WO2024138708A1 - Data collection method and apparatus, and device and storage medium - Google Patents

Abstract

The present disclosure relates to a data collection method and apparatus, and a device and a storage medium. The method comprises: receiving data collection instruction information; and measuring a reference signal on the basis of the data collection instruction information, so as to obtain sample data. By means of data collection instruction information, a terminal is instructed to collect corresponding sample data, such that when a beam prediction model is operated, the sample data, which is collected by the terminal, can be used for carrying out operations, thereby improving the accuracy of an output result from the beam prediction model.

Description

A data collection method, device, equipment and storage medium Technical Field

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

Background technique

In new radio (NR), especially when the communication frequency band is in frequency range 2, beam-based transmission and reception are required to ensure coverage due to the rapid attenuation of high-frequency channels.

In some beam management processes, the network device configures a reference signal resource set for beam measurement. The terminal measures the reference signal resources in the reference signal resource set. The terminal reports some of the stronger reference signal resource identifiers (IDs) and the corresponding layer 1 reference signal received power (L1-RSRP) and/or layer 1 signal to interference plus noise ratio (L1-SINR) to the network device.

In the current traditional method, the reference signal resource set configured by the network device includes X reference signals, and each reference signal corresponds to a different transmit beam of the network device. For at least one reference signal, the terminal needs to use all receive beams to measure the reference signal. Therefore, the number of beam pairs that the terminal needs to measure is M*N. Among them, M represents the number of transmit beams of the network device, and N is the number of receive beams of the terminal.

In some cases, in order to reduce the number of beam pairs measured by the terminal, an artificial intelligence (AI) model can be used for beam prediction. For example, the number of beam pairs that the terminal originally needs to measure is M*N, but due to the existence of the corresponding AI model, for spatial domain beam prediction, the terminal only needs to measure part of the M*N beam pairs. The beam quality of the measured part of the beam pairs is input into the AI model, and the beam information of the M*N beam pairs can be output. For time domain beam prediction, the terminal can measure the beam quality of the beam pairs at historical time, and predict the beam information of the beam pairs at future times through the AI model.

However, the AI models mentioned above need to be trained. The model training process requires a lot of data support. Obviously, there is no corresponding solution to effectively collect data for AI model training.

Summary of the invention

In order to overcome the problems existing in the related art, the present disclosure provides a data collection method, device, equipment and storage medium.

According to a first aspect of an embodiment of the present disclosure, a data collection method is provided, which is applied to a terminal and includes: receiving data collection indication information; measuring a reference signal based on the data collection indication information to obtain sample data.

According to a second aspect of an embodiment of the present disclosure, a data collection method is provided. The method is applied to a network device and includes: sending data collection indication information, where the data collection indication information is used to instruct a terminal to measure a reference signal to obtain sample data.

According to a third aspect of an embodiment of the present disclosure, a data collection device is provided, which is configured in a terminal and includes: a receiving module for receiving data collection indication information; and a processing module for measuring a reference signal based on the data collection indication information to obtain sample data.

According to a fourth aspect of an embodiment of the present disclosure, a data collection device is provided. The device is configured in a network device, and the device includes: a sending module, used to send data collection indication information, and the data collection indication information is used to instruct a terminal to measure a reference signal to obtain sample data.

According to a fifth aspect of an embodiment of the present disclosure, there is provided a data collection device, comprising: a processor; a memory for storing processor executable instructions; wherein the processor is configured to: execute the first aspect and any one of the methods in the first aspect.

According to a sixth aspect of an embodiment of the present disclosure, there is provided a data collection device, comprising: a processor; a memory for storing processor executable instructions; wherein the processor is configured to: execute the second aspect and any one of the methods in the second aspect.

According to the seventh aspect of an embodiment of the present disclosure, a non-temporary computer-readable storage medium is provided. When instructions in the storage medium are executed by a processor of a terminal, the terminal is enabled to execute the first aspect and any one of the methods in the first aspect.

According to an eighth aspect of an embodiment of the present disclosure, a non-temporary computer-readable storage medium is provided. When instructions in the storage medium are executed by a processor of a terminal, the terminal is enabled to execute the second aspect and any one of the methods in the second aspect.

The technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: instructing the terminal to collect corresponding sample data through data collection indication information, so that when the beam prediction model is operated, the sample data collected by the terminal can be used for operation, thereby improving the accuracy of the output result of the beam prediction model.

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

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

Fig. 1 is a schematic diagram of a wireless communication system according to an exemplary embodiment.

Fig. 2 is a flow chart of a data collection method according to an exemplary embodiment.

Fig. 3 is a flow chart of another data collection method according to an exemplary embodiment.

Fig. 4 is a flow chart of yet another data collection method according to an exemplary embodiment.

Fig. 5 is a flow chart of yet another data collection method according to an exemplary embodiment.

Fig. 6 is a flow chart of another data collection method according to an exemplary embodiment.

Fig. 7 is a flow chart of yet another data collection method according to an exemplary embodiment.

Fig. 8 is a schematic diagram of a data collection device according to an exemplary embodiment.

Fig. 9 is a schematic diagram of another data collection device according to an exemplary embodiment.

Fig. 10 is a schematic diagram of a data collection device according to an exemplary embodiment.

Fig. 11 is a schematic diagram of another data collection device according to an exemplary embodiment.

Detailed ways

Here, exemplary embodiments will be described in detail, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present disclosure.

The communication method involved in the present disclosure can be applied to the wireless communication system 100 shown in Figure 1. The network system may include a network device 110 and a terminal 120. It can be understood that the wireless communication system shown in Figure 1 is only for schematic illustration, and the wireless communication system may also include other network devices, for example, core network devices, wireless relay devices, and wireless backhaul devices, which are not shown in Figure 1. The embodiment of the present disclosure does not limit the number of network devices and the number of terminals included in the wireless communication system.

It can be further understood that the wireless communication system of the embodiment of the present disclosure is a network that provides wireless communication functions. The wireless communication system can adopt different communication technologies, such as Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency-Division Multiple Access (OFDMA), Single Carrier FDMA (SC-FDMA), and Carrier Sense Multiple Access with Collision Avoidance. According to the capacity, rate, delay and other factors of different networks, networks can be divided into 2G (English: Generation) networks, 3G networks, 4G networks or future evolution networks, such as the 5th Generation Wireless Communication System (5G) network. 5G networks can also be called New Radio (NR). For the convenience of description, the present disclosure sometimes refers to wireless communication networks as networks.

Furthermore, the network device 110 involved in the present disclosure may also be referred to as a wireless access network device. The wireless access network device may be: a base station, an evolved Node B (eNB), a home base station, an access point (AP) in a wireless fidelity (WIFI) system, a wireless relay node, a wireless backhaul node, a transmission point (TP) or a transmission and receiving point (TRP), etc. It may also be a gNB in an NR system, or it may also be a component or a part of a device constituting a base station, etc. When it is a vehicle-to-everything (V2X) communication system, the network device may also be a vehicle-mounted device. It should be understood that in the embodiments of the present disclosure, the specific technology and specific device form adopted by the network device are not limited.

Furthermore, the terminal 120 involved in the present disclosure may also be referred to as a terminal device, a user equipment (UE), a mobile station (MS), a mobile terminal (MT), etc., which is a device that provides voice and/or data connectivity to users. For example, the terminal may be a handheld device with a wireless connection function, a vehicle-mounted device, etc. At present, some examples of terminals are: a smart phone (Mobile Phone), a pocket computer (Pocket Personal Computer, PPC), a handheld computer, a personal digital assistant (Personal Digital Assistant, PDA), a laptop computer, a tablet computer, a wearable device, or a vehicle-mounted device, etc. In addition, when it is a vehicle-to-everything (V2X) communication system, the terminal device may also be a vehicle-mounted device. It should be understood that the embodiments of the present disclosure do not limit the specific technology and specific device form adopted by the terminal.

In the embodiments of the present disclosure, the network device 110 and the terminal 120 may use any feasible wireless communication technology to achieve mutual data transmission. Among them, the transmission channel corresponding to the data or control information sent by the network device 110 to the terminal 120 is called a downlink channel (downlink, DL), and the transmission channel corresponding to the data or control information sent by the terminal 120 to the network device 110 is called an uplink channel (uplink, UL). It can be understood that the network device involved in the embodiments of the present disclosure may be a base station. Of course, the network device may also be any other possible network device, and the terminal may be any possible terminal, which is not limited by the present disclosure.

In NR, especially when the communication frequency band is in frequency range 2, beam-based transmission and reception are required to ensure coverage due to the rapid attenuation of high-frequency channels.

In some beam management processes, the network device configures a reference signal resource set for beam measurement. The terminal measures the reference signal resources in the reference signal resource set. The terminal reports some of the stronger reference signal resource IDs and the corresponding L1-RSRP and/or L1-SINR to the network device.

In the current traditional method, the reference signal resource set configured by the network device includes X reference signals, and each reference signal corresponds to a different transmit beam of the network device. For at least one reference signal, the terminal needs to use all receive beams to measure the reference signal to obtain the beam measurement quality corresponding to all receive beams. In some cases, one or more best beam measurement qualities and/or the beam identifier corresponding to the best beam measurement quality can be determined. Therefore, the maximum number of beam pairs that the terminal needs to measure is M*N. Among them, M represents the number of transmit beams of the network device, and N is the number of receive beams of the terminal.

In some cases, in order to reduce the number of beam pairs measured by the terminal, an AI model can be used for beam prediction. For example, the number of beam pairs that the terminal originally needs to measure is M*N, but due to the existence of the corresponding AI model, for spatial domain beam prediction, the terminal only needs to measure part of the M*N beam pairs. For example, measure 1/8 of the beam pairs, 1/4 of the beam pairs, and so on in the M*N beam pairs. The beam quality of the measured partial beam pairs is input into the AI model, and the beam information or preferred beam information of the M*N beam pairs can be output. Among them, the preferred beam information may include at least one of the preferred transmit beam, the preferred receive beam and the preferred beam pair. The preferred beam pair is a beam pair consisting of a preferred transmit beam and a preferred receive beam. The preferred beam information may indicate a beam whose beam quality predicted by the AI model meets the conditions. For time domain beam prediction, the terminal can measure the beam quality of the beam pairs at historical moments, and predict the beam information of the beam pairs at future moments through the AI model.

In some AI model-based beam prediction scenarios, predictions for spatial beams may be included, such as predicting beam information of beams in set A based on beam information of beams in set B. Wherein set B may be a subset of set A, or set B is a wide beam and set A is a narrow beam. And/or, predictions for time-domain beams may be included, such as predicting beam information of set A in future moments based on beam information of set B in historical moments. Wherein, in the case of time-domain beam prediction, the relationship between set B and set A may be the same as set A, except that set B may be a subset of set A, or set B is a wide beam and set A is a narrow beam.

In the related art, the implementation process of beam prediction by the beam prediction model can refer to the following method.

When the beam prediction model is a spatial prediction, the terminal measures the L1-RSRP and/or L1-SINR of set B and inputs it into the AI model. The AI model can predict that the information of set A includes at least one of the following: the L1-RSRP and/or L1-SINR corresponding to the beam, and/or the L1-RSRP and/or L1-SINR corresponding to the beam pair; the preferred transmit beam ID, the preferred receive beam ID and/or the preferred beam pair ID in set A. The beam pair includes a transmit beam and a receive beam.

Among them, the relationship between set B and set A can include the following two types:

The first relationship is that set B is a subset of set A. For example, if set A contains 32 reference signals (each reference signal corresponds to a beam direction), then set B contains some of the reference signals, for example, set B contains 8 of the 32 reference signals.

The second relationship is that set B is a wide beam and set A is a narrow beam. For example, set A contains 32 reference signals (each reference signal corresponds to a beam direction, and the 32 reference signals cover a 120-degree direction). And set B contains another Y reference signals, for example, Y=8. And these Y reference signals cover the same 120-degree direction, that is, the beam direction of each reference signal in set B covers the beam directions of multiple reference signals in set A. It can be understood that the 32/Y reference signals in set A and the same reference signal in set B are in a quasi co-location (QCL) type (Type) D relationship.

When the beam prediction model is a time domain prediction, the terminal measures the L1-RSRP and/or L1-SINR of set B at the historical moment and inputs it into the AI model to predict at least one of the following items of set A at the future moment: the L1-RSRP and/or L1-SINR corresponding to the beam, and/or the L1-RSRP and/or L1-SINR corresponding to the beam pair; the preferred transmit beam ID, preferred receive beam ID and/or preferred beam pair ID in set A. Among them, the beam pair includes a transmit beam and a receive beam. In addition to the above two relationships between set B and set A, there is another relationship in which set B and set A are the same.

In the case of beam prediction based on the AI model, and the AI model is a time domain prediction. Since the beam information corresponding to the reference signal at the future moment can be predicted based on the AI model, the base station may not send the reference signal at the future moment. The terminal obtains the L1-RSRP and/or L1-SINR of the reference signal at the future moment based on the measurement results of the reference signal at the historical moment and the AI model, and can report it to the network device.

In some solutions that do not use AI models for beam prediction, the L1-RSRP and/or L1-SINR measured by the terminal corresponding to the reference signal at the future time also needs to be sent to the base station. That is, the terminal measures the L1-RSRP and/or L1-SINR at the future time to obtain beam information and reports the beam information to the network device.

