the AI neural network is composed of neurons.

Figure 3 shows a schematic diagram of the composition of a neuron. As shown in Figure 3, a ₁ , a ₂ ,...a _K is the input, w is the weight (or multiplicative coefficient), and b is the bias (or is called the additive coefficient), and σ(.) is the activation function. Common excitement Living functions include Sigmoid, tanh, Rectified Linear Unit (ReLU), etc.

The parameters of neural networks can be optimized through optimization algorithms. An optimization algorithm is a type of algorithm that can assist developers or users in minimizing or maximizing an objective function (also known as: loss function). The objective function is often a mathematical combination of model parameters and data. For example: given data X and its corresponding label Y, developers can build a neural network model f(.), and through this neural network model f(.), the predicted output can be obtained based on the input x f(x), and the difference between the predicted value and the true value (f(x)-Y) can be calculated, which is the loss function. Among them, the developer's purpose is to find appropriate w and b so that the value of the above loss function can be minimized. The smaller the loss value, the closer the model is to the real situation.

The optimization algorithm in the embodiment of this application may be based on the error back propagation (Error Back Propagation, BP) algorithm. The basic idea of BP algorithm is that the learning process consists of two processes: forward propagation of signals and back propagation of errors. During forward propagation, the input sample is passed in from the input layer, processed layer by layer by each hidden layer, and then transmitted to the output layer. If the actual output of the output layer does not match the expected output, it will enter the error backpropagation stage. Error backpropagation is to backpropagate the output error in some form to the input layer layer by layer through the hidden layer, and allocate the error to all units in each layer, thereby obtaining the error signal of each layer unit. This error signal is used as a correction for each unit. The basis for the weight. This process of adjusting the weights of each layer in forward signal propagation and error back propagation is carried out over and over again. The process of continuous adjustment of weights is the learning and training process of the network. This process continues until the error of the network output is reduced to an acceptable level, or until a preset number of learning times.

Common optimization algorithms include gradient descent (Gradient Descent, GD), stochastic gradient descent (Stochastic Gradient Descent, SGD), mini-batch gradient descent (Mini-Batch Gradient Descent), momentum method (Momentum), and stochastic gradient descent with momentum ( Nesterov), adaptive gradient descent (ADAptive GRADient descent, Adagrad), Adadelta, root mean square error reduction (Root Mean Square Prop, RMSprop), adaptive momentum estimation (Adaptive Moment Estimation, Adam), etc.

When these optimization algorithms perform error backpropagation, they calculate the derivative/partial derivative of the current neuron based on the error/loss obtained by the loss function, plus the influence of the learning rate, previous gradient/derivative/partial derivative, etc., so that it can Get the gradient and pass it to the previous layer.

Some concepts and/or terms involved in the determination methods provided in the embodiments of this application are explained below.

Beam Indication mechanism

After beam measurement and beam reporting, the network side equipment can perform beam indication on the downlink and uplink channels or reference signals, which is used to establish beam links between the network side equipment and the UE to achieve channel or reference signal transmission.

For the beam indication of the Physical Downlink Control Channel (PDCCH), the network uses Radio Resource Control (RRC) signaling to configure K transmission configuration indications (Transmission Configuration Indication) for each core set (CORESET). TCI) state; when K>1, a TCI state is instructed or activated by the Media Access Control Layer (MAC CE). When K=1, no additional MAC CE command is required. When the UE monitors the PDCCH, it uses the same quasi-colocation (QCL) for all search spaces in the CORESET, that is, the same TCI state to monitor the PDCCH. The reference signal (Reference Signal) in this TCI state (such as periodic CSI-RS resource, semi-persistent CSI-RS resource, SS block, etc.) and the UE-specific PDCCH demodulation reference signal (DemodulationReference Sgnal, DMRS) port are spatial QCL . The UE can learn which receiving beam is used to receive the PDCCH according to the TCI status.

For the beam indication of PDSCH, the network side device configures M TCI states through RRC signaling, then uses the MAC CE command to activate 2 ^N TCI states, and then notifies the TCI state through the N-bit TCI field of DCI. The TCI state in the TCI state The Reference Signal and the DMRS port of the PDSCH to be scheduled are QCL. The UE can learn which receiving beam is used to receive the PDSCH according to the TCI status.

For CSI-RS beam indication, when the CSI-RS type is periodic CSI-RS, the network side device uses RRC Signaling configures QCL information for CSI-RS resources. When the CSI-RS type is semi-persistent CSI-RS, the network side device indicates its QCL information when activating a CSI-RS resource from the CSI-RS resource set configured in RRC through the MAC CE command. When the CSI-RS type is aperiodic CSI-RS, the network side device configures QCL for the CSI-RS resource through RRC signaling and uses DCI to trigger CSI-RS.

For the beam indication of the Physical Uplink Control Channel (PUCCH), the network side device uses RRC signaling to configure the spatial relationship information (Spatial Relation Information) for each PUCCH resource through the PUCCH-SpatialRelationInfo parameter. When it is a PUCCH resource When the configured Spatial Relation Information contains multiple spatial relation information, use MAC-CE to indicate or activate one of the spatial relation information. When the spatial relation information configured for the PUCCH resource only contains 1, no additional MAC CE command is required.

For the beam indication of PUSCH, the spatial relation information of PUSCH is the uplink scheduling request indication information in DCI when the downlink control channel information (DCI) carried by PDCCH schedules the physical uplink shared channel (Physical Uplink Shared Channel, PUSCH). Each SRI code point (codepoint) of the Schduling Request Indication field (SRI field) indicates an SRI, which is used to indicate the Spatial Relation Information of PUSCH.

