WO2025081436A1

WO2025081436A1 - Model performance monitoring method, device, and storage medium

Info

Publication number: WO2025081436A1
Application number: PCT/CN2023/125501
Authority: WO
Inventors: 李明菊
Original assignee: Beijing Xiaomi Mobile Software Co Ltd
Current assignee: Beijing Xiaomi Mobile Software Co Ltd
Priority date: 2023-10-19
Filing date: 2023-10-19
Publication date: 2025-04-24
Anticipated expiration: 2026-04-19
Also published as: CN120202634A

Abstract

The present disclosure relates to a model performance monitoring method, a device, and a storage medium. The method comprises: receiving first information sent by a network device, wherein the first information comprises configuration of a reference signal resource, and the reference signal resource is used for a terminal device to perform beam measurement; performing beam measurement on the basis of the first information to obtain a beam measurement result; and sending performance monitoring data to the network device on the basis of the beam measurement result, wherein the performance monitoring data is used for determining the performance of a first AI model. In this way, the terminal device can perform beam measurement on the basis of the first information sent by the network device, and send performance monitoring data to the network device on the basis of the beam measurement result, and the network device can determine the performance of the first AI model on the basis of the performance monitoring data, thereby achieving performance monitoring of AI models.

Description

Model performance monitoring method, device and storage medium

Technical Field

本公开涉及通信技术领域，尤其涉及一种模型性能监测方法、设备和存储介质。The present disclosure relates to the field of communication technology, and in particular to a model performance monitoring method, device and storage medium.

Background Art

在无线通信系统中，多收发节点(Multiple Transmission Receive Point，MTRP)场景下需要多个收发点(Transmission Receive Point，TRP)的波束同时为终端设备服务。在波束测量过程中，可以通过人工智能(Artificial Intelligence，AI)模型对每个TRP对应的波束进行波束预测，波束预测的准确率依赖于AI模型的性能。因此，如何对AI模型进行性能监控成为亟待解决的问题。In wireless communication systems, multiple transmission receive points (MTRP) need to have beams from multiple transmission receive points (TRP) serving the terminal device at the same time in the multiple transmission receive point (MTRP) scenario. During beam measurement, the beam corresponding to each TRP can be predicted using an artificial intelligence (AI) model. The accuracy of beam prediction depends on the performance of the AI model. Therefore, how to monitor the performance of the AI model has become an urgent problem to be solved.

发明内容Summary of the invention

本公开实施例提出了一种模型性能监测方法、设备和存储介质。The embodiments of the present disclosure provide a model performance monitoring method, device and storage medium.

根据本公开实施例的第一方面，提出了一种模型性能监测方法，所述方法包括：According to a first aspect of an embodiment of the present disclosure, a model performance monitoring method is proposed, the method comprising:

接收网络设备发送的第一信息，所述第一信息包括参考信号资源的配置，所述参考信号资源用于终端设备进行波束测量；Receiving first information sent by a network device, where the first information includes configuration of a reference signal resource, where the reference signal resource is used by the terminal device to perform beam measurement;

根据所述第一信息进行波束测量，得到波束测量结果；Performing beam measurement according to the first information to obtain a beam measurement result;

根据所述波束测量结果，向所述网络设备发送性能监测数据，所述性能监测数据用于确定第一AI模型的性能。Based on the beam measurement result, performance monitoring data is sent to the network device, and the performance monitoring data is used to determine the performance of the first AI model.

根据本公开实施例的第二方面，提出了一种模型性能监测方法，所述方法包括：According to a second aspect of an embodiment of the present disclosure, a model performance monitoring method is proposed, the method comprising:

向终端设备发送第一信息，所述第一信息包括参考信号资源的配置，所述参考信号资源用于终端设备进行波束测量；Sending first information to a terminal device, where the first information includes configuration of a reference signal resource, where the reference signal resource is used by the terminal device to perform beam measurement;

接收所述终端设备根据波束测量结果发送的性能监测数据，所述波束测量结果是所述终端设备根据所述第一信息进行波束测量得到的，所述性能监测数据用于确定第一AI模型的性能。Receive performance monitoring data sent by the terminal device according to the beam measurement result, where the beam measurement result is obtained by the terminal device performing beam measurement according to the first information, and the performance monitoring data is used to determine the performance of the first AI model.

根据本公开实施例的第三方面，提出了一种终端设备，包括：According to a third aspect of an embodiment of the present disclosure, a terminal device is provided, including:

收发模块，被配置为接收网络设备发送的第一信息，所述第一信息包括参考信号资源的配置，所述参考信号资源用于终端设备进行波束测量；A transceiver module is configured to receive first information sent by a network device, where the first information includes a configuration of a reference signal resource, where the reference signal resource is used by the terminal device to perform beam measurement;

处理模块，被配置为根据所述第一信息进行波束测量，得到波束测量结果；A processing module, configured to perform beam measurement according to the first information to obtain a beam measurement result;

所述收发模块，还被配置为根据所述波束测量结果，向所述网络设备发送性能监测数据，所述性能监测数据用于确定第一AI模型的性能。The transceiver module is also configured to send performance monitoring data to the network device based on the beam measurement result, and the performance monitoring data is used to determine the performance of the first AI model.

根据本公开实施例的第四方面，提出了一种网络设备，包括：According to a fourth aspect of an embodiment of the present disclosure, a network device is provided, including:

收发模块，被配置为向终端设备发送第一信息，所述第一信息包括参考信号资源的配置，所述参考信号资源用于终端设备进行波束测量；A transceiver module is configured to send first information to a terminal device, where the first information includes a configuration of a reference signal resource, where the reference signal resource is used by the terminal device to perform beam measurement;

所述收发模块，还被配置为接收所述终端设备根据波束测量结果发送的性能监测数据，所述波束测量结果是所述终端设备根据所述第一信息进行波束测量得到的，所述性能监测数据用于确定第一AI模型的性能。The transceiver module is also configured to receive performance monitoring data sent by the terminal device according to the beam measurement result, where the beam measurement result is obtained by the terminal device performing beam measurement according to the first information, and the performance monitoring data is used to determine the performance of the first AI model.

根据本公开实施例的第五方面，提出了一种通信设备，包括：一个或多个处理器；其中，该通信设备可以用于执行第一方面或第二方面的可选实现方式。According to a fifth aspect of an embodiment of the present disclosure, a communication device is proposed, comprising: one or more processors; wherein the communication device can be used to execute an optional implementation of the first aspect or the second aspect.

根据本公开实施例的第六方面，提出了一种存储介质，该存储介质存储有指令，当该指令在通信设备上运行时，使得该通信设备执行如第一方面或第二方面的可选实现方式所描述的方法。According to a sixth aspect of an embodiment of the present disclosure, a storage medium is proposed, which stores instructions. When the instructions are executed on a communication device, the communication device executes the method described in the optional implementation of the first aspect or the second aspect.

本公开实施例提供的技术方案可以包括以下有益效果：接收网络设备发送的第一信息，所述第一信息包括参考信号资源的配置，所述参考信号资源用于终端设备进行波束测量；根据所述第一信息进行波束测量，得到波束测量结果；根据所述波束测量结果，向所述网络设备发送性能监测数据，所述性能监测数据用于确定第一AI模型的性能。这样，终端设备可以根据网络设备发送的第一信息进行波束测量，并根据波束测量结果向网络设备发送性能监测数据，网络设备可以根据该性能监测数据确定该第一AI模型的性能，从而实现了对AI模型的性能监控。The technical solution provided by the embodiment of the present disclosure may include the following beneficial effects: receiving the first information sent by the network device, the first information including the configuration of the reference signal resource, the reference signal resource is used for the terminal device to perform beam measurement; performing beam measurement according to the first information to obtain the beam measurement result; sending performance monitoring data to the network device according to the beam measurement result, the performance monitoring data is used to determine the performance of the first AI model. In this way, the terminal device can perform beam measurement according to the first information sent by the network device, and send performance monitoring data to the network device according to the beam measurement result, and the network device can determine the performance of the first AI model according to the performance monitoring data, thereby realizing performance monitoring of the AI model.

应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，并不能限制本公开。 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

为了更清楚地说明本公开实施例中的技术方案，以下对实施例描述所需的附图进行介绍，以下附图仅仅是本公开的一些实施例，不对本公开的保护范围造成具体限制。In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for describing the embodiments are introduced below. The following drawings are only some embodiments of the present disclosure and do not impose specific limitations on the protection scope of the present disclosure.

图1是根据本公开实施例示出的通信系统的架构示意图。FIG1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure.

图2A是根据本公开实施例示出的一种模型性能监测方法的交互示意图。FIG2A is an interactive schematic diagram of a model performance monitoring method according to an embodiment of the present disclosure.

图2B是根据本公开实施例示出的一种模型性能监测方法的交互示意图。FIG2B is an interactive schematic diagram of a model performance monitoring method according to an embodiment of the present disclosure.

图2C是根据本公开实施例示出的一种模型性能监测方法的交互示意图。FIG2C is an interactive schematic diagram of a model performance monitoring method according to an embodiment of the present disclosure.

图3A是根据本公开实施例示出的一种模型性能监测方法的流程示意图。FIG3A is a flow chart of a model performance monitoring method according to an embodiment of the present disclosure.

图3B是根据本公开实施例示出的一种模型性能监测方法的流程示意图。FIG3B is a flow chart of a model performance monitoring method according to an embodiment of the present disclosure.

图3C是根据本公开实施例示出的一种模型性能监测方法的流程示意图。FIG3C is a flow chart of a model performance monitoring method according to an embodiment of the present disclosure.

图4A是根据本公开实施例示出的一种模型性能监测方法的流程示意图。FIG4A is a flow chart of a model performance monitoring method according to an embodiment of the present disclosure.

图4B是根据本公开实施例示出的一种模型性能监测方法的流程示意图。FIG4B is a flow chart of a model performance monitoring method according to an embodiment of the present disclosure.

图4C是根据本公开实施例示出的一种模型性能监测方法的流程示意图。FIG4C is a flow chart of a model performance monitoring method according to an embodiment of the present disclosure.

图5是根据本公开实施例示出的一种模型性能监测方法的交互示意图。FIG5 is an interactive schematic diagram of a model performance monitoring method according to an embodiment of the present disclosure.

图6A是本公开实施例提出的一种终端设备的结构示意图。FIG6A is a schematic diagram of the structure of a terminal device proposed in an embodiment of the present disclosure.

图6B是本公开实施例提出的一种网络设备的结构示意图。FIG6B is a schematic diagram of the structure of a network device proposed in an embodiment of the present disclosure.

图7A是本公开实施例提出的通信设备的结构示意图。FIG. 7A is a schematic diagram of the structure of a communication device proposed in an embodiment of the present disclosure.

图7B是本公开实施例提出的芯片的结构示意图。FIG. 7B is a schematic diagram of the structure of a chip proposed in an embodiment of the present disclosure.

DETAILED DESCRIPTION

第一方面，本公开实施例提出了一种模型性能监测方法，所述方法包括：In a first aspect, an embodiment of the present disclosure provides a model performance monitoring method, the method comprising:

在上述实施例中，终端设备可以根据网络设备发送的第一信息进行波束测量，并根据波束测量结果向网络设备发送性能监测数据，网络设备可以根据该性能监测数据确定该第一AI模型的性能，从而实现了对AI模型的性能监控。In the above embodiment, the terminal device can perform beam measurement based on the first information sent by the network device, and send performance monitoring data to the network device based on the beam measurement result. The network device can determine the performance of the first AI model based on the performance monitoring data, thereby realizing performance monitoring of the AI model.

结合第一方面的一些实施例，在一些实施例中，In conjunction with some embodiments of the first aspect, in some embodiments,

所述第一AI模型为用于执行波束预测的模型，所述第一信息包括以下至少一项：The first AI model is a model for performing beam prediction, and the first information includes at least one of the following:

第一参考信号资源集合，所述第一参考信号资源集合中的参考信号资源对应待测量的波束；A first reference signal resource set, wherein the reference signal resources in the first reference signal resource set correspond to the beam to be measured;

第二参考信号资源集合，所述第二参考信号资源集合中的参考信号资源对应待预测的波束；a second reference signal resource set, wherein the reference signal resources in the second reference signal resource set correspond to beams to be predicted;

第三参考信号资源集合，所述第三参考信号资源集合包括用于干扰测量的资源；a third reference signal resource set, wherein the third reference signal resource set includes resources used for interference measurement;

所述第一参考信号资源集合与所述第二参考信号资源集合之间的关系。The relationship between the first reference signal resource set and the second reference signal resource set.

在上述实施例中，提供了用于进行波束测量的参考信号资源的配置，以便终端设备能够进行波束测量。In the above embodiment, configuration of reference signal resources for beam measurement is provided so that the terminal device can perform beam measurement.

结合第一方面的一些实施例，在一些实施例中，所述第一AI模型为用于执行空域波束预测的模型，所述第一参考信号资源集合与所述第二参考信号资源集合之间的关系包括以下至少一种：In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is a model for performing spatial beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

所述第一参考信号资源集合为所述第二参考信号资源集合的子集；The first reference signal resource set is a subset of the second reference signal resource set;

所述第一参考信号资源集合对应的波束为宽波束，所述第二参考信号资源集合对应的波束为窄波束，且所述第一参考信号资源集合与所述第二参考信号资源集合对应的波束覆盖范围相同。The beam corresponding to the first reference signal resource set is a wide beam, the beam corresponding to the second reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set are the same.

在上述实施例中，提供了空域波束预测模型对应的测量波束和预测波束之间的关系，以便终端设备可以准确进行波束测量。In the above embodiment, a relationship between the measurement beam and the prediction beam corresponding to the spatial beam prediction model is provided so that the terminal device can accurately perform beam measurement.

结合第一方面的一些实施例，在一些实施例中，所述第一AI模型为用于执行时域波束预测的模型，所述第一参考信号资源集合与所述第二参考信号资源集合之间的关系包括以下至少一种：In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is a model for performing time domain beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

所述第一参考信号资源集合与所述第二参考信号资源集合相同；The first reference signal resource set is the same as the second reference signal resource set;

所述第一参考信号资源集合对应的波束为宽波束，所述第二参考信号资源集合对应的波束为窄波束，且所述第一参考信号资源集合与所述第二参考信号资源集合对应的波束覆盖范围相同。 The beam corresponding to the first reference signal resource set is a wide beam, the beam corresponding to the second reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set are the same.

在上述实施例中，提供了时域波束预测模型对应的测量波束和预测波束之间的关系，以便终端设备可以准确进行波束测量。In the above embodiment, a relationship between the measurement beam and the prediction beam corresponding to the time domain beam prediction model is provided so that the terminal device can accurately perform beam measurement.

结合第一方面的一些实施例，在一些实施例中，所述第一参考信号资源集合包括多个第四参考信号资源集合，不同第四参考信号资源集合对应不同的收发点TRP；所述第二参考信号资源集合包括多个第五参考信号资源集合，不同第五参考信号资源集合对应不同的TRP。In combination with some embodiments of the first aspect, in some embodiments, the first reference signal resource set includes multiple fourth reference signal resource sets, and different fourth reference signal resource sets correspond to different transceiver points TRP; the second reference signal resource set includes multiple fifth reference signal resource sets, and different fifth reference signal resource sets correspond to different TRPs.

在上述实施例中，不同的TRP可以设置不同的参考信号资源集合，以使终端设备能够基于组的方式测量并上报测量结果。In the above embodiment, different TRPs may set different reference signal resource sets so that the terminal device can measure and report the measurement results in a group-based manner.

结合第一方面的一些实施例，在一些实施例中，所述第一AI模型为用于执行空域波束预测的模型，所述第四参考信号资源集合与所述第五参考信号资源集合之间的关系包括以下至少一种：In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is a model for performing spatial beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

所述第四参考信号资源集合为所述第五参考信号资源集合的子集；The fourth reference signal resource set is a subset of the fifth reference signal resource set;

所述第四参考信号资源集合对应的波束为宽波束，所述第五参考信号资源集合对应的波束为窄波束，且所述第四参考信号资源集合与所述第五参考信号资源集合对应的波束覆盖范围相同。The beam corresponding to the fourth reference signal resource set is a wide beam, the beam corresponding to the fifth reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the fourth reference signal resource set and the fifth reference signal resource set are the same.

在上述实施例中，终端设备可以基于每个TRP的参考信号资源进行波束测量和上报，提高了终端设备基于多波束传输的通信性能。In the above embodiment, the terminal device can perform beam measurement and reporting based on the reference signal resources of each TRP, thereby improving the communication performance of the terminal device based on multi-beam transmission.

结合第一方面的一些实施例，在一些实施例中，所述第一AI模型为用于执行时域波束预测的模型，所述第四参考信号资源集合与所述第五参考信号资源集合之间的关系包括以下至少一种：In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is a model for performing time domain beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

所述第四参考信号资源集合与所述第五参考信号资源集合相同；The fourth reference signal resource set is the same as the fifth reference signal resource set;

结合第一方面的一些实施例，在一些实施例中，所述性能监测数据包括以下至少一项：In conjunction with some embodiments of the first aspect, in some embodiments, the performance monitoring data includes at least one of the following:

所述第一AI模型的性能值；The performance value of the first AI model;

第一数据，所述第一数据包括以下至少一项：所述第一AI模型的输入数据、所述第一AI模型的输出数据、所述输出数据对应的测量数据，所述输出数据是所述第一AI模型根据输入数据输出的数据；first data, the first data including at least one of the following: input data of the first AI model, output data of the first AI model, and measurement data corresponding to the output data, where the output data is data output by the first AI model according to the input data;

指定事件，所述指定事件基于所述第一AI模型的性能值与第一门限值或第一偏移值的比较结果触发；A specified event, wherein the specified event is triggered based on a comparison result between a performance value of the first AI model and a first threshold value or a first offset value;

第一操作信息，所述第一操作信息用于指示对所述第一AI模型进行管理操作，所述管理操作包括以下任一项：激活所述第一AI模型、去激活所述第一AI模型、切换所述第一AI模型、不使用AI模型。First operation information, where the first operation information is used to indicate a management operation to be performed on the first AI model, where the management operation includes any one of the following: activating the first AI model, deactivating the first AI model, switching the first AI model, and not using the AI model.

在上述实施例中，终端设备可以向网络设备发送一个或多个性能监测数据，以便网络设备对第一A模型进行性能监测，或者通过第一AI模型进行推理。In the above embodiment, the terminal device can send one or more performance monitoring data to the network device so that the network device can perform performance monitoring on the first A model or perform inference through the first AI model.

结合第一方面的一些实施例，在一些实施例中，所述性能值包括以下至少一项：In conjunction with some embodiments of the first aspect, in some embodiments, the performance value includes at least one of the following:

波束预测准确率；Beam prediction accuracy;

波束对预测准确率，所述波束对包括所述终端设备能够同时接收和/或同时发送的波束对；A beam pair prediction accuracy rate, wherein the beam pair includes a beam pair that the terminal device can simultaneously receive and/or simultaneously transmit;

波束质量差异度，所述波束质量差异度为第一波束的测量波束质量与第二波束的测量波束质量的差值，所述第一波束为预测的波束质量最强的波束，所述第二波束为测量的波束质量最强的波束；A beam quality difference, where the beam quality difference is a difference between a measured beam quality of a first beam and a measured beam quality of a second beam, where the first beam is a beam with the strongest predicted beam quality and the second beam is a beam with the strongest measured beam quality;

预测波束质量差异度，所述预测波束质量差异度为所述第一波束的预测波束质量与所述第一波束的测量波束质量的差值。A predicted beam quality difference is a difference between a predicted beam quality of the first beam and a measured beam quality of the first beam.

在上述实施例中，网络设备可以通过该性能值中的一项或多项对第一AI模型进行性能监测。In the above embodiment, the network device can monitor the performance of the first AI model through one or more of the performance values.

结合第一方面的一些实施例，在一些实施例中，所述波束预测准确率为预测的至少一个波束中包括实际最佳波束的准确率。In combination with some embodiments of the first aspect, in some embodiments, the beam prediction accuracy is the accuracy of including an actual optimal beam in the predicted at least one beam.

在上述实施例中，网络设备可以根据预测的实际最佳波束的准确率确定第一AI模型的性能。In the above embodiment, the network device may determine the performance of the first AI model based on the accuracy of the predicted actual optimal beam.

结合第一方面的一些实施例，在一些实施例中，所述波束对预测准确率为预测的至少一个波束对中包括实际最佳波束对的准确率。In combination with some embodiments of the first aspect, in some embodiments, the beam pair prediction accuracy is the accuracy of at least one predicted beam pair including an actual optimal beam pair.

在上述实施例中，网络设备可以根据预测的实际最佳波束对的准确率确定第一AI模型的性能。In the above embodiment, the network device may determine the performance of the first AI model based on the accuracy of the predicted actual optimal beam pair.