It can be understood that the operation process of the AI model may include model training, model performance monitoring and model deduction. Among them, the training of the model can be to use some existing sample data as the input and output labels of the model to train a model for beam prediction, that is, a beam prediction model. The performance monitoring of the model may refer to the use of the model. Due to possible changes in the scene, changes in the terminal's mobile speed, different beam-related parameter configurations of the base station and the terminal, the performance of the model may not meet the business needs, so it is necessary to monitor the performance of the model to optimize/switch/update the model. The deduction of the model refers to providing input to the model and using the model to obtain the corresponding output. Among them, the training of the model requires a large amount of data for training. The performance monitoring of the model also requires a certain amount of data for performance monitoring, and the model deduction also requires some data. However, there is currently no corresponding solution for effectively collecting the data required for the operation of the AI model. It can be seen that how to collect the data needed for the operation of the AI model is a problem that needs to be solved.

Therefore, the present disclosure instructs the terminal to collect corresponding sample data through data collection indication information, so that when the beam prediction model is operated, the sample data collected by the terminal can be used to operate, thereby improving the accuracy of the output result of the beam prediction model.

FIG2 is a flow chart of a data collection method according to an exemplary embodiment. As shown in FIG2 , the method is applied to a terminal and may include the following steps:

In step S11, data collection instruction information is received.

In some embodiments, the terminal may receive data collection indication information sent by the network device.

It is understandable that the data collection indication information can be used to instruct the terminal to collect sample data, such as when to measure the reference signal, which reference signals to measure, how much data to collect for beam prediction model operation, etc. The present disclosure does not limit the content indicated by the data collection indication information.

In some embodiments, the reference signal may be a channel state information reference signal (CSI-RS), a demodulation reference signal (DMRS), a synchronization signal block (SSB), etc., which is not limited in the present disclosure.

In step S12, the reference signal is measured based on the data collection indication information to obtain sample data.

In some embodiments, the terminal may measure the reference signal based on the data collection indication information received in S11 to obtain corresponding sample data. The reference signal may be, for example, a reference signal in one or more reference signal sets.

For example, the data collection indication information received by the terminal indicates which reference signals the terminal measures. It is understandable that in actual application, the network device often sends multiple reference signals, so multiple reference signals can be included in a reference signal set. That is, a reference signal set can include one or more reference signals. The terminal can measure the reference signal indicated by the data collection indication information or the reference signal in the reference signal set to obtain corresponding sample data.

For another example, the reference signal measured by the terminal may also be based on protocol provisions or pre-configured. The data collection indication information may indicate when the terminal performs the measurement, such as the time when the measurement starts and/or the time when the measurement ends. The terminal measures the corresponding reference signal within the corresponding time based on the data collection indication information. Of course, the data collection indication information may also be used only to indicate that the terminal can measure the reference signal. And the terminal determines which reference signals and/or at what time to measure based on protocol provisions or pre-configured methods.

It is understandable that the sample data measured by the terminal may include one or more data used for beam prediction model operation. Of course, the sample data may also include more possible information according to actual conditions, such as measurement time, partial identification information, etc., which is not limited in the present disclosure.

The present disclosure instructs the terminal to collect corresponding sample data through data collection indication information, so that when the beam prediction model is operated, the sample data collected by the terminal can be used to operate, thereby improving the accuracy of the output result of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, the sample data may include input sample data. The input sample data is used as input when the beam prediction model operates. The reference signal may include a reference signal in the first reference signal set and/or the second reference signal set, the input of the beam prediction model is the beam information corresponding to the reference signal in the first reference signal set, and the output of the beam prediction model is the beam information or preferred beam information corresponding to the reference signal in the second reference signal set. The input sample data may include at least one of the following parameters: beam information of the first transmit beam corresponding to the reference signal in the first reference signal set, or beam information of the first beam pair corresponding to the reference signal in the first reference signal set, wherein the first beam pair includes a first transmit beam and a first receive beam; reference signal identifier of the reference signal in the first reference signal set; first receive beam identifier; first beam pair identifier.

In some embodiments, the sample data may include input sample data. The input sample data may be used as input data when performing beam prediction model operations. Among them, the reference signal measured by the terminal may include a reference signal in the first reference signal set and/or a reference signal in the second reference signal set. The input of the beam prediction model may be the beam information corresponding to the reference signal in the first reference signal set, the output of the beam prediction model may be the beam information corresponding to the reference signal in the second reference signal set, and the output of the beam prediction model may also be preferred beam information. It can be understood that the preferred beam information may include one or more of the beam information of the preferred transmit beam, the beam information of the preferred receive beam, and the beam information of the preferred beam pair. Among them, the preferred beam pair includes a preferred transmit beam and a preferred receive beam.

For example, the first reference signal set may be set B, and the second reference signal set may be set A. The input of the beam prediction model may be beam information corresponding to the reference signals in set B, such as L1-RSRP and/or L1-SINR corresponding to each reference signal in set B. The output of the beam prediction model may include beam information corresponding to the reference signals in set A, such as L1-RSRP and/or L1-SINR corresponding to each reference signal in set A.

Of course, in some cases, the output of the beam prediction model may also include preferred beam information, such as preferred K beam information. The preferred K beam information may be the first K beam information whose beam quality satisfies the condition, which may be recorded as the first (top) K beam information. Usually, the top K beam information may be the top K beam identifier. The identifier may be an identity (ID) or an index.

For the conditions that the beam quality in the top K beam information must meet, reference may be made to related technologies, such as the beam quality meeting a threshold or selecting the top K beams after sorting according to the beam quality, etc., and this disclosure does not make any limitations.

It can be understood that the input sample data can be the input data related to set B for beam prediction model operation.

In some embodiments, the input sample data may include beam information of a first transmit beam corresponding to a reference signal in a first reference signal set, or beam information of a first beam pair corresponding to a reference signal in a first reference signal set, wherein the first beam pair includes a first transmit beam and a first receive beam.

For example, the input sample data may include beam information of a transmit beam in set B, or beam information of a beam pair in set B. It can be understood that the first transmit beam may be a transmit beam in set B, and the beam pair consisting of a transmit beam and a receive beam in set B may be referred to as a first beam pair. That is, the first beam pair includes a transmit beam in set B and a receive beam used by the terminal for reference signal measurement. Of course, the first receive beam may include all receive beams used by the terminal for reference signal measurement, or may include part of all receive beams, which is not limited in the present disclosure.

It can be understood that the transmitted beam is the beam indicated by the reference signal.

The beam information may be L1-RSRP and/or L1-SINR of a beam or a beam pair.

In some embodiments, the input sample data may include a reference signal identifier of a reference signal in the first reference signal set, wherein the identifier may be an ID or an index.

For example, the input sample data may include the reference signal ID of a reference signal in set B.

In some embodiments, the input sample data may include a first receive beam identification.

For example, the input sample data may include a first receive (RX) beam ID. It can be understood that the first RX beam ID is used to indicate the first RX beam.

In some embodiments, the input sample data may include a first beam pair identification.

For example, the input sample data may include a first beam pair ID. The first beam pair ID may include a combined ID of a reference signal ID of a reference signal in set B and a first RX beam ID. That is, the first beam pair ID may indicate a corresponding first beam pair, and a first receive beam and a first transmit beam corresponding to the first beam pair. It is understood that the transmit beam of the first beam pair may be indicated by the reference signal ID of the reference signal in set B.

Of course, in other embodiments, the input sample data may include any two, three or four of the above items. For example, the input sample data may include: beam information of the first transmit beam corresponding to the reference signal in the first reference signal set, or beam information of the first beam pair corresponding to the reference signal in the first reference signal set, and reference signal identifiers of the reference signals in the first reference signal set; reference signal identifiers and first receive beam identifiers of the reference signals in the first reference signal set; beam information of the first transmit beam corresponding to the reference signal in the first reference signal set, or beam information of the first beam pair corresponding to the reference signal in the first reference signal set, and reference signal identifiers of the reference signals in the first reference signal set, and first beam pair identifiers; beam information of the first transmit beam corresponding to the reference signal in the first reference signal set, or beam information of the first beam pair corresponding to the reference signal in the first reference signal set, and reference signal identifiers of the reference signals in the first reference signal set, and first receive beam identifiers, and first beam pair identifiers. For the convenience of description, the present disclosure does not list them one by one here.

In some embodiments, when the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is the beam information corresponding to the second reference signal set, the terminal may collect the input sample data involved above. Wherein, the first reference signal set and the second reference signal set involved in the present disclosure are different, which is used to indicate that the reference signals in the first reference signal set and the reference signals in the second reference signal set are different, that is, there is no identical reference signal in the first reference signal set and the second reference signal set.

For example, set B is different from set A, and the output of the beam prediction model is the beam information corresponding to set A, such as the beam prediction model outputs L1-RSRP and/or L1-SINR of each beam or beam pair in set A.

It can be understood that in this embodiment, set B can be a wide beam and set A can be a narrow beam. Of course, in general, the beam prediction model can output the L1-RSRP and/or L1-SINR of all beams, some beams, all beam pairs or some beam pairs in set A.

In some embodiments, when the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is the beam information corresponding to the second reference signal set, the terminal can collect the input sample data involved above.

For example, set B is a subset of set A, or set B is equal to set A, and the output of the beam prediction model is the beam information corresponding to set A, such as the beam prediction model outputs the L1-RSRP and/or L1-SINR of all beams, part of the beams, all beam pairs or part of the beam pairs in set A.

In some embodiments, when the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is preferred beam information, the terminal may collect the input sample data involved above.

For example, set B is different from set A, and the output of the beam prediction model is preferred beam information, such as the beam prediction model outputs top K transmit beam information, top K receive beam information and/or top K beam pair information.

It can be understood that in this embodiment, set B can be a wide beam and set A can be a narrow beam. The preferred beam information can be a preferred transmission beam identifier, a preferred reception beam identifier and/or a preferred beam pair identifier. For example, top K transmission beam ID, top K reception beam ID and/or top K beam pair ID.

In some embodiments, when the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is preferred beam information, the terminal may collect the input sample data involved above.

For example, set B is a subset of set A, or set B is equal to set A, and the output of the beam prediction model is the preferred beam information, such as the beam prediction model outputs top K transmit beam information, top K receive beam information and/or top K beam pair information.

The sample data collected by the terminal disclosed in the present invention may include a variety of input sample data for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, the sample data may include sample label data. The sample label data is used as a label when the beam prediction model operates. In response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying the first condition, the sample label data includes at least one of the following parameters: beam information of the second transmit beam corresponding to the reference signal in the second reference signal set, or beam information of the second beam pair corresponding to the reference signal in the second reference signal set, wherein the second beam pair includes a second transmit beam and a receive beam; a reference signal identifier of the reference signal in the second reference signal set; a second receive beam identifier; and a second beam pair identifier.

In some embodiments, the sample data may further include sample label data. The sample label data may be used as a label when the beam prediction model is operated. For example, the sample label data may be used as a label output by the beam prediction model when the beam prediction model is trained or monitored.

It will be appreciated that the sample label data may be labels associated with set A for beam prediction model operations.

In some embodiments, in response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying the first condition, the sample label data may include beam information of a second transmit beam corresponding to a reference signal in the second reference signal set, or beam information of a second beam pair corresponding to a reference signal in the second reference signal set, wherein the second beam pair includes a second transmit beam and a second receive beam.

For example, the sample label data may include beam information of a transmit beam in set A, or beam information of a beam pair in set A. It can be understood that the second transmit beam may be a transmit beam in set A, and the beam pair consisting of the transmit beam and the receive beam in set A may be referred to as a second beam pair. That is, the second beam pair includes the transmit beam in set A and the receive beam used by the terminal for reference signal measurement. Of course, the second receive beam may generally include all receive beams used by the terminal for reference signal measurement. Of course, the second receive beam may also include part of all receive beams.

In some embodiments, in response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying a first condition, the sample label data may include a reference signal identifier of a reference signal in the second reference signal set.

For example, the sample label data may include the reference signal ID of the reference signal in set A.

In some embodiments, in response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying a first condition, the sample label data may include a second receive beam identifier.

For example, the sample tag data may include a second RX beam ID. It is understood that the second RX beam ID is used to indicate the second RX beam.

It can be understood that the first RX beam and the second RX beam can be completely or partially the same, or can be completely different, and the present disclosure is not limited to this.

In some embodiments, in response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying a first condition, the sample label data may include a second beam pair identifier.

For example, the sample tag data may include a second beam pair ID. The second beam pair ID may include a combined ID of a reference signal ID of a reference signal in set A and a second RX beam ID. That is, the second beam pair ID may indicate a corresponding second beam pair, and a second receive beam and a second transmit beam corresponding to the second beam pair. It is understood that the transmit beam of the second beam pair may be indicated by the reference signal ID of the reference signal in set A.

Of course, in other embodiments, in response to the first reference signal set, the second reference signal set and the output of the beam prediction model satisfying the first condition, the sample label data may include any two, three or four of the above items. For example, the sample label data may include: beam information of the second transmit beam corresponding to the reference signal in the second reference signal set, or beam information of the second beam pair corresponding to the reference signal in the second reference signal set, and reference signal identifier of the reference signal in the second reference signal set; reference signal identifier and second receive beam identifier of the reference signal in the second reference signal set; beam information of the second transmit beam corresponding to the reference signal in the second reference signal set, or beam information of the second beam pair corresponding to the reference signal in the second reference signal set, and reference signal identifier of the reference signal in the second reference signal set, and second beam pair identifier; beam information of the second transmit beam corresponding to the reference signal in the second reference signal set, or beam information of the second beam pair corresponding to the reference signal in the second reference signal set, and reference signal identifier of the reference signal in the second reference signal set, and second receive beam identifier, and second beam pair identifier. For the convenience of description, the present disclosure will not list them one by one here.