For SRS beam indication, when the SRS type is periodic SRS, the network configures Spatial Relation Information for the SRS resource through RRC signaling. When the SRS type is semi-persistent SRS, the network uses the MAC CE command to activate one from a set of Spatial Relation Information configured by RRC. When the SRS type is aperiodic SRS, the network configures Spatial Relation Information for the SRS resource through RRC signaling.

Beam indication can be implemented using unified transmission configuration indication status (unified TCI indication), that is, the TCI domain in a DCI indicates subsequent reference signals and beam information of multiple channels.

It should be noted that the above-mentioned beam information, Spatial Relation information, Spatial Domain Transmission Filter information, Spatial Filter Spatial Filter information, TCI State information, QCL information, QCL parameters, Spatial Relation information, beam correlation relationships, etc. The meaning expressed is the same or similar. Among them, downlink beam information can usually be represented by TCI state information and QCL information. Uplink beam information can usually be represented using Spatial Relation information.

Demodulation sensitivity calculation method

Receiving sensitivity, which can be achieved through the demodulation formula, where the demodulation formula is: S (dBm) = thermal noise (dBm) + 10log (BW) + NF (dB) + demodulation threshold, the thermal noise is -174dbm/Hz .

Ignoring the demodulation threshold, taking 30GHz, 120kH SCS as an example,

The noise floor on one subcarrier=-174+10*log10(120*10^3)+10=-174+50.8+10=-113.2dBm.

Therefore, for high-frequency large sub-carrier spacing, the energy of the noise floor is relatively large.

About Beam Measurement And Beam Reporting

Since analog beamforming is transmitted with full bandwidth, and each polarization direction array element on the panel of each high-frequency antenna array can only transmit analog beams in a time-division multiplexing manner, the shaping weight of the analog beam is calculated by This is achieved by adjusting the parameters of equipment such as RF front-end phase shifters.

In the related art, the polling method can be used to train the simulated beamforming vector, that is, the array elements of each polarization direction of each antenna panel sequentially send training signals (i.e., candidate shaping vectors) at an agreed time in a time division multiplexing manner. ), the terminal feeds back the beam report after measurement, so that the network side can use the training signal to implement simulated beam transmission when transmitting services next time. The content of the beam report usually includes several optimal transmit beam identifiers and the measured received power of each transmit beam.

When performing beam measurements, the network side device configures a reference signal resource set (RS resource set), which includes at least one reference signal resource, such as SSB resource or CSI-RS resource. The UE measures the L1-RSRP/L1-SINR of each RS resource and reports at least one of the best measurement results to the network side device. The report content includes SSBRI or CRI, and L1-RSRP/L1-SINR. The report content reflects at least one optimal beam and its quality for the network side device to determine the beam used to send channels or signals to the UE.

When the UE feedback report contains only one L1-RSRP, the 7-bit quantization method is used, the quantization step is 1dB, and the quantization range is -140dBm to -44dBm. When the UE is instructed that the feedback report contains multiple L1-RSRPs, or Group Based Beam Report is enabled, the strongest RSRP quantization uses 7-bit quantization, and the remaining RSRP quantization uses the 4-bit differential quantization method. The steps are 2dB.

Figure 4 shows a schematic structural diagram of a feedback report.

Figure 5 shows a schematic diagram of the feedback report structure of group-based beam reporting.

Among them, the number of feedback reports is determined by the parameters configured by the network side device to the UE, and through the RRC configuration parameters, and the number of RS and RSRP that should be included in the feedback report of the UE. The value of the quantity configuration is 1, 2 ,3,4, the default value is 1. In addition, the number limit is based on the UE capability, and the UE will first report the maximum number it can support.

Beam prediction using AI methods:

Figure 6 shows a schematic diagram of beam prediction using the AI method. As shown in Figure 6, the RSRP of some beam pairs can be used as input, and the output of the AI model is the RSRP result of all beam pairs. The beam pairs are composed of transmitting beams and receiving beams, and the input number of the AI model is the number of selected partial beam pairs, and the output number is the number of all beam pairs.

Figure 7 shows a schematic diagram of using AI methods to enhance beam prediction performance. As shown in Figure 7, associated information can be added to the input side. The associated information is generally angle-related information corresponding to the beam pairs selected for input, beam ID information, etc. Therefore, the number of inputs of this model is still related to the number of selected partial beam pairs, and the number of outputs is still equal to the number of all beam pairs.

Figure 8 shows a schematic diagram of using AI methods to improve enhanced beam prediction performance. As shown in Figure 8, this method mainly affects the output of the AI model by changing the expected information.

The input type of the AI model includes at least one of the following:

Beam quality related information;

Beam-related associated information;

The A-side sends beam-related association information;

Relevant information related to the B-side receiving beam;

Related information related to the beam expected by the B-side;

The associated information related to the B-side receiving beam expected by the B-side;

End B expects end A to send beam-related associated information;

Time-related information related to beam quality information;

Desired forecast time related information.

The associated information related to the beam refers to the beam information corresponding to the beam. The beam information includes but is not limited to at least one of the following:

Beam ID information;

Beam angle information;

Beam gain information;

Beamwidth information, etc.

The beam ID information is used to characterize the identity of the beam, including but not limited to at least one of the following:

Send beam ID;

Receive beam ID;

beamID;

The reference signal set ID corresponding to the beam;

The reference signal resource ID corresponding to the beam;

Uniquely identified random ID;

The coded value processed by the additional AI network;

Beam angle related information, etc.

The beam angle information is used to characterize the angle information corresponding to the beam, including but not limited to at least one of the following:

angle information;

Send angle information;

Receive angle information.