结合第一方面的一些实施例，在一些实施例中，所述指定事件包括以下至少一项：第一事件、第二事件、第三事件、第四事件、第五事件、第六事件、第七事件、第八事件；In conjunction with some embodiments of the first aspect, in some embodiments, the designated event includes at least one of the following: a first event, a second event, a third event, a fourth event, a fifth event, a sixth event, a seventh event, and an eighth event;

所述根据所述波束测量结果，向所述网络设备发送所述性能监测数据包括以下至少一项：The sending the performance monitoring data to the network device according to the beam measurement result includes at least one of the following:

根据所述波束测量结果确定所述波束预测准确率小于第一准确率阈值，向所述网络设备发送所述第一事件；Determine, according to the beam measurement result, that the beam prediction accuracy is less than a first accuracy threshold, and send the first event to the network device;

根据所述波束测量结果确定所述波束预测准确率大于第二准确率阈值，向所述网络设备发送所述第二事件； Determine, according to the beam measurement result, that the beam prediction accuracy is greater than a second accuracy threshold, and send the second event to the network device;

根据所述波束测量结果确定所述波束对预测准确率小于第三准确率阈值，向所述网络设备发送所述第三事件；Determine, according to the beam measurement result, that the beam pair prediction accuracy is less than a third accuracy threshold, and send the third event to the network device;

根据所述波束测量结果确定所述波束对预测准确率大于第四准确率阈值，向所述网络设备发送所述第四事件；Determine, according to the beam measurement result, that the beam pair prediction accuracy is greater than a fourth accuracy threshold, and send the fourth event to the network device;

根据所述波束测量结果确定所述波束质量差异度小于第一差异度阈值，向所述网络设备发送所述第五事件；Determine, according to the beam measurement result, that the beam quality difference is less than a first difference threshold, and send the fifth event to the network device;

根据所述波束测量结果确定所述波束质量差异度大于第二差异度阈值，向所述网络设备发送所述第六事件；Determine, according to the beam measurement result, that the beam quality difference is greater than a second difference threshold, and send the sixth event to the network device;

根据所述波束测量结果确定所述预测波束质量差异度小于第三差异度阈值，向所述网络设备发送所述第七事件；Determine, according to the beam measurement result, that the predicted beam quality difference is less than a third difference threshold, and send the seventh event to the network device;

根据所述波束测量结果确定所述预测波束质量差异度大于第四差异度阈值，向所述网络设备发送所述第八事件。Determine, according to the beam measurement result, that the predicted beam quality difference is greater than a fourth difference threshold, and send the eighth event to the network device.

在上述实施例中，终端设备可以根据波束测量结果对预测结果进行判断，并根据判断结果触发不同的事件，以便网络设备确定第一AI模型的性能。In the above embodiment, the terminal device can judge the prediction result based on the beam measurement result, and trigger different events based on the judgment result, so that the network device can determine the performance of the first AI model.

结合第一方面的一些实施例，在一些实施例中，所述根据所述波束测量结果，向所述网络设备发送所述性能监测数据包括：In combination with some embodiments of the first aspect, in some embodiments, sending the performance monitoring data to the network device according to the beam measurement result includes:

根据所述波束测量结果确定所述输出数据和所述输出数据对应的测量数据；Determine the output data and the measurement data corresponding to the output data according to the beam measurement result;

根据所述输出数据和所述输出数据对应的测量数据，确定所述第一操作信息；determining the first operation information according to the output data and the measurement data corresponding to the output data;

向所述网络设备发送所述第一操作信息。The first operation information is sent to the network device.

在上述实施例中，终端设备可以根据输出数据和输出数据对应的测量数据确定对第一AI模型的决定，并告知网络设备。In the above embodiment, the terminal device can determine the decision on the first AI model based on the output data and the measurement data corresponding to the output data, and inform the network device.

结合第一方面的一些实施例，在一些实施例中，所述根据所述输出数据和所述输出数据对应的测量数据，确定所述第一操作信息包括：In conjunction with some embodiments of the first aspect, in some embodiments, determining the first operation information according to the output data and the measurement data corresponding to the output data includes:

所述第一AI模型处于非激活状态，根据所述输出数据和所述输出数据对应的测量数据确定所述第一AI模型的性能满足性能需求，确定所述第一操作信息为激活所述第一AI模型；或者，The first AI model is in an inactive state, and it is determined that the performance of the first AI model meets the performance requirement according to the output data and the measurement data corresponding to the output data, and the first operation information is determined to activate the first AI model; or

所述第一AI模型处于激活状态，根据所述输出数据和所述输出数据对应的测量数据确定所述第一AI模型的性能不满足性能需求，确定所述第一操作信息为去激活所述第一AI模型。The first AI model is in an activated state. It is determined according to the output data and the measurement data corresponding to the output data that the performance of the first AI model does not meet the performance requirement, and the first operation information is determined to be to deactivate the first AI model.

在上述实施例中，终端设备可以根据第一AI模型的当前状态确定对AI模型的管理操作。In the above embodiment, the terminal device can determine the management operation of the AI model according to the current state of the first AI model.

结合第一方面的一些实施例，在一些实施例中，所述第一AI模型为用于执行空域波束预测的模型，所述输入数据包括以下至少一项：In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is a model for performing spatial beam prediction, and the input data includes at least one of the following:

所述第一参考信号资源集合对应的N个波束的波束质量，所述波束质量包括层1参考信号接收功率L1-RSRP或层1信号与干扰加噪声比L1-SINR，其中，N为正整数；beam qualities of the N beams corresponding to the first reference signal resource set, the beam qualities comprising layer 1 reference signal received power L1-RSRP or layer 1 signal to interference plus noise ratio L1-SINR, where N is a positive integer;

所述第一参考信号资源集合对应的N个波束对应的参考信号资源的标识；identifiers of reference signal resources corresponding to the N beams corresponding to the first reference signal resource set;

第二信息，所述第二信息用于指示所述第一AI模型输出的基于组的波束信息中至少一个组内包含的波束为所述终端设备支持的能够同时接收和/或同时发送的两个波束。The second information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device and can be received and/or sent simultaneously.

在上述实施例中，输入数据可以包括多种不同类型，以便通过不同的数据对第一AI模型进行性能监控，或者通过第一AI模型进行更多的预测。In the above embodiment, the input data may include multiple different types, so as to monitor the performance of the first AI model through different data, or make more predictions through the first AI model.

结合第一方面的一些实施例，在一些实施例中，所述第一AI模型为用于执行空域波束预测的模型，所述输出数据包括以下至少一项：In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is a model for performing spatial beam prediction, and the output data includes at least one of the following:

至少一个组；at least one group;

每个组对应的两个参考信号资源的标识，其中，所述参考信号资源为所述第二参考信号资源集合内的参考信号资源；identifiers of two reference signal resources corresponding to each group, wherein the reference signal resources are reference signal resources in the second reference signal resource set;

每个所述参考信号资源的标识对应的波束质量；a beam quality corresponding to an identifier of each of the reference signal resources;

至少一个第三波束；at least one third beam;

每个所述第三波束对应的参考信号资源的标识，其中，所述参考信号资源为所述第二参考信号资源集合内的参考信号资源；an identifier of a reference signal resource corresponding to each of the third beams, wherein the reference signal resource is a reference signal resource in the second reference signal resource set;

每个所述第三波束对应的波束质量；a beam quality corresponding to each of the third beams;

第三信息，所述第三信息用于指示所述第一AI模型输出的基于组的波束信息中至少一个组内包含的波束为所述终端设备支持的能够同时接收和/或同时发送的两个波束。The third information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and/or sent simultaneously.

在上述实施例中，输出数据可以包括多种不同类型，以便通过不同的数据对第一AI模型进行性能监控，或者对第一AI模型进行训练。In the above embodiment, the output data may include multiple different types, so as to monitor the performance of the first AI model or train the first AI model through different data.

结合第一方面的一些实施例，在一些实施例中，所述第一AI模型为用于执行时域波束预测的模型，所述输入数据包括以下至少一项：In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is a model for performing time domain beam prediction, The input data includes at least one of the following:

至少一个历史时间；at least one historical time;

每个所述历史时间对应的所述第一参考信号资源集合对应的N个波束的波束质量，所述波束质量包括L1-RSRP或L1-SINR，其中，N为正整数；beam quality of N beams corresponding to the first reference signal resource set corresponding to each of the historical times, the beam quality comprising L1-RSRP or L1-SINR, where N is a positive integer;

每个所述历史时间对应的所述第一参考信号资源集合对应的N个波束对应的参考信号资源的标识；identifiers of reference signal resources corresponding to the N beams corresponding to the first reference signal resource set corresponding to each of the historical times;

第四信息，所述第四信息用于指示所述第一AI模型输出的基于组的波束信息中至少一个组内包含的波束为所述终端设备支持的能够同时接收和/或同时发送的两个波束。The fourth information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and/or sent simultaneously.

结合第一方面的一些实施例，在一些实施例中，所述第一AI模型为用于执行时域波束预测的模型，所述输出数据包括以下至少一项：In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is a model for performing time-domain beam prediction, and the output data includes at least one of the following:

至少一个未来时间，所述未来时间为通过所述第一AI模型进行波束预测的波束对应时间；At least one future time, where the future time is a beam corresponding time for beam prediction by the first AI model;

每个所述未来时间对应的至少一个组；At least one group corresponding to each of the future times;

每个所述未来时间对应的每个组对应的两个参考信号资源的标识，其中，所述参考信号资源为所述第二参考信号资源集合内的参考信号资源；identifiers of two reference signal resources corresponding to each group corresponding to each of the future times, wherein the reference signal resources are reference signal resources in the second reference signal resource set;

每个所述未来时间对应的每个所述参考信号资源的标识对应的波束质量；a beam quality corresponding to an identifier of each of the reference signal resources corresponding to each of the future times;

每个所述未来时间对应的至少一个第四波束；at least one fourth beam corresponding to each of the future times;

每个所述未来时间对应的每个所述第四波束对应的参考信号资源的标识，其中，所述参考信号资源为所述第二参考信号资源集合内的参考信号资源；an identifier of a reference signal resource corresponding to each of the fourth beams corresponding to each of the future times, wherein the reference signal resource is a reference signal resource in the second reference signal resource set;

每个所述未来时间对应每个所述第四波束对应的波束质量；The beam quality corresponding to each of the fourth beams at each of the future times;

第五信息，所述第五信息用于指示所述第一AI模型输出的基于组的波束信息中至少一个未来时间对应的至少一个组内包含的波束为所述终端设备支持的能够同时接收和/或同时发送的两个波束。The fifth information is used to indicate that the beams contained in at least one group corresponding to at least one future time in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and/or sent simultaneously.

第二方面，本公开实施例提出了一种模型性能监测方法，所述方法包括：In a second aspect, an embodiment of the present disclosure provides a model performance monitoring method, the method comprising:

结合第一方面的一些实施例，在一些实施例中，所述第一AI模型为用于执行波束预测的模型，所述第一信息包括以下至少一项：In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is a model for performing beam prediction, and the first information includes at least one of the following:

结合第二方面的一些实施例，在一些实施例中，所述第一AI模型为用于执行时域波束预测的模型，所述第一参考信号资源集合与所述第二参考信号资源集合之间的关系包括以下至少一种：In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model is a model for performing time domain beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

结合第二方面的一些实施例，在一些实施例中，所述第一参考信号资源集合包括多个第四参考信号资源集合，不同第四参考信号资源集合对应不同的收发点TRP；所述第二参考信号资源集合包括多个第五参考信号资源集合，不同第五参考信号资源集合对应不同的TRP。In combination with some embodiments of the second aspect, in some embodiments, the first reference signal resource set includes multiple fourth reference signal resource sets, and different fourth reference signal resource sets correspond to different transceiver points TRP; the second reference signal resource set includes multiple fifth reference signal resource sets, and different fifth reference signal resource sets correspond to different TRPs.

结合第二方面的一些实施例，在一些实施例中，所述第一AI模型为用于执行空域波束预测的模型，所述第四参考信号资源集合与所述第五参考信号资源集合之间的关系包括以下至少一种：In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model is a model for performing spatial beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

所述第四参考信号资源集合为所述第五参考信号资源集合的子集； The fourth reference signal resource set is a subset of the fifth reference signal resource set;

结合第二方面的一些实施例，在一些实施例中，所述第一AI模型为用于执行时域波束预测的模型，所述第四参考信号资源集合与所述第五参考信号资源集合之间的关系包括以下至少一种：In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model is a model for performing time domain beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

结合第二方面的一些实施例，在一些实施例中，所述性能监测数据包括以下至少一项：In conjunction with some embodiments of the second aspect, in some embodiments, the performance monitoring data includes at least one of the following:

所述第一AI模型的性能值；The performance value of the first AI model;

第一操作信息，所述第一操作信息用于指示对所述第一AI模型进行管理操作，所述管理操作包括激活所述第一AI模型、去激活所述第一AI模型、切换所述第一AI模型、不使用AI模型。First operation information, where the first operation information is used to indicate a management operation to be performed on the first AI model, where the management operation includes activating the first AI model, deactivating the first AI model, switching the first AI model, and not using the AI model.

结合第二方面的一些实施例，在一些实施例中，所述性能值包括以下至少一项：In conjunction with some embodiments of the second aspect, in some embodiments, the performance value includes at least one of the following:

波束预测准确率；Beam prediction accuracy;

结合第二方面的一些实施例，在一些实施例中，所述波束预测准确率为预测的至少一个波束中包括实际最佳波束的准确率。In combination with some embodiments of the second aspect, in some embodiments, the beam prediction accuracy is the accuracy of including an actual optimal beam in at least one predicted beam.

结合第二方面的一些实施例，在一些实施例中，所述波束对预测准确率包括预测的至少一个波束对中包括实际最佳波束对的准确率。In combination with some embodiments of the second aspect, in some embodiments, the beam pair prediction accuracy includes the accuracy of including an actual optimal beam pair in the predicted at least one beam pair.

结合第二方面的一些实施例，在一些实施例中，所述指定事件包括以下至少一项：第一事件、第二事件、第三事件、第四事件、第五事件、第六事件、第七事件、第八事件；In conjunction with some embodiments of the second aspect, in some embodiments, the designated event includes at least one of the following: a first event, a second event, a third event, a fourth event, a fifth event, a sixth event, a seventh event, and an eighth event;

所述接收所述终端设备根据波束测量结果发送的性能监测数据包括以下至少一项：The receiving performance monitoring data sent by the terminal device according to the beam measurement result includes at least one of the following:

接收所述终端设备发送的所述第一事件，所述第一事件是所述终端设备根据所述波束测量结果确定所述波束预测准确率小于第一准确率阈值时触发的；Receiving the first event sent by the terminal device, where the first event is triggered when the terminal device determines, based on the beam measurement result, that the beam prediction accuracy is less than a first accuracy threshold;

接收所述终端设备发送的所述第二事件，所述第二事件是所述终端设备根据所述波束测量结果确定所述波束预测准确率大于第二准确率阈值时触发的；receiving the second event sent by the terminal device, where the second event is triggered when the terminal device determines, based on the beam measurement result, that the beam prediction accuracy is greater than a second accuracy threshold;

接收所述终端设备发送的所述第三事件，所述第三事件是所述终端设备根据所述波束测量结果确定所述波束对预测准确率小于第三准确率阈值时触发的；receiving the third event sent by the terminal device, where the third event is triggered when the terminal device determines, based on the beam measurement result, that the prediction accuracy of the beam pair is less than a third accuracy threshold;

接收所述终端设备发送的所述第四事件，所述第四事件是所述终端设备根据所述波束测量结果确定所述波束对预测准确率大于第四准确率阈值时触发的；receiving the fourth event sent by the terminal device, where the fourth event is triggered when the terminal device determines, based on the beam measurement result, that the beam pair prediction accuracy is greater than a fourth accuracy threshold;

接收所述终端设备发送的所述第五事件，所述第五事件是所述终端设备根据所述波束测量结果确定所述波束质量差异度小于第一差异度阈值时触发的；receiving the fifth event sent by the terminal device, where the fifth event is triggered when the terminal device determines, according to the beam measurement result, that the beam quality difference is less than a first difference threshold;

接收所述终端设备发送的所述第六事件，所述第六事件是所述终端设备根据所述波束测量结果确定所述波束质量差异度大于第二差异度阈值时触发的；receiving the sixth event sent by the terminal device, where the sixth event is triggered when the terminal device determines, according to the beam measurement result, that the beam quality difference is greater than a second difference threshold;

接收所述终端设备发送的所述第七事件，所述第七事件是所述终端设备根据所述波束测量结果确定所述预测波束质量差异度小于第三差异度阈值时触发的；receiving the seventh event sent by the terminal device, where the seventh event is triggered when the terminal device determines, based on the beam measurement result, that the predicted beam quality difference is less than a third difference threshold;

接收所述终端设备发送的所述第八事件，所述第八事件是所述终端设备根据所述波束测量结果确定所述预测波束质量差异度大于第四差异度阈值时触发的。Receive the eighth event sent by the terminal device, where the eighth event is triggered when the terminal device determines, based on the beam measurement result, that the predicted beam quality difference is greater than a fourth difference threshold.

结合第二方面的一些实施例，在一些实施例中，所述第一操作信息是所述终端设备根据所述输出数据和所述输出数据对应的测量数据确定的。In combination with some embodiments of the second aspect, in some embodiments, the first operation information is determined by the terminal device according to the output data and measurement data corresponding to the output data.

结合第二方面的一些实施例，在一些实施例中，所述第一AI模型为用于执行空域波束预测的模型，所述输入数据包括以下至少一项：In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model is a model for performing spatial beam prediction, and the input data includes at least one of the following:

所述第一参考信号资源集合对应的N个波束对应的参考信号资源的标识； identifiers of reference signal resources corresponding to the N beams corresponding to the first reference signal resource set;

结合第二方面的一些实施例，在一些实施例中，所述第一AI模型为用于执行空域波束预测的模型，所述输出数据包括以下至少一项：In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model is a model for performing spatial beam prediction, and the output data includes at least one of the following:

至少一个组；at least one group;

至少一个第三波束；at least one third beam;

结合第二方面的一些实施例，在一些实施例中，所述第一AI模型为用于执行时域波束预测的模型，所述输入数据包括以下至少一项：In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model is a model for performing time-domain beam prediction, and the input data includes at least one of the following:

至少一个历史时间；at least one historical time;

结合第二方面的一些实施例，在一些实施例中，所述第一AI模型为用于执行时域波束预测的模型，所述输出数据包括以下至少一项：In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model is a model for performing time-domain beam prediction, and the output data includes at least one of the following:

多个未来时间，所述未来时间为通过所述第一AI模型进行波束预测的波束对应的时间；multiple future times, where the future times are times corresponding to beams predicted by the first AI model;

每个所述未来时间对应的每个所述第四波束对应的波束质量；a beam quality corresponding to each of the fourth beams corresponding to each of the future time;

第三方面，本公开实施例提出了一种模型性能监测方法，所述方法包括：In a third aspect, an embodiment of the present disclosure provides a model performance monitoring method, the method comprising:

网络设备向终端设备发送第一信息，所述第一信息包括参考信号资源的配置，所述参考信号资源用于终端设备进行波束测量；The network device sends first information to the terminal device, where the first information includes configuration of reference signal resources, where the reference signal resources are used by the terminal device to perform beam measurement;

终端设备根据所述第一信息进行波束测量，得到波束测量结果；The terminal device performs beam measurement according to the first information to obtain a beam measurement result;

终端设备根据所述波束测量结果，向所述网络设备发送性能监测数据，所述性能监测数据用于确定第一AI模型的性能。The terminal device sends performance monitoring data to the network device based on the beam measurement result, and the performance monitoring data is used to determine the performance of the first AI model.

第四方面，本公开实施例提出了一种终端设备，该终端设备可以包括收发模块、处理模块中的至少一者；其中，该终端设备可以用于执行第一方面的可选实现方式。In a fourth aspect, an embodiment of the present disclosure proposes a terminal device, which may include at least one of a transceiver module and a processing module; wherein the terminal device may be used to execute the optional implementation method of the first aspect.

第五方面，本公开实施例提出了一种网络设备，该网络设备可以包括收发模块、处理模块中的至少一者；其中，该网络设备可以用于执行第二方面的可选实现方式。In a fifth aspect, an embodiment of the present disclosure proposes a network device, which may include at least one of a transceiver module and a processing module; wherein the network device may be used to execute the optional implementation method of the second aspect.

第六方面，本公开实施例提出了一种终端设备，该终端设备可以包括：一个或多个处理器；其中，该终端设备可以用于执行第一方面的可选实现方式。In a sixth aspect, an embodiment of the present disclosure proposes a terminal device, which may include: one or more processors; wherein the terminal device can be used to execute an optional implementation method of the first aspect.

第七方面，本公开实施例提出了一种网络设备，该网络设备可以包括：一个或多个处理器；其中，该网络设备可以用于执行第二方面的可选实现方式。In a seventh aspect, an embodiment of the present disclosure proposes a network device, which may include: one or more processors; wherein the network device can be used to execute the optional implementation method of the second aspect.

第八方面，本公开实施例提出了一种通信设备，该通信设备可以包括：一个或多个处理器；其中，该通信设备可以用于执行第一方面或第二方面的可选实现方式。In an eighth aspect, an embodiment of the present disclosure proposes a communication device, which may include: one or more processors; wherein the communication device can be used to execute an optional implementation method of the first aspect or the second aspect.