The sample data collected by the terminal of the present invention may include a variety of tags for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, the first condition includes at least one of the following: the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is the beam information corresponding to the second reference signal set; the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is the beam information corresponding to the second reference signal set.

In some embodiments, the first condition may be that the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is beam information corresponding to the second reference signal set.

For example, the first condition is that set B and set A are different, and the output of the beam prediction model is the beam information corresponding to set A, such as the beam prediction model outputs the L1-RSRP and/or L1-SINR of all beams, part of the beams, all beam pairs or part of the beam pairs in set A.

It will be appreciated that in this embodiment, set B may be a wide beam and set A may be a narrow beam.

In some embodiments, the first condition may be that the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is the beam information corresponding to the second reference signal set.

For example, the first condition is that set B is a subset of set A, or set B is equal to set A, and the output of the beam prediction model is the beam information corresponding to set A, such as the beam prediction model outputs the L1-RSRP and/or L1-SINR of all beams, part of the beams, all beam pairs or part of the beam pairs in set A.

In some embodiments, when the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is the beam information corresponding to the second reference signal set, the input sample data may be included in the sample label data.

It can be understood that since set B is a subset of set A, or set B is equal to set A. Therefore, the input data for beam prediction model operation related to set B can be part of the data or all of the data in the label for beam prediction model operation related to set A. That is, the input sample data can be part of the data in the sample label data, or the input sample data can be exactly the same as the sample label data.

For example, when set B is a subset of set A, the input sample data can be part of the sample label data. For another example, when set B is equal to set A, the input sample data can be exactly the same as the sample label data.

In some embodiments, in the case where set B is a subset of set A, the network device configuration may select which part of the beams or beam pairs in set A are used as set B. Of course, if the network device is not configured accordingly, the terminal may randomly select part of the beams or beam pairs from set A as set B when measuring set B. Alternatively, the terminal may select part of the beams or beam pairs from set A as set B based on a protocol specification or a pre-set selection method, which is not specifically limited in the present disclosure.

In some embodiments, for the case where the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is the beam information corresponding to the second reference signal set. If the sample data includes input sample data and sample label data, the sample label data may not include data that is the same as the input sample data. For example, the sample label data is the remaining data of set A minus set B. Of course, in other examples, the sample data may only include sample label data, then the sample label data at this time is the data corresponding to set A, and the input sample data can be determined from the sample label data.

It should be understood that the first condition in the present disclosure may also include: the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is the beam information corresponding to the second reference signal set; and, the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is the beam information corresponding to the second reference signal set.

The present disclosure is adapted to various reference signal sets and beam prediction models. In such cases, the terminal collects sample data including various tags for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate the beam prediction model, thereby improving the accuracy of the output result of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, the sample data includes sample label data. The sample label data is used as a label when the beam prediction model operates. In response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying the second condition, the sample label data includes: a preferred transmit beam identifier or a third beam pair identifier in the second reference signal set, wherein the third beam pair includes a preferred transmit beam and a preferred receive beam.

In some embodiments, the sample data may further include sample label data. The sample label data may be used as a label when the beam prediction model is operated. For example, the sample label data may be used as a label output by the beam prediction model when the beam prediction model is trained or monitored.

In some embodiments, in response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying the second condition, the sample label data may include a preferred transmit beam identifier or a third beam pair identifier in the second reference signal set, wherein the third beam pair includes a preferred transmit beam and a preferred receive beam.

For example, the sample label data may include top K transmit beam identifiers and/or top K beam pair identifiers in set A. The top K beam pairs may include top K transmit beams and top K preferred receive beams.

It can be understood that in this embodiment, set B can be a wide beam, and set A can be a narrow beam. The top K transmit beam identifier can be a top K transmit beam ID, and the top K beam pair identifier can be a top K beam pair ID.

For example, the preferred transmission beam included in the third beam pair may be the same as the preferred transmission beam in the second reference signal set.

For another example, the preferred transmission beam included in the third beam pair may not be completely the same as or different from the preferred transmission beam in the second reference signal set. It can be understood that the second reference signal set is set A, and the preferred transmission beam in the second reference signal set is the preferred transmission beam in set A output by the beam prediction model; the third beam pair is the preferred beam pair in set A output by the beam prediction model. Since the beam pair is composed of a receiving beam and a transmitting beam. It can be imagined that some transmitting beams may not meet the conditions corresponding to the preferred transmission beam, but if they form a beam pair with some receiving beams, they may meet the conditions corresponding to the preferred beam pair. And some transmitting beams meet the conditions corresponding to the preferred transmission beam, but if they form a beam pair with some receiving beams, they may not meet the conditions corresponding to the preferred beam pair. Therefore, the preferred transmission beam included in the preferred beam pair in set A may be partially the same as or completely different from the preferred transmission beam in set A.

The sample data collected by the terminal of the present invention may include a variety of tags for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, the second condition includes at least one of the following: the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is preferred beam information; the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is preferred beam information.

In some embodiments, the second condition may be that the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is preferred beam information.

For example, the second condition is that set B and set A are different, and the output of the beam prediction model is preferred beam information, such as the beam prediction model outputs top K transmit beam information, top K receive beam information and/or top K beam pair information.

It can be understood that in this embodiment, set B can be a wide beam and set A can be a narrow beam. The preferred beam information can be a preferred transmission beam identifier, a preferred reception beam identifier and/or a preferred beam pair identifier. For example, top K transmission beam ID, top K reception beam ID and/or top K beam pair ID.

Among them, the preferred beam information output by the beam prediction model may be one or more of the preferred transmit beam in the second reference signal set, the preferred receive beam in the second reference signal set, and the preferred beam pair in the second reference signal set output by the beam prediction model. The preferred beam pair in the second reference signal set includes a preferred transmit beam and a preferred receive beam. It can be understood that the preferred transmit beam included in the preferred beam pair may be the same as, partially the same as, or completely different from the preferred transmit beam in the second reference signal set. The preferred receive beam included in the preferred beam pair may be the same as, partially the same as, or completely different from the preferred receive beam in the second reference signal set.

Since a beam pair is composed of a receiving beam and a transmitting beam. For example, some transmitting beams may not meet the conditions corresponding to the preferred transmitting beams, but when they form a beam pair with some receiving beams, they may meet the conditions corresponding to the preferred beam pair. Some transmitting beams meet the conditions corresponding to the preferred transmitting beams, but when they form a beam pair with some receiving beams, they may not meet the conditions corresponding to the preferred beam pair. For another example, some receiving beams may not meet the conditions corresponding to the preferred receiving beams, but when they form a beam pair with some transmitting beams, they may meet the conditions corresponding to the preferred beam pair. Some receiving beams meet the conditions corresponding to the preferred receiving beams, but when they form a beam pair with some transmitting beams, they may not meet the conditions corresponding to the preferred beam pair.

Therefore, the preferred receiving beam included in the preferred beam pair may be the same as, partially the same as, or completely different from the preferred receiving beam in the second reference signal set.

In some embodiments, the second condition may be that the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is preferred beam information.

For example, the second condition is that set B is a subset of set A, or set B is equal to set A, and the output of the beam prediction model is preferred beam information, such as the beam prediction model outputs top K transmitting beam information, top K receiving beam information and/or top K beam pair information.

It can be understood that the preferred beam information can be a preferred transmission beam identifier, a preferred reception beam identifier and/or a preferred beam pair identifier, such as top K transmission beam IDs, top K reception beam IDs and/or top K beam pair IDs.

It should be understood that the third condition in the present disclosure may also include: the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is preferred beam information; and, the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is preferred beam information.

The present disclosure is adapted to various reference signal sets and beam prediction models. In such cases, the terminal collects sample data including various tags for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate the beam prediction model, thereby improving the accuracy of the output result of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, the sample data includes at least one of the following parameters: the number of sample data; the scene and/or sample parameters corresponding to the sample data; the beam prediction model identifier and/or the function of the beam prediction model; and the timestamp corresponding to the sample data.

In some embodiments, the sample data may include the quantity of the sample data.

It can be understood that the number of sample data is used to identify how many sample data the terminal reports to the network device.

In some embodiments, the sample data may include scenes and/or sample parameters corresponding to the sample data.

For example, the scene corresponding to the sample data may refer to the scene in which the terminal that measured the sample data is located, such as an urban macro cell (Uma) scene, an urban micro cell (Umi) scene, an indoor scene, etc.

For another example, the sample parameters corresponding to the sample data may be parameters such as the number of terminal Rx beams, beam width, beam angle, moving speed of the terminal, antenna configuration of the terminal, number of antenna panels, etc.

Of course, the scenarios may include more possible scenarios according to actual conditions, and the sample parameters may include more possible parameters according to actual conditions, which are not limited in the present disclosure.

In some embodiments, the sample data may include a beam prediction model identification and/or a function of a beam prediction model.

Among them, the beam prediction model identifier can be used to uniquely indicate which AI model is operated. The function of the beam prediction model can indicate what the AI model is used for. For example, it indicates that the AI model is used for beam prediction, for position prediction, for channel state information (CSI) compression, for terminal mobility management, etc.

In some embodiments, the sample data may include a timestamp corresponding to the sample data.

For example, the timestamp corresponding to the sample data may be indicated explicitly or implicitly. Explicit indication means indicating the timestamp corresponding to each sample data. Implicit indication means that the length of the time period corresponding to each sample data is the same, and each sample data is recorded, stored, transmitted, etc. in chronological order. For example, each sample data is arranged in chronological order.

It should be understood that the sample data in the present disclosure may also include any two, three or four of the above-mentioned items. For example, the sample data may also include: the number of sample data, the timestamp corresponding to the sample data; the scene and/or sample parameters corresponding to the sample data, and the timestamp corresponding to the sample data; the number of sample data, the scene and/or sample parameters corresponding to the sample data, and the timestamp corresponding to the sample data; the number of sample data, the scene and/or sample parameters corresponding to the sample data, and the timestamp corresponding to the sample data; the number of sample data, the scene and/or sample parameters corresponding to the sample data, and the beam prediction model identifier and/or the function of the beam prediction model, and the timestamp corresponding to the sample data. For the convenience of description, the present disclosure does not list them one by one here.

In some embodiments, the sample data may include input sample data and/or sample label data, and may also include one or more of the number of sample data, the scene and/or sample parameters corresponding to the sample data, the beam prediction model identifier and/or the function of the beam prediction model, and the timestamp corresponding to the sample data. This disclosure is not limited thereto.

The present disclosure provides a variety of information included in the sample data, so that the network device can use the sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output result of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, the sample data is used to operate the beam prediction model. The data collection indication information includes at least measurement configuration information. The measurement configuration information may include at least one of the following information: at least one reference signal set; data collection start time; data collection end time.

In some embodiments, the sample data obtained by the terminal for measuring the reference signal can be used to perform corresponding operations on the beam prediction model. Among them, the beam prediction model can be an AI model for beam prediction. The data collection indication information received by the terminal shall include at least measurement configuration information. Among them, the measurement configuration information may include parameters instructing the terminal to perform measurements. The terminal can measure the reference signal based on the measurement configuration information to obtain sample data.

For example, the measurement configuration information includes some parameters for instructing the terminal to perform measurements, such as when to perform measurements, which reference signals the terminal measures, etc. The terminal can perform corresponding measurements on the reference signals based on these parameters and obtain corresponding sample data.

In some embodiments, the measurement configuration information may include at least one reference signal set.

For example, the network device informs the terminal which reference signal sets are configured through measurement configuration information. When multiple reference signal sets are configured, the relationship between the reference signal sets also needs to be indicated. For example, two reference signal sets are configured, namely set B and set A. The measurement configuration information also needs to indicate the beam relationship between set B and set A. That is, it indicates that set B is a subset of set A, or set B is the same as set A, or set B and set A are different. If set B is a subset of set A, it can indicate which reference signals in set A the reference signals contained in set B are. If set B and set A are different, it can also indicate which reference signals in set A the beam corresponding to a reference signal of set B can cover.

In some embodiments, the measurement configuration information may include a data collection start time, wherein the data collection start time indicates a time when the network device sends a reference signal.

For example, the data collection start time may be an implicit indication, such as a time indication of sending a reference signal through a network device. In another example, the data collection start time may be an explicit indication, that is, directly indicating the start time of data collection by the terminal.

In some embodiments, the measurement configuration information may include a data collection end time, wherein the data collection end time indicates the time when the terminal ends the measurement.

For example, the data collection end time may be an implicit indication, such as an indication of the time when the network device stops sending the reference signal. For another example, the data collection end time may be an explicit indication, that is, directly indicating the end time of data collection for the terminal.

It should be understood that the measurement configuration information in the present disclosure may also include any two or three of the above-mentioned items, and the measurement configuration information may also include: at least one reference signal set, data collection start time; at least one reference signal set, data collection end time; data collection start time, data collection end time; at least one reference signal set, data collection start time, data collection end time.

The present disclosure provides a variety of information included in the measurement configuration information, which facilitates the terminal to collect corresponding sample data. When the beam prediction model is operated, the sample data of the terminal can be used to operate, thereby improving the accuracy of the output result of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, FIG3 is a flow chart of another data collection method according to an exemplary embodiment. As shown in FIG3, the beam prediction model operates on the terminal. The method may also include the following steps:

In step S21, a data collection request is sent.