Among them, angle information is information used to represent angles, such as angle, radian, index code value, code value after additional AI network processing, etc.

However, since the training location and inference location of the AI model may be uncertain, the training location and inference location may be at the same location, such as at the UE, base station or central node, or the model training location and inference location are at Two locations, for example, the training location is at the base station and the inference location is at the UE.

Therefore, due to the uncertainty of the training position and inference position of the AI model, the performance of the AI model may be reduced; and, since there are many feasible solutions for the implementation of the AI model, it may cause the AI model to mismatch. situation arises. In addition, the implementation of the AI model solution requires some auxiliary information interaction to ensure the normal use of the AI model. Therefore, a method is urgently needed to ensure the normal use of the AI model and improve the performance of the AI model.

The determination method provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings through some embodiments and their application scenarios.

The receiving method provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings through some embodiments and application scenarios.

Embodiment 1

This embodiment of the present application provides a determination method, and FIG. 9 shows a flow chart of a receiving method provided by this embodiment of the present application. As shown in Figure 9, the receiving method provided by the embodiment of the present application may include the following steps 201 and 202.

Step 201: The first device receives the target information sent by the second device.

The target information includes any of the following: target configuration information, first capability information or second capability information.

The target configuration information is used to indicate a first quantity, and the first quantity is the number of transmission beams of the second device.

Step 202: The first device selects an artificial intelligence AI model corresponding to the target information based on the target information.

The first capability information is used to indicate a second quantity, and the second quantity is the number of receiving beams of the second device;

The second capability information is used to indicate the number of repetitions of the first resource, which is a reference signal resource associated with the input and/or output of the AI model.

Embodiments of the present application provide a receiving method. A first device receives target information sent by a second device; the target information includes any of the following: target configuration information, first capability information, or second capability information; where the target configuration information is To indicate the first quantity, the first quantity is the number of transmission beams of the second device; the first device selects an artificial intelligence AI model corresponding to the target information according to the target information; the first capability information is used to indicate the second quantity, and the second The number is the number of receiving beams of the second device; the second capability information is used to indicate the number of repetitions of the first resource, and the first resource is a reference signal resource associated with the input and/or output of the AI model. Since the first device can receive the target information sent by the second device, that is, receive the number of transmit beams, the number of receive beams, and the reference signal resources associated with the input and/or output of the AI model, and the first device can receive the target information according to the received Target information, select an applicable AI model corresponding to the target information; therefore, it can be ensured that through interactive interaction before AI training, the model deployment end can obtain a sufficient number of AI model input parameters, or obtain all The required number of AI model input parameters can enable the first device to use and process the AI model normally, This avoids the shortage of resources for beam training or beam inference, or choosing the wrong AI model during AI model training or inference; therefore, not only can the AI model be used normally, but the performance of the AI model can also be improved. .

Optionally, in this embodiment of the present application, the first device is a UE and the second device is a network-side device; the target information includes target configuration information; the above step 202 can be implemented specifically through the following step 202a.

Step 202a: The UE selects an AI model corresponding to the target configuration information according to the target configuration information.

In the embodiment of this application, since the UE can select an AI model corresponding to the target configuration information according to the target configuration information, it can be ensured that the UE selects a suitable AI model before training the AI, or selects an AI that can be used normally. model, therefore, the applicability of the AI model can be guaranteed.

Optionally, in the embodiment of this application, the first number is greater than or equal to the first target number; the first target number is the number of beams associated with the input and/or output results of the AI model;

or,

The first quantity is greater than or equal to the second target quantity;

The second target quantity is less than or equal to the first target quantity.

Wherein, the second target quantity is a part of the first target quantity.

Optionally, in this embodiment of the present application, the second target quantity is related to the first target quantity, and the second target quantity is determined based on the first target quantity.

For example, the second target quantity is the quantity corresponding to the product of the first target quantity and the preset coefficient; or, the second target quantity is the quantity corresponding to the difference between the first target quantity and the preset value; or, the second target quantity The quantity is the quantity corresponding to the product of the first target quantity and the preset percentage; specifically, it can be determined according to the actual application situation, and the embodiment of the present application does not impose any restrictions here.

Optionally, in this embodiment of the present application, the first device is a network side device, and the second device is a UE; the target information includes the first capability information; the above step 202 can be specifically implemented through the following step 202b.

Step 202b: The network side device selects an AI model corresponding to the first capability information based on the first capability information, and/or determines the number of beams associated with the input and/or output results of the AI model based on the first capability information.

In this embodiment of the present application, the UE can select an AI model corresponding to the first capability information based on the first capability information, thereby ensuring that the UE selects an appropriate AI model based on the capabilities of its own AI model before training the AI. Either select an AI model that can be used normally, or determine the number of beams associated with the input and/or output results of the AI model based on the first capability information, thereby determining the input and/or output parameter information of the AI model, so , which can ensure the applicability of the AI model while improving the performance of the AI model.

Optionally, in the embodiment of the present application, the second number is greater than or equal to the third target number; the third target number is the number of beams associated with the input and/or output results of the AI model;

or,

The second quantity is greater than or equal to the fourth target quantity;

The fourth target quantity is less than or equal to the third target quantity.

Among them, the fourth target quantity is part of the third target quantity.

Optionally, in this embodiment of the present application, the fourth target quantity is related to the third target quantity, and the fourth target quantity is determined based on the third target quantity.