第九方面，本公开实施例提出了一种通信系统，该通信系统可以包括：终端设备和网络设备；其中，该终端设备被配置为执行如第一方面的可选实现方式所描述的方法，该网络设备被配置为执行如第二方面的可选实现方式所描述的方法。In a ninth aspect, an embodiment of the present disclosure provides a communication system, which may include: a terminal device and a network device; wherein: The terminal device is configured to execute the method described in the optional implementation manner of the first aspect, and the network device is configured to execute the method described in the optional implementation manner of the second aspect.

第十方面，本公开实施例提出了一种存储介质，该存储介质存储有指令，当该指令在通信设备上运行时，使得该通信设备执行如第一方面或第二方面的可选实现方式所描述的方法。In a tenth aspect, an embodiment of the present disclosure proposes a storage medium storing instructions, which, when executed on a communication device, enables the communication device to execute the method described in the optional implementation of the first aspect or the second aspect.

第十一方面，本公开实施例提出了一种程序产品，该程序产品被通信设备执行时，使得该通信设备执行如第一方面或第二方面的可选实现方式所描述的方法。In an eleventh aspect, an embodiment of the present disclosure proposes a program product, which, when executed by a communication device, enables the communication device to execute the method described in the optional implementation manner of the first aspect or the second aspect.

第十二方面，本公开实施例提出了计算机程序，当其在计算机上运行时，使得计算机执行如第一方面或第二方面的可选实现方式所描述的方法。In a twelfth aspect, an embodiment of the present disclosure proposes a computer program, which, when executed on a computer, enables the computer to execute the method described in the optional implementation of the first aspect or the second aspect.

第十三方面，本公开实施例提供了一种芯片或芯片系统。该芯片或芯片系统包括处理电路，被配置为执行如第一方面或第二方面的可选实现方式所描述的方法。In a thirteenth aspect, an embodiment of the present disclosure provides a chip or a chip system. The chip or chip system includes a processing circuit configured to execute the method described in the optional implementation of the first aspect or the second aspect.

可以理解地，上述网络设备、终端设备、通信设备、通信系统、存储介质、程序产品、计算机程序、芯片或芯片系统均可以用于执行本公开实施例所提出的方法。因此，其所能达到的有益效果可以参考对应方法中的有益效果，此处不再赘述。It is understandable that the above network devices, terminal devices, communication devices, communication systems, storage media, program products, computer programs, chips or chip systems can all be used to execute the methods proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods, which will not be repeated here.

本公开实施例提出了一种模型性能监测方法、设备和存储介质。在一些实施例中，模型性能监测方法与信息处理方法、通信方法等术语可以相互替换；模型性能监测装置与信息处理装置、通信装置、通信设备等术语可以相互替换；模型性能监测系统、通信系统等术语可以相互替换。The embodiments of the present disclosure provide a model performance monitoring method, device and storage medium. In some embodiments, the model performance monitoring method and information processing method, communication method and other terms can be replaced with each other; the model performance monitoring device and information processing device, communication device, communication equipment and other terms can be replaced with each other; the model performance monitoring system and communication system and other terms can be replaced with each other.

本公开实施例并非穷举，仅为部分实施例的示意，不作为对本公开保护范围的具体限制。在不矛盾的情况下，某一实施例中的每个步骤均可以作为独立实施例来实施，且各步骤之间可以任意组合，例如，在某一实施例中去除部分步骤后的方案也可以作为独立实施例来实施，且在某一实施例中各步骤的顺序可以任意交换，另外，某一实施例中的可选实现方式可以任意组合；此外，各实施例之间可以任意组合，例如，不同实施例的部分或全部步骤可以任意组合，某一实施例可以与其他实施例的可选实现方式任意组合。The embodiments of the present disclosure are not exhaustive, but are only illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined, for example, some or all of the steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

在各本公开实施例中，如果没有特殊说明以及逻辑冲突，各实施例之间的术语和/或描述具有一致性，且可以互相引用，不同实施例中的技术特征根据其内在的逻辑关系可以组合形成新的实施例。In each embodiment of the present disclosure, unless otherwise specified or there is a logical conflict, the terms and/or descriptions between the embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form a new embodiment based on their internal logical relationships.

本公开实施例中所使用的术语只是为了描述特定实施例的目的，而并非作为对本公开的限制。The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

在本公开实施例中，除非另有说明，以单数形式表示的元素，如“一个”、“一种”、“该”、“上述”、“所述”、“前述”、“这一”等，可以表示“一个且只有一个”，也可以表示“一个或多个”、“至少一个”等。例如，在翻译中使用如英语中的“a”、“an”、“the”等冠词(article)的情况下，冠词之后的名词可以理解为单数表达形式，也可以理解为复数表达形式。In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular form, such as "a", "an", "the", "above", "said", "aforementioned", "this", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun after the article may be understood as a singular expression or a plural expression.

在一些实施例中，“多个”可以指两个或两个以上。In some embodiments, "plurality" may refer to two or more than two.

在一些实施例中，“至少一者(至少一项、至少一个)(at least one of)”、“一个或多个(一项或多项)(one or more)”、“多个(a plurality of)”、“多个(multiple)等术语可以相互替换。In some embodiments, the terms "at least one", "one or more", "a plurality of", "multiple", etc. can be used interchangeably.

在一些实施例中，“A、B中的至少一者”、“A和/或B”、“在一情况下A，在另一情况下B”、“响应于一情况A，响应于另一情况B”等记载方式，根据情况可以包括以下技术方案：在一些实施例中A(与B无关地执行A)；在一些实施例中B(与A无关地执行B)；在一些实施例中从A和B中选择执行(A和B被选择性执行)；在一些实施例中A和B(A和B都被执行)。当有A、B、C等更多分支时也类似上述。In some embodiments, "at least one of A and B", "A and/or B", "A in one case, B in another case", "in response to one case A, in response to another case B", etc., may include the following technical solutions according to the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, A and B (both A and B are executed). When there are more branches such as A, B, C, etc., the above is also similar.

在一些实施例中，“A或B”等记载方式，根据情况可以包括以下技术方案：在一些实施例中A(与B无关地执行A)；在一些实施例中B(与A无关地执行B)；在一些实施例中从A和B中选择执行(A和B被选择性执行)。当有A、B、C等更多分支时也类似上述。In some embodiments, the recording method of "A or B" may include the following technical solutions according to the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). When there are more branches such as A, B, C, etc., the above is also similar.

本公开实施例中的“第一”、“第二”等前缀词，仅仅为了区分不同的描述对象，不对描述对象的位置、顺序、优先级、数量或内容等构成限制，对描述对象的陈述参见权利要求或实施例中上下文的描述，不应因为使用前缀词而构成多余的限制。例如，描述对象为“字段”，则“第一字段”和“第二字段”中“字段”之前的序数词并不限制“字段”之间的位置或顺序，“第一”和“第二”并不限制其修饰的“字段”是否在同一个消息中，也不限制“第一字段”和“第二字段”的先后顺序。再如，描述对象为“等级”，则“第一等级”和“第二等级”中“等级”之前的序数词并不限制“等级”之间的优先级。再如，描述对象的数量并不受序数词的限制，可以是一个或者多个，以“第一装置”为例，其中“装置”的数量可以是一个或者多个。此外，不同前缀词修饰的对象可以相同或不同，例如，描述对象为“装置”，则“第一装置”和“第二装置”可以是相同的装置或者不同的装置，其类型可以相同或不同；再如，描述对象为“信息”，则“第一信息”和“第二信息”可以是相同的信息或者不同的信息，其内容可以相同或不同。The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects, and do not constitute restrictions on the position, order, priority, quantity or content of the description objects. The statement of the description object refers to the description in the context of the claims or embodiments, and should not constitute unnecessary restrictions due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields", and the "first" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number, and can be one or more. Taking the "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes may be the same or different. For example, if the description object is "device", then the "first device" and the "second device" may be the same device or different devices, and their types may be the same or different. For another example, if the description object is "information", then the "first information" and the "second information" may be the same information or different information, and their contents may be the same or different.

在一些实施例中，“包括A”、“包含A”、“用于指示A”、“携带A”，可以解释为直接携带A，也可以解释为间接指示A。In some embodiments, “including A”, “comprising A”, “used to indicate A”, and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

在一些实施例中，“响应于……”、“响应于确定……”、“在……的情况下”、“在……时”、“当……时”、“若……”、“如果……”等术语可以相互替换。 In some embodiments, terms such as "in response to ...", "in response to determining ...", "in the case of ...", "at the time of ...", "when ...", "if ...", "if ...", etc. can be used interchangeably.

在一些实施例中，“大于”、“大于或等于”、“不小于”、“多于”、“多于或等于”、“不少于”、“高于”、“高于或等于”、“不低于”、“以上”等术语可以相互替换，“小于”、“小于或等于”、“不大于”、“少于”、“少于或等于”、“不多于”、“低于”、“低于或等于”、“不高于”、“以下”等术语可以相互替换。In some embodiments, terms such as "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not lower than", and "above" can be replaced with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "no more than", "lower than", "lower than or equal to", "not higher than", and "below" can be replaced with each other.

在一些实施例中，装置等可以解释为实体的、也可以解释为虚拟的，其名称不限定于实施例中所记载的名称。“装置”、“设备(equipment)”、“设备(device)”、“电路”、“网元”、“节点”、“功能”、“单元”、“部件(section)”、“系统”、“网络”、“芯片”、“芯片系统”、“实体”、“主体”等术语可以相互替换。In some embodiments, devices and the like may be interpreted as physical or virtual, and their names are not limited to the names described in the embodiments. Terms such as "device", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", and "subject" may be used interchangeably.

在一些实施例中，“网络”可以解释为网络中包含的装置(例如，接入网设备、核心网设备等)。In some embodiments, "network" may be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).

在一些实施例中，“接入网设备(Access Network Device，AN Device)”也、“无线接入网设备(Radio Access Network Device，RAN Device)”、“基站(Base Station，BS)”、“无线基站(Radio Base Station)”、“固定台(Fixed Station)”、“节点(Node)”、“接入点(Access Point)”、“发送点(Transmission Point，TP)”、“接收点(Reception Point，RP)”、“发送和/或接收点(Transmission/Reception Point，TRP)”、“面板(Panel)”、“天线面板(Antenna Panel)”、“天线阵列(Antenna Array)”、“小区(Cell)”、“宏小区(Macro Cell)”、“小型小区(Small Cell)”、“毫微微小区(Femto Cell)”、“微微小区(Pico Cell)”、“扇区(Sector)”、“小区组(Cell Group)”、“服务小区”、“载波(Carrier)”、“分量载波(Component Carrier)”、“带宽部分(Bandwidth Part，BWP)”等术语可以相互替换。In some embodiments, “Access Network Device (AN Device)” also refers to “Radio Access Network Device (RAN Device)”, “Base Station (BS)”, “Radio Base Station (Radio Base Station)”, “Fixed Station (Fixed Station)”, “Node (Node)”, “Access Point (Access Point)”, “Transmission Point (TP)”, “Reception Point (RP)”, “Transmission and/or Reception Point (Transmission /Reception Point, TRP)","Panel"","Antenna Panel"","Antenna Panel"","Antenna Array"","Cell"","Macro Cell"","Small Cell"","Femto Cell"","Pico Cell"","Sector"","Cell Group"","Serving Cell"","Carrier"","Component Carrier" and "Bandwidth Part" (BWP) are interchangeable.

在一些实施例中，“终端(Terminal)”、“终端设备(Terminal Device)”、“用户设备(User Equipment，UE)”、“用户终端(User Terminal)”、“移动台(Mobile Station，MS)”、“移动终端(Mobile Terminal，MT)”、订户站(Subscriber Station)、移动单元(Mobile Unit)、订户单元(Subscriber Unit)、无线单元(Wireless Unit)、远程单元(Remote Unit)、移动设备(Mobile Device)、无线设备(Wireless Device)、无线通信设备(Wireless Communication Device)、远程设备(Remote Device)、移动订户站(Mobile Subscriber Station)、接入终端(Access Terminal)、移动终端(Mobile Terminal)、无线终端(Wireless Terminal)、远程终端(Remote Terminal)、手持设备(Handset)、用户代理(User Agent)、移动客户端(Mobile Client)、客户端(Client)等术语可以相互替换。In some embodiments, the terms "terminal", "terminal device", "user equipment (UE)", "user terminal (User Terminal)", "mobile station (Mobile Station, MS)", "mobile terminal (Mobile Terminal, MT)", subscriber station (Subscriber Station), mobile unit (Mobile Unit), subscriber unit (Subscriber Unit), wireless unit (Wireless Unit), remote unit (Remote Unit), mobile device (Mobile Device), wireless device (Wireless Device), wireless communication device (Wireless Communication Device), remote device (Remote Device), mobile subscriber station (Mobile Subscriber Station), access terminal (Access Terminal), mobile terminal (Mobile Terminal), wireless terminal (Wireless Terminal), remote terminal (Remote Terminal), handset (Handset), user agent (User Agent), mobile client (Mobile Client), client (Client) and the like can be used interchangeably.

在一些实施例中，接入网设备、核心网设备、或网络设备可以被替换为终端。例如，针对将接入网设备、核心网设备、或网络设备以及终端间的通信置换为多个终端间的通信(例如，设备对设备(device-to-device，D2D)、车联网(vehicle-to-everything，V2X)等)的结构，也可以应用本公开的各实施例。在该情况下，也可以设为终端具有接入网设备所具有的全部或部分功能的结构。此外，“上行”、“下行”等术语也可以被替换为与终端间通信对应的术语(例如，“侧行(side)”)。例如，上行信道、下行信道等可以被替换为侧行信道或直连信道，上行链路、下行链路等可以被替换为侧行链路或直连链路。In some embodiments, the access network device, the core network device, or the network device can be replaced by a terminal. For example, the various embodiments of the present disclosure can also be applied to a structure in which the communication between the access network device, the core network device, or the network device and the terminal is replaced by the communication between multiple terminals (for example, device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, it can also be set as a structure in which the terminal has all or part of the functions of the access network device. In addition, terms such as "uplink" and "downlink" can also be replaced by terms corresponding to communication between terminals (for example, "side"). For example, uplink channels, downlink channels, etc. can be replaced by side channels or direct channels, and uplinks, downlinks, etc. can be replaced by side links or direct links.

在一些实施例中，终端可以被替换为接入网设备、核心网设备、或网络设备。在该情况下，也可以设为接入网设备、核心网设备、或网络设备具有终端所具有的全部或部分功能的结构。In some embodiments, the terminal may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, the core network device, or the network device may also be configured to have a structure that has all or part of the functions of the terminal.

在一些实施例中，获取数据、信息等可以遵照所在地国家的法律法规。In some embodiments, acquisition of data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

在一些实施例中，可以在得到用户同意后获取数据、信息等。In some embodiments, data, information, etc. may be obtained with the user's consent.

此外，本公开实施例的表格中的每一元素、每一行、或每一列均可以作为独立实施例来实施，任意元素、任意行、任意列的组合也可以作为独立实施例来实施。In addition, each element, each row, or each column in the table of the embodiments of the present disclosure may be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns may also be implemented as an independent embodiment.

图1是根据本公开实施例示出的一种通信系统的架构示意图。如图1所示，该通信系统100可以包括终端设备(Terminal Device)101、网络设备102。FIG1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure. As shown in FIG1 , the communication system 100 may include a terminal device 101 and a network device 102.

在一些实施例中，终端设备101可以包括手机(mobile phone)、可穿戴设备、物联网设备、具备通信功能的汽车、智能汽车、平板电脑(Pad)、带无线收发功能的电脑、虚拟现实(Virtual Reality，VR)终端设备、增强现实(Augmented Reality，AR)终端设备、工业控制(Industrial Control)中的无线终端设备、无人驾驶(Self-Driving)中的无线终端设备、远程手术(Remote Medical Surgery)中的无线终端设备、智能电网(Smart Grid)中的无线终端设备、运输安全(Transportation Safety)中的无线终端设备、智慧城市(Smart City)中的无线终端设备、智慧家庭(Smart Home)中的无线终端设备中的至少一者，但不限于此。In some embodiments, the terminal device 101 may include a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer (Pad), a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control (Industrial Control), a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in smart grid (Smart Grid), a wireless terminal device in transportation safety (Transportation Safety), a wireless terminal device in a smart city (Smart City), and at least one of a wireless terminal device in a smart home (Smart Home), but is not limited to these.

在一些实施例中，网络设备102可以包括接入网设备、核心网设备中的至少一者。In some embodiments, the network device 102 may include at least one of an access network device and a core network device.

在一些实施例中，接入网设备可以是将终端设备接入到无线网络的节点或设备，接入网设备可以包括5G通信系统中的演进节点B(evolved NodeB，eNB)、下一代演进节点B(next generation eNB，ng-eNB)、下一代节点B(next generation NodeB，gNB)、节点B(node B，NB)、家庭节点B(home node B，HNB)、家庭演进节点B(home evolved nodeB，HeNB)、无线回传设备、无线网络控制器(Radio Network Controller，RNC)、基站控制器(Base Station Controller，BSC)、基站收发台(Base Transceiver Station，BTS)、基带单元(Base Band Unit，BBU)、移动交换中心、6G通信系统中的基站、开放型基站(Open RAN)、云基站(Cloud RAN)、其他通信系统中的基站、Wi-Fi系统中的接入节点中的至少一者，但不限于此。 In some embodiments, the access network device may be a node or device that accesses a terminal device to a wireless network. The access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, and at least one of an access node in a Wi-Fi system, but is not limited thereto.

在一些实施例中，本公开的技术方案可适用于Open RAN架构，此时，本公开实施例所涉及的接入网设备间或者接入网设备内的接口可变为Open RAN的内部接口，这些内部接口之间的流程和信息交互可以通过软件或者程序实现。In some embodiments, the technical solution of the present disclosure may be applicable to the Open RAN architecture. In this case, the interfaces between access network devices or within access network devices involved in the embodiments of the present disclosure may become internal interfaces of Open RAN, and the processes and information interactions between these internal interfaces may be implemented through software or programs.

在一些实施例中，接入网设备可以由集中单元(Central Unit，CU)与分布式单元(Distributed Unit，DU)组成的，其中，CU也可以称为控制单元(Control Unit)，采用CU-DU的结构可以将接入网设备的协议层拆分开，部分协议层的功能放在CU集中控制，剩下部分或全部协议层的功能分布在DU中，由CU集中控制DU，但不限于此。In some embodiments, the access network device may be composed of a centralized unit (Central Unit, CU) and a distributed unit (Distributed Unit, DU), wherein the CU may also be called a control unit (Control Unit). The CU-DU structure may be used to split the protocol layer of the access network device, with some functions of the protocol layer being centrally controlled by the CU, and the remaining part or all of the functions of the protocol layer being distributed in the DU, and the DU being centrally controlled by the CU, but not limited to this.

在一些实施例中，核心网设备可以是一个设备，也可以是多个设备或设备群。核心网可以包括演进分组核心(Evolved Packet Core，EPC)、5G核心网络(5G Core Network，5GCN)、下一代核心(Next Generation Core，NGC)中的至少一者。In some embodiments, the core network device may be one device, or may be multiple devices or a group of devices. The core network may include at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC).

可以理解的是，本公开实施例描述的通信系统是为了更加清楚的说明本公开实施例的技术方案，并不构成对于本公开实施例提出的技术方案的限定，本领域普通技术人员可知，随着系统架构的演变和新业务场景的出现，本公开实施例提出的技术方案对于类似的技术问题同样适用。It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure. A person of ordinary skill in the art can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.

下述本公开实施例可以应用于图1所示的通信系统100、或部分主体，但不限于此。图1所示的各主体是示例，通信系统可以包括图1中的全部或部分主体，也可以包括图1以外的其他主体，各主体数量和形态为任意，各主体可以是实体的也可以是虚拟的，各主体之间的连接关系是示例，各主体之间可以不连接也可以连接，其连接可以是任意方式，可以是直接连接也可以是间接连接，可以是有线连接也可以是无线连接。The following embodiments of the present disclosure may be applied to the communication system 100 shown in FIG1 , or part of the subject, but are not limited thereto. The subjects shown in FIG1 are examples, and the communication system may include all or part of the subjects in FIG1 , or may include other subjects other than FIG1 , and the number and form of the subjects are arbitrary, and the subjects may be physical or virtual, and the connection relationship between the subjects is an example, and the subjects may be connected or disconnected, and the connection may be in any manner, and may be a direct connection or an indirect connection, and may be a wired connection or a wireless connection.