In some embodiments, the beam prediction model can be operated on the terminal. In this scenario, the terminal can send a data collection request to the network device. The data collection request is used to instruct the network device to configure data collection indication information. That is, the data collection request is used to instruct the network device to configure measurement configuration information.

For example, the terminal may send a data collection request to the network device, and the terminal receives measurement configuration information sent by the network device. The measurement configuration information is configured by the network device based on the data collection request and sent to the terminal.

The present disclosure can be applicable to scenarios where a beam prediction model is operated in a terminal. The measurement configuration information is used to instruct the terminal to collect corresponding sample data so that the terminal can use the sample data of the terminal to operate when the beam prediction model is operated, thereby improving the accuracy of the output results of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, the data collection request may include at least one of the following parameters: the number of sample data; and reference signal set configuration information.

In some embodiments, the data collection request may include the quantity of sample data.

It can be understood that the number of sample data is used to inform the network device of the number of sample data used for beam prediction model operation.

In some embodiments, the data collection request may include reference signal set configuration information.

It can be understood that the reference signal set configuration information informs the network device whether the terminal uses one reference signal set or two reference signal sets to collect sample data.

For example, a reference signal set may correspond to a scenario where set B is the same as set A, or set B is a subset of set A. Two reference signal sets may correspond to a scenario where set B is a wide beam and set A is a narrow beam, or set B is a subset of set A. In other words, the reference signal set configuration information may indicate the relationship between set B and set A.

The present disclosure provides a variety of information included in a data collection request, which is convenient for a network device to configure and send a corresponding reference signal in a scenario where a beam prediction model is operated in a terminal. The terminal can collect corresponding sample data so that the terminal can use the sample data of the terminal to operate when the beam prediction model is operated, thereby improving the accuracy of the output result of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, the data collection indication information also includes report configuration information. The report configuration information is used to indicate at least one of the following reporting methods: periodic reporting; non-periodic reporting; reporting based on radio resource control (RRC) signaling or uplink control information (UCI).

In some embodiments, the data collection instruction information received by the terminal may further include report configuration information. The report configuration information may include parameters instructing the terminal to report the sample data to the network device, such as a reporting method instructing the terminal to report the sample data. The terminal may report the sample data based on the report configuration information.

In some embodiments, the reporting configuration information is used to indicate that the measurement result is reported in a periodic reporting manner.

In some embodiments, the reporting configuration information is used to indicate that the measurement result is reported in an aperiodic reporting manner.

In some embodiments, the reporting configuration information is used to indicate that the measurement result is reported based on RRC signaling or UCI reporting.

It should be understood that the measurement configuration information in the present disclosure may also include any two or three of the above-mentioned items, for example, the report configuration information is used to indicate: periodic reporting, non-periodic reporting; periodic reporting, and reporting based on RRC signaling or UCI; non-periodic reporting, and reporting based on RRC signaling or UCI; periodic reporting, and non-periodic reporting, and reporting based on RRC signaling or UCI.

The present disclosure provides a variety of reporting methods that can be applied to different reporting scenarios. When operating the beam prediction model, the network device can use the sample data reported by the terminal to perform operations, thereby improving the accuracy of the output results of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, FIG4 is a flow chart of another data collection method according to an exemplary embodiment. As shown in FIG4, the beam prediction model operates on the network device. In S12, the reference signal is measured based on the data collection indication information to obtain sample data, which may include the following steps:

In step S31, the reference signal is measured based on the measurement configuration information to obtain sample data.

In some embodiments, the beam prediction model may be operated on a network device. In this scenario, the terminal may perform corresponding measurements on the reference signal based on the measurement configuration information and obtain corresponding sample data.

The method may further comprise the following steps:

In step S32, sample data is sent based on the report configuration information.

In some embodiments, the terminal may send the sample data obtained in S31 to the network device based on the report configuration information.

For example, the report configuration information may include parameters instructing the terminal to report the sample data to the network device. For example, instructing the terminal to report the sample data in a manner. The terminal may send the sample data to the network device based on the reporting manner indicated by the report configuration information, so that the network device operates the beam prediction model using the sample data sent by the terminal.

The present disclosure can be applicable to the scenario where the beam prediction model is operated in the network device. By measuring the configuration information and reporting the configuration information, the terminal is instructed to collect the corresponding sample data and report it to the network device, so that the network device can use the sample data reported by the terminal to operate when the beam prediction model is operated, thereby improving the accuracy of the output result of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, in response to the report configuration information being used to indicate periodic reporting, sending the sample data in S32 includes: sending one or more sample data.

In some embodiments, in response to the report configuration information indicating periodic reporting, one or more sample data may be sent. That is, the sample data sent by the terminal to the network device may be one or more sample data. In other words, the terminal periodically reports one or more sample data.

It can be understood that the amount of sample data in the sample data reported by the terminal each time mainly depends on the amount of sample data available to the terminal in the corresponding reporting period.

The present disclosure is applicable to the situation where measurement results are reported periodically, so that the network equipment can use the sample data reported by the terminal to operate the beam prediction model when operating, thereby improving the accuracy of the output results of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, in response to the report configuration information being used to indicate non-periodic reporting, the sample data at least includes the quantity of the sample data.

In some embodiments, in response to the reporting configuration information being used to indicate aperiodic reporting, the sample data should at least include the quantity of the sample data.

It is understandable that since the terminal reports sample data non-periodically, the number of sample data in each reported sample data may be different. Therefore, each time the terminal reports sample data, it should include information indicating the number of sample data, that is, it needs to inform how much sample data is reported.

The present disclosure is applicable to the case of non-periodic reporting of measurement results, so that the network equipment can use the sample data reported by the terminal to operate the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, in response to the report configuration information used to indicate reporting based on RRC signaling or UCI, sending the sample data in S32 includes: sending the sample data through a physical uplink shared channel (physical uplink shared channel, PUSCH) or a physical uplink control channel (physical uplink control channel, PUCCH).

In some embodiments, in response to the reporting configuration information indicating reporting based on RRC signaling or UCI, the terminal may send sample data to the network device via PUSCH or PUCCH, wherein the sample data is carried on the RRC signaling or UCI.

The present disclosure is applicable to situations where measurement results are reported based on RRC signaling or UCI, so that network equipment can use sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, operating the beam prediction model includes at least one of the following: training the beam prediction model; monitoring the beam prediction model; and performing deduction based on the beam prediction model.

In some embodiments, the operating beam prediction model may be a training beam prediction model.

In some embodiments, the operating beam prediction model may be a monitoring beam prediction model.

In some embodiments, the operating beam prediction model may be derived based on the beam prediction model.

It should be understood that the operating beam prediction model in the present disclosure may also include: training the beam prediction model and monitoring the beam prediction model; training the beam prediction model and performing deductions based on the beam prediction model; monitoring the beam prediction model and performing deductions based on the beam prediction model; training the beam prediction model and monitoring the beam prediction model and performing deductions based on the beam prediction model.

The present disclosure provides a variety of scenarios for operating beam prediction models, so that in different scenarios, sample data of the terminal can be used to operate the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

Based on the same concept, the present disclosure also provides a data collection method executed on a network device.

FIG. 5 is a flow chart of another data collection method according to an exemplary embodiment. As shown in FIG. 5 , the method is applied to a network device and may include the following steps:

In step S41, data collection instruction information is sent.

In some embodiments, the network device may send data collection indication information to the terminal. It is understood that the data collection indication information may be used to instruct the terminal to collect sample data, such as when to measure the reference signal, which reference signals to measure, how much data to collect for beam prediction model operation, etc. The present disclosure does not limit the content indicated by the data collection indication information, so that the terminal measures the reference signal and obtains the sample data.

In some embodiments, the reference signal may be CSI-RS, DMRS, SSB, etc., which is not limited in the present disclosure.

In some embodiments, the network device may send a reference signal to the terminal, wherein the reference signal may be, for example, a reference signal in one or more reference signal sets.

The present disclosure instructs the terminal to collect corresponding sample data through data collection indication information, so that when the beam prediction model is operated, the sample data collected by the terminal can be used to operate, thereby improving the accuracy of the output result of the beam prediction model.

The present disclosure can be applicable to the scenario where the beam prediction model is operated in the network device. By measuring the configuration information and reporting the configuration information, the terminal is instructed to collect the corresponding sample data and report it to the network device, so that the network device can use the sample data reported by the terminal to operate when the beam prediction model is operated, thereby improving the accuracy of the output result of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, the sample data may include input sample data. The input sample data is used as input when the beam prediction model operates. The reference signal may include a reference signal in the first reference signal set and/or the second reference signal set, the input of the beam prediction model is the beam information corresponding to the reference signal in the first reference signal set, and the output of the beam prediction model is the beam information or preferred beam information corresponding to the reference signal in the second reference signal set. The input sample data may include at least one of the following parameters: beam information of the first transmit beam corresponding to the reference signal in the first reference signal set, or beam information of the first beam pair corresponding to the reference signal in the first reference signal set, wherein the first beam pair includes a first transmit beam and a first receive beam; a reference signal identifier of the reference signal in the first reference signal set; a first receive beam identifier; and a first beam pair identifier.

In some embodiments, the sample data may include input sample data. The input sample data may be used as input data when performing beam prediction model operations. Among them, the reference signal measured by the terminal may include a reference signal in the first reference signal set and/or a reference signal in the second reference signal set. The input of the beam prediction model may be the beam information corresponding to the reference signal in the first reference signal set, the output of the beam prediction model may be the beam information corresponding to the reference signal in the second reference signal set, and the output of the beam prediction model may also be preferred beam information. It can be understood that the preferred beam information may include one or more of the beam information of the preferred transmit beam, the beam information of the preferred receive beam, and the beam information of the preferred beam pair. Among them, the preferred beam pair includes a preferred transmit beam and a preferred receive beam.

For example, the first reference signal set may be set B, and the second reference signal set may be set A. The input of the beam prediction model may be beam information corresponding to the reference signals in set B, such as L1-RSRP and/or L1-SINR corresponding to each reference signal in set B. The output of the beam prediction model may include beam information corresponding to the reference signals in set A, such as L1-RSRP and/or L1-SINR corresponding to each reference signal in set A.

Of course, in some cases, the output of the beam prediction model may also include preferred beam information, such as preferred K beam information. The preferred K beam information may be the first K beam information whose beam quality meets the conditions, which may be recorded as top K beam information. Usually, the top K beam information may be the top K beam identifier. The identifier may be an ID or an index.

For the conditions that the beam quality in the top K beam information must meet, reference may be made to related technologies, such as the beam quality meeting a threshold or selecting the top K beams after sorting according to the beam quality, etc., and this disclosure does not make any limitations.

It can be understood that the input sample data can be the input data related to set B for beam prediction model operation.

In some embodiments, the input sample data may include beam information of a first transmit beam corresponding to a reference signal in a first reference signal set, or beam information of a first beam pair corresponding to a reference signal in a first reference signal set, wherein the first beam pair includes a first transmit beam and a first receive beam.

For example, the input sample data may include beam information of a transmit beam in set B, or beam information of a beam pair in set B. It can be understood that the first transmit beam may be a transmit beam in set B, and the beam pair consisting of a transmit beam and a receive beam in set B may be referred to as a first beam pair. That is, the first beam pair includes a transmit beam in set B and a receive beam used by the terminal for reference signal measurement. Of course, the first receive beam may include all receive beams used by the terminal for reference signal measurement, or may include part of all receive beams, which is not limited in the present disclosure.

It can be understood that the transmitted beam is the beam indicated by the reference signal.

The beam information may be L1-RSRP and/or L1-SINR of a beam or a beam pair.

In some embodiments, the input sample data may include a reference signal identifier of a reference signal in the first reference signal set, wherein the identifier may be an ID or an index.

For example, the input sample data may include the reference signal ID of a reference signal in set B.

In some embodiments, the input sample data may include a first receive beam identification.

For example, the input sample data may include a first RX beam ID. It will be appreciated that the first RX beam ID is used to indicate the first RX beam.

In some embodiments, the input sample data may include a first beam pair identification.

For example, the input sample data may include a first beam pair ID. The first beam pair ID may include a combined ID of a reference signal ID of a reference signal in set B and a first RX beam ID. That is, the first beam pair ID may indicate a corresponding first beam pair, and a first receive beam and a first transmit beam corresponding to the first beam pair. It is understood that the transmit beam of the first beam pair may be indicated by the reference signal ID of the reference signal in set B.

Of course, in other embodiments, the input sample data may include any two, three or four of the above items. For example, the input sample data may include: beam information of the first transmit beam corresponding to the reference signal in the first reference signal set, or beam information of the first beam pair corresponding to the reference signal in the first reference signal set, and reference signal identifiers of the reference signals in the first reference signal set; reference signal identifiers and first receive beam identifiers of the reference signals in the first reference signal set; beam information of the first transmit beam corresponding to the reference signal in the first reference signal set, or beam information of the first beam pair corresponding to the reference signal in the first reference signal set, and reference signal identifiers of the reference signals in the first reference signal set, and first beam pair identifiers; beam information of the first transmit beam corresponding to the reference signal in the first reference signal set, or beam information of the first beam pair corresponding to the reference signal in the first reference signal set, and reference signal identifiers of the reference signals in the first reference signal set, and first receive beam identifiers, and first beam pair identifiers. For the convenience of description, the present disclosure does not list them one by one here.

In some embodiments, when the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is the beam information corresponding to the second reference signal set, the terminal may collect the input sample data involved above. Wherein, the first reference signal set and the second reference signal set involved in the present disclosure are different, which is used to indicate that the reference signals in the first reference signal set and the reference signals in the second reference signal set are different, that is, there is no identical reference signal in the first reference signal set and the second reference signal set.