Exemplarily, the fourth target quantity is the quantity corresponding to the product of the third target quantity and the preset coefficient; or, the fourth target quantity is the quantity corresponding to the difference between the third target quantity and the preset value; or, the fourth target quantity The quantity is the quantity corresponding to the product of the third target quantity and the preset percentage; specifically, it can be determined according to the actual application situation, and the embodiment of the present application does not impose any restrictions here.

Optionally, the receiving method provided by the embodiment of this application also includes the following step 301.

Step 301: The network side device determines the third quantity based on the first capability information.

The third quantity is associated with the quantity of the second resource; the second resource is configured by the network side device and is used for beam scanning. Reference signal resources.

In the embodiment of this application, the network side device can configure the reference signal resources required by the UE for beam scanning according to the UE's capability information, thereby ensuring that the number of reference signal resources sent to the UE is the AI model of the UE. Therefore, the normal use of the AI model can be guaranteed.

Optionally, in this embodiment of the present application, the repeated configuration state of the second resource is enabled.

Optionally, in this embodiment of the present application, the repeated configuration of the second resource association is enabled.

Optionally, in this embodiment of the present application, the third quantity is associated with the repetition quantity of the second resource;

The number of duplicates for the secondary resource includes any of the following:

The number of symbols occupied by the second resource;

The number of repetitions of the second resource;

The number of repetitions of the beam corresponding to the second resource.

Optionally, in this embodiment of the present application, the input and/or output quantity of the AI model is associated with the second capability information.

It should be noted that association in the embodiments of this application can be understood as: including; or association can be understood as: yes; or association can be understood as: having an association relationship, that is, the two are related and the two are related. relation.

Optionally, in this embodiment of the present application, the number of inputs to the AI model is less than or equal to the number of repetitions of the first resource;

The amount of target information in the input of the AI model is less than or equal to the number of repetitions of the first resource;

Target information can be at least one of the following:

Reference signal received power RSRP information;

Beam information of the beam;

Beam information of the transmit beam;

Beam information for the receive beam.

Optionally, in this embodiment of the present application, the fourth number is less than or equal to the number of repetitions of the first resource;

The fourth quantity is any of the following:

The number corresponding to the output results of the AI model;

The number of beams used to monitor the AI model.

Embodiment 2

This embodiment of the present application provides a receiving method, which may include the following steps 11 and 12.

Step 11. The network side device sends the target configuration information.

The target configuration information is used to indicate the number of transmission beams supported by the network side device.

Step 12: The UE receives the target configuration information, and selects the AI model corresponding to the target configuration information according to the target configuration information.

Optionally, in this embodiment of the present application, the number of transmitting beams indicated by the target configuration information is related to the number of beams associated with the input and/or output numbers of the AI model.

Optionally, in this embodiment of the present application, the number of transmitting beams indicated by the target configuration information is greater than or equal to the number of beams associated with the input and/or output results of the AI model.

Exemplarily, Figure 10 shows an interaction diagram of a receiving method provided by an embodiment of the present application. As shown in Figure 10, the AI model performs AI model training and AI model reasoning on the UE side. The receiving method provided by an embodiment of the present application Including the following steps a and b.

Step a: The network side device (such as a base station) sends target configuration information.

Step b: The UE determines the number of transmit beams of the base station according to the target configuration information, and selects an AI model.

For example, the number of beams corresponding to the output number of AI model 1 is 16, and the number of beams corresponding to the output number of AI model 2 is 32. If the UE determines that the number of transmit beams of the base station is equal to 32 based on the target configuration information sent by the base station, then select AI Model 2.

Embodiment 3

This embodiment of the present application provides a receiving method, which may include the following steps 13 to 15.

Step 13: The UE sends the first capability information.

In this embodiment of the present application, the first capability information is used to indicate information on the number of receiving beams of the UE.

Step 14: The network side device receives the first capability information, and performs an AI model based on the first capability information, and/or determines the number of beams associated with the input and/or output results of the AI model.

Optionally, in this embodiment of the present application, the number of receiving beams indicated by the first capability information is related to the number of beams associated with the input and/or output results of the AI model.

Optionally, in this embodiment of the present application, the number of receiving beams indicated by the first capability information is greater than or equal to the number of beams associated with the input and/or output results of the AI model.

Step 15: The network side device determines the third quantity based on the first capability information.

In this embodiment of the present application, the third quantity is associated with a second resource, and the second resource is a reference signal resource configured by the network side device for beam scanning.

Optionally, in this embodiment of the present application, the number of second resources indicates the number of repetitions and/or the minimum number of repetitions of the second resource.

Optionally, in this embodiment of the present application, the repetition number and/or the minimum repetition number of the second resource represents the number of reference second resources, or represents the number of beams corresponding to the second resource.

Optionally, in this embodiment of the present application, the repeated configuration of the second resource association is turned on, that is, repetition on.

Embodiment 4

This embodiment of the present application provides a receiving method, which may include the following step 16.

Step 16: The UE sends the second capability information.

In this embodiment of the present application, the second capability information is used to indicate the number of repetitions of the first resource, which is a reference signal resource associated with the input and/or output of the AI model.

Optionally, in this embodiment of the present application, the number of inputs and/or the number of outputs of the AI model is related to the number of repetitions and/or the minimum number of repetitions of the first resource.

Optionally, in this embodiment of the present application, the amount of target information in the input of the AI model is less than or equal to the number of repetitions of the first resource.

Optionally, in this embodiment of the present application, the target information may be at least one of the following:

Reference signal received power RSRP information;

Beam information of the beam;

Beam information of the transmit beam;

Beam information for the receive beam.

Optionally, in this embodiment of the present application, the fourth number is less than or equal to the number of repetitions of the first resource;

The fourth quantity is any of the following:

The number corresponding to the output results of the AI model;

The number of beams used to monitor the AI model.