本公开各实施例可以应用于长期演进(Long Term Evolution，LTE)、LTE-Advanced(LTE-A)、LTE-Beyond(LTE-B)、SUPER 3G、IMT-Advanced、第四代移动通信系统(4th generation mobile communication system，4G)、)、第五代移动通信系统(5th generation mobile communication system，5G)、5G新空口(new radio，NR)、未来无线接入(Future Radio Access，FRA)、新无线接入技术(New-Radio Access Technology，RAT)、新无线(New Radio，NR)、新无线接入(New Radio Access，NX)、未来一代无线接入(Future generation radio access，FX)、Global System for Mobile communications(GSM(注册商标))、CDMA2000、超移动宽带(Ultra Mobile Broadband，UMB)、IEEE 802.11(Wi-Fi(注册商标))、IEEE 802.16(WiMAX(注册商标))、IEEE 802.20、超宽带(Ultra-WideBand，UWB)、蓝牙(Bluetooth(注册商标))、陆上公用移动通信网(Public Land Mobile Network，PLMN)网络、设备到设备(Device-to-Device，D2D)系统、机器到机器(Machine to Machine，M2M)系统、物联网(Internet of Things，IoT)系统、车联网(Vehicle-to-Everything，V2X)、利用其他通信方法的系统、基于它们而扩展的下一代系统等。此外，也可以将多个系统组合(例如，LTE或者LTE-A与5G的组合等)应用。The embodiments of the present disclosure may be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, the fourth generation mobile communication system (4G), the fifth generation mobile communication system (5G), 5G new radio (NR), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New Radio Access (NX), Future generation radio access ... The present invention relates to wireless communication systems such as LTE, Wi-Fi (X), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) network, Device to Device (D2D) system, Machine to Machine (M2M) system, Internet of Things (IoT) system, Vehicle to Everything (V2X), systems using other communication methods, and next-generation systems expanded based on them. In addition, a combination of multiple systems (for example, a combination of LTE or LTE-A with 5G, etc.) may also be applied.

在本公开的一些实施例中，上述通信系统可以引入第一人工智能(Artificial Intelligence，AI)模型，也可以是其他用于预测的模型。该第一AI模型可以是一个或多个模型，该第一AI模型可以包括一个或多个功能。该第一AI模型可以部署在终端设备侧，也可以部署在网络设备侧。In some embodiments of the present disclosure, the above-mentioned communication system may introduce a first artificial intelligence (AI) model, or may be other models for prediction. The first AI model may be one or more models, and the first AI model may include one or more functions. The first AI model may be deployed on the terminal device side or on the network device side.

在NR通信中，针对FR2(frequency range 2)通信频段，由于高频信道的衰减比较快，为了保证覆盖范围，可以使用基于波束(beam)的发送和接收。In NR communication, for the FR2 (frequency range 2) communication band, since the high-frequency channel attenuates relatively quickly, beam-based transmission and reception can be used to ensure coverage.

在一些实施例中，网络设备可以配置用于波束测量的参考信号资源集合，终端设备可以对该参考信号资源集合中的参考信号资源进行测量，并上报测量结果中信号质量比较强的X个参考信号资源的ID，以及X个参考信号资源中每个参考信号资源的层1参考信号接收功率(Layer 1-Reference Signal Receiving Power，L1-RSRP)和/或层1信号与干扰加噪声比(Layer 1-Signal to Interference plus Noise Ratio，L1-SINR)。网络设备配置的参考信号资源集合中包括X个参考信号资源，每个参考信号资源对应网络设备的不同发送波束，针对每个参考信号资源，终端设备需要通过全部接收波束对该参考信号资源进行测量，确定每个接收波束对应的波束测量质量，并从多个波束测量质量中确定最强的波束测量质量。在上述测量过程中，若网络设备的发送波束的数量为M，终端设备的接收波束的数量为N，则终端设备需要测量的波束对的数量为M*N。In some embodiments, the network device may configure a reference signal resource set for beam measurement, and the terminal device may measure the reference signal resources in the reference signal resource set, and report the IDs of X reference signal resources with relatively strong signal quality in the measurement results, as well as the layer 1 reference signal receiving power (Layer 1-Reference Signal Receiving Power, L1-RSRP) and/or layer 1 signal to interference plus noise ratio (Layer 1-Signal to Interference plus Noise Ratio, L1-SINR) of each reference signal resource in the X reference signal resources. The reference signal resource set configured by the network device includes X reference signal resources, each reference signal resource corresponds to a different transmission beam of the network device, and for each reference signal resource, the terminal device needs to measure the reference signal resource through all receiving beams, determine the beam measurement quality corresponding to each receiving beam, and determine the strongest beam measurement quality from multiple beam measurement qualities. In the above measurement process, if the number of transmitting beams of the network device is M and the number of receiving beams of the terminal device is N, the number of beam pairs that the terminal device needs to measure is M*N.

在一些实施例中，通过AI模型进行波束预测。示例地，对于空域波束预测，终端设备可以只测量其中的一部分波束对，例如，终端设备测量的波束对可以是M*N个波束对中的1/8、1/4等，将测量得到的部分波束对的波束测量质量输入AI模型中，通过AI模型预测得到M*N个波束对的波束质量。示例地，对于空域波束预测，终端设备可以只测量其中的一部分波束，例如，终端设备测量的波束可以是M个发送波束中的1/8、1/4等，将测量得到的部分波束的波束测量质量输入AI模型中，通过AI模型预测得到M个发送波束的波束质量。对于时域波束预测，终端设备可以测量历史时间的波束对的波束质量，得到波束历史测量质量，根据该波束历史测量质量，通过AI模型预测未来时间的波束对的波束质量。同样，对于时域波束预测，波束对也可以换成发送波束。 In some embodiments, beam prediction is performed through an AI model. For example, for spatial beam prediction, the terminal device may only measure a portion of the beam pairs. For example, the beam pairs measured by the terminal device may be 1/8, 1/4, etc. of the M*N beam pairs. The measured beam measurement quality of the partial beam pairs is input into the AI model, and the beam quality of the M*N beam pairs is predicted by the AI model. For example, for spatial beam prediction, the terminal device may only measure a portion of the beams. For example, the beams measured by the terminal device may be 1/8, 1/4, etc. of the M transmit beams. The measured beam measurement quality of the partial beams is input into the AI model, and the beam quality of the M transmit beams is predicted by the AI model. For time domain beam prediction, the terminal device may measure the beam quality of the beam pairs at historical times to obtain the beam historical measurement quality. Based on the beam historical measurement quality, the AI model is used to predict the beam quality of the beam pairs at future times. Similarly, for time domain beam prediction, the beam pairs can also be replaced with transmit beams.

在一些实施例中，对于空域波束预测，可以基于波束集合setB中波束的测量结果预测波束集合setA中波束的测量结果；对于时域波束预测，可以基于历史时间的setB中波束的测量结果，预测未来时间的setA中波束的测量结果。In some embodiments, for spatial domain beam prediction, the measurement results of the beams in beam set setA can be predicted based on the measurement results of the beams in beam set setB; for time domain beam prediction, the measurement results of the beams in setA at future times can be predicted based on the measurement results of the beams in setB at historical times.

在一些实施例中，对于空域波束预测，终端设备可以测量setB中每个波束的L1-RSRP，将测量得到的多个L1-RSRP输入AI模型，得到setA中每个波束的L1-RSRP。In some embodiments, for spatial beam prediction, the terminal device may measure the L1-RSRP of each beam in setB, input the measured multiple L1-RSRPs into the AI model, and obtain the L1-RSRP of each beam in setA.

其中，setB与setA的关系可以包括以下至少一种：The relationship between setB and setA may include at least one of the following:

setB可以是setA的子集，示例地，setA包括32个参考信号(每个参考信号对应一个波束方向)，setB包括N个参考信号，N<32，例如，N＝8；setB may be a subset of setA. For example, setA includes 32 reference signals (each reference signal corresponds to a beam direction), and setB includes N reference signals, where N<32, for example, N=8.

setB对应的波束为宽波束，setA对应的波束为窄波束，示例地，setA包括32个参考信号，每个参考信号对应一个波束方向，32个参考信号覆盖的范围为120度，setB包括N个参考信号，例如N＝8，N个参考信号覆盖的范围也是120度，也就是说，setB中多个参考信号的波束方向覆盖了setA中多个参考信号的波束方向，也可以理解为setA中的32/N个参考信号与setB中的同一个参考信号为准共站址(Quasi Co-Location，QCL)Type D的关系。The beam corresponding to setB is a wide beam, and the beam corresponding to setA is a narrow beam. For example, setA includes 32 reference signals, each reference signal corresponds to a beam direction, and the range covered by the 32 reference signals is 120 degrees. setB includes N reference signals, for example, N=8, and the range covered by the N reference signals is also 120 degrees. That is to say, the beam directions of multiple reference signals in setB cover the beam directions of multiple reference signals in setA. It can also be understood that the 32/N reference signals in setA and the same reference signal in setB are in a quasi co-location (Quasi Co-Location, QCL) Type D relationship.

在一些实施例中，对于时域波束预测，终端设备可以测量历史时间的setB中每个波束的L1-RSRP，将测量得到的多个L1-RSRP输入AI模型，预测未来时间setA中每个波束的L1-RSRP。In some embodiments, for time domain beam prediction, the terminal device can measure the L1-RSRP of each beam in setB at historical time, input the measured multiple L1-RSRPs into the AI model, and predict the L1-RSRP of each beam in setA at future time.

setB可以是setA的子集；setB can be a subset of setA;

setB与setA相同；setB is the same as setA;

setB对应的波束为宽波束，setA对应的波束为窄波束。The beam corresponding to setB is a wide beam, and the beam corresponding to setA is a narrow beam.

在一些实施例中，AI模型的输出数据主要包括L1-RSRP和/或波束(pair)ID，但是，在MTRP场景下，可能需要多个TRP的波束同时为终端设备服务。这种情况下，就需要终端设备进行基于分组的波束上报(group based beam report)，终端设备需要对所有波束进行测量，其参考信号资源开销比较大，终端设备测量的复杂度也比较高。这种情况下，可以通过AI模型对每个TRP对应的波束进行波束预测，波束预测的准确率依赖于AI模型的性能。因此，如何对AI模型进行性能监控成为亟待解决的问题。In some embodiments, the output data of the AI model mainly includes L1-RSRP and/or beam (pair) ID. However, in the MTRP scenario, multiple TRP beams may be required to serve the terminal device at the same time. In this case, the terminal device is required to perform group-based beam reporting (group based beam report), and the terminal device needs to measure all beams. Its reference signal resource overhead is relatively large, and the complexity of the terminal device measurement is also relatively high. In this case, the AI model can be used to predict the beam corresponding to each TRP, and the accuracy of the beam prediction depends on the performance of the AI model. Therefore, how to monitor the performance of the AI model has become an urgent problem to be solved.

图2A是根据本公开实施例示出的一种模型性能监测方法的交互示意图。该方法可以由上述通信系统执行。如图2A所示，该方法可以包括：FIG2A is an interactive schematic diagram of a model performance monitoring method according to an embodiment of the present disclosure. The method may be executed by the above communication system. As shown in FIG2A , the method may include:

步骤S2101、网络设备向终端设备发送第一信息。Step S2101: The network device sends first information to the terminal device.

在一些实施例中，终端设备可以接收第一信息。例如，终端设备可以接收网络设备发送的第一信息。再例如，终端设备也可以接收其他实体发送的第一信息。In some embodiments, the terminal device may receive the first information. For example, the terminal device may receive the first information sent by the network device. For another example, the terminal device may also receive the first information sent by other entities.

在一些实施例中，该第一信息的名称不做限定，例如可以是“测量请求信息”、“测量配置信息”、“测量指示信息”等。In some embodiments, the name of the first information is not limited, and may be, for example, "measurement request information", "measurement configuration information", "measurement indication information", etc.

在一些实施例中，第一AI模型为用于执行波束预测的模型，该第一信息可以包括以下至少一项：In some embodiments, the first AI model is a model for performing beam prediction, and the first information may include at least one of the following:

第一参考信号资源集合，该第一参考信号资源集合中的参考信号资源对应待测量的波束；A first reference signal resource set, where reference signal resources in the first reference signal resource set correspond to beams to be measured;

第二参考信号资源集合，该第二参考信号资源集合中的参考信号资源对应待预测的波束；a second reference signal resource set, wherein the reference signal resources in the second reference signal resource set correspond to the beam to be predicted;

第三参考信号资源集合，该第三参考信号资源集合包括用于干扰测量的资源；a third reference signal resource set, the third reference signal resource set comprising resources for interference measurement;

该第一参考信号资源集合与该第二参考信号资源集合之间的关系。A relationship between the first reference signal resource set and the second reference signal resource set.

其中，“待测量的波束”是实际使用模型时的定义，“待测量的波束”可以理解为作为AI模型的输入需要实际测量的波束，这里的波束可以理解为发送波束，或发送接收波束对。Among them, "beam to be measured" is the definition when the model is actually used. "Beam to be measured" can be understood as the beam that needs to be actually measured as the input of the AI model. The beam here can be understood as a transmitting beam, or a transmitting and receiving beam pair.

在一些实施例中，波束可以与参考信号资源对应，该参考信号资源可以对应承载参考信号，每个参考信号可以对应一个波束方向。针对每个波束，网络设备可以配置一个参考信号对应的参考信号资源，终端设备可以根据该参考信号对该波束进行测量。In some embodiments, a beam may correspond to a reference signal resource, the reference signal resource may correspond to a reference signal, and each reference signal may correspond to a beam direction. For each beam, a network device may configure a reference signal resource corresponding to a reference signal, and a terminal device may measure the beam according to the reference signal.

在一些实施例中，若波束测量结果为L1-SINR，则该第一信息可以包括该第三参考信号资源集合。In some embodiments, if the beam measurement result is L1-SINR, the first information may include the third reference signal resource set.

在一些实施例中，第三参考信号资源集合可以包括两个用于干扰测量的资源集合，其中一个对应第一参考信号资源集合，另一个对应第二参考信号资源集合。In some embodiments, the third reference signal resource set may include two resource sets used for interference measurement, one of which corresponds to the first reference signal resource set, and the other corresponds to the second reference signal resource set.

在一些实施例中，该第三参考信号资源可以包括与该第一参考信号资源集合和该第二参考信号资源集合对应的每个波束对应的用于进行干扰测量的波束。In some embodiments, the third reference signal resource may include a beam for interference measurement corresponding to each beam corresponding to the first reference signal resource set and the second reference signal resource set.

在一些实施例中，若该第一AI模型为用于执行空域波束测量的模型，则该第一参考信号资源集合与该第二参考信号资源集合之间的关系包括以下至少一种：In some embodiments, if the first AI model is a model for performing spatial beam measurement, the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

该第一参考信号资源集合为该第二参考信号资源集合的子集；The first reference signal resource set is a subset of the second reference signal resource set;

该第一参考信号资源集合对应的波束为宽波束，该第二参考信号资源集合对应的波束为窄波束，且该第一参考信号资源集合与该第二参考信号资源集合对应的波束覆盖范围相同。The beam corresponding to the first reference signal resource set is a wide beam, the beam corresponding to the second reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set are the same.

在一些实施例中，若该第一参考信号资源集合为该第二参考信号资源集合的子集，则该第一信息可以只包括第二参考信号资源集合。In some embodiments, if the first reference signal resource set is a subset of the second reference signal resource set, the first information may be Only the second reference signal resource set is included.

其中，该第一参考信号资源集合为该第二参考信号资源集合的子集，表示该第一参考信号资源集合对应的待测量的波束是该第二参考信号资源集合对应的待预测的波束的子集。示例地，若该第二参考信号资源集合对应的待预测的波束包括32个波束，该第一参考信号资源集合对应的待测量的波束可以包括该第二参考信号资源集合对应的待预测的32个波束中的4个波束，则该第一参考信号资源集合为该第二参考信号资源集合的子集。The first reference signal resource set is a subset of the second reference signal resource set, indicating that the beam to be measured corresponding to the first reference signal resource set is a subset of the beam to be predicted corresponding to the second reference signal resource set. For example, if the beam to be predicted corresponding to the second reference signal resource set includes 32 beams, and the beam to be measured corresponding to the first reference signal resource set may include 4 beams among the 32 beams to be predicted corresponding to the second reference signal resource set, then the first reference signal resource set is a subset of the second reference signal resource set.

该第一参考信号资源集合为该第二参考信号资源集合的子集，可以通过该第一参考信号资源集合对应该第二参考信号资源集合中的哪几个参考信号资源表示。示例地，该第二参考信号资源集合对应的波束集合为setA，setA包括32个波束(为方便说明，32个波束可以记为波束1、波束2、波束3、波束4、波束5，……，波束30、波束31、波束32)，该第一参考信号资源集合对应的波束集合为setB，setB包括4个波束(为方便说明，4个波束可以记为波束1、波束2、波束3、波束4)，setB中的波束1对应setA中的波束8、setB中的波束2对应setA中的波束16、setB中的波束3对应setA中的波束24、setB中的波束4对应setA中的波束32。通过setB中波束与setA中波束的对应关系，可以看出setB中的4个波束为setA的32个波束的子集。The first reference signal resource set is a subset of the second reference signal resource set, and can be represented by which reference signal resources in the second reference signal resource set the first reference signal resource set corresponds to. For example, the beam set corresponding to the second reference signal resource set is setA, setA includes 32 beams (for convenience of explanation, the 32 beams can be recorded as beam 1, beam 2, beam 3, beam 4, beam 5, ..., beam 30, beam 31, beam 32), the beam set corresponding to the first reference signal resource set is setB, setB includes 4 beams (for convenience of explanation, the 4 beams can be recorded as beam 1, beam 2, beam 3, beam 4), beam 1 in setB corresponds to beam 8 in setA, beam 2 in setB corresponds to beam 16 in setA, beam 3 in setB corresponds to beam 24 in setA, and beam 4 in setB corresponds to beam 32 in setA. Through the correspondence between the beams in setB and the beams in setA, it can be seen that the 4 beams in setB are a subset of the 32 beams in setA.

需要说明的是，上述第一参考信号资源集合对应的波束数量和第二参考信号资源集合对应的波束数量为示例性说明，该第一参考信号资源集合对应的波束与该第二参考信号资源集合对应的波束的对应关系也为示例性说明，本公开实施例对此不作限定。It should be noted that the number of beams corresponding to the first reference signal resource set and the number of beams corresponding to the second reference signal resource set are exemplary illustrations, and the correspondence between the beams corresponding to the first reference signal resource set and the beams corresponding to the second reference signal resource set is also an exemplary illustration, and the embodiments of the present disclosure do not limit this.

该第一参考信号资源集合与该第二参考信号资源集合对应的波束覆盖范围相同，可以表示为第一参考信号资源集合对应的每个宽波束可以覆盖该第二参考信号资源集合对应的多个窄波束。示例地，若该第一参考信号资源集合对应的波束集合为setB，setB包括8个宽波束(为方便说明，8个波束可以记为波束1、波束2、波束3、波束4，……，波束8)，该第二参考信号资源集合对应的波束集合为setA，setA包括32个窄波束(为方便说明，32个波束可以记为波束1、波束2、波束3、波束4、波束5，……，波束30、波束31、波束32)，则该第一参考信号资源集合对应的每个宽波束与该宽波束覆盖的第二参考信号资源集合对应的窄波束的对应关系，可以表示为setB中的波束1覆盖了setA中的波束1、波束2、波束3以波束4，setB中的波束2覆盖了setA中的波束5、波束6、波束7以波束8，以此类推，setB中的波束8覆盖了setA中的波束29、波束30、波束31以波束32。The beam coverage corresponding to the first reference signal resource set is the same as that corresponding to the second reference signal resource set, which can be expressed as each wide beam corresponding to the first reference signal resource set can cover multiple narrow beams corresponding to the second reference signal resource set. For example, if the beam set corresponding to the first reference signal resource set is setB, setB includes 8 wide beams (for the convenience of explanation, the 8 beams can be recorded as beam 1, beam 2, beam 3, beam 4, ..., beam 8), the beam set corresponding to the second reference signal resource set is setA, setA includes 32 narrow beams (for the convenience of explanation, the 32 beams can be recorded as beam 1, beam 2, beam 3, beam 4, beam 5, ..., beam 30, beam 31, beam 32 ), the correspondence between each wide beam corresponding to the first reference signal resource set and the narrow beam corresponding to the second reference signal resource set covered by the wide beam can be expressed as beam 1 in setB covers beam 1, beam 2, beam 3 to beam 4 in setA, beam 2 in setB covers beam 5, beam 6, beam 7 to beam 8 in setA, and so on, beam 8 in setB covers beam 29, beam 30, beam 31 to beam 32 in setA.

在一些实施例中，若第一参考信号资源集合为第二参考信号资源集合的子集，则网络设备可以向终端设备发送第二参考信号资源集合。In some embodiments, if the first reference signal resource set is a subset of the second reference signal resource set, the network device may send the second reference signal resource set to the terminal device.

需要说明的是，“网络设备可以向终端设备发送第二参考信号资源集合”可以解释为网络设备向终端设备发送第二参考信号资源集合，不向终端设备发送第一参考信号资源集合。It should be noted that “the network device may send a second reference signal resource set to the terminal device” may be interpreted as the network device sending the second reference signal resource set to the terminal device but not sending the first reference signal resource set to the terminal device.