For example, set B is different from set A, and the output of the beam prediction model is the beam information corresponding to set A, such as the beam prediction model outputs L1-RSRP and/or L1-SINR of each beam or beam pair in set A.

It can be understood that in this embodiment, set B can be a wide beam and set A can be a narrow beam. Of course, in general, the beam prediction model outputs the L1-RSRP and/or L1-SINR of all beams, some beams, all beam pairs, or some beam pairs in set A.

In some embodiments, when the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is the beam information corresponding to the second reference signal set, the terminal can collect the input sample data involved above.

For example, set B is a subset of set A, or set B is equal to set A, and the output of the beam prediction model is the beam information corresponding to set A, such as the beam prediction model outputs the L1-RSRP and/or L1-SINR of all beams, part of the beams, all beam pairs or part of the beam pairs in set A.

In some embodiments, when the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is preferred beam information, the terminal may collect the input sample data involved above.

For example, set B is different from set A, and the output of the beam prediction model is preferred beam information, such as the beam prediction model outputs top K transmit beam information, top K receive beam information and/or top K beam pair information.

It can be understood that in this embodiment, set B can be a wide beam and set A can be a narrow beam. The preferred beam information can be a preferred transmission beam identifier, a preferred reception beam identifier and/or a preferred beam pair identifier. For example, top K transmission beam ID, top K reception beam ID and/or top K beam pair ID.

In some embodiments, when the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is preferred beam information, the terminal may collect the input sample data involved above.

For example, set B is a subset of set A, or set B is equal to set A, and the output of the beam prediction model is the preferred beam information, such as the beam prediction model outputs top K transmit beam information, top K receive beam information and/or top K beam pair information.

The sample data collected by the terminal disclosed in the present invention may include a variety of input sample data for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, the sample data may include sample label data. The sample label data is used as a label when the beam prediction model operates. In response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying the first condition, the sample label data includes at least one of the following parameters: beam information of the second transmit beam corresponding to the reference signal in the second reference signal set, or beam information of the second beam pair corresponding to the reference signal in the second reference signal set, wherein the second beam pair includes a second transmit beam and a receive beam; a reference signal identifier of the reference signal in the second reference signal set; a second receive beam identifier; and a second beam pair identifier.

In some embodiments, the sample data may further include sample label data. The sample label data may be used as a label when the beam prediction model is operated. For example, the sample label data may be used as a label output by the beam prediction model when the beam prediction model is trained or monitored.

It will be appreciated that the sample label data may be labels associated with set A for beam prediction model operations.

In some embodiments, in response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying the first condition, the sample label data may include beam information of a second transmit beam corresponding to a reference signal in the second reference signal set, or beam information of a second beam pair corresponding to a reference signal in the second reference signal set, wherein the second beam pair includes a second transmit beam and a second receive beam.

For example, the sample label data may include beam information of a transmit beam in set A, or beam information of a beam pair in set A. It can be understood that the second transmit beam may be a transmit beam in set A, and the beam pair consisting of the transmit beam and the receive beam in set A may be referred to as a second beam pair. That is, the second beam pair includes the transmit beam in set A and the receive beam used by the terminal for reference signal measurement. Of course, the second receive beam may generally include all receive beams used by the terminal for reference signal measurement. Of course, the second receive beam may also include part of all receive beams.

In some embodiments, in response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying a first condition, the sample label data may include a reference signal identifier of a reference signal in the second reference signal set.

For example, the sample label data may include the reference signal ID of the reference signal in set A.

In some embodiments, in response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying a first condition, the sample label data may include a second receive beam identifier.

For example, the sample tag data may include a second RX beam ID. It is understood that the second RX beam ID is used to indicate the second RX beam.

It can be understood that the first RX beam and the second RX beam can be completely or partially the same, or can be completely different, and the present disclosure is not limited to this.

In some embodiments, in response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying a first condition, the sample label data may include a second beam pair identifier.

For example, the sample tag data may include a second beam pair ID. The second beam pair ID may include a combined ID of a reference signal ID of a reference signal in set A and a second RX beam ID. That is, the second beam pair ID may indicate a corresponding second beam pair, and a second receive beam and a second transmit beam corresponding to the second beam pair. It is understood that the transmit beam of the second beam pair may be indicated by the reference signal ID of the reference signal in set A.

Of course, in other embodiments, in response to the first reference signal set, the second reference signal set and the output of the beam prediction model satisfying the first condition, the sample label data may include any two, three or four of the above items. For example, the sample label data may include: beam information of the second transmit beam corresponding to the reference signal in the second reference signal set, or beam information of the second beam pair corresponding to the reference signal in the second reference signal set, and reference signal identifier of the reference signal in the second reference signal set; reference signal identifier and second receive beam identifier of the reference signal in the second reference signal set; beam information of the second transmit beam corresponding to the reference signal in the second reference signal set, or beam information of the second beam pair corresponding to the reference signal in the second reference signal set, and reference signal identifier of the reference signal in the second reference signal set, and second beam pair identifier; beam information of the second transmit beam corresponding to the reference signal in the second reference signal set, or beam information of the second beam pair corresponding to the reference signal in the second reference signal set, and reference signal identifier of the reference signal in the second reference signal set, and second receive beam identifier, and second beam pair identifier. For the convenience of description, the present disclosure will not list them one by one here.

The sample data collected by the terminal of the present invention may include a variety of tags for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, the first condition includes at least one of the following: the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is the beam information corresponding to the second reference signal set; the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is the beam information corresponding to the second reference signal set.

In some embodiments, the first condition may be that the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is beam information corresponding to the second reference signal set.

For example, the first condition is that set B and set A are different, and the output of the beam prediction model is the beam information corresponding to set A, such as the beam prediction model outputs the L1-RSRP and/or L1-SINR of all beams, part of the beams, all beam pairs or part of the beam pairs in set A.

It will be appreciated that in this embodiment, set B may be a wide beam and set A may be a narrow beam.

In some embodiments, the first condition may be that the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is the beam information corresponding to the second reference signal set.

For example, the first condition is that set B is a subset of set A, or set B is equal to set A, and the output of the beam prediction model is the beam information corresponding to set A, such as the beam prediction model outputs the L1-RSRP and/or L1-SINR of all beams, part of the beams, all beam pairs or part of the beam pairs in set A.

In some embodiments, when the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is the beam information corresponding to the second reference signal set, the input sample data may be included in the sample label data.

It can be understood that since set B is a subset of set A, or set B is equal to set A. Therefore, the input data for beam prediction model operation related to set B can be part of or all of the data in the label for beam prediction model operation related to set A. That is, the input sample data can be part of the data in the sample label data, or the input sample data can be exactly the same as the sample label data.

For example, when set B is a subset of set A, the input sample data can be part of the sample label data. For another example, when set B is equal to set A, the input sample data can be exactly the same as the sample label data.

In some embodiments, in the case where set B is a subset of set A, the network device configuration may select which part of the beams or beam pairs in set A are used as set B. Of course, if the network device is not configured accordingly, the terminal may randomly select part of the beams or beam pairs from set A as set B when measuring set B. Alternatively, the terminal may select part of the beams or beam pairs from set A as set B based on a protocol specification or a pre-set selection method, which is not specifically limited in the present disclosure.

In some embodiments, for the case where the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is the beam information corresponding to the second reference signal set. If the sample data includes input sample data and sample label data, the sample label data may not include data that is the same as the input sample data. For example, the sample label data is the remaining data of set A minus set B. Of course, in other examples, the sample data may only include sample label data, then the sample label data at this time is the data corresponding to set A, and the input sample data can be determined from the sample label data.

It should be understood that the first condition in the present disclosure may also include: the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is the beam information corresponding to the second reference signal set; and, the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is the beam information corresponding to the second reference signal set.

The present disclosure is adapted to various reference signal sets and beam prediction models. In such cases, the terminal collects sample data including various tags for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate the beam prediction model, thereby improving the accuracy of the output result of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, the sample data includes sample label data. The sample label data is used as a label when the beam prediction model operates. In response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying the second condition, the sample label data includes: a preferred transmit beam identifier or a third beam pair identifier in the second reference signal set, wherein the third beam pair includes a preferred transmit beam and a preferred receive beam.

In some embodiments, the sample data may further include sample label data. The sample label data may be used as a label when the beam prediction model is operated. For example, the sample label data may be used as a label output by the beam prediction model when the beam prediction model is trained or monitored.

In some embodiments, in response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying the second condition, the sample label data may include a preferred transmit beam identifier or a third beam pair identifier in the second reference signal set, wherein the third beam pair includes a preferred transmit beam and a preferred receive beam.

For example, the sample label data may include top K transmit beam identifiers and/or top K beam pair identifiers in set A. The top K beam pairs may include top K transmit beams and top K preferred receive beams.

It can be understood that in this embodiment, set B can be a wide beam, and set A can be a narrow beam. The top K transmit beam identifier can be a top K transmit beam ID, and the top K beam pair identifier can be a top K beam pair ID.

For example, the preferred transmission beam included in the third beam pair may be the same as the preferred transmission beam in the second reference signal set.

For another example, the preferred transmission beam included in the third beam pair may not be completely the same as or different from the preferred transmission beam in the second reference signal set. It can be understood that the second reference signal set is set A, and the preferred transmission beam in the second reference signal set is the preferred transmission beam in set A output by the beam prediction model; the third beam pair is the preferred beam pair in set A output by the beam prediction model. Since the beam pair is composed of a receiving beam and a transmitting beam. It can be imagined that some transmitting beams may not meet the conditions corresponding to the preferred transmission beam, but if they form a beam pair with some receiving beams, they may meet the conditions corresponding to the preferred beam pair. And some transmitting beams meet the conditions corresponding to the preferred transmission beam, but if they form a beam pair with some receiving beams, they may not meet the conditions corresponding to the preferred beam pair. Therefore, the preferred transmission beam included in the preferred beam pair in set A may be partially the same as or completely different from the preferred transmission beam in set A.

The sample data collected by the terminal of the present invention may include a variety of tags for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, the second condition includes at least one of the following: the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is preferred beam information; the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is preferred beam information.

In some embodiments, the second condition may be that the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is preferred beam information.

For example, the second condition is that set B and set A are different, and the output of the beam prediction model is preferred beam information, such as the beam prediction model outputs top K transmit beam information, top K receive beam information and/or top K beam pair information.

It can be understood that in this embodiment, set B can be a wide beam and set A can be a narrow beam. The preferred beam information can be a preferred transmission beam identifier, a preferred reception beam identifier and/or a preferred beam pair identifier. For example, top K transmission beam ID, top K reception beam ID and/or top K beam pair ID.

Among them, the preferred beam information output by the beam prediction model can be one or more of the preferred transmit beam in the second reference signal set, the preferred receive beam in the second reference signal set, and the preferred beam pair in the second reference signal set output by the beam prediction model. The preferred beam pair in the second reference signal set includes a preferred transmit beam and a preferred receive beam. It can be understood that the preferred transmit beam included in the preferred beam pair can be the same as, partially the same as, or completely different from the preferred transmit beam in the second reference signal set. The preferred receive beam included in the preferred beam pair can be the same as, partially the same as, or completely different from the preferred receive beam in the second reference signal set.

Since a beam pair is composed of a receiving beam and a transmitting beam. For example, some transmitting beams may not meet the conditions corresponding to the preferred transmitting beams, but when they form a beam pair with some receiving beams, they may meet the conditions corresponding to the preferred beam pair. Some transmitting beams meet the conditions corresponding to the preferred transmitting beams, but when they form a beam pair with some receiving beams, they may not meet the conditions corresponding to the preferred beam pair. For another example, some receiving beams may not meet the conditions corresponding to the preferred receiving beams, but when they form a beam pair with some transmitting beams, they may meet the conditions corresponding to the preferred beam pair. Some receiving beams meet the conditions corresponding to the preferred receiving beams, but when they form a beam pair with some transmitting beams, they may not meet the conditions corresponding to the preferred beam pair.

Therefore, the preferred receiving beam included in the preferred beam pair may be the same as, partially the same as, or completely different from the preferred receiving beam in the second reference signal set.

In some embodiments, the second condition may be that the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is preferred beam information.

For example, the second condition is that set B is a subset of set A, or set B is equal to set A, and the output of the beam prediction model is preferred beam information, such as the beam prediction model outputs top K transmitting beam information, top K receiving beam information and/or top K beam pair information.

It can be understood that the preferred beam information can be a preferred transmission beam identifier, a preferred reception beam identifier and/or a preferred beam pair identifier, such as top K transmission beam IDs, top K reception beam IDs and/or top K beam pair IDs.

It should be understood that the third condition in the present disclosure may also include: the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is preferred beam information; and, the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is preferred beam information.

The present disclosure is adapted to various reference signal sets and beam prediction models. In such cases, the terminal collects sample data including various tags for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate the beam prediction model, thereby improving the accuracy of the output result of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, the sample data includes at least one of the following parameters: the number of sample data; the scene and/or sample parameters corresponding to the sample data; the beam prediction model identifier and/or the function of the beam prediction model; and the timestamp corresponding to the sample data.

In some embodiments, the sample data may include the quantity of the sample data.

It can be understood that the number of sample data is used to identify how many sample data the terminal reports to the network device.

In some embodiments, the sample data may include scenes and/or sample parameters corresponding to the sample data.

For example, the scene corresponding to the sample data may refer to the scene in which the terminal that measured the sample data is located, such as Uma scene, Umi scene, indoor scene, etc.