Optionally, in this embodiment of the present application, the repetition number and/or the minimum repetition number of the first resource represents the number of first resources, or represents the number of beams corresponding to the first resource.

For the receiving method provided by the embodiment of the present application, the execution subject may be the sending device. In the embodiment of the present application, the sending device executing the sending method is taken as an example to describe the sending device provided by the embodiment of the present application.

Figure 11 shows a possible structural schematic diagram of the receiving device involved in the embodiment of the present application. As shown in FIG. 11 , the receiving device 40 may include: a receiving module 41 and a selecting module 42 .

Wherein, the receiving module 41 is used to receive the target information sent by the second device; the target information includes any of the following: target configuration information, first capability information or second capability information; wherein the target configuration information is used to indicate the first quantity , the first number is the number of transmit beams of the second device. The selection module 42 is used to select the AI model corresponding to the target information according to the target information received by the receiving module 41; the first capability information is used to indicate the second quantity, The second number is the number of receiving beams of the second device; the second capability information is used to indicate the number of repetitions of the first resource, and the first resource is a reference signal resource associated with the input and/or output of the AI model.

The embodiment of the present application provides a receiving device. Since the first device can receive the target information sent by the second device, that is, the number of receiving and sending beams, the number of receiving beams, and the reference signal resources associated with the input and/or output of the AI model, Moreover, the first device can select an applicable AI model corresponding to the target information based on the received target information; therefore, it can be ensured that the model deployment end can obtain sufficient AI through interactive interaction before AI training is performed. The number of model input parameters, or the required number of AI model input parameters can be obtained, so that the first device can use and process the AI model normally, thus avoiding the need for beam training during AI model training or inference. Either there is a shortage of beam inference resources, or the wrong AI model is selected; therefore, not only can the AI model be used normally, but the performance of the AI model can also be improved.

In a possible implementation manner, the first device is a UE and the second device is a network side device; the target information includes target configuration information; the selection module 42 is specifically used to select the AI corresponding to the target configuration information according to the target configuration information. Model.

In a possible implementation manner, the first number is greater than or equal to the first target number; the first target number is the number of beams associated with the input and/or output results of the AI model;

or,

The first quantity is greater than or equal to the second target quantity;

The second target quantity is less than or equal to the first target quantity.

In a possible implementation manner, the first device is a network side device, and the second device is a UE; the target information includes first capability information; the selection module 42 is specifically configured to select the first capability information according to the first capability information. The corresponding AI model, and/or the number of beams associated with the input and/or output results of the AI model is determined based on the first capability information.

In a possible implementation manner, the second number is greater than or equal to the third target number; the third target number is the number of beams associated with the input and/or output results of the AI model;

or,

The second quantity is greater than or equal to the fourth target quantity;

The fourth target quantity is less than or equal to the third target quantity.

In a possible implementation manner, the above-mentioned device 40 further includes: a determining module; a determining module configured to determine a third quantity according to the first capability information; the third quantity is associated with the quantity of the second resource; the second resource is the network side Reference signal resources configured by the device for beam scanning.

In one possible implementation manner, the repeated configuration state of the second resource is enabled.

In one possible implementation manner, the third quantity is associated with the repeated quantity of the second resource;

The number of duplicates for the secondary resource includes any of the following:

The number of symbols occupied by the second resource;

The number of repetitions of the second resource;

The number of repetitions of the beam corresponding to the second resource.

In one possible implementation manner, the input and/or output quantities of the AI model are associated with the second capability information.

In one possible implementation manner, the number of inputs to the AI model is less than or equal to the number of repetitions of the first resource;

The amount of target information in the input of the AI model is less than or equal to the number of repetitions of the first resource;

Target information can be at least one of the following:

Reference signal received power RSRP information;

Beam information of the beam;

Beam information of the transmit beam;

Beam information for the receive beam.

In one possible implementation manner, the fourth number is less than or equal to the number of repetitions of the first resource;

The fourth quantity is any of the following:

The number corresponding to the output results of the AI model;

The number of beams used to monitor the AI model.

The receiving device in the embodiment of the present application may be an electronic device, such as an electronic device with an operating system, or may be a component in the electronic device, such as an integrated circuit or chip. The electronic device may be a terminal or other devices other than the terminal. For example, terminals may include but are not limited to the types of terminals 11 listed above, and other devices may be servers, network attached storage (Network Attached Storage, NAS), etc., which are not specifically limited in the embodiment of this application.

The receiving device provided by the embodiments of the present application can implement each process implemented by the above method embodiments and achieve the same technical effect. To avoid duplication, details will not be described here.

Optionally, as shown in Figure 12, this embodiment of the present application also provides a communication device 600, which includes a processor 601 and a memory 602. The memory 602 stores programs or instructions that can be run on the processor 601, for example. , when the communication device 600 is a terminal, when the program or instruction is executed by the processor 601, each step of the above receiving method embodiment is implemented, and the same technical effect can be achieved. When the communication device 600 is a network-side device, when the program or instruction is executed by the processor 601, each step of the above receiving method embodiment is implemented, and the same technical effect can be achieved. To avoid duplication, the details are not repeated here.

An embodiment of the present application also provides a UE (terminal), which includes a processor and a communication interface. The processor is configured to receive target information sent by a second device. This terminal embodiment corresponds to the above-mentioned method embodiment when the first device is a UE. Each implementation process and implementation manner of the above-mentioned method embodiment can be applied to this terminal embodiment, and can achieve the same technical effect. Specifically, FIG. 13 is a schematic diagram of the hardware structure of a UE that implements an embodiment of the present application.