在一些实施例中，若该第一AI模型为用于执行时域波束测量的模型，则该第一参考信号资源集合与该第二参考信号资源集合之间的关系包括以下至少一种：In some embodiments, if the first AI model is a model for performing time domain beam measurement, the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

该第一参考信号资源集合与该第二参考信号资源集合相同；The first reference signal resource set is the same as the second reference signal resource set;

在一些实施例中，若该第一参考信号资源集合为该第二参考信号资源集合的子集，则该第一信息可以只包括第二参考信号资源集合。In some embodiments, if the first reference signal resource set is a subset of the second reference signal resource set, the first information may only include the second reference signal resource set.

其中，该第一参考信号资源集合与该第二参考信号资源集合相同，表示该第一参考信号资源集合对应的待测量的波束与该第二参考信号资源集合对应的待预测的波束相同。对于时域波束预测模型，可以通过历史时间测量得到的第一参考信号资源集合对应的每个波束的波束测量结果，预测未来时间该第二参考信号资源集合对应的每个波束的波束测量结果。这样，终端设备可以在未来时间不进行任何测量。Among them, the first reference signal resource set is the same as the second reference signal resource set, indicating that the beam to be measured corresponding to the first reference signal resource set is the same as the beam to be predicted corresponding to the second reference signal resource set. For the time domain beam prediction model, the beam measurement result of each beam corresponding to the first reference signal resource set obtained by historical time measurement can be used to predict the beam measurement result of each beam corresponding to the second reference signal resource set in the future time. In this way, the terminal device does not need to perform any measurement in the future time.

在一些实施例中，该第一参考信号资源集合可以包括多个第四参考信号资源集合，不同第四参考信号资源集合对应不同的收发点TRP；该第二参考信号资源集合可以包括多个第五参考信号资源集合，不同第五参考信号资源集合对应不同的TRP。In some embodiments, the first reference signal resource set may include multiple fourth reference signal resource sets, and different fourth reference signal resource sets correspond to different transceiver points TRP; the second reference signal resource set may include multiple fifth reference signal resource sets, and different fifth reference signal resource sets correspond to different TRPs.

例如，若TRP包括第一TRP和第二TRP，该第一参考信号资源可以包括第四参考信号资源集合A和第四参考信号资源集合B，该第二参考信号资源集合包括第五参考信号资源集合A和第五参考信号资源集合B，则第四参考信号资源集合A与第五参考信号资源集合A对应，第四参考信号资源集合B与第五参考信号资源集合B对应，第四参考信号资源集合A和第五参考信号资源集合A与第一TRP对应，第四参考信号资源集合B和第五参考信号资源集合B与第二TRP对应。即第四参考信号资源集合A是第一TRP对应的待测量的波束，第五参考信号资源集合A是第一TRP对应的待预测的波束；第四参考信号资源集合B是第二TRP对应的待测量的波束，第五参考信号资源集合B是第二TRP对应的待预测的波束。 For example, if the TRP includes the first TRP and the second TRP, the first reference signal resource may include the fourth reference signal resource set A and the fourth reference signal resource set B, and the second reference signal resource set includes the fifth reference signal resource set A and the fifth reference signal resource set B, then the fourth reference signal resource set A corresponds to the fifth reference signal resource set A, the fourth reference signal resource set B corresponds to the fifth reference signal resource set B, the fourth reference signal resource set A and the fifth reference signal resource set A correspond to the first TRP, and the fourth reference signal resource set B and the fifth reference signal resource set B correspond to the second TRP. That is, the fourth reference signal resource set A is the beam to be measured corresponding to the first TRP, and the fifth reference signal resource set A is the beam to be predicted corresponding to the first TRP; the fourth reference signal resource set B is the beam to be measured corresponding to the second TRP, and the fifth reference signal resource set B is the beam to be predicted corresponding to the second TRP.

在一些实施例中，“不同第四参考信号资源集合对应不同的TRP”和“不同第五参考信号资源集合对应不同的TRP”可以理解为不同第五参考信号资源集合对应不同的第四参考信号资源集合，也就是说，第五参考信号资源集合与第四参考信号资源集合一一对应。In some embodiments, "different fourth reference signal resource sets correspond to different TRPs" and "different fifth reference signal resource sets correspond to different TRPs" can be understood as different fifth reference signal resource sets corresponding to different fourth reference signal resource sets, that is, the fifth reference signal resource set corresponds one-to-one to the fourth reference signal resource set.

在一些实施例中，若该第一AI模型为用于执行空域波束测量的模型，则该第四参考信号资源集合与该第五参考信号资源集合之间的关系包括以下至少一种：In some embodiments, if the first AI model is a model for performing spatial beam measurement, the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

该第四参考信号资源集合为该第五参考信号资源集合的子集；The fourth reference signal resource set is a subset of the fifth reference signal resource set;

该第四参考信号资源集合对应的波束为宽波束，该第五参考信号资源集合对应的波束为窄波束，且该第四参考信号资源集合与该第五参考信号资源集合对应的波束覆盖范围相同。The beam corresponding to the fourth reference signal resource set is a wide beam, the beam corresponding to the fifth reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the fourth reference signal resource set and the fifth reference signal resource set are the same.

在一些实施例中，若该第一AI模型为用于执行时域波束测量的模型，则该第四参考信号资源集合与该第五参考信号资源集合之间的关系包括以下至少一种：In some embodiments, if the first AI model is a model for performing time domain beam measurement, the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

该第四参考信号资源集合与该第五参考信号资源集合相同；The fourth reference signal resource set is the same as the fifth reference signal resource set;

需要说明的是，第四参考信号资源集合与第五参考信号资源集合之间的关系的说明，可以参考上述第一参考信号资源集合与第二参考信号资源集合之间的关系的说明，此处不再赘述。It should be noted that, for the description of the relationship between the fourth reference signal resource set and the fifth reference signal resource set, reference may be made to the description of the relationship between the first reference signal resource set and the second reference signal resource set, which will not be repeated here.

同样需要说明的是，在该第四参考信号资源和该第五参考信号资源对应同一个TRP时，该第四参考信号资源与该第五参考信号资源具备上述关系。It should also be noted that when the fourth reference signal resource and the fifth reference signal resource correspond to the same TRP, the fourth reference signal resource and the fifth reference signal resource have the above-mentioned relationship.

步骤S2102、终端设备根据第一信息进行波束测量，得到波束测量结果。Step S2102: The terminal device performs beam measurement according to the first information to obtain a beam measurement result.

在一些实施例中，该波束测量结果可以包括波束质量。In some embodiments, the beam measurements may include beam quality.

在一些实施例中，该波束质量可以包括L1-RSRP或L1-SINR。In some embodiments, the beam quality may include L1-RSRP or L1-SINR.

在一些实施例中，该波束测量结果可以包括第一波束测量结果和第二波束测量结果。该第一波束测量结果和该第二波束测量结果可以是L1-RSRP。终端设备基于该第一参考信号资源集合对应的波束进行波束测量，可以得到第一波束测量结果；终端设备基于该第二参考信号资源集合对应的波束进行波束测量，可以得到第二波束测量结果。In some embodiments, the beam measurement result may include a first beam measurement result and a second beam measurement result. The first beam measurement result and the second beam measurement result may be L1-RSRP. The terminal device performs beam measurement based on the beam corresponding to the first reference signal resource set to obtain a first beam measurement result; the terminal device performs beam measurement based on the beam corresponding to the second reference signal resource set to obtain a second beam measurement result.

在一些实施例中，该第一波束测量结果和该第二波束测量结果可以是L1-SINR。终端设备可以基于该第一参考信号资源集合和该第三参考信号资源集合对应的波束进行波束测量，得到该第一波束测量结果，基于该第二参考信号资源集合和该第三参考信号资源集合对应的波束进行波束测量，得到该第二波束测量结果。In some embodiments, the first beam measurement result and the second beam measurement result may be L1-SINR. The terminal device may perform beam measurement based on the beam corresponding to the first reference signal resource set and the third reference signal resource set to obtain the first beam measurement result, and perform beam measurement based on the beam corresponding to the second reference signal resource set and the third reference signal resource set to obtain the second beam measurement result.

在一些实施例中，该第一波束测量结果可以是L1-RSRP，该第二波束测量结果可以是L1-SINR。终端设备可以基于该第一参考信号资源集合对应的波束进行波束测量，得到该第一波束测量结果，基于该第二参考信号资源集合和该第三参考信号资源集合对应的波束进行波束测量，得到该第二波束测量结果。In some embodiments, the first beam measurement result may be L1-RSRP, and the second beam measurement result may be L1-SINR. The terminal device may perform beam measurement based on the beam corresponding to the first reference signal resource set to obtain the first beam measurement result, and perform beam measurement based on the beams corresponding to the second reference signal resource set and the third reference signal resource set to obtain the second beam measurement result.

在一些实施例中，该第一波束测量结果可以包括该第一参考信号资源集合对应的N个波束的L1-RSRP或者L1-SINR，其中，N为正整数。In some embodiments, the first beam measurement result may include L1-RSRP or L1-SINR of N beams corresponding to the first reference signal resource set, where N is a positive integer.

在一些实施例中，该第一波束测量结果可以包括该第一参考信号资源集合对应的N个波束的L1-RSRP或者L1-SINR，以及N个参考信号资源的标识。In some embodiments, the first beam measurement result may include L1-RSRP or L1-SINR of N beams corresponding to the first reference signal resource set, and identifiers of the N reference signal resources.

其中，“N”可以解释为第一参考信号资源集合对应的的全部波束，也可以解释为第一参考信号资源集合对应的的部分波束，本公开实施例对此不作限定。Here, "N" can be interpreted as all beams corresponding to the first reference signal resource set, or can be interpreted as part of the beams corresponding to the first reference signal resource set, which is not limited in the embodiments of the present disclosure.

在一些实施例中，该第二波束测量结果可以包括该第二参考信号资源集合中的至少一个组中每个组分别对应的两个参考信号资源的标识。其中组可以是波束组。In some embodiments, the second beam measurement result may include identifiers of two reference signal resources corresponding to each group in at least one group in the second reference signal resource set, wherein the group may be a beam group.

在一些实施例中，该第二波束测量结果可以包括该第二参考信号资源集合中的至少一个组中每个组分别对应的两个参考信号资源的标识。其中组可以是终端支持同时接收的波束组，终端支持同时发送的波束组，或终端支持同时接收且同时发送的波束组。In some embodiments, the second beam measurement result may include identifiers of two reference signal resources corresponding to each group in at least one group in the second reference signal resource set, wherein the group may be a beam group supported by the terminal for simultaneous reception, a beam group supported by the terminal for simultaneous transmission, or a beam group supported by the terminal for simultaneous reception and transmission.

在一些实施例中，该第二波束测量结果可以包括该第二参考信号资源集合中的至少一个组中每个组分别对应的两个参考信号资源的标识，以及每个参考信号资源标识对应的L1-RSRP或者L1-SINR。其中组可以是波束组。In some embodiments, the second beam measurement result may include identifiers of two reference signal resources corresponding to each group in at least one group in the second reference signal resource set, and L1-RSRP or L1-SINR corresponding to each reference signal resource identifier, wherein the group may be a beam group.

在一些实施例中，该第二波束测量结果可以包括该第二参考信号资源集合中的至少一个组中每个组分别对应的两个参考信号资源的标识，以及每个参考信号资源标识对应的L1-RSRP或者L1-SINR。其中组可以是终端支持同时接收的波束组，终端支持同时发送的波束组，或终端支持同时接收且同时发送的波束组。In some embodiments, the second beam measurement result may include identifiers of two reference signal resources corresponding to each group in at least one group in the second reference signal resource set, and L1-RSRP or L1-SINR corresponding to each reference signal resource identifier. The group may be a beam group that the terminal supports simultaneous reception, a beam group that the terminal supports simultaneous transmission, or a beam group that the terminal supports simultaneous reception and transmission.

在一些实施例中，该第二波束测量结果可以包括该第二参考信号资源集合中至少一个波束的参考信号资源标识，其中该波束不能与其它波束形成组。In some embodiments, the second beam measurement result may include a reference signal resource identifier of at least one beam in the second reference signal resource set, wherein the beam cannot form a group with other beams.

在一些实施例中，该第二波束测量结果可以包括该第二参考信号资源集合中至少一个波束的参考信号资源的标识，以及该参考信号资源的标识对应的L1-RSRP或者L1-SINR，其中该波束不能与其它波束形成组。In some embodiments, the second beam measurement result may include a reference signal of at least one beam in the second reference signal resource set. The resource identifier and the L1-RSRP or L1-SINR corresponding to the reference signal resource identifier, where the beam cannot be grouped with other beams.

步骤S2103、终端设备根据波束测量结果确定输出数据和输出数据对应的测量数据。Step S2103: The terminal device determines the output data and the measurement data corresponding to the output data according to the beam measurement result.

在一些实施例中，该输出数据和输出数据对应的测量数据可以用于确定第一AI模型的性能。In some embodiments, the output data and the measurement data corresponding to the output data can be used to determine the performance of the first AI model.

在一些实施例中，终端设备可以根据该波束测量结果确定第一AI模型的输入数据。In some embodiments, the terminal device can determine the input data of the first AI model based on the beam measurement results.

在一些实施例中，若该第一AI模型为用于执行空域波束测量的模型，则该第一AI模型的输入数据可以包括以下至少一项：In some embodiments, if the first AI model is a model for performing spatial beam measurement, the input data of the first AI model may include at least one of the following:

该第一参考信号资源集合对应的N个波束的L1-RSRP，其中，N为正整数；L1-RSRP of N beams corresponding to the first reference signal resource set, where N is a positive integer;

该第一参考信号资源集合对应的N个波束对应的参考信号资源的标识；identifiers of reference signal resources corresponding to the N beams corresponding to the first reference signal resource set;

第二信息，该第二信息用于指示第一AI模型输出的基于组的波束信息中至少一个组内包含的波束为终端设备支持的能够同时接收和/或同时发送的两个波束。The second information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device and can be received and/or sent simultaneously.

在一些实施例中，参考信号资源的标识可以是同步信号块(Synchronization Signal Block，SSB)ID或信道状态信息参考信号(Channel State Information-Reference Signal，CSI-RS)ID。In some embodiments, the identifier of the reference signal resource can be a synchronization signal block (Synchronization Signal Block, SSB) ID or a channel state information reference signal (Channel State Information-Reference Signal, CSI-RS) ID.

在一些实施例中，该第二信息可以是网络设备指示给终端设备的，也可以是终端设备自主确定的。In some embodiments, the second information may be indicated by the network device to the terminal device, or may be determined autonomously by the terminal device.

在一些实施例中，若第一AI模型可以预测终端设备支持同时接收和/或同时发送的两个波束为一组，则输入数据可以包含第二信息，指示希望第一AI模型仅输出终端设备支持同时接收的两个波束为一组；或指示希望第一AI模型仅输出终端设备支持同时发送的两个波束为一组；或指示第一AI模型输出终端设备支持同时接收且同时发送的两个波束为一组。若第一AI模型仅可以预测终端设备支持同时接收的两个波束为一组，或第一AI模型仅可以预测终端设备支持同时发送的两个波束为一组，或第一AI模型仅可以预测终端设备支持同时接收且同时发送的两个波束为一组，那输入数据可以不包含第二信息。In some embodiments, if the first AI model can predict that the terminal device supports two beams that are simultaneously received and/or simultaneously transmitted as a group, the input data may include second information, indicating that the first AI model is expected to output only two beams that the terminal device supports to be simultaneously received as a group; or indicating that the first AI model is expected to output only two beams that the terminal device supports to be simultaneously transmitted as a group; or indicating that the first AI model is expected to output two beams that the terminal device supports to be simultaneously received and transmitted as a group. If the first AI model can only predict that the terminal device supports two beams that are simultaneously received as a group, or the first AI model can only predict that the terminal device supports two beams that are simultaneously transmitted as a group, or the first AI model can only predict that the terminal device supports two beams that are simultaneously received and transmitted as a group, then the input data may not include the second information.

在一些实施例中，若该第一AI模型为用于执行时域波束测量的模型，则该第一AI模型的输入数据可以包括以下至少一项：In some embodiments, if the first AI model is a model for performing time-domain beamforming, the input data of the first AI model may include at least one of the following:

至少一个历史时间；at least one historical time;

每个历史时间对应的第一参考信号资源集合对应的N个波束的L1-RSRP或L1-SINR，其中，N为正整数；L1-RSRP or L1-SINR of N beams corresponding to the first reference signal resource set corresponding to each historical time, where N is a positive integer;

每个历史时间对应的第一参考信号资源集合对应的N个波束对应的参考信号资源的标识；Identifications of reference signal resources corresponding to N beams corresponding to the first reference signal resource set corresponding to each historical time;

第四信息，该第四信息用于指示第一AI模型输出的基于组的波束信息中至少一个组内包含的波束为终端设备支持的能够同时接收和/或同时发送的两个波束。The fourth information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and/or sent simultaneously.

在一些实施例中，该历史时间可以是对该第一参考信号资源集合执行测量的时间。In some embodiments, the historical time may be a time when measurement is performed on the first reference signal resource set.

在一些实施例中，该历史时间可以包括多个，每个历史时间可以是对该第一参考信号资源集合执行一次测量的时间。In some embodiments, the historical time may include multiple times, and each historical time may be a time when a measurement is performed on the first reference signal resource set.

在一些实施例中，不同历史时间对应的第一参考信号资源可以相同，也可以不同，本公开实施例对此不作限定。In some embodiments, the first reference signal resources corresponding to different historical times may be the same or different, which is not limited in the embodiments of the present disclosure.

其中，第四信息可以包含在输入数据或不包含在输入数据中，详细情况同对第二信息的描述。The fourth information may be included in the input data or not included in the input data, and the details are the same as the description of the second information.

在一些实施例中，该第四信息可以是网络设备指示给终端设备的，也可以是终端设备自主确定的。In some embodiments, the fourth information may be indicated by the network device to the terminal device, or may be determined autonomously by the terminal device.

在一些实施例中，终端设备确定该第一AI模型的输入数据后，可以将该输入数据输入该第一AI模型，得到该第一AI模型输出的输出数据。In some embodiments, after the terminal device determines the input data of the first AI model, it can input the input data into the first AI model to obtain output data output by the first AI model.

在一些实施例中，若该第一AI模型为用于执行空域波束测量的模型，则该第一AI模型的输出数据可以包括以下至少一项：In some embodiments, if the first AI model is a model for performing spatial beam measurement, the output data of the first AI model may include at least one of the following:

至少一个组；at least one group;

至少一个第三波束；at least one third beam;

在一些实施例中，组可以是波束组。In some embodiments, the groups may be beam groups.

在一些实施例中，每个组对应的两个参考信号资源可以是第二参考信号资源集合中的两个波束。In some embodiments, the two reference signal resources corresponding to each group may be two beams in the second reference signal resource set.

在一些实施例中，每个组对应的两个参考信号资源可以分别是两个第五参考信号资源集合中的两个波束。In some embodiments, the two reference signal resources corresponding to each group may be two reference signal resources in two fifth reference signal resource sets. bundle.

例如，若波束组包括参考信号资源A和参考信号资源B，则参考信号资源A可以是第五参考信号资源集合A中的一个波束，参考信号资源B可以是第五参考信号资源集合B中的一个波束。For example, if the beam group includes reference signal resource A and reference signal resource B, reference signal resource A may be a beam in the fifth reference signal resource set A, and reference signal resource B may be a beam in the fifth reference signal resource set B.

在一些实施例中，若该输出数据包括第三波束，则该输出数据中不包括与第三波束以组的形式进行上报的任一波束。In some embodiments, if the output data includes a third beam, the output data does not include any beam reported in a group with the third beam.

在一些实施例中，终端设备可以分别指示每个组内包含的波束为终端设备支持的能够同时接收和/或同时发送的两个波束。In some embodiments, the terminal device may respectively indicate that the beams contained in each group are two beams supported by the terminal device that can be received and/or transmitted simultaneously.

在一些实施例中，终端设备可以同时指示每个组内包含的波束为终端设备支持的能够同时接收和/或同时发送的两个波束。In some embodiments, the terminal device may simultaneously indicate that the beams contained in each group are two beams supported by the terminal device that can be received and/or transmitted simultaneously.

在一些实施例中，若第一AI模型可以预测终端设备支持同时接收和/或同时发送的两个波束为一组，且输入数据不包含第二信息时，输出数据可以包含第三信息。若第一AI模型可以预测终端设备支持同时接收和/或同时发送的两个波束为一组，且输入数据包含第二信息时，输出数据不需要包含第三信息。若第一AI模型仅可以预测终端设备支持同时接收的两个波束为一组，或第一AI模型仅可以预测终端设备支持同时发送的两个波束为一组，或第一AI模型仅可以预测终端设备支持同时接收且同时发送的两个波束为一组，则输出数据可以不包含第三信息。In some embodiments, if the first AI model can predict that the terminal device supports two beams that are simultaneously received and/or simultaneously transmitted as a group, and the input data does not include the second information, the output data may include the third information. If the first AI model can predict that the terminal device supports two beams that are simultaneously received and/or simultaneously transmitted as a group, and the input data includes the second information, the output data does not need to include the third information. If the first AI model can only predict that the terminal device supports two beams that are simultaneously received as a group, or the first AI model can only predict that the terminal device supports two beams that are simultaneously transmitted as a group, or the first AI model can only predict that the terminal device supports two beams that are simultaneously received and transmitted as a group, the output data may not include the third information.