For another example, the sample parameters corresponding to the sample data may be parameters such as the number of terminal Rx beams, beam width, beam angle, moving speed of the terminal, antenna configuration of the terminal, number of antenna panels, etc.

Of course, the scenarios may include more possible scenarios according to actual conditions, and the sample parameters may include more possible parameters according to actual conditions, which are not limited in the present disclosure.

In some embodiments, the sample data may include a beam prediction model identification and/or a function of a beam prediction model.

The beam prediction model identifier can be used to uniquely indicate which AI model is being operated. The function of the beam prediction model can indicate what the AI model is used for. For example, it indicates that the AI model is used for beam prediction, for location prediction, for CSI compression, for terminal mobility management, and so on.

In some embodiments, the sample data may include a timestamp corresponding to the sample data.

For example, the timestamp corresponding to the sample data may be indicated explicitly or implicitly. Explicit indication means indicating the timestamp corresponding to each sample data. Implicit indication means that the length of the time period corresponding to each sample data is the same, and each sample data is recorded, stored, transmitted, etc. in chronological order. For example, each sample data is arranged in chronological order.

It should be understood that the sample data in the present disclosure may also include any two, three or four of the above-mentioned items. For example, the sample data may also include: the number of sample data, the timestamp corresponding to the sample data; the scene and/or sample parameters corresponding to the sample data, and the timestamp corresponding to the sample data; the number of sample data, the scene and/or sample parameters corresponding to the sample data, and the timestamp corresponding to the sample data; the number of sample data, the scene and/or sample parameters corresponding to the sample data, and the timestamp corresponding to the sample data; the number of sample data, the scene and/or sample parameters corresponding to the sample data, and the beam prediction model identifier and/or the function of the beam prediction model, and the timestamp corresponding to the sample data. For the convenience of description, the present disclosure does not list them one by one here.

In some embodiments, the sample data may include input sample data and/or sample label data, and may also include one or more of the number of sample data, the scene and/or sample parameters corresponding to the sample data, the beam prediction model identifier and/or the function of the beam prediction model, and the timestamp corresponding to the sample data. This disclosure is not limited thereto.

The present disclosure provides a variety of information included in the sample data, so that the network device can use the sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output result of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, the sample data is used to operate the beam prediction model. The data collection indication information at least includes measurement configuration information. The measurement configuration information includes at least one of the following information: at least one reference signal set; data collection start time; data collection end time.

In some embodiments, the sample data obtained by the terminal for measuring the reference signal can be used to perform corresponding operations on the beam prediction model. Among them, the beam prediction model can be an AI model for beam prediction. The data collection indication information sent by the network device shall include at least measurement configuration information. Among them, the measurement configuration information may include parameters instructing the terminal to measure. The terminal can measure the reference signal based on the measurement configuration information to obtain sample data.

For example, the measurement configuration information includes some parameters for instructing the terminal to perform measurements, such as when to perform measurements, which reference signals the terminal measures, etc., so that the terminal can perform corresponding measurements on the reference signals based on these parameters and obtain corresponding sample data.

In some embodiments, the measurement configuration information may include at least one reference signal set.

For example, the network device informs the terminal which reference signal sets are configured through measurement configuration information. When multiple reference signal sets are configured, the relationship between the reference signal sets also needs to be indicated. For example, two reference signal sets are configured, namely set B and set A. The measurement configuration information also needs to indicate the beam relationship between set B and set A. That is, it indicates that set B is a subset of set A, or set B is the same as set A, or set B and set A are different. If set B is a subset of set A, it can indicate which reference signals in set A the reference signals contained in set B are. If set B and set A are different, it can also indicate which reference signals in set A the beam corresponding to a reference signal of set B can cover.

In some embodiments, the measurement configuration information may include a data collection start time, wherein the data collection start time indicates a time when the network device sends a reference signal set.

For example, the data collection start time may be an implicit indication, such as a time indication of sending a reference signal through a network device. In another example, the data collection start time may be an explicit indication, that is, directly indicating the start time of data collection by the terminal.

In some embodiments, the measurement configuration information may include a data collection end time, wherein the data collection end time indicates the time when the terminal ends the measurement.

For example, the data collection end time may be an implicit indication, such as an indication of the time when the network device stops sending the reference signal. For another example, the data collection end time may be an explicit indication, that is, directly indicating the end time of data collection for the terminal.

It should be understood that the measurement configuration information in the present disclosure may also include any two or three of the above-mentioned items, and the measurement configuration information may also include: at least one reference signal set, data collection start time; at least one reference signal set, data collection end time; data collection start time, data collection end time; at least one reference signal set, data collection start time, data collection end time.

The present disclosure provides a variety of information included in the measurement configuration information, which facilitates the terminal to collect corresponding sample data, so that the sample data of the terminal can be used to operate the beam prediction model, thereby improving the accuracy of the output result of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, FIG6 is a flow chart of another data collection method according to an exemplary embodiment. As shown in FIG6, the sample data is used to operate the beam prediction model. In response to the beam prediction model operating on the terminal, the data collection indication information includes: measurement configuration information. The method may also include the following steps:

In step S51, a data collection request is received.

In some embodiments, the beam prediction model can be operated on the terminal. In this scenario, the network device can receive a data collection request sent by the terminal. The data collection request is used to instruct the network device to configure data collection indication information. That is, the data collection request is used to instruct the network device to configure measurement configuration information.

For example, the network device may receive a data collection request sent by the terminal, configure measurement configuration information based on the data collection request, and send the measurement configuration information to the terminal.

The present disclosure can be applicable to scenarios where a beam prediction model is operated in a terminal. The measurement configuration information is used to instruct the terminal to collect corresponding sample data so that the terminal can use the sample data of the terminal to operate when the beam prediction model is operated, thereby improving the accuracy of the output results of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, the data collection request may include at least one of the following parameters: the number of sample data; and reference signal set configuration information.

In some embodiments, the data collection request may include the quantity of sample data.

It can be understood that the number of sample data is used to inform the network device of the number of sample data used for beam prediction model operation.

In some embodiments, the data collection request may include reference signal set configuration information.

It can be understood that the reference signal set configuration information informs the network device whether the terminal uses one reference signal set or two reference signal sets to collect sample data.

For example, a reference signal set may correspond to a scenario where set B is the same as set A, or set B is a subset of set A. Two reference signal sets may correspond to a scenario where set B is a wide beam and set A is a narrow beam, or set B is a subset of set A. In other words, the reference signal set configuration information may indicate the relationship between set B and set A.

The present disclosure provides a variety of information included in a data collection request, which is convenient for a network device to configure and send a corresponding reference signal in a scenario where a beam prediction model is operated in a terminal. The terminal can collect corresponding sample data so that the terminal can use the sample data of the terminal to operate when the beam prediction model is operated, thereby improving the accuracy of the output result of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, the data collection indication information also includes report configuration information. The report configuration information is used to indicate at least one of the following reporting methods: periodic reporting; aperiodic reporting; reporting based on RRC signaling or UCI.

In some embodiments, the data collection instruction information sent by the network device may further include report configuration information. The report configuration information may include parameters instructing the terminal to report the sample data to the network device, such as a reporting method instructing the terminal to report the sample data. The terminal may report the sample data based on the report configuration information.

In some embodiments, the reporting configuration information is used to indicate that the measurement result is reported in a periodic reporting manner.

In some embodiments, the reporting configuration information is used to indicate that the measurement result is reported in an aperiodic reporting manner.

In some embodiments, the reporting configuration information is used to indicate that the measurement result is reported based on RRC signaling or UCI reporting.

It should be understood that the measurement configuration information in the present disclosure may also include any two or three of the above-mentioned items, for example, the report configuration information is used to indicate: periodic reporting, non-periodic reporting; periodic reporting, and reporting based on RRC signaling or UCI; non-periodic reporting, and reporting based on RRC signaling or UCI; periodic reporting, and non-periodic reporting, and reporting based on RRC signaling or UCI.

The present disclosure provides a variety of reporting methods that can be applied to different reporting scenarios. When operating the beam prediction model, the network device can use the sample data reported by the terminal to perform operations, thereby improving the accuracy of the output results of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, FIG. 7 is a flow chart of another data collection method according to an exemplary embodiment. As shown in FIG. 7, the beam prediction model operates on the network device. Among them, the data collection indication information is used to instruct the terminal to measure the reference signal to obtain sample data, and the report configuration information is used to instruct the terminal to send the sample data. The method may also include the following steps:

In step S61, sample data is received.

In some embodiments, in a scenario where a beam prediction model is operated on a network device, the data collection indication information sent by the network device may include measurement configuration information and report configuration information. The measurement configuration information may include parameters for instructing the terminal to perform measurements, which are used to instruct the terminal to measure a reference signal to obtain sample data. So that the terminal can measure the reference signal based on the measurement configuration information to obtain sample data. The report configuration information may include parameters for instructing the terminal to report the sample data to the network device, which are used to instruct the terminal to send the sample data.

In some embodiments, the network device may receive sample data sent by the terminal based on the report configuration information.

For example, the report configuration information may include parameters instructing the terminal to report the sample data to the network device. For example, instructing the terminal to report the sample data in a manner so that the terminal can send the sample data to the network device based on the reporting manner indicated by the report configuration information. The network device can operate the beam prediction model using the received sample data.

The present disclosure can be applicable to the scenario where the beam prediction model is operated in the network device. By measuring the configuration information and reporting the configuration information, the terminal is instructed to collect the corresponding sample data and report it to the network device, so that the network device can use the sample data reported by the terminal to operate when the beam prediction model is operated, thereby improving the accuracy of the output result of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, in response to the report configuration information being used to indicate periodic reporting, receiving sample data in S61 may include: receiving one or more sample data.

In some embodiments, in response to the report configuration information indicating periodic reporting, the network device may receive one or more sample data. That is, the network device receives sample data sent by the terminal, which may be one or more sample data. In other words, the terminal periodically reports one or more sample data.

It can be understood that the amount of sample data in the sample data reported by the terminal each time mainly depends on the amount of sample data available to the terminal in the corresponding reporting period.

The present disclosure is applicable to the situation where measurement results are reported periodically, so that the network equipment can use the sample data reported by the terminal to operate the beam prediction model when operating, thereby improving the accuracy of the output results of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, in response to the report configuration information being used to indicate non-periodic reporting, the sample data at least includes the quantity of the sample data.

In some embodiments, in response to the reporting configuration information being used to indicate aperiodic reporting, the sample data should at least include the quantity of the sample data.

It is understandable that since the terminal reports sample data aperiodically, the number of sample data in each reported sample data may be different. Therefore, each time the terminal reports sample data, it should include information indicating the number of sample data, that is, it needs to inform how much sample data is reported.

The present disclosure is applicable to the case of non-periodic reporting of measurement results, so that the network equipment can use the sample data reported by the terminal to operate the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, in response to the report configuration information indicating reporting based on RRC signaling or UCI, receiving the sample data in S61 includes: receiving the sample data through PUSCH or PUCCH.

In some embodiments, in response to the report configuration information indicating reporting based on RRC signaling or UCI, the network device may receive sample data sent by the terminal via PUSCH or PUCCH, wherein the sample data is carried on the RRC signaling or UCI.

The present disclosure is applicable to situations where measurement results are reported based on RRC signaling or UCI, so that network equipment can use sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In the data collection method provided by the embodiment of the present disclosure, operating the beam prediction model includes at least one of the following: training the beam prediction model; monitoring the beam prediction model; and performing deduction based on the beam prediction model.

In some embodiments, the operating beam prediction model may be a training beam prediction model.

In some embodiments, the operating beam prediction model may be a monitoring beam prediction model.

In some embodiments, the operating beam prediction model may be derived based on the beam prediction model.

It should be understood that the operating beam prediction model in the present disclosure may also include: training the beam prediction model and monitoring the beam prediction model; training the beam prediction model and performing deductions based on the beam prediction model; monitoring the beam prediction model and performing deductions based on the beam prediction model; training the beam prediction model and monitoring the beam prediction model and performing deductions based on the beam prediction model.

The present disclosure provides a variety of scenarios for operating beam prediction models, so that in different scenarios, sample data of the terminal can be used to operate the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

It should be noted that those skilled in the art can understand that the various implementation methods/embodiments involved in the embodiments of the present disclosure can be used in conjunction with the aforementioned embodiments or can be used independently. Whether used alone or in conjunction with the aforementioned embodiments, the implementation principle is similar. In the implementation of the present disclosure, some embodiments are described in terms of implementation methods used together. Of course, those skilled in the art can understand that such examples are not limitations of the embodiments of the present disclosure.

Based on the same concept, the embodiments of the present disclosure also provide a data collection device and equipment.

It is understandable that the data collection device and equipment provided by the embodiments of the present disclosure include hardware structures and/or software modules corresponding to the execution of each function in order to realize the above functions. In combination with the units and algorithm steps of each example disclosed in the embodiments of the present disclosure, the embodiments of the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the technical solution of the embodiments of the present disclosure.

Fig. 8 is a schematic diagram of a data collection device according to an exemplary embodiment. Referring to Fig. 8, the device 200 is configured in a terminal, and the device 200 includes: a receiving module 201, which is used to receive data collection indication information; a processing module 202, which is used to measure a reference signal based on the data collection indication information to obtain sample data.

The present disclosure instructs the terminal to collect corresponding sample data through data collection indication information, so that when the beam prediction model is operated, the sample data collected by the terminal can be used to operate, thereby improving the accuracy of the output result of the beam prediction model.