The UE 100 includes but is not limited to: at least one of the radio frequency unit 101, the network module 102, the audio output unit 103, the input unit 104, the sensor 105, the display unit 106, the user input unit 107, the interface unit 108, the memory 109, the processor 110, etc. Some parts.

Those skilled in the art can understand that the UE 100 may also include a power supply (such as a battery) that supplies power to various components. The power supply may be logically connected to the processor 110 through a power management system, thereby achieving management of charging, discharging, and power consumption management through the power management system. and other functions. The terminal structure shown in FIG. 13 does not constitute a limitation on the terminal. The terminal may include more or fewer components than shown in the figure, or some components may be combined or arranged differently, which will not be described again here.

It should be understood that in the embodiment of the present application, the input unit 104 may include a graphics processing unit (Graphics Processing Unit, GPU) 1041 and a microphone 1042. The graphics processor 1041 is responsible for the image capture device (GPU) in the video capture mode or the image capture mode. Process the image data of still pictures or videos obtained by cameras (such as cameras). The display unit 106 may include a display panel 1061, which may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 107 includes a touch panel 1071 and at least one of other input devices 1072 . Touch panel 1071 is also called a touch screen. The touch panel 1071 may include two parts: a touch detection device and a touch controller. Other input devices 1072 may include, but are not limited to, physical keyboards, function keys (such as volume control keys, switch keys, etc.), trackballs, mice, and joysticks, which will not be described again here.

In this embodiment of the present application, after receiving downlink data from the network side device, the radio frequency unit 101 can transmit it to the processor 110 for processing; in addition, the radio frequency unit 101 can send uplink data to the network side device. Generally, the radio frequency unit 101 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc.

Memory 109 may be used to store software programs or instructions as well as various data. The memory 109 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, Image playback function, etc.) etc. Additionally, memory 109 may include volatile memory or non-volatile memory, or memory 109 Both volatile and non-volatile memory can be included. Among them, the non-volatile memory can be read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electrically removable memory. Erase programmable read-only memory (Electrically EPROM, EEPROM) or flash memory. Volatile memory can be random access memory (Random Access Memory, RAM), static random access memory (Static RAM, SRAM), dynamic random access memory (Dynamic RAM, DRAM), synchronous dynamic random access memory (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDRSDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory (Synch link DRAM) , SLDRAM) and direct memory bus random access memory (Direct Rambus RAM, DRRAM). Memory 109 in embodiments of the present application includes, but is not limited to, these and any other suitable types of memory.

The processor 110 may include one or more processing units; optionally, the processor 110 integrates an application processor and a modem processor, where the application processor mainly handles operations related to the operating system, user interface, application programs, etc., Modem processors mainly process wireless communication signals, such as baseband processors. It can be understood that the above modem processor may not be integrated into the processor 110 .

Wherein, the radio frequency unit 101 is used to receive target information sent by the second device; the target information includes any of the following: target configuration information, first capability information or second capability information; wherein the target configuration information is used to indicate the first quantity , the first quantity is the number of transmission beams of the second device; the processor 110 is used to select an AI model corresponding to the target information according to the target information; the first capability information is used to indicate the second quantity, and the second quantity is the second The number of receiving beams of the device; the second capability information is used to indicate the number of repetitions of the first resource, which is a reference signal resource associated with the input and/or output of the AI model.

The embodiment of the present application provides a UE, because the first device can receive the target information sent by the second device, that is, the number of receiving sending beams, the number of receiving beams, and the reference signal resources associated with the input and/or output of the AI model, and , the first device can select an applicable AI model corresponding to the target information based on the received target information; therefore, it can be ensured that the model deployment end can obtain sufficient AI models through interactive interactions before AI training is performed. The number of input parameters, or the required number of input parameters of the AI model is obtained, so that the first device can use and process the AI model normally, thereby avoiding the need for beam training or inference during AI model training or inference. It is a shortage of beam inference resources, or the wrong AI model is selected; therefore, not only can the AI model be used normally, but the performance of the AI model can also be improved.

In a possible implementation manner, the first device is a UE and the second device is a network side device; the target information includes target configuration information; the processor 110 is specifically configured to select an AI corresponding to the target configuration information according to the target configuration information. Model.

In a possible implementation manner, the first device is a network side device, and the second device is a UE; the target information includes the first capability information; the processor 110 is specifically configured to select the first capability information according to the first capability information. The corresponding AI model, and/or the number of beams associated with the input and/or output results of the AI model is determined based on the first capability information.

Optionally, the processor 110 is configured to determine a third quantity according to the first capability information; the third quantity is associated with the quantity of second resources; and the second resources are reference signal resources configured by the network side device for beam scanning.

An embodiment of the present application also provides a network side device, including a processor and a communication interface. The processor is configured to receive target information sent by the second device. This network-side device embodiment corresponds to the above-mentioned method embodiment when the first device is a network-side device. Each implementation process and implementation manner of the above-mentioned method embodiment can be applied to this network-side device embodiment, and can achieve the same technical effects.

Specifically, the embodiment of the present application also provides a network side device. As shown in FIG. 14 , the network side device 700 includes: an antenna 71 , a radio frequency device 72 , a baseband device 73 , a processor 74 and a memory 75 . The antenna 71 is connected to the radio frequency device 72 . In the uplink direction, the radio frequency device 72 receives information through the antenna 71 and converts the received information Sent to baseband device 73 for processing. In the downlink direction, the baseband device 73 processes the information to be sent and sends it to the radio frequency device 72. The radio frequency device 72 processes the received information and then sends it out through the antenna 71.