需要说明的是，输出数据包括的都是第一AI模型预测出来的信息。It should be noted that the output data includes all the information predicted by the first AI model.

在一些实施例中，若该第一AI模型为用于执行时域波束测量的模型，则该第一AI模型的输出数据可以包括以下至少一项：In some embodiments, if the first AI model is a model for performing time-domain beamforming, the output data of the first AI model may include at least one of the following:

在一些实施例中，该未来时间可以是通过该第一AI模型进行波束预测的时间。In some embodiments, the future time may be the time when beam prediction is performed by the first AI model.

在一些实施例中，该未来时间可以包括多个，在每个未来时间可以通过该第一AI模型进行一次波束预测。In some embodiments, the future time may include multiple times, and beam prediction may be performed once by the first AI model at each future time.

在一些实施例中，终端设备可以分别上报每个未来时间对应的输出数据。In some embodiments, the terminal device may report output data corresponding to each future time separately.

在一些实施例中，在每个未来时间，终端设备可以分别指示每个组内包含的波束为终端设备支持的能够同时接收和/或同时发送的两个波束。In some embodiments, at each future time, the terminal device may respectively indicate that the beams contained in each group are two beams supported by the terminal device that can be received and/or transmitted simultaneously.

在一些实施例中，在每个未来时间，终端设备可以同时指示每个组内包含的波束为终端设备支持的能够同时接收和/或同时发送的两个波束。In some embodiments, at each future time, the terminal device may simultaneously indicate that the beams included in each group are two beams supported by the terminal device that can be received and/or transmitted simultaneously.

在一些实施例中，终端设备可以同时指示多个未来时间、多个组内包含的波束为终端设备支持的能够同时接收和/或同时发送的两个波束。In some embodiments, the terminal device may simultaneously indicate multiple future times, and the beams included in multiple groups are two beams supported by the terminal device that can be received and/or transmitted simultaneously.

在一些实施例中，第五信息可以包含在输出数据或不包含在输出数据中，详细情况同对第三信息的描述。In some embodiments, the fifth information may be included in the output data or not included in the output data, and the details are the same as the description of the third information.

在一些实施例中，该输出数据对应的测量数据可以是第二波束测量结果中与该输出数据对应的波束的测量数据。In some embodiments, the measurement data corresponding to the output data may be measurement data of the beam corresponding to the output data in the second beam measurement result.

步骤S2104、终端设备根据输出数据和输出数据对应的测量数据，确定第一操作信息。Step S2104: The terminal device determines first operation information according to the output data and the measurement data corresponding to the output data.

在一些实施例中，该第一操作信息可以用于指示对第一AI模型进行管理操作。In some embodiments, the first operation information may be used to indicate a management operation to be performed on the first AI model.

在一些实施例中，该管理操作包括以下任一项：激活第一AI模型、去激活第一AI模型、切换第一AI模型、不使用AI模型。In some embodiments, the management operation includes any one of the following: activating the first AI model, deactivating the first AI model, switching the first AI model, and not using the AI model.

在一些实施例中，“切换第一AI模型”可以解释为去激活第一AI模型，激活第二AI模型，其中，该第二AI模型可以是除该第一AI模型之外的任一模型，本公开实施例对此不作限定。In some embodiments, “switching the first AI model” may be interpreted as deactivating the first AI model and activating the second AI model, wherein the second AI model may be any model except the first AI model, which is not limited in the embodiments of the present disclosure.

在一些实施例中，“不使用AI模型”可以解释为不使用任何AI模型，也可以称为fallback，即回退到传统模式(不使用AI模型的模式)。In some embodiments, “not using the AI model” can be interpreted as not using any AI model, and can also be called fallback, that is, falling back to the traditional mode (a mode that does not use the AI model).

在一些实施例中，响应于该第一AI模型处于非激活状态，根据该输出数据和该输出数据对应的测量数据确定该第一AI模型的性能满足性能需求，确定该第一操作信息为激活该第一AI模型。In some embodiments, in response to the first AI model being in an inactive state, according to the output data and the measurement corresponding to the output data The data determines that the performance of the first AI model meets the performance requirements, and determines that the first operation information is to activate the first AI model.

在一些实施例中，响应于该第一AI模型处于激活状态，根据该输出数据和该输出数据对应的测量数据确定该第一AI模型的性能不满足性能需求，确定该第一操作信息为去激活该第一AI模型。In some embodiments, in response to the first AI model being in an activated state, it is determined that the performance of the first AI model does not meet performance requirements based on the output data and the measurement data corresponding to the output data, and the first operation information is determined to deactivate the first AI model.

在一些实施例中，若该输出数据与该输出数据对应的测量数据之间的差值小于或等于第一差值阈值，则可以确定该第一AI模型的性能满足性能需求；若该输出数据与该输出数据对应的测量数据之间的差值大于第一差值阈值，则可以确定该第一AI模型的性能不满足性能需求。In some embodiments, if the difference between the output data and the measured data corresponding to the output data is less than or equal to a first difference threshold, it can be determined that the performance of the first AI model meets the performance requirements; if the difference between the output data and the measured data corresponding to the output data is greater than the first difference threshold, it can be determined that the performance of the first AI model does not meet the performance requirements.

需要说明的是，上述确定该第一AI模型的性能是否满足性能需求的方法为举例说明，本公开实施例对此不作限定。It should be noted that the above method of determining whether the performance of the first AI model meets the performance requirements is for illustration only and is not limited to this in the embodiments of the present disclosure.

步骤S2105、终端设备向网络设备发送第一操作信息。Step S2105: The terminal device sends first operation information to the network device.

在一些实施例中，网络设备可以接收第一操作信息。例如，网络设备可以接收终端设备发送的第一操作信息。再例如，网络设备也可以接收其他实体发送的第一操作信息。In some embodiments, the network device may receive the first operation information. For example, the network device may receive the first operation information sent by the terminal device. For another example, the network device may also receive the first operation information sent by other entities.

在一些实施例中，该第一操作信息的名称不做限定，例如可以是“模型操作报告”、“模型操作指示”、“模型操作信息”、“模型处理信息”等。In some embodiments, the name of the first operation information is not limited, and may be, for example, “model operation report”, “model operation instruction”, “model operation information”, “model processing information”, etc.

在一些实施例中，该第一AI模型部署在终端设备侧，终端设备向网络设备发送该第一操作信息，可以将终端设备对第一AI模型的处理决定告知网络设备。In some embodiments, the first AI model is deployed on the terminal device side, and the terminal device sends the first operation information to the network device, which can inform the network device of the terminal device's processing decision on the first AI model.

采用上述方法，终端设备可以根据网络设备发送的第一信息进行波束测量，得到波束测量结果，根据该波束测量结果确定第一AI模型的输出数据和该输出数据对应的测量数据，并根据该输出数据和该输出数据对应的测量数据确定针对该第一AI模型的第一操作信息，并将该第一操作信息告知网络设备，从而实现了对该第一AI模型的性能监控。Using the above method, the terminal device can perform beam measurement based on the first information sent by the network device to obtain a beam measurement result, determine the output data of the first AI model and the measurement data corresponding to the output data based on the beam measurement result, and determine the first operation information for the first AI model based on the output data and the measurement data corresponding to the output data, and inform the network device of the first operation information, thereby realizing performance monitoring of the first AI model.

本公开实施例所涉及的方法可以包括上述步骤S2101～步骤S2105中的至少一者。例如，步骤S2101可以作为独立实施例来实施，步骤S2105可以作为独立实施例来实施，步骤S2102+S2103+S2104可以作为独立实施例来实施，但不限于此。The method involved in the embodiment of the present disclosure may include at least one of the above steps S2101 to S2105. For example, step S2101 may be implemented as an independent embodiment, step S2105 may be implemented as an independent embodiment, and steps S2102+S2103+S2104 may be implemented as independent embodiments, but are not limited thereto.

在一些实施例中，上述步骤S2101～步骤S2105均为可选步骤。例如，步骤S2101、S2105是可选的，在不同实施例中可以对这些步骤中的一个或多个步骤进行省略或替代。再例如，步骤S2102、S2103是可选的，在不同实施例中可以对这些步骤中的一个或多个步骤进行省略或替代。In some embodiments, the above steps S2101 to S2105 are all optional steps. For example, steps S2101 and S2105 are optional, and one or more of these steps may be omitted or replaced in different embodiments. For another example, steps S2102 and S2103 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

在一些实施例中，可参见图2A所对应的说明书之前或之后记载的其他可选实现方式。In some embodiments, reference may be made to other optional implementations described before or after the specification corresponding to FIG. 2A .

在一些实施例中，信息等的名称不限定于实施例中所记载的名称，“信息(information)”、“消息(message)”、“信号(signal)”、“信令(signaling)”、“报告(report)”、“配置(configuration)”、“指示(indication)”、“指令(instruction)”、“命令(command)”、“信道”、“参数(parameter)”、“域”、“字段”、“符号(symbol)”、“码元(symbol)”、“码本(codebook)”、“码字(codeword)”、“码点(codepoint)”、“比特(bit)”、“数据(data)”、“程序(program)”、“码片(chip)”等术语可以相互替换。In some embodiments, the names of information, etc. are not limited to the names recorded in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "code element", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

在一些实施例中，“获取”、“获得”、“得到”、“接收”、“传输”、“双向传输”、“发送和/或接收”可以相互替换，其可以解释为从其他主体接收，从协议中获取，从高层获取，自身处理得到、自主实现等多种含义。In some embodiments, "obtain", "obtain", "get", "receive", "transmit", "bidirectional transmission", "send and/or receive" can be interchangeable, and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from high levels, obtaining by self-processing, autonomous implementation, etc.

在一些实施例中，“发送”、“发射”、“上报”、“下发”、“传输”、“双向传输”、“发送和/或接收”等术语可以相互替换。In some embodiments, terms such as "send", "transmit", "report", "send", "transmit", "bidirectional transmission", "send and/or receive" can be used interchangeably.

在一些实施例中，“特定(certain)”、“预定(preseted)”、“预设”、“设定”、“指示(indicated)”、“某一”、“任意”、“第一”等术语可以相互替换，“特定A”、“预定A”、“预设A”、“设定A”、“指示A”、“某一A”、“任意A”、“第一A”可以解释为在协议等中预先规定的A，也可以解释为通过设定、配置、或指示等得到的A，也可以解释为特定A、某一A、任意A、或第一A等，但不限于此。In some embodiments, terms such as "certain", "preset", "preset", "set", "indicated", "some", "any", and "first" can be interchangeable, and "specific A", "preset A", "preset A", "set A", "indicated A", "some A", "any A", and "first A" can be interpreted as A pre-defined in a protocol, etc., or as A obtained through setting, configuration, or indication, etc., and can also be interpreted as specific A, some A, any A, or first A, etc., but is not limited to this.

图2B是根据本公开实施例示出的一种模型性能监测方法的交互示意图。该方法可以由上述通信系统执行。如图2B所示，该方法可以包括：FIG2B is an interactive schematic diagram of a model performance monitoring method according to an embodiment of the present disclosure. The method may be executed by the above communication system. As shown in FIG2B , the method may include:

步骤S2201、网络设备向终端设备发送第一信息。Step S2201: The network device sends first information to the terminal device.

该步骤S2201的可选实现方式可以参见图2A的步骤S2101的可选实现方式、及图2A所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S2201 can refer to the optional implementation of step S2101 in FIG. 2A and other related parts in the embodiment involved in FIG. 2A , which will not be described in detail here.

步骤S2202、终端设备根据第一信息进行波束测量，得到波束测量结果。Step S2202: The terminal device performs beam measurement according to the first information to obtain a beam measurement result.

该步骤S2202的可选实现方式可以参见图2A的步骤S2102的可选实现方式、及图2A所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S2202 can refer to the optional implementation of step S2102 in FIG. 2A and other related parts in the embodiment involved in FIG. 2A , which will not be described in detail here.

步骤S2203、终端设备根据波束测量结果向网络设备发送指定事件。Step S2203: The terminal device sends a specified event to the network device according to the beam measurement result.

在一些实施例中，该指定事件可以用于确定第一AI模型的性能。In some embodiments, the specified event may be used to determine the performance of the first AI model.

在一些实施例中，该指定事件可以基于第一AI模型的性能值与第一门限值或第一偏移值的比较结果触发。 In some embodiments, the designated event may be triggered based on a comparison result between a performance value of the first AI model and a first threshold value or a first offset value.

在一些实施例中，该第一门限值或该第一偏移值可以是协议规定的，也可以是网络设备配置的，也可以是经验值，本公开实施例对此不作限定。In some embodiments, the first threshold value or the first offset value may be specified by a protocol, configured by a network device, or an empirical value, which is not limited in the embodiments of the present disclosure.

在一些实施例中，该指定事件可以包括以下至少一项：第一事件、第二事件、第三事件、第四事件、第五事件、第六事件、第七事件、第八事件。In some embodiments, the designated event may include at least one of the following: a first event, a second event, a third event, a fourth event, a fifth event, a sixth event, a seventh event, and an eighth event.

需要说明的是，上述指定事件为举例说明，本公开实施例对该指定事件包括的具体事件不作限定。It should be noted that the above-mentioned designated events are for illustration only, and the embodiments of the present disclosure do not limit the specific events included in the designated events.

在一些实施例中，终端设备根据该波束测量结果确定波束预测准确率小于第一准确率阈值，向网络设备发送第一事件。In some embodiments, the terminal device determines that the beam prediction accuracy is less than a first accuracy threshold based on the beam measurement result, and sends a first event to the network device.

在一些实施例中，根据该波束测量结果确定波束预测准确率大于第二准确率阈值，向网络设备发送第二事件。In some embodiments, it is determined based on the beam measurement result that the beam prediction accuracy is greater than a second accuracy threshold, and a second event is sent to the network device.

在一些实施例中，根据该波束测量结果确定波束对预测准确率小于第三准确率阈值，向网络设备发送所述第三事件。In some embodiments, it is determined based on the beam measurement result that the beam pair prediction accuracy is less than a third accuracy threshold, and the third event is sent to the network device.

在一些实施例中，根据该波束测量结果确定波束对预测准确率大于第四准确率阈值，向网络设备发送所述第四事件。In some embodiments, it is determined based on the beam measurement result that the beam pair prediction accuracy is greater than a fourth accuracy threshold, and the fourth event is sent to the network device.

在一些实施例中，根据该波束测量结果确定波束质量差异度小于第一差异度阈值，向网络设备发送所述第五事件。In some embodiments, it is determined based on the beam measurement result that the beam quality difference is less than a first difference threshold, and the fifth event is sent to the network device.

在一些实施例中，根据该波束测量结果确定波束质量差异度大于第二差异度阈值，向网络设备发送所述第六事件。In some embodiments, it is determined based on the beam measurement result that the beam quality difference is greater than a second difference threshold, and the sixth event is sent to the network device.

在一些实施例中，根据该波束测量结果确定预测波束质量差异度小于第三差异度阈值，向网络设备发送所述第七事件。In some embodiments, it is determined based on the beam measurement result that the predicted beam quality difference is less than a third difference threshold, and the seventh event is sent to the network device.

在一些实施例中，根据该波束测量结果确定预测波束质量差异度大于第四差异度阈值，向网络设备发送所述第八事件。In some embodiments, it is determined based on the beam measurement result that the predicted beam quality difference is greater than a fourth difference threshold, and the eighth event is sent to the network device.

例如，该第一准确率阈值可以是80％，该第二准确率阈值可以是90％，该第一差异度阈值可以是1dB，该第二差异度阈值可以是3dB，在波束预测准确率小于80％时触发第一事件，在波束预测准确率大于90％时触发第二事件，在波束质量差异度小于1dB时触发第五事件，在波束质量差异度大于3dB时触发第七事件。For example, the first accuracy threshold may be 80%, the second accuracy threshold may be 90%, the first difference threshold may be 1dB, and the second difference threshold may be 3dB. The first event is triggered when the beam prediction accuracy is less than 80%, the second event is triggered when the beam prediction accuracy is greater than 90%, the fifth event is triggered when the beam quality difference is less than 1dB, and the seventh event is triggered when the beam quality difference is greater than 3dB.

在一些实施例中，在该波束测量结果能够触发多个指定事件时，可以向网络设备发送多个指定事件。In some embodiments, when the beam measurement result can trigger multiple designated events, multiple designated events can be sent to the network device.

例如，若根据该波束测量结果确定波束预测准确率小于或等于第一准确率阈值，且波束对预测准确率小于或等于第二准确率阈值，可以向网络设备发送第一事件和第三事件；若根据该波束测量结果确定波束质量差异度小于或等于第一差异度阈值，且预测波束质量差异度小于或等于第二差异度阈值，可以向网络设备发送第五事件和第七事件。For example, if it is determined based on the beam measurement result that the beam prediction accuracy is less than or equal to the first accuracy threshold, and the beam pair prediction accuracy is less than or equal to the second accuracy threshold, the first event and the third event can be sent to the network device; if it is determined based on the beam measurement result that the beam quality difference is less than or equal to the first difference threshold, and the predicted beam quality difference is less than or equal to the second difference threshold, the fifth event and the seventh event can be sent to the network device.

在一些实施例中，终端设备确定触发该指定事件后，可以向网络设备上报该指定事件的ID。In some embodiments, after the terminal device determines that the designated event is triggered, it can report the ID of the designated event to the network device.

在一些实施例中，终端设备还可以向网络设备上报该指定事件对应的性能值。In some embodiments, the terminal device may also report the performance value corresponding to the specified event to the network device.

在一些实施例中，上述步骤均为可选步骤。In some embodiments, the above steps are all optional steps.

图2C是根据本公开实施例示出的一种模型性能监测方法的交互示意图。该方法可以由上述通信系统执行。如图2C所示，该方法可以包括：FIG2C is an interactive schematic diagram of a model performance monitoring method according to an embodiment of the present disclosure. The method may be executed by the above communication system. As shown in FIG2C , the method may include:

步骤S2301、网络设备向终端设备发送第一信息。Step S2301: The network device sends first information to the terminal device.

该步骤S2301的可选实现方式可以参见图2A的步骤S2101的可选实现方式、及图2A所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S2301 can refer to the optional implementation of step S2101 in FIG. 2A and other related parts in the embodiment involved in FIG. 2A , which will not be described in detail here.

步骤S2302、终端设备根据第一信息进行波束测量，得到波束测量结果。Step S2302: The terminal device performs beam measurement according to the first information to obtain a beam measurement result.

该步骤S2302的可选实现方式可以参见图2A的步骤S2102的可选实现方式、及图2A所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S2302 can refer to the optional implementation of step S2102 in FIG. 2A and other related parts in the embodiment involved in FIG. 2A , which will not be described in detail here.

步骤S2303、终端设备根据波束测量结果，向网络设备发送性能监测数据。Step S2303: The terminal device sends performance monitoring data to the network device based on the beam measurement result.

在一些实施例中，网络设备可以接收性能监测数据。例如，网络设备可以接收终端设备发送的性能监测数据。再例如，网络设备也可以接收其他实体发送的性能监测数据。In some embodiments, the network device may receive performance monitoring data. For example, the network device may receive performance monitoring data sent by a terminal device. For another example, the network device may also receive performance monitoring data sent by other entities.

在一些实施例中，该性能监测数据的名称不做限定，例如可以是“性能报告”、“性能监测报告”、“模型监测报告”、“模型性能监测报告”等。In some embodiments, the name of the performance monitoring data is not limited, and may be, for example, "performance report", "performance monitoring report", "model monitoring report", "model performance monitoring report", etc.

在一些实施例中，该性能监测数据可以用于确定第一AI模型的性能。In some embodiments, the performance monitoring data may be used to determine the performance of the first AI model.

在一些实施例中，该性能监测数据包括以下至少一项：In some embodiments, the performance monitoring data includes at least one of the following:

所述第一AI模型的性能值；The performance value of the first AI model;

指定事件，所述指定事件基于所述第一AI模型的性能值与第一门限值或第一偏移值的比较结果触发； A specified event, wherein the specified event is triggered based on a comparison result between a performance value of the first AI model and a first threshold value or a first offset value;

在一些实施例中，该性能值用于指示该第一AI模型的性能指标。In some embodiments, the performance value is used to indicate a performance indicator of the first AI model.

需要说明的是，该输入数据、该输出数据以及该输出数据对应的测量数据可以参考步骤S2103中的定义，此处不再赘述。It should be noted that the input data, the output data and the measurement data corresponding to the output data can refer to the definition in step S2103, which will not be repeated here.

同样需要说明的是，该指定事件可以参考步骤S2203中的定义，该第一操作信息可以参考步骤S2104中的定义，此处不再赘述。It should also be noted that the designated event can refer to the definition in step S2203, and the first operation information can refer to the definition in step S2104, which will not be repeated here.

在一些实施例中，所述性能值可以包括以下至少一项：In some embodiments, the performance value may include at least one of the following:

波束预测准确率；Beam prediction accuracy;

在一些实施例中，终端设备包括两个panel(天线面板)，可以向网络设备上报配对的波束对，该波束对中包括波束质量最强的波束。In some embodiments, the terminal device includes two panels (antenna panels) and can report a paired beam pair to the network device, where the beam pair includes a beam with the strongest beam quality.