In some embodiments, the sample data includes input sample data, wherein the input sample data serves as input when a beam prediction model operates; the reference signal includes a first reference signal set and/or a second reference signal set, the input of the beam prediction model is beam information corresponding to the reference signal in the first reference signal set, and the output of the beam prediction model is beam information or preferred beam information corresponding to the reference signal in the second reference signal set; the input sample data includes at least one of the following parameters: beam information of a first transmitting beam corresponding to a reference signal in the first reference signal set, or beam information of a first beam pair corresponding to a reference signal in the first reference signal set, wherein the first beam pair includes a first transmitting beam and a first receiving beam; a reference signal identifier of the reference signal in the first reference signal set; a first receiving beam identifier; and a first beam pair identifier.

The sample data collected by the terminal disclosed in the present invention may include a variety of input sample data for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In some embodiments, the sample data includes sample label data, wherein the sample label data serves as a label when the beam prediction model operates; in response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying the first condition, the sample label data includes at least one of the following parameters: beam information of the second transmit beam corresponding to the reference signal in the second reference signal set, or beam information of the second beam pair corresponding to the reference signal in the second reference signal set, wherein the second beam pair includes a second transmit beam and a second receive beam; a reference signal identifier of the reference signal in the second reference signal set; a second receive beam identifier; and a second beam pair identifier.

The sample data collected by the terminal of the present invention may include a variety of tags for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In some embodiments, the first condition includes at least one of the following: the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is the beam information corresponding to the second reference signal set; the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is the beam information corresponding to the second reference signal set.

The present disclosure is adapted to various reference signal sets and beam prediction models. In such cases, the terminal collects sample data including various tags for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate the beam prediction model, thereby improving the accuracy of the output result of the beam prediction model.

In some embodiments, the sample data includes sample label data, wherein the sample label data serves as a label when the beam prediction model operates; in response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying a second condition, the sample label data includes: a preferred transmit beam identifier or a third beam pair identifier in the second reference signal set, wherein the third beam pair includes a preferred transmit beam and a preferred receive beam.

The sample data collected by the terminal of the present invention may include a variety of tags for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In some embodiments, the second condition includes at least one of the following: the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is preferred beam information; the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is preferred beam information.

The present disclosure is adapted to various reference signal sets and beam prediction models. In such cases, the terminal collects sample data including various tags for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate the beam prediction model, thereby improving the accuracy of the output result of the beam prediction model.

In some embodiments, the sample data includes at least one of the following parameters: the number of sample data; the scene and/or sample parameters corresponding to the sample data; the beam prediction model identifier and/or the function of the beam prediction model; and the timestamp corresponding to the sample data.

The present disclosure provides a variety of information included in the sample data, so that the network device can use the sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output result of the beam prediction model.

In some embodiments, the sample data is used to operate a beam prediction model, and the data collection indication information includes at least measurement configuration information; the measurement configuration information includes at least one of the following information: at least one reference signal set; data collection start time; data collection end time.

The present disclosure provides a variety of information included in the measurement configuration information, which facilitates the terminal to collect corresponding sample data, so that the sample data of the terminal can be used to operate the beam prediction model, thereby improving the accuracy of the output result of the beam prediction model.

In some implementations, the beam prediction model is operated on a terminal; the apparatus 200 further includes: a sending module 203, configured to send a data collection request, wherein the data collection request is used to instruct a network device to configure data collection indication information.

The present disclosure can be applicable to scenarios where a beam prediction model is operated in a terminal. The measurement configuration information is used to instruct the terminal to collect corresponding sample data so that the terminal can use the sample data of the terminal to operate when the beam prediction model is operated, thereby improving the accuracy of the output results of the beam prediction model.

In some implementations, the data collection request includes at least one of the following parameters: the number of sample data; and reference signal set configuration information.

The present disclosure provides a variety of information included in a data collection request, which is convenient for a network device to configure and send a corresponding reference signal in a scenario where a beam prediction model is operated in a terminal. The terminal can collect corresponding sample data so that the terminal can use the sample data of the terminal to operate when the beam prediction model is operated, thereby improving the accuracy of the output result of the beam prediction model.

In some implementations, the data collection indication information also includes report configuration information; the report configuration information is used to indicate at least one of the following reporting methods: periodic reporting; non-periodic reporting; reporting based on radio resource control RRC signaling or uplink control information UCI.

The present disclosure provides a variety of reporting methods that can be applied to different reporting scenarios. When operating the beam prediction model, the network device can use the sample data reported by the terminal to perform operations, thereby improving the accuracy of the output results of the beam prediction model.

In some implementations, the beam prediction model operates on a network device; the processing module 202 is further used to measure a reference signal based on measurement configuration information to obtain sample data; the processing module 202 is further used to send sample data based on report configuration information.

The present disclosure can be applicable to the scenario where the beam prediction model is operated in the network device. By measuring the configuration information and reporting the configuration information, the terminal is instructed to collect the corresponding sample data and report it to the network device, so that the network device can use the sample data reported by the terminal to operate when the beam prediction model is operated, thereby improving the accuracy of the output result of the beam prediction model.

In some implementations, in response to the report configuration information being used to indicate periodic reporting, the sending module 203 is further used to: send one or more sample data.

The present disclosure is applicable to the situation where measurement results are reported periodically, so that the network equipment can use the sample data reported by the terminal to operate the beam prediction model when operating, thereby improving the accuracy of the output results of the beam prediction model.

In some implementations, in response to the report configuration information being used to indicate aperiodic reporting, the sample data at least includes the quantity of the sample data.

The present disclosure is applicable to the case of non-periodic reporting of measurement results, so that the network equipment can use the sample data reported by the terminal to operate the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In some implementations, in response to the report configuration information being used to indicate reporting based on RRC signaling or UCI, the sending module 203 is further used to: send the sample data via a physical uplink shared channel PUSCH or a physical uplink control channel PUCCH.

The present disclosure is applicable to situations where measurement results are reported based on RRC signaling or UCI, so that network equipment can use sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In some embodiments, operating the beam prediction model includes at least one of: training the beam prediction model; monitoring the beam prediction model; and performing deduction based on the beam prediction model.

The present disclosure provides a variety of scenarios for operating beam prediction models, so that in different scenarios, sample data of the terminal can be used to operate the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

Fig. 9 is a schematic diagram of another data collection device according to an exemplary embodiment. Referring to Fig. 9, the device 300 is configured in a network device, and the device 300 includes: a sending module 301, which is used to send data collection indication information, and the data collection indication information is used to instruct the terminal to measure the reference signal to obtain sample data.

The present disclosure instructs the terminal to collect corresponding sample data through data collection indication information, so that when the beam prediction model is operated, the sample data collected by the terminal can be used to operate, thereby improving the accuracy of the output result of the beam prediction model.

In some embodiments, the sample data includes input sample data, wherein the input sample data is used as input when a beam prediction model is operated; the reference signal includes a first reference signal set and/or a second reference signal set, the input of the beam prediction model is the beam information corresponding to the reference signal in the first reference signal set, and the output of the beam prediction model is the beam information or preferred beam information corresponding to the reference signal in the second reference signal set; the input sample data includes at least one of the following parameters: beam information of a first transmitting beam corresponding to a reference signal in the first reference signal set, or beam information of a first beam pair corresponding to a reference signal in the first reference signal set, wherein the first beam pair includes a first transmitting beam and a first receiving beam; a reference signal identifier of the reference signal in the first reference signal set; a first receiving beam identifier; and a first beam pair identifier.

The sample data collected by the terminal disclosed in the present invention may include a variety of input sample data for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In some embodiments, the sample data includes sample label data, wherein the sample label data serves as a label when the beam prediction model operates; in response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying the first condition, the sample label data includes at least one of the following parameters: beam information of the second transmit beam corresponding to the reference signal in the second reference signal set, or beam information of the second beam pair corresponding to the reference signal in the second reference signal set, wherein the second beam pair includes a second transmit beam and a second receive beam; a reference signal identifier of the reference signal in the second reference signal set; a second receive beam identifier; and a second beam pair identifier.

The sample data collected by the terminal of the present invention may include a variety of tags for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In some embodiments, the first condition includes at least one of the following: the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is the beam information corresponding to the second reference signal set; the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is the beam information corresponding to the second reference signal set.

The present disclosure is adapted to various reference signal sets and beam prediction models. In such cases, the terminal collects sample data including various tags for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate the beam prediction model, thereby improving the accuracy of the output result of the beam prediction model.

In some embodiments, the sample data includes sample label data, wherein the sample label data serves as a label when the beam prediction model operates; in response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying a second condition, the sample label data includes: a preferred transmit beam identifier or a third beam pair identifier in the second reference signal set, wherein the third beam pair includes a preferred transmit beam and a preferred receive beam.

The sample data collected by the terminal of the present invention may include a variety of tags for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In some embodiments, the second condition includes at least one of the following: the first reference signal set and the second reference signal set are different, and the output of the beam prediction model is preferred beam information; the first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is preferred beam information.

The present disclosure is adapted to various reference signal sets and beam prediction models. In such cases, the terminal collects sample data including various tags for beam prediction model operations, so that the network device can use the sample data reported by the terminal to operate the beam prediction model, thereby improving the accuracy of the output result of the beam prediction model.

In some embodiments, the sample data includes at least one of the following parameters: the number of sample data; the scene and/or sample parameters corresponding to the sample data; the beam prediction model identifier and/or the function of the beam prediction model; and the timestamp corresponding to the sample data.

The present disclosure provides a variety of information included in the sample data, so that the network device can use the sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output result of the beam prediction model.

In some embodiments, the sample data is used to operate a beam prediction model; the data collection indication information includes at least measurement configuration information; the measurement configuration information includes at least one of the following information: at least one reference signal set; data collection start time; data collection end time.

The present disclosure provides a variety of information included in the measurement configuration information, which facilitates the terminal to collect corresponding sample data, so that the sample data of the terminal can be used to operate the beam prediction model, thereby improving the accuracy of the output result of the beam prediction model.

In some implementations, the beam prediction model is operated on the terminal; the apparatus 300 further includes: a receiving module 302, configured to receive a data collection request, wherein the data collection request is used to instruct the network device to configure data collection indication information.

The present disclosure can be applicable to scenarios where a beam prediction model is operated in a terminal. The measurement configuration information is used to instruct the terminal to collect corresponding sample data so that the terminal can use the sample data of the terminal to operate when the beam prediction model is operated, thereby improving the accuracy of the output results of the beam prediction model.

In some implementations, the data collection request includes at least one of the following parameters: the number of sample data; and reference signal set configuration information.

The present disclosure provides a variety of information included in a data collection request, which is convenient for a network device to configure and send a corresponding reference signal in a scenario where a beam prediction model is operated in a terminal. The terminal can collect corresponding sample data so that the terminal can use the sample data of the terminal to operate when the beam prediction model is operated, thereby improving the accuracy of the output result of the beam prediction model.

In some implementations, the data collection indication information also includes report configuration information; the report configuration information is used to indicate at least one of the following reporting methods: periodic reporting; non-periodic reporting; reporting based on radio resource control RRC signaling or uplink control information UCI.

The present disclosure provides a variety of reporting methods that can be applied to different reporting scenarios. When operating the beam prediction model, the network device can use the sample data reported by the terminal to perform operations, thereby improving the accuracy of the output results of the beam prediction model.

In some embodiments, the beam prediction model operates on a network device, wherein the data collection indication information is used to instruct the terminal to measure the reference signal to obtain sample data, and the report configuration information is used to instruct the terminal to send the sample data; the receiving module 302 is also used to receive the sample data.

The present disclosure can be applicable to the scenario where the beam prediction model is operated in the network device. By measuring the configuration information and reporting the configuration information, the terminal is instructed to collect the corresponding sample data and report it to the network device, so that the network device can use the sample data reported by the terminal to operate when the beam prediction model is operated, thereby improving the accuracy of the output result of the beam prediction model.

In some implementations, in response to the report configuration information being used to indicate periodic reporting, the receiving module 302 is further used to: receive one or more sample data.

The present disclosure is applicable to the situation where measurement results are reported periodically, so that the network equipment can use the sample data reported by the terminal to operate the beam prediction model when operating, thereby improving the accuracy of the output results of the beam prediction model.

In some implementations, in response to the report configuration information being used to indicate aperiodic reporting, the sample data at least includes the quantity of the sample data.

The present disclosure is applicable to the case of non-periodic reporting of measurement results, so that the network equipment can use the sample data reported by the terminal to operate the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In some implementations, in response to the report configuration information being used to indicate reporting based on RRC signaling or UCI, the receiving module 302 is further used to: receive sample data via a physical uplink shared channel PUSCH or a physical uplink control channel PUCCH.

The present disclosure is applicable to situations where measurement results are reported based on RRC signaling or UCI, so that network equipment can use sample data reported by the terminal to operate when operating the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

In some embodiments, operating the beam prediction model includes at least one of: training the beam prediction model; monitoring the beam prediction model; and performing deduction based on the beam prediction model.

The present disclosure provides a variety of scenarios for operating beam prediction models, so that in different scenarios, sample data of the terminal can be used to operate the beam prediction model, thereby improving the accuracy of the output results of the beam prediction model.

Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

Fig. 10 is a schematic diagram of a data collection device according to an exemplary embodiment. For example, the device 400 may be any terminal such as a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

10 , device 400 may include one or more of the following components: a processing component 402 , a memory 404 , a power component 406 , a multimedia component 408 , an audio component 410 , an input/output (I/O) interface 412 , a sensor component 414 , and a communication component 416 .