The method performed by the network side device in the above embodiment can be implemented in the baseband device 73, which includes a baseband processor.

The baseband device 73 may include, for example, at least one baseband board on which multiple chips are disposed, as shown in FIG. Program to perform the network device operations shown in the above method embodiments.

The network side device may also include a network interface 76, which is, for example, a common public radio interface (CPRI).

Specifically, the network side device 700 in this embodiment of the present invention also includes: instructions or programs stored in the memory 75 and executable on the processor 74. The processor 74 calls the instructions or programs in the memory 75 to execute the various operations shown in Figure 14. The method of module execution and achieving the same technical effect will not be described in detail here to avoid duplication.

Wherein, the radio frequency device 72 is used to receive target information sent by the second device; the target information includes any of the following: target configuration information, first capability information or second capability information; wherein the target configuration information is used to indicate the first quantity , the first quantity is the number of transmission beams of the second device; the processor 74 is used to select the AI model corresponding to the target information according to the target information; the first capability information is used to indicate the second quantity, and the second quantity is the second The number of receiving beams of the device; the second capability information is used to indicate the number of repetitions of the first resource, which is a reference signal resource associated with the input and/or output of the AI model.

The embodiment of the present application provides a network-side device, because the first device can receive the target information sent by the second device, that is, the number of receiving and sending beams, the number of receiving beams, and the reference signal resources associated with the input and/or output of the AI model. , and the first device can select an applicable AI model corresponding to the target information based on the received target information; therefore, it can be ensured that the model deployment end can obtain sufficient information through interactive interaction before AI training is performed. The number of AI model input parameters, or the required number of AI model input parameters can be obtained, so that the first device can use and process the AI model normally, thereby avoiding the need for beams during AI model training or inference. There is a shortage of resources for training or beam inference, or the wrong AI model is selected; therefore, not only can the AI model be used normally, but the performance of the AI model can also be improved.

In a possible implementation manner, the first device is a UE and the second device is a network side device; the target information includes target configuration information; the processor 74 is specifically configured to select an AI corresponding to the target configuration information according to the target configuration information. Model.

In a possible implementation manner, the first device is a network side device, and the second device is a UE; the target information includes the first capability information; the processor 74 is specifically configured to select the first capability information according to the first capability information. The corresponding AI model, and/or the number of beams associated with the input and/or output results of the AI model is determined based on the first capability information.

Optionally, the processor 74 is configured to determine a third quantity according to the first capability information; the third quantity is associated with the quantity of second resources; and the second resources are reference signal resources configured by the network side device for beam scanning.

Embodiments of the present application also provide a readable storage medium. Programs or instructions are stored on the readable storage medium. When the program or instructions are executed by a processor, each process of the above receiving method embodiment is implemented and the same can be achieved. To avoid repetition, the technical effects will not be repeated here.

Wherein, the processor is the processor in the terminal described in the above embodiment. The readable storage medium includes computer readable storage media, such as computer read-only memory ROM, random access memory RAM, magnetic disk or optical disk, etc.

An embodiment of the present application further provides a chip. The chip includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement each of the above receiving method embodiments. The process can achieve the same technical effect. To avoid repetition, it will not be described again here.

It should be understood that the chips mentioned in the embodiments of this application can also be called system-level chips, system chips, and system-on-chips. Or system-on-a-chip, etc.

Embodiments of the present application further provide a computer program/program product. The computer program/program product is stored in a storage medium. The computer program/program product is executed by at least one processor to implement the above receiving method embodiment. Each process can achieve the same technical effect. To avoid duplication, it will not be described again here.

Embodiments of the present application also provide a receiving system, including: a UE and a network side device. The terminal can be used to perform the steps of the receiving method as described above. The network side device can be used to perform the steps of the receiving method as described above. step.

It should be noted that, in this document, the terms "comprising", "comprises" or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article or device that includes a series of elements not only includes those elements, It also includes other elements not expressly listed or inherent in the process, method, article or apparatus. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of additional identical elements in a process, method, article or apparatus that includes that element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, but may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved. Functions may be performed, for example, the methods described may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better. implementation. Based on this understanding, the technical solution of the present application can be embodied in the form of a computer software product that is essentially or contributes to the existing technology. The computer software product is stored in a storage medium (such as ROM/RAM, disk , CD), including several instructions to cause a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of this application.

The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above-mentioned specific implementations. The above-mentioned specific implementations are only illustrative and not restrictive. Those of ordinary skill in the art will Inspired by this application, many forms can be made without departing from the purpose of this application and the scope protected by the claims, all of which fall within the protection of this application.

Claims (26)

1. A receiving method, the method includes:

   The first device receives the target information sent by the second device;

   The target information includes any of the following: target configuration information, first capability information or second capability information;

   Wherein, the target configuration information is used to indicate a first quantity, and the first quantity is the number of transmission beams of the second device;

   The first device selects an artificial intelligence AI model corresponding to the target information based on the target information;

   The first capability information is used to indicate a second quantity, and the second quantity is the number of receiving beams of the second device;

   The second capability information is used to indicate the number of repetitions of the first resource, which is a reference signal resource associated with the input and/or output of the AI model.
2. The method according to claim 1, wherein the first device is the UE, and the second device is the network side device; the target information includes the target configuration information;

   The first device selects an artificial intelligence AI model corresponding to the target information based on the target information, including:

   The UE selects the AI model corresponding to the target configuration information according to the target configuration information.
3. The method according to any one of claims 1 or 2, wherein,

   The first number is greater than or equal to the first target number; the first target number is the number of beams associated with the input and/or output results of the AI model;

   or,

   The first quantity is greater than or equal to the second target quantity;