在一些实施例中，所述波束预测准确率可以是预测的至少一个波束对中是否包括实际最强波束。In some embodiments, the beam prediction accuracy may be whether the predicted at least one beam pair includes the actual strongest beam.

例如，若预测的至少一个波束对中包括实际最强波束，则波束预测准确；若预测的至少一个波束对中不包括实际最强波束，则波束预测不准确。For example, if the predicted at least one beam pair includes the actual strongest beam, the beam prediction is accurate; if the predicted at least one beam pair does not include the actual strongest beam, the beam prediction is inaccurate.

在一些实施例中，实际最强波束可以是测量的L1-RSRP和/或L1-SINR最强的波束。In some embodiments, the actual strongest beam may be the beam with the strongest measured L1-RSRP and/or L1-SINR.

在一些实施例中，波束对预测准确率可以是预测的至少一个波束对中是否包括实际最佳波束对。实际最佳波束对包括实际测量的L1-RSRP和/或L1-SINR最强的波束1以及与之配对的波束2。预测的波束对可以包括多个波束对的预测准确率，例如，该多个波束对可以包括第一波束对和第二波束对。In some embodiments, the beam pair prediction accuracy may be whether the predicted at least one beam pair includes an actual best beam pair. The actual best beam pair includes beam 1 with the strongest actually measured L1-RSRP and/or L1-SINR and beam 2 paired therewith. The predicted beam pair may include the prediction accuracy of multiple beam pairs, for example, the multiple beam pairs may include a first beam pair and a second beam pair.

该预测的第一波束对包括波束A和波束B，预测的第二波束对包括波束C和波束D。而实际测量的最佳波束对也称为实际测量的第一波束对，该第一波束对可以包括波束E和波束F。波束E是测量的L1-SINR最强的波束，波束F需要满足以下至少一项条件：波束F是能够与波束A配对的多个波束中L1-SINR最强的波束、波束F的L1-SINR大于第一门限值、波束F与波束E的L1-SINR的差值小于第一偏移值。波束对预测准确是指波束A和波束B与波束E和波束F相同，或波束C和波束D与波束E和波束F相同。否则为波束对预测不准确。而波束对预测准确率可以理解为统计M次模型输出，其中波束对预测准确的模型输出次数为N，那么波束对预测准确率为N/M。The predicted first beam pair includes beam A and beam B, and the predicted second beam pair includes beam C and beam D. The best beam pair actually measured is also called the first beam pair actually measured, and the first beam pair may include beam E and beam F. Beam E is the beam with the strongest L1-SINR measured, and beam F needs to meet at least one of the following conditions: beam F is the beam with the strongest L1-SINR among multiple beams that can be paired with beam A, the L1-SINR of beam F is greater than the first threshold value, and the difference between the L1-SINR of beam F and beam E is less than the first offset value. Accurate prediction of the beam pair means that beam A and beam B are the same as beam E and beam F, or beam C and beam D are the same as beam E and beam F. Otherwise, the beam pair prediction is inaccurate. The beam pair prediction accuracy can be understood as counting M model outputs, where the number of model outputs for accurate beam pair prediction is N, then the beam pair prediction accuracy is N/M.

而实际测量的第二最佳波束对包括波束X和波束Y。波束X是除波束E和波束F之外的L1-SINR最强的波束，波束Y需要满足以下至少一项条件：波束Y是能够与波束X配对的多个波束中除波束E和波束F外L1-SINR最强的波束、波束Y的L1-SINR大于第一门限值、波束Y与波束X的L1-SINR的差值小于第一偏移值。The second best beam pair actually measured includes beam X and beam Y. Beam X is the beam with the strongest L1-SINR except beam E and beam F, and beam Y needs to meet at least one of the following conditions: beam Y is the beam with the strongest L1-SINR except beam E and beam F among multiple beams that can be paired with beam X, the L1-SINR of beam Y is greater than the first threshold value, and the difference between the L1-SINR of beam Y and beam X is less than the first offset value.

需要说明的是，若实际测量的波束对还包括第三波束对、第四波束对等，该第三波束对和该第四波束对的波束对的确定方法可以参考上述第一波束对和第二波束对的波束对来确定，此处不再赘述。It should be noted that if the beam pairs actually measured also include a third beam pair, a fourth beam pair, etc., the method for determining the beam pairs of the third beam pair and the fourth beam pair can be determined by referring to the beam pairs of the first beam pair and the second beam pair mentioned above, and will not be repeated here.

在一些实施例中，终端设备可以根据该波束测量结果确定该第一AI模型的性能值，向网络设备发送包含该性能值的性能监测数据。In some embodiments, the terminal device can determine the performance value of the first AI model based on the beam measurement result, and send performance monitoring data containing the performance value to the network device.

在一些实施例中，终端设备可以根据该波束测量结果确定该第一AI模型的性能值，根据该性能值确定指定事件，向网络设备发送包含该指定事件的性能监测数据。In some embodiments, the terminal device can determine the performance value of the first AI model based on the beam measurement result, determine a specified event based on the performance value, and send performance monitoring data containing the specified event to the network device.

在一些实施例中，终端设备可以根据该波束测量结果确定该第一数据，向网络设备发送包含该第一数据的性能监测数据。In some embodiments, the terminal device may determine the first data based on the beam measurement result, and send performance monitoring data including the first data to the network device.

在一些实施例中，若该第一AI模型部署在终端设备侧，则终端设备向网络设备发送的第一数据可以包括该输出数据和该输出数据对应的测量数据。In some embodiments, if the first AI model is deployed on the terminal device side, the first data sent by the terminal device to the network device may include the output data and the measurement data corresponding to the output data.

在一些实施例中，若该第一AI模型部署在网络设备侧，则终端设备向网络设备发送的第一数据可以包括该输入数据和该输出数据对应的测量数据。In some embodiments, if the first AI model is deployed on the network device side, the first data sent by the terminal device to the network device may include measurement data corresponding to the input data and the output data.

在一些实施例中，该输入数据和测量数据可以在同一个性能监测报告中，也可以在不同的性能监测报告中。In some embodiments, the input data and the measurement data may be in the same performance monitoring report or in different performance monitoring reports.

在一些实施例中，终端设备可以根据该波束测量结果确定该第一数据，根据该第一数据确定该第一操作信息，向网络设备发送包含该第一操作信息的性能监测数据。In some embodiments, the terminal device may determine the first data based on the beam measurement result, determine the first operation information based on the first data, and send performance monitoring data including the first operation information to the network device.

需要说明的是，终端设备可以向网络设备发送性能监测数据中的一项或多项，本公开实施例对此不作限定。 It should be noted that the terminal device can send one or more items of performance monitoring data to the network device, and the embodiments of the present disclosure are not limited to this.

图3A是根据本公开实施例示出的一种模型性能监测方法的流程示意图。如图3A所示，本公开实施例涉及模型性能监测方法，该方法可以由终端设备执行。该方法可以包括：FIG3A is a flow chart of a model performance monitoring method according to an embodiment of the present disclosure. As shown in FIG3A , the embodiment of the present disclosure relates to a model performance monitoring method, which can be executed by a terminal device. The method may include:

步骤S3101、获取第一信息。Step S3101, obtain first information.

该步骤S3101的可选实现方式可以参见图2A的步骤S2101的可选实现方式、及图2A所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S3101 can refer to the optional implementation of step S2101 in FIG. 2A and other related parts in the embodiment involved in FIG. 2A , which will not be described in detail here.

在一些实施例中，终端设备可以接收由网络设备发送的第一信息，但不限于此，终端设备也可以接收由其他主体发送的第一信息。In some embodiments, the terminal device may receive the first information sent by the network device, but is not limited thereto, and the terminal device may also receive the first information sent by other entities.

在一些实施例中，终端设备可以获取由协议规定的第一信息。In some embodiments, the terminal device may obtain first information specified by the protocol.

在一些实施例中，终端设备可以从高层(upper layer(s))获取第一信息。In some embodiments, the terminal device can obtain the first information from an upper layer(s).

在一些实施例中，步骤S3101可以被省略，终端设备可以自主实现第一信息所指示的参考信号资源，或上述功能为缺省或默认。In some embodiments, step S3101 may be omitted, and the terminal device may autonomously implement the reference signal resources indicated by the first information, or the above function may be default or acquiescent.

步骤S3102、根据第一信息进行波束测量，得到波束测量结果。Step S3102: Perform beam measurement according to the first information to obtain a beam measurement result.

该步骤S3102的可选实现方式可以参见图2A的步骤S2102的可选实现方式、及图2A所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S3102 can refer to the optional implementation of step S2102 in FIG. 2A and other related parts in the embodiment involved in FIG. 2A , which will not be described in detail here.

步骤S3103、根据波束测量结果确定输出数据和输出数据对应的测量数据。Step S3103: Determine output data and measurement data corresponding to the output data according to the beam measurement result.

该步骤S3103的可选实现方式可以参见图2A的步骤S2103的可选实现方式、及图2A所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S3103 can refer to the optional implementation of step S2103 in FIG. 2A and other related parts in the embodiment involved in FIG. 2A , which will not be described in detail here.

步骤S3104、根据输出数据和输出数据对应的测量数据，确定第一操作信息。Step S3104: Determine first operation information according to the output data and the measurement data corresponding to the output data.

该步骤S3104的可选实现方式可以参见图2A的步骤S2104的可选实现方式、及图2A所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S3104 can refer to the optional implementation of step S2104 in FIG. 2A and other related parts in the embodiment involved in FIG. 2A , which will not be described in detail here.

步骤S3105、发送第一操作信息。Step S3105: Send first operation information.

该步骤S3105的可选实现方式可以参见图2A的步骤S2105的可选实现方式、及图2A所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S3105 can refer to the optional implementation of step S2105 in FIG. 2A and other related parts in the embodiment involved in FIG. 2A , which will not be described in detail here.

本公开实施例所涉及的方法可以包括上述步骤S3101～步骤S3105中的至少一者。例如，步骤S3101可以作为独立实施例来实施，步骤S3105可以作为独立实施例来实施，步骤S3102+S3103+S3104可以作为独立实施例来实施，但不限于此。The method involved in the embodiment of the present disclosure may include at least one of the above steps S3101 to S3105. For example, step S3101 may be implemented as an independent embodiment, step S3105 may be implemented as an independent embodiment, and steps S3102+S3103+S3104 may be implemented as independent embodiments, but are not limited thereto.

在一些实施例中，上述步骤S3101～步骤S3105均为可选步骤。例如，步骤S3101、S3105是可选的，在不同实施例中可以对这些步骤中的一个或多个步骤进行省略或替代。In some embodiments, the above steps S3101 to S3105 are all optional steps. For example, steps S3101 and S3105 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

在一些实施例中，可参见图3A所对应的说明书之前或之后记载的其他可选实现方式。In some embodiments, reference may be made to other optional implementations described before or after the specification corresponding to FIG. 3A .

图3B是根据本公开实施例示出的一种模型性能监测方法的流程示意图。如图3B所示，本公开实施例涉及模型性能监测方法，该方法可以由终端设备执行。该方法可以包括：FIG3B is a flow chart of a model performance monitoring method according to an embodiment of the present disclosure. As shown in FIG3B , the embodiment of the present disclosure relates to a model performance monitoring method, which can be executed by a terminal device. The method may include:

步骤S3201、获取第一信息。Step S3201, obtain first information.

该步骤S3201的可选实现方式可以参见图2B的步骤S2201、以及图2B所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S3201 can refer to step S2201 in FIG. 2B and other related parts of the embodiment involved in FIG. 2B , which will not be described in detail here.

步骤S3202、根据第一信息进行波束测量，得到波束测量结果。Step S3202: Perform beam measurement according to the first information to obtain a beam measurement result.

该步骤S3202的可选实现方式可以参见图2B的步骤S2202、以及图2B所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S3202 can refer to step S2202 of FIG. 2B and other related parts of the embodiment involved in FIG. 2B , which will not be described in detail here.

步骤S3203、根据波束测量结果发送指定事件。Step S3203: Send a specified event according to the beam measurement result.

该步骤S3203的可选实现方式可以参见图2B的步骤S2203、以及图2B所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S3203 can refer to step S2203 of FIG. 2B and other related parts of the embodiment involved in FIG. 2B , which will not be described in detail here.

图3C是根据本公开实施例示出的一种模型性能监测方法的流程示意图。如图3C所示，本公开实施例涉及模型性能监测方法，该方法可以由终端设备执行。该方法可以包括：FIG3C is a flow chart of a model performance monitoring method according to an embodiment of the present disclosure. As shown in FIG3C , the embodiment of the present disclosure relates to a model performance monitoring method, which can be executed by a terminal device. The method may include:

步骤S3301、获取第一信息。Step S3301, obtain first information.

该步骤S3301的可选实现方式可以参见图2A的步骤S2101、图2B的步骤S2201、图3A的步骤S3101、图3B的步骤S3201的可选实现方式、以及图2A、图2B、图3A、图3B所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation method of step S3301 can refer to the optional implementation methods of step S2101 in Figure 2A, step S2201 in Figure 2B, step S3101 in Figure 3A, step S3201 in Figure 3B, and other related parts in the embodiments involved in Figures 2A, 2B, 3A, and 3B, which will not be repeated here.

步骤S3302、根据第一信息进行波束测量，得到波束测量结果。Step S3302: Perform beam measurement according to the first information to obtain a beam measurement result.

该步骤S3302的可选实现方式可以参见图2A的步骤S2102、图2B的步骤S2202、图3A的步骤S3102、图3B的步骤S3202的可选实现方式、以及图2A、图2B、图3A、图3B所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation method of step S3302 can be found in step S2102 of Figure 2A, step S2202 of Figure 2B, step S3102 of Figure 3A, and step S3202 of Figure 3B, as well as other related parts in the embodiments involved in Figures 2A, 2B, 3A, and 3B, which will not be repeated here.

步骤S3303、根据波束测量结果发送性能监测数据。Step S3303: Send performance monitoring data according to the beam measurement result.

该步骤S3303的可选实现方式可以参见图2A的步骤S2105、图2B的步骤S2203、图3A的步骤S3105、图3B的步骤S3203的可选实现方式、以及图2A、图2B、图3A、图3B所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S3303 can refer to step S2105 of FIG. 2A , step S2203 of FIG. 2B , step S3105 of FIG. 3A , The optional implementation of step S3203 of FIG. 3B and other related parts of the embodiments involved in FIG. 2A , FIG. 2B , FIG. 3A , and FIG. 3B are not described in detail here.

在一些实施例中，所述第一AI模型为用于执行波束预测的模型，所述第一信息包括以下至少一项：In some embodiments, the first AI model is a model for performing beam prediction, and the first information includes at least one of the following:

在一些实施例中，所述第一AI模型为用于执行空域波束预测的模型，所述第一参考信号资源集合与所述第二参考信号资源集合之间的关系包括以下至少一种：In some embodiments, the first AI model is a model for performing spatial beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

在一些实施例中，所述第一AI模型为用于执行时域波束预测的模型，所述第一参考信号资源集合与所述第二参考信号资源集合之间的关系包括以下至少一种：In some embodiments, the first AI model is a model for performing time-domain beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

在一些实施例中，所述第一参考信号资源集合包括多个第四参考信号资源集合，不同第四参考信号资源集合对应不同的收发点TRP；所述第二参考信号资源集合包括多个第五参考信号资源集合，不同第五参考信号资源集合对应不同的TRP。In some embodiments, the first reference signal resource set includes multiple fourth reference signal resource sets, and different fourth reference signal resource sets correspond to different transceiver points TRP; the second reference signal resource set includes multiple fifth reference signal resource sets, and different fifth reference signal resource sets correspond to different TRPs.

在一些实施例中，所述第一AI模型为用于执行空域波束预测的模型，所述第四参考信号资源集合与所述第五参考信号资源集合之间的关系包括以下至少一种：In some embodiments, the first AI model is a model for performing spatial beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

在一些实施例中，所述第一AI模型为用于执行时域波束预测的模型，所述第四参考信号资源集合与所述第五参考信号资源集合之间的关系包括以下至少一种：In some embodiments, the first AI model is a model for performing time-domain beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

在一些实施例中，所述性能监测数据包括以下至少一项：In some embodiments, the performance monitoring data includes at least one of the following:

所述第一AI模型的性能值；The performance value of the first AI model;

第一数据，所述第一数据包括所述第一AI模型的输出数据和/或所述输出数据对应的测量数据，所述输出数据是所述第一AI模型根据输入数据输出的数据；first data, the first data including output data of the first AI model and/or measurement data corresponding to the output data, the output data being data output by the first AI model according to input data;

在一些实施例中，所述性能值包括以下至少一项：In some embodiments, the performance value includes at least one of the following:

波束预测准确率；Beam prediction accuracy;

在一些实施例中，所述波束预测准确率为预测的至少一个波束中包括实际最佳波束的准确率。In some embodiments, the beam prediction accuracy is the accuracy of including an actual optimal beam in the predicted at least one beam.

在一些实施例中，所述波束对预测准确率为预测的至少一个波束对中包括实际最佳波束对的准确率。In some embodiments, the beam pair prediction accuracy is an accuracy rate of at least one predicted beam pair including an actual optimal beam pair.

在一些实施例中，所述指定事件包括以下至少一项：第一事件、第二事件、第三事件、第四事件、第五事件、第六事件、第七事件、第八事件；In some embodiments, the designated event includes at least one of the following: a first event, a second event, a third event, a fourth event, a fifth event, a sixth event, a seventh event, an eighth event;

根据所述波束测量结果确定所述波束预测准确率小于或等于第一准确率阈值，向所述网络设备发送所述第一事件；Determine, according to the beam measurement result, that the beam prediction accuracy is less than or equal to a first accuracy threshold, and send the beam prediction accuracy to the network device. Describe the first incident;

根据所述波束测量结果确定所述波束预测准确率大于所述第一准确率阈值，向所述网络设备发送所述第二事件；Determine, according to the beam measurement result, that the beam prediction accuracy is greater than the first accuracy threshold, and send the second event to the network device;

根据所述波束测量结果确定所述波束对预测准确率小于或等于第二准确率阈值，向所述网络设备发送所述第三事件；Determine, according to the beam measurement result, that the beam pair prediction accuracy is less than or equal to a second accuracy threshold, and send the third event to the network device;

根据所述波束测量结果确定所述波束对预测准确率大于所述第二准确率阈值，向所述网络设备发送所述第四事件；Determine, according to the beam measurement result, that the beam pair prediction accuracy is greater than the second accuracy threshold, and send the fourth event to the network device;

根据所述波束测量结果确定所述波束质量差异度小于或等于第一差异度阈值，向所述网络设备发送所述第五事件；Determine, according to the beam measurement result, that the beam quality difference is less than or equal to a first difference threshold, and send the fifth event to the network device;

根据所述波束测量结果确定所述波束质量差异度大于所述第一差异度阈值，向所述网络设备发送所述第六事件；Determine, according to the beam measurement result, that the beam quality difference is greater than the first difference threshold, and send the sixth event to the network device;

根据所述波束测量结果确定所述预测波束质量差异度小于或等于第二差异度阈值，向所述网络设备发送所述第七事件；Determine, according to the beam measurement result, that the predicted beam quality difference is less than or equal to a second difference threshold, and send the seventh event to the network device;

根据所述波束测量结果确定所述预测波束质量差异度大于所述第二差异度阈值，向所述网络设备发送所述第八事件。Determine, according to the beam measurement result, that the predicted beam quality difference is greater than the second difference threshold, and send the eighth event to the network device.

在一些实施例中，所述根据所述波束测量结果，向所述网络设备发送所述性能监测数据包括：In some embodiments, sending the performance monitoring data to the network device according to the beam measurement result includes:

在一些实施例中，所述根据所述输出数据和所述输出数据对应的测量数据，确定所述第一操作信息包括：In some embodiments, determining the first operation information according to the output data and the measurement data corresponding to the output data includes:

响应于所述第一AI模型处于非激活状态，根据所述输出数据和所述输出数据对应的测量数据确定所述第一AI模型的性能满足性能需求，确定所述第一操作信息为激活所述第一AI模型；或者，In response to the first AI model being in an inactive state, determining that performance of the first AI model meets performance requirements according to the output data and measurement data corresponding to the output data, and determining that the first operation information is to activate the first AI model; or

响应于所述第一AI模型处于激活状态，根据所述输出数据和所述输出数据对应的测量数据确定所述第一AI模型的性能不满足性能需求，确定所述第一操作信息为去激活所述第一AI模型。In response to the first AI model being in an activated state, it is determined that performance of the first AI model does not meet performance requirements according to the output data and measurement data corresponding to the output data, and the first operation information is determined to be deactivating the first AI model.

在一些实施例中，所述第一AI模型为用于执行空域波束预测的模型，所述输入数据包括以下至少一项：In some embodiments, the first AI model is a model for performing spatial beam prediction, and the input data includes at least one of the following:

在一些实施例中，所述第一AI模型为用于执行空域波束预测的模型，所述输出数据包括以下至少一项：In some embodiments, the first AI model is a model for performing spatial beam prediction, and the output data includes at least one of the following:

至少一个组；at least one group;

至少一个第三波束；at least one third beam;

在一些实施例中，所述第一AI模型为用于执行时域波束预测的模型，所述输入数据包括以下至少一项：In some embodiments, the first AI model is a model for performing time-domain beam prediction, and the input data includes at least one of the following:

至少一个历史时间；at least one historical time;

第四信息，所述第四信息用于指示所述第一AI模型输出的基于组的波束信息中至少一个组内包含的波束为所述终端设备支持的能够同时接收和/或同时发送的两个波束。 The fourth information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and/or sent simultaneously.

在一些实施例中，所述第一AI模型为用于执行时域波束预测的模型，所述输出数据包括以下至少一项：In some embodiments, the first AI model is a model for performing time-domain beam prediction, and the output data includes at least one of the following:

图4A是根据本公开实施例示出的一种模型性能监测方法的流程示意图。如图4A所示，本公开实施例涉及模型性能监测方法，该方法可以由网络设备执行。该方法可以包括：FIG4A is a flow chart of a model performance monitoring method according to an embodiment of the present disclosure. As shown in FIG4A , the embodiment of the present disclosure relates to a model performance monitoring method, which can be executed by a network device. The method may include:

步骤S4101、发送第一信息。Step S4101, sending the first information.

该步骤S4101的可选实现方式可以参见图2A的步骤S2101的可选实现方式、及图2A所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S4101 can refer to the optional implementation of step S2101 in FIG. 2A and other related parts in the embodiment involved in FIG. 2A , which will not be described in detail here.

步骤S4102、获取第一操作信息。Step S4102: Obtain first operation information.

该步骤S4102的可选实现方式可以参见图2A的步骤S2105的可选实现方式、及图2A所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S4102 can refer to the optional implementation of step S2105 in FIG. 2A and other related parts of the embodiment involved in FIG. 2A , which will not be described in detail here.

在一些实施例中，网络设备可以接收由终端设备发送的第一操作信息，但不限于此，网络设备也可以接收由其他主体发送的第一操作信息。In some embodiments, the network device may receive the first operation information sent by the terminal device, but is not limited thereto, and the network device may also receive the first operation information sent by other entities.

图4B是根据本公开实施例示出的一种模型性能监测方法的流程示意图。如图4B所示，本公开实施例涉及模型性能监测方法，该方法可以由网络设备执行。该方法可以包括：FIG4B is a flow chart of a model performance monitoring method according to an embodiment of the present disclosure. As shown in FIG4B , the embodiment of the present disclosure relates to a model performance monitoring method, which can be executed by a network device. The method may include:

步骤S4201、发送第一信息。Step S4201, sending the first information.

该步骤S4201的可选实现方式可以参见图2A的步骤S2101、图4A的步骤S4101的可选实现方式、及图2、图4A所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S4201 can refer to step S2101 in FIG. 2A , the optional implementation of step S4101 in FIG. 4A , and other related parts in the embodiments involved in FIG. 2 and FIG. 4A , which will not be described in detail here.

步骤S4202、获取指定事件。Step S4202: Get the specified event.

该步骤S4202的可选实现方式可以参见图2B的步骤S2203的可选实现方式、及图2B所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S4202 can refer to the optional implementation of step S2203 in FIG. 2B and other related parts in the embodiment involved in FIG. 2B , which will not be described in detail here.

在一些实施例中，网络设备可以接收由终端设备发送的指定事件，但不限于此，网络设备也可以接收由其他主体发送的指定事件。In some embodiments, the network device may receive a specified event sent by a terminal device, but is not limited thereto, and the network device may also receive a specified event sent by other entities.

图4C是根据本公开实施例示出的一种模型性能监测方法的流程示意图。如图4C所示，本公开实施例涉及模型性能监测方法，该方法可以由网络设备执行。该方法可以包括：FIG4C is a flow chart of a model performance monitoring method according to an embodiment of the present disclosure. As shown in FIG4C , the embodiment of the present disclosure relates to a model performance monitoring method, which can be executed by a network device. The method may include:

步骤S4301、发送第一信息。Step S4301, sending the first information.

该步骤S4301的可选实现方式可以参见图2A的步骤S2101、图2B的步骤S2201、图4A的步骤S4101的可选实现方式、以及图2A、图2B、图4A所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S4301 can refer to the optional implementation of step S2101 in Figure 2A, step S2201 in Figure 2B, step S4101 in Figure 4A, and other related parts in the embodiments involved in Figures 2A, 2B, and 4A, which will not be repeated here.

步骤S4302、获取性能监测数据。Step S4302: Obtain performance monitoring data.

该步骤S4302的可选实现方式可以参见图2A的步骤S2105、图2B的步骤S2203、图4A的步骤S4102、图4B的步骤S4202的可选实现方式、以及图2A、图2B、图4A、图4B所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation method of step S4302 can refer to step S2105 of Figure 2A, step S2203 of Figure 2B, step S4102 of Figure 4A, the optional implementation method of step S4202 of Figure 4B, and other related parts in the embodiments involved in Figures 2A, 2B, 4A, and 4B, which will not be repeated here.

在一些实施例中，所述第一AI模型为用于执行空域波束预测的模型，所述第一参考信号资源集合与所述第二参考信号资源集合之间的关系包括以下至少一种： In some embodiments, the first AI model is a model for performing spatial beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

所述第一AI模型的性能值；The performance value of the first AI model;

第一数据，所述第一数据包括所述第一AI模型的输出数据和/或与所述输出数据对应的测量数据，所述输出数据是所述第一AI模型根据输入数据输出的数据；first data, the first data including output data of the first AI model and/or measurement data corresponding to the output data, the output data being data output by the first AI model according to input data;

波束预测准确率；Beam prediction accuracy;

在一些实施例中，所述波束预测准确率为预测的至少一个波束中包括实际最佳波束的准确率。In some embodiments, the beam prediction accuracy is an accuracy rate of including an actual optimal beam in the predicted at least one beam.

在一些实施例中，所述波束对预测准确率包括预测的至少一个波束对中包括实际最佳波束对的准确率。In some embodiments, the beam pair prediction accuracy rate includes an accuracy rate of including an actual optimal beam pair in the predicted at least one beam pair.

接收所述终端设备发送的所述第一事件，所述第一事件是所述终端设备根据所述波束测量结果确定所述波束预测准确率小于或等于第一准确率阈值时触发的；Receiving the first event sent by the terminal device, where the first event is triggered when the terminal device determines, based on the beam measurement result, that the beam prediction accuracy is less than or equal to a first accuracy threshold;

接收所述终端设备发送的所述第二事件，所述第二事件是所述终端设备根据所述波束测量结果确定所述波束预测准确率大于所述第一准确率阈值时触发的；receiving the second event sent by the terminal device, where the second event is triggered when the terminal device determines, based on the beam measurement result, that the beam prediction accuracy is greater than the first accuracy threshold;

接收所述终端设备发送的所述第三事件，所述第三事件是所述终端设备根据所述波束测量结果确定所述波束对预测准确率小于或等于第二准确率阈值时触发的；receiving the third event sent by the terminal device, where the third event is triggered when the terminal device determines, based on the beam measurement result, that the beam pair prediction accuracy is less than or equal to a second accuracy threshold;

接收所述终端设备发送的所述第四事件，所述第四事件是所述终端设备根据所述波束测量结果确定所述波束对预测准确率大于所述第二准确率阈值时触发的；receiving the fourth event sent by the terminal device, where the fourth event is triggered when the terminal device determines, based on the beam measurement result, that the beam pair prediction accuracy is greater than the second accuracy threshold;

接收所述终端设备发送的所述第五事件，所述第五事件是所述终端设备根据所述波束测量结果确定所述波束质量差异度小于或等于第一差异度阈值时触发的； receiving the fifth event sent by the terminal device, where the fifth event is triggered when the terminal device determines, based on the beam measurement result, that the beam quality difference is less than or equal to a first difference threshold;

接收所述终端设备发送的所述第六事件，所述第六事件是所述终端设备根据所述波束测量结果确定所述波束质量差异度大于所述第一差异度阈值时触发的；receiving the sixth event sent by the terminal device, where the sixth event is triggered when the terminal device determines, according to the beam measurement result, that the beam quality difference is greater than the first difference threshold;

接收所述终端设备发送的所述第七事件，所述第七事件是所述终端设备根据所述波束测量结果确定所述预测波束质量差异度小于或等于第二差异度阈值时触发的；receiving the seventh event sent by the terminal device, where the seventh event is triggered when the terminal device determines, based on the beam measurement result, that the predicted beam quality difference is less than or equal to a second difference threshold;

接收所述终端设备发送的所述第八事件，所述第八事件是所述终端设备根据所述波束测量结果确定所述预测波束质量差异度大于所述第二差异度阈值时触发的。Receive the eighth event sent by the terminal device, where the eighth event is triggered when the terminal device determines, based on the beam measurement result, that the predicted beam quality difference is greater than the second difference threshold.

在一些实施例中，所述第一操作信息是所述终端设备根据所述输出数据和所述输出数据对应的测量数据确定的。In some embodiments, the first operation information is determined by the terminal device according to the output data and measurement data corresponding to the output data.

至少一个组；at least one group;

至少一个第三波束；at least one third beam;

至少一个历史时间；at least one historical time;

图5是根据本公开实施例示出的一种模型性能监测方法的交互示意图。如图5所示，本公开实施例涉及模型性能监测方法，该方法可以由通信系统执行。该方法可以包括：FIG5 is an interactive schematic diagram of a model performance monitoring method according to an embodiment of the present disclosure. As shown in FIG5 , the embodiment of the present disclosure relates to a model performance monitoring method, which can be executed by a communication system. The method may include:

步骤S5101、网络设备向终端设备发送第一信息。Step S5101: The network device sends first information to the terminal device.

该步骤S5101的可选实现方式可以参见图2A的步骤S2101、图3A的步骤S3101、图4A的步骤S4101 的可选实现方式、及图2A、图3A、图4A所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S5101 can refer to step S2101 in FIG. 2A , step S3101 in FIG. 3A , and step S4101 in FIG. 4A . The optional implementation methods and other related parts of the embodiments involved in Figures 2A, 3A, and 4A are not repeated here.

步骤S5102、终端设备根据第一信息进行波束测量，得到波束测量结果。Step S5102: The terminal device performs beam measurement according to the first information to obtain a beam measurement result.

该步骤S5102的可选实现方式可以参见图2A的步骤S2102、图3A的步骤S3102的可选实现方式、以及图2A、图3A所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S5102 can refer to the optional implementation of step S2102 in Figure 2A, the optional implementation of step S3102 in Figure 3A, and other related parts in the embodiments involved in Figures 2A and 3A, which will not be repeated here.

步骤S5103、终端设备根据波束测量结果，向网络设备发送性能监测数据。Step S5103: The terminal device sends performance monitoring data to the network device based on the beam measurement result.

该步骤S5103的可选实现方式可以参见图2A的步骤S2105、图3A的步骤S3105的可选实现方式、以及图2A、图3A所涉及的实施例中其他关联部分，此处不再赘述。The optional implementation of step S5103 can refer to the optional implementation of step S2105 in FIG. 2A , step S3105 in FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be described in detail here.

在一些实施例中，上述方法可以包括上述通信系统、终端设备、网络设备等的实施例所述的方法，此处不再赘述。In some embodiments, the above method may include the method described in the above embodiments of the communication system, terminal equipment, network equipment, etc., which will not be repeated here.

在一些实施例中，第一AI模型用于执行空域波束预测的模型，该第一AI模型的输入数据可以包括以下至少一项：In some embodiments, the first AI model is used to perform a model for spatial beam prediction, and the input data of the first AI model may include at least one of the following:

setB1和setB2内的波束的L1-RSRP或L1-SINR(或者还可以增加波束标识ID，即参考信号资源ID：SSB ID或CSI-RS resource ID)；L1-RSRP or L1-SINR of the beams in setB1 and setB2 (or the beam identification ID, i.e., reference signal resource ID: SSB ID or CSI-RS resource ID)

setB内波束的L1-RSRP或L1-SINR(或者还可以增加波束标识ID，即参考信号资源ID：SSB ID或CSI-RS resource ID)，setB表示不区分setB1和setB2，将setB1和setB2混合成一个setB；L1-RSRP or L1-SINR of the beam in setB (or the beam identification ID, i.e., reference signal resource ID: SSB ID or CSI-RS resource ID, can be added). setB means that setB1 and setB2 are not distinguished, and setB1 and setB2 are mixed into one setB.

在输入数据为L1-SINR时，setBi内每个参考信号资源对应配置一个用于测量干扰的参考信号资源；When the input data is L1-SINR, each reference signal resource in setBi is configured with a corresponding reference signal resource for measuring interference;

希望输出的波束组(或波束对)包含的两个波束为终端支持同时接收和/或同时发送的两个波束。It is desired that the two beams included in the output beam group (or beam pair) are two beams that the terminal supports simultaneous reception and/or transmission.

其中，setB1和setB2对应不同的参考信号资源集合，即对应不同的TRP，setB1与setA1对应同一个TRP，setB2与setA2对应同一个TRP。Among them, setB1 and setB2 correspond to different reference signal resource sets, that is, correspond to different TRPs, setB1 and setA1 correspond to the same TRP, and setB2 and setA2 correspond to the same TRP.

set Bi与set Ai的关系可以包括以下至少一项：set Bi为set Ai的子集，set Bi为宽波束而set Ai为窄波束(set Bi的一个宽波束覆盖set Ai的多个窄波束)。The relationship between set Bi and set Ai may include at least one of the following: set Bi is a subset of set Ai, set Bi is a wide beam and set Ai is a narrow beam (one wide beam of set Bi covers multiple narrow beams of set Ai).

在setB不区分setB1和setB2时，setA也不区分setA1和setA2。When setB does not distinguish between setB1 and setB2, setA does not distinguish between setA1 and setA2.

在一些实施例那个，波束可以是beam，QCL Type D，空域设置(spatial setting)，空域滤波(spatial filter)，空域关系信息(spatial relation info)，传输配置指示(Transmission Configuration Indication，TCI)state。In some embodiments, the beam can be beam, QCL Type D, spatial setting, spatial filter, spatial relation information (spatial relation info), and Transmission Configuration Indication (TCI) state.

在一些实施例中，第一AI模型用于执行空域波束预测的模型，该第一AI模型的输出数据可以包括以下至少一项：In some embodiments, the first AI model is used to perform a model for spatial beam prediction, and the output data of the first AI model may include at least one of the following:

N个beam pair(波束组)，每个beam pair对应的两个参考信号资源ID，其中，两个参考信号资源是set A中的两个，或者是set A1和set A2里分别有一个；N beam pairs, each with two reference signal resource IDs, where the two reference signal resources are either two in set A, or one in set A1 and one in set A2;

N个beam pair，每个beam pair对应的两个参考信号资源ID，以及每个参考信号资源ID对应的L1-SINR；N beam pairs, two reference signal resource IDs corresponding to each beam pair, and L1-SINR corresponding to each reference signal resource ID;

M个beam，即没有找到与之配成pair的另一beam，所以以单个形式上报；M beams, that is, no other beam is found to form a pair with it, so it is reported as a single beam;

beam对应的L1-SINR。L1-SINR corresponding to the beam.

在一些实施例中，第一AI模型用于执行时域波束预测的模型时，该第一AI模型的输入数据与该第一AI模型用于执行空域波束预测的输入数据相比，还包括多个历史时间，每个历史时间均包括该第一AI模型为用于执行空域波束预测时的一份输入数据。在set Bi<set Ai时，即set Bi为set Ai的子集时，多个历史时间的set Bi不变，或多个历史时间的set Bi包含的波束不同，比如多个set Bi可以合成一个set Ai。In some embodiments, when the first AI model is used to perform a model for time-domain beam prediction, the input data of the first AI model, compared with the input data of the first AI model used to perform spatial-domain beam prediction, further includes multiple historical times, each of which includes a portion of input data when the first AI model is used to perform spatial-domain beam prediction. When set Bi<set Ai, that is, when set Bi is a subset of set Ai, the sets Bi of multiple historical times remain unchanged, or the sets Bi of multiple historical times contain different beams, for example, multiple sets Bi can be synthesized into one set Ai.

在一些实施例中，第一AI模型用于执行时域波束预测的模型时，set Bi与set Ai的关系还可以包括set Bi与set Ai相同。In some embodiments, when the first AI model is used to execute a time domain beam prediction model, the relationship between set Bi and set Ai may also include set Bi being the same as set Ai.

在一些实施例中，第一AI模型用于执行时域波束预测的模型，该第一AI模型的输出数据与该第一AI模型用于执行空域波束预测的输出数据相比，还包括多个未来时间，每个未来时间均包括该第一AI模型为用于执行空域波束预测时的一份输出数据。In some embodiments, the first AI model is used to perform a model for time domain beam prediction. The output data of the first AI model, compared with the output data of the first AI model used to perform spatial domain beam prediction, also includes multiple future times, and each future time includes a portion of the output data of the first AI model when it is used to perform spatial domain beam prediction.

在一些实施例中，终端设备可以接收网络设备发送的第一信息，基于该第一信息确定参考信号资源，获得性能监测报告，并将性能监测报告发送给网络设备。In some embodiments, the terminal device may receive first information sent by the network device, determine a reference signal resource based on the first information, obtain a performance monitoring report, and send the performance monitoring report to the network device.

在一些实施例中，该性能监测报告可以包括用于模型性能监测的数据，该性能监测数据可以包括用于计算performance metric(性能指标)的数据，或计算出来的performance metric或基于performance metric与门限比较触发的event(事件)或做出的模型管理的操作决定(去激活模型或激活模型或切换模型或fallback(回退)到非AI模式)。In some embodiments, the performance monitoring report may include data for model performance monitoring, and the performance monitoring data may include data for calculating a performance metric, or a calculated performance metric, or an event triggered based on a comparison of the performance metric with a threshold, or an operational decision made for model management (deactivating the model or activating the model or switching the model or fallback to a non-AI mode).

在一些实施例中，用于模型性能监测的performance metric可以包括以下至少一项：In some embodiments, the performance metric used for model performance monitoring may include at least one of the following:

Top-1波束预测准确率；Top-1 beam prediction accuracy;

波束对预测准确性；beam pair prediction accuracy;

L1-SINR的difference(差异度)L1-SINR difference

Predicted(预测)L1-SINR difference(差异度)。Predicted L1-SINR difference.

在一些实施例中，终端设备包括两个panel(天线面板)，在上报配对的波束对时，波束对可以包含 L1-SINR最强的波束。若预测的多个波束对中包含实际最强的L1-SINR的波束，则表示Top-1波束预测准确率为准确。In some embodiments, the terminal device includes two panels (antenna panels). When reporting the paired beam pair, the beam pair may include: The beam with the strongest L1-SINR. If the predicted multiple beam pairs include the beam with the strongest actual L1-SINR, it means that the Top-1 beam prediction accuracy is accurate.

在一些实施例中，预测的波束对为实际终端能同时接收和/或同时发送的波束对。In some embodiments, the predicted beam pair is a beam pair that the actual terminal can simultaneously receive and/or simultaneously transmit.

在一些实施例中，该波束对可以包括第一波束对，该第一波束对可以包括第一波束和第二波束，若第一波束是set A1中L1-SINR最强的波束，则该波束对预测准确性可以通过以下至少一种方式确定：与该第一波束配对的第二波束是否是set A2中能够与该第一波束配对的多个波束中L1-SINR最强的波束、第二波束的L1-SINR是否大于第一门限值、第二波束与第一波束的L1-SINR的差值是否小于第一偏移值。In some embodiments, the beam pair may include a first beam pair, which may include a first beam and a second beam. If the first beam is the beam with the strongest L1-SINR in set A1, the prediction accuracy of the beam pair may be determined by at least one of the following methods: whether the second beam paired with the first beam is the beam with the strongest L1-SINR among multiple beams in set A2 that can be paired with the first beam, whether the L1-SINR of the second beam is greater than a first threshold value, and whether the difference between the L1-SINR of the second beam and the first beam is less than a first offset value.

在一些实施例中，该波束对还可以包括第二波束对，该第二波束对可以包括第三波束和第四波束，该第三波束可以是去掉第一波束和第二波束之后L1-SINR最强的波束，与上述第二波束类似，可以确定第四波束是否为另一个set中与第三波束配对的多个波束中L1-SINR最强的波束，或者第四波束的L1-SINR是否大于第一门限值，或者第四波束与第三波束的L1-SINR的差值是否小于第一偏移值。In some embodiments, the beam pair may also include a second beam pair, the second beam pair may include a third beam and a fourth beam, the third beam may be the beam with the strongest L1-SINR after removing the first beam and the second beam. Similar to the above-mentioned second beam, it can be determined whether the fourth beam is the beam with the strongest L1-SINR among multiple beams paired with the third beam in another set, or whether the L1-SINR of the fourth beam is greater than the first threshold value, or whether the difference between the L1-SINR