The processing component 402 generally controls the overall operation of the device 400, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 402 may include one or more processors 420 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 402 may include one or more modules to facilitate the interaction between the processing component 402 and other components. For example, the processing component 402 may include a multimedia module to facilitate the interaction between the multimedia component 408 and the processing component 402.

The memory 404 is configured to store various types of data to support operations on the device 400. Examples of such data include instructions for any application or method operating on the device 400, contact data, phone book data, messages, pictures, videos, etc. The memory 404 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

The power component 406 provides power to the various components of the device 400. The power component 406 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 400.

The multimedia component 408 includes a screen that provides an output interface between the device 400 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 408 includes a front camera and/or a rear camera. When the device 400 is in an operating mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each front camera and the rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 410 is configured to output and/or input audio signals. For example, the audio component 410 includes a microphone (MIC), and when the device 400 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 404 or sent via the communication component 416. In some embodiments, the audio component 410 also includes a speaker for outputting audio signals.

I/O interface 412 provides an interface between processing component 402 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 414 includes one or more sensors for providing various aspects of status assessment for the device 400. For example, the sensor assembly 414 can detect the open/closed state of the device 400, the relative positioning of components, such as the display and keypad of the device 400, and the sensor assembly 414 can also detect the position change of the device 400 or a component of the device 400, the presence or absence of user contact with the device 400, the orientation or acceleration/deceleration of the device 400, and the temperature change of the device 400. The sensor assembly 414 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 414 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 414 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 416 is configured to facilitate wired or wireless communication between the device 400 and other devices. The device 400 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 416 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 416 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

In an exemplary embodiment, the device 400 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 404 including instructions, which can be executed by a processor 420 of the device 400 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

FIG11 is a schematic diagram of another data collection device according to an exemplary embodiment. For example, device 500 may be provided as a base station, or a server. Referring to FIG11 , device 500 includes a processing component 522, which further includes one or more processors, and a memory resource represented by a memory 532 for storing instructions executable by the processing component 522, such as an application. The application stored in the memory 532 may include one or more modules, each of which corresponds to a set of instructions. In addition, the processing component 522 is configured to execute instructions to perform the above method.

The device 500 may also include a power supply component 526 configured to perform power management of the device 500, a wired or wireless network interface 550 configured to connect the device 500 to a network, and an input/output (I/O) interface 558. The device 500 may operate based on an operating system stored in the memory 532, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or the like.

The present disclosure instructs the terminal to collect corresponding sample data through data collection indication information, so that when the beam prediction model is operated, the sample data collected by the terminal can be used to operate, thereby improving the accuracy of the output result of the beam prediction model.

It is further understood that in the present disclosure, "plurality" refers to two or more than two, and other quantifiers are similar thereto. "And/or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and/or B may represent: A exists alone, A and B exist at the same time, and B exists alone. The character "/" generally indicates that the associated objects before and after are in an "or" relationship. The singular forms "a", "the", and "the" are also intended to include plural forms, unless the context clearly indicates other meanings.

It is further understood that the terms "first", "second", etc. are used to describe various information, but such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other, and do not indicate a specific order or degree of importance. In fact, the expressions "first", "second", etc. can be used interchangeably. For example, without departing from the scope of the present disclosure, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information.

It is further understood that the meanings of the words "in response to" and "if" involved in the present disclosure depend on the context and the actual usage scenario. For example, the word "in response to" used herein can be interpreted as "at..." or "when..." or "if" or "if".

It is further understood that, although the operations are described in a specific order in the drawings in the embodiments of the present disclosure, it should not be understood as requiring the operations to be performed in the specific order shown or in a serial order, or requiring the execution of all the operations shown to obtain the desired results. In certain environments, multitasking and parallel processing may be advantageous.

Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any modifications, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure.

It should be understood that the present disclosure is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the scope of the appended claims.

Claims (38)

A data collection method, characterized in that the method is applied to a terminal, comprising: receiving data collection instruction information; The reference signal is measured based on the data collection indication information to obtain sample data. The method according to claim 1, characterized in that the sample data comprises input sample data, wherein the input sample data is used as input when the beam prediction model is operated; The reference signal includes a reference signal in a first reference signal set and/or a second reference signal set, the input of the beam prediction model is beam information corresponding to the reference signal in the first reference signal set, and the output of the beam prediction model is beam information or preferred beam information corresponding to the reference signal in the second reference signal set; the input sample data includes at least one of the following parameters: beam information of a first transmit beam corresponding to a reference signal in the first reference signal set, or beam information of a first beam pair corresponding to a reference signal in the first reference signal set, wherein the first beam pair includes the first transmit beam and a first receive beam; a reference signal identifier of a reference signal in the first reference signal set; A first receive beam identifier; The first beam pair is identified. The method according to claim 1 or 2, characterized in that the sample data includes sample label data, wherein the sample label data is used as a label when the beam prediction model operates; In response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying a first condition, the sample label data includes at least one of the following parameters: beam information of a second transmit beam corresponding to a reference signal in the second reference signal set, or beam information of a second beam pair corresponding to a reference signal in the second reference signal set, wherein the second beam pair includes the second transmit beam and a second receive beam; A reference signal identifier of a reference signal in the second reference signal set; A second receiving beam identifier; The second beam pair identifier. The method according to claim 3, characterized in that the first condition includes at least one of the following: The first reference signal set and the second reference signal set are different, and the output of the beam prediction model is beam information corresponding to the second reference signal set; The first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is the beam information corresponding to the second reference signal set. The method according to claim 1 or 2, characterized in that the sample data includes sample label data, wherein the sample label data is used as a label when the beam prediction model operates; In response to the first reference signal set, the second reference signal set and the output of the beam prediction model satisfying the second condition, the sample label data includes: a preferred transmit beam identifier or a third beam pair identifier in the second reference signal set, wherein the third beam pair includes the preferred transmit beam and the preferred receive beam. The method according to claim 5, characterized in that the second condition includes at least one of the following: The first reference signal set and the second reference signal set are different, and the output of the beam prediction model is the preferred beam information; The first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and an output of the beam prediction model is the preferred beam information. The method according to any one of claims 1 to 6, characterized in that the sample data includes at least one of the following parameters: The number of sample data; The scene and/or sample parameters corresponding to the sample data; A beam prediction model identifier and/or a beam prediction model function; The timestamp corresponding to the sample data. The method according to any one of claims 1 to 7, characterized in that the sample data is used to operate a beam prediction model, and the data collection indication information at least includes measurement configuration information; The measurement configuration information includes at least one of the following information: at least one of said reference signal sets; The start time of data collection; The time when data collection ends. The method according to claim 8, characterized in that the beam prediction model is operated on a terminal; The method further comprises: Sending a data collection request, wherein the data collection request is used to instruct the network device to configure the data collection indication information. The method according to claim 9, wherein the data collection request includes at least one of the following parameters: The number of sample data; Reference signal set configuration information. The method according to claim 8, characterized in that the data collection instruction information also includes report configuration information; The report configuration information is used to indicate at least one of the following reporting methods: Periodic reporting; Non-periodic reporting; Based on radio resource control RRC signaling or uplink control information UCI reporting. The method according to claim 11, characterized in that the beam prediction model is operated on a network device; The measuring the reference signal based on the data collection indication information to obtain sample data includes: Measuring a reference signal based on the measurement configuration information to obtain the sample data; The method further comprises: The sample data is sent based on the report configuration information. The method according to claim 11 or 12 is characterized in that, in response to the report configuration information being used to indicate periodic reporting, the sending of the sample data includes: sending one or more sample data. The method according to claim 11 or 12 is characterized in that, in response to the report configuration information being used to indicate non-periodic reporting, the sample data at least includes the quantity of sample data. The method according to claim 11 or 12 is characterized in that, in response to the report configuration information being used to indicate reporting based on the RRC signaling or the UCI, the sending of the sample data includes: sending the sample data via a physical uplink shared channel PUSCH or a physical uplink control channel PUCCH. The method according to any one of claims 2 to 15, wherein the operating beam prediction model comprises at least one of the following: Train the beam prediction model; Monitoring beam prediction model; Deduction is performed based on the beam prediction model. A data collection method, characterized in that the method is applied to a network device, comprising: Sending data collection indication information, where the data collection indication information is used to instruct the terminal to measure the reference signal to obtain sample data. The method according to claim 17, characterized in that the sample data comprises input sample data, wherein the input sample data is used as input when the beam prediction model is operated; The reference signal includes a reference signal in a first reference signal set and/or a second reference signal set, the input of the beam prediction model is the beam information corresponding to the reference signal in the first reference signal set, and the output of the beam prediction model is the beam information or the preferred beam information corresponding to the reference signal in the second reference signal set; the input sample data includes at least one of the following parameters: beam information of a first transmit beam corresponding to a reference signal in the first reference signal set, or beam information of a first beam pair corresponding to a reference signal in the first reference signal set, wherein the first beam pair includes the first transmit beam and a first receive beam; a reference signal identifier of a reference signal in the first reference signal set; A first receive beam identifier; The first beam pair is identified. The method according to claim 17 or 18, characterized in that the sample data includes sample label data, wherein the sample label data is used as a label when the beam prediction model operates; In response to the first reference signal set, the second reference signal set, and the output of the beam prediction model satisfying a first condition, the sample label data includes at least one of the following parameters: beam information of a second transmit beam corresponding to a reference signal in the second reference signal set, or beam information of a second beam pair corresponding to a reference signal in the second reference signal set, wherein the second beam pair includes the second transmit beam and a second receive beam; A reference signal identifier of a reference signal in the second reference signal set; A second receiving beam identifier; The second beam pair identifier. The method according to claim 19, wherein the first condition includes at least one of the following: The first reference signal set and the second reference signal set are different, and the output of the beam prediction model is beam information corresponding to the second reference signal set; The first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and the output of the beam prediction model is beam information corresponding to the second reference signal set. The method according to claim 17 or 18, characterized in that the sample data includes sample label data, wherein the sample label data is used as a label when the beam prediction model operates; In response to the first reference signal set, the second reference signal set and the output of the beam prediction model satisfying the second condition, the sample label data includes: a preferred transmit beam identifier or a third beam pair identifier in the second reference signal set, wherein the third beam pair includes the preferred transmit beam and the preferred receive beam. The method according to claim 21, characterized in that the second condition includes at least one of the following: The first reference signal set and the second reference signal set are different, and the output of the beam prediction model is the preferred beam information; The first reference signal set is a subset of the second reference signal set, or the first reference signal set is the same as the second reference signal set, and an output of the beam prediction model is the preferred beam information. The method according to any one of claims 17 to 22, wherein the sample data comprises at least one of the following parameters: The number of sample data; The scene and/or sample parameters corresponding to the sample data; A beam prediction model identifier and/or a beam prediction model function; The timestamp corresponding to the sample data. The method according to any one of claims 17 to 23, characterized in that the sample data is used to operate a beam prediction model, and the data collection indication information at least includes measurement configuration information; The measurement configuration information includes at least one of the following information: at least one of said reference signal sets; The start time of data collection; The time when data collection ends. The method according to claim 24, characterized in that the beam prediction model is operated on the terminal; The method further comprises: A data collection request is received, wherein the data collection request is used to instruct the network device to configure the data collection indication information. The method according to claim 25, characterized in that the data collection request includes at least one of the following parameters: The number of sample data; Reference signal set configuration information. The method according to claim 24, characterized in that the data collection instruction information also includes report configuration information; The report configuration information is used to indicate at least one of the following reporting methods: Periodic reporting; Non-periodic reporting; Based on radio resource control RRC signaling or uplink control information UCI reporting. The method according to claim 27, characterized in that the beam prediction model is operated on a network device, wherein the data collection indication information is used to instruct the terminal to measure a reference signal to obtain the sample data, and the report configuration information is used to instruct the terminal to send the sample data; The method further comprises: Receive sample data. The method according to claim 27 or 28 is characterized in that, in response to the report configuration information being used to indicate periodic reporting, the receiving the sample data includes: receiving one or more sample data. The method according to claim 27 or 28 is characterized in that, in response to the report configuration information being used to indicate non-periodic reporting, the sample data at least includes the quantity of sample data. The method according to claim 27 or 28 is characterized in that, in response to the report configuration information being used to indicate reporting based on the RRC signaling or the UCI, the receiving of the sample data includes: receiving the sample data through a physical uplink shared channel PUSCH or a physical uplink control channel PUCCH. The method according to any one of claims 18 to 15, wherein the operating beam prediction model comprises at least one of the following: Train the beam prediction model; Monitoring beam prediction model; Deduction is performed based on the beam prediction model. A data collection device, characterized in that the device is configured in a terminal, and the device comprises: A receiving module, used for receiving data collection instruction information; The processing module is used to measure the reference signal based on the data collection indication information to obtain sample data. A data collection device, characterized in that the device is configured in a network device, and the device comprises: The sending module is used to send data collection indication information, where the data collection indication information is used to instruct the terminal to measure the reference signal to obtain sample data. A data collection device, comprising: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to: execute the method described in any one of claims 1 to 16. A data collection device, comprising: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to: execute the method described in any one of claims 17 to 32. A non-temporary computer-readable storage medium, characterized in that when the instructions in the storage medium are executed by a processor of a terminal, the terminal is enabled to execute the method described in any one of claims 1 to 16. A non-temporary computer-readable storage medium, characterized in that when the instructions in the storage medium are executed by a processor of a network device, the network device is enabled to execute the method described in any one of claims 17 to 32.

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