   The second target quantity is less than or equal to the first target quantity.
4. The method according to claim 1, wherein the first device is the network side device, and the second device is the UE; the target information includes the first capability information;

   The first device selects an artificial intelligence AI model corresponding to the target information based on the target information, including:

   The network side device selects an AI model corresponding to the first capability information based on the first capability information, and/or determines the input and/or output results associated with the AI model based on the first capability information. Number of beams.
5. The method according to claim 1 or 4, wherein,

   The second number is greater than or equal to the third target number; the third target number is the number of beams associated with the input and/or output results of the AI model;

   or,

   The second quantity is greater than or equal to the fourth target quantity;

   The fourth target quantity is less than or equal to the third target quantity.
6. The method of claim 4, further comprising:

   The network side device determines a third quantity based on the first capability information;

   The third number is associated with the number of second resources; the second resources are reference signal resources configured by the network side device for beam scanning.
7. The method of claim 6, wherein

   The repeated configuration status of the second resource is enabled.
8. The method of claim 6, wherein

   The third quantity is associated with the repetition quantity of the second resource;

   The number of repetitions of the second resource includes any of the following:

   The number of symbols occupied by the second resource;

   The number of repetitions of the second resource;

   The number of repetitions of the beam corresponding to the second resource.
9. The method of claim 1, wherein,

   The number of inputs and/or outputs of the AI model is associated with the second capability information.
10. The method of claim 9, wherein

    The number of inputs to the AI model is less than or equal to the number of repetitions of the first resource;

    The amount of target information in the input of the AI model is less than or equal to the number of repetitions of the first resource;

    The target information may be at least one of the following:

    Reference signal received power RSRP information;

    Beam information of the beam;

    Beam information of the transmission beam;

    Beam information of the receiving beam.
11. The method of claim 9, wherein

    The fourth number is less than or equal to the number of repetitions of the first resource;

    The fourth quantity is any of the following:

    The number corresponding to the output results of the AI model;

    The number of beams used to monitor the AI model.
12. A receiving device, the device includes: a receiving module and a selecting module;

    The receiving module is used to receive target information sent by the second device;

    The target information includes any of the following: target configuration information, first capability information or second capability information;

    Wherein, the target configuration information is used to indicate a first quantity, and the first quantity is the number of transmission beams of the second device;

    The selection module is configured to select an artificial intelligence AI model corresponding to the target information according to the target information received by the receiving module;

    The first capability information is used to indicate a second quantity, and the second quantity is the number of receiving beams of the second device;

    The second capability information is used to indicate the number of repetitions of the first resource, which is a reference signal resource associated with the input and/or output of the AI model.
13. The device according to claim 12, wherein the first device is the UE, and the second device is the network side device; the target information includes the target configuration information;

    The selection module is specifically configured to select the AI model corresponding to the target configuration information according to the target configuration information.
14. The device according to any one of claims 12 or 13, wherein,

    The first number is greater than or equal to the first target number; the first target number is the number of beams associated with the input and/or output results of the AI model;

    or,

    The first quantity is greater than or equal to the second target quantity;

    The second target quantity is less than or equal to the first target quantity.
15. The device according to claim 12, wherein the first device is the network side device, and the second device is the UE; the target information includes the first capability information;

    The selection module is specifically configured to select an AI model corresponding to the first capability information based on the first capability information, and/or determine the input and/or output of the AI model based on the first capability information. The number of beams associated with the result.
16. The device according to claim 11 or 15, wherein,

    The second quantity is greater than or equal to the third target quantity; the third target quantity is the input of the AI model and/or the number of beams associated with the output result;

    or,

    The second quantity is greater than or equal to the fourth target quantity;

    The fourth target quantity is less than or equal to the third target quantity.
17. The device according to claim 15, wherein the device further comprises: a determining module;

    The determination module is used to determine a third quantity according to the first capability information;

    The third number is associated with the number of second resources; the second resources are reference signal resources configured by the network side device for beam scanning.
18. The device of claim 17, wherein:

    The repeated configuration status of the second resource is enabled.
19. The device of claim 17, wherein:

    The third quantity is associated with the repetition quantity of the second resource;

    The number of repetitions of the second resource includes any of the following:

    The number of symbols occupied by the second resource;

    The number of repetitions of the second resource;

    The number of repetitions of the beam corresponding to the second resource.
20. The device of claim 12, wherein:

    The number of inputs and/or outputs of the AI model is associated with the second capability information.
21. The device of claim 20, wherein:

    The number of inputs to the AI model is less than or equal to the number of repetitions of the first resource;

    The amount of target information in the input of the AI model is less than or equal to the number of repetitions of the first resource;

    The target information may be at least one of the following:

    Reference signal received power RSRP information;

    Beam information of the beam;

    Beam information of the transmission beam;

    Beam information of the receiving beam.
22. The device of claim 20, wherein:

    The fourth number is less than or equal to the number of repetitions of the first resource;

    The fourth quantity is any of the following:

    The number corresponding to the output results of the AI model;

    The number of beams used to monitor the AI model.
23. A device, characterized in that it includes a processor and a memory, the memory stores a program or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the implementation of claims 1 to 11 is achieved. The steps of the receiving method described in any one of the above.
24. A readable storage medium, characterized in that the readable storage medium stores programs or instructions, and when the programs or instructions are executed by a processor, the steps of the receiving method according to any one of claims 1 to 11 are implemented. .
25. A computer program product, which is executed by at least one processor to implement the receiving method according to any one of claims 1 to 11.
26. A user equipment UE, including the UE being configured to perform the receiving method according to any one of claims 1 to 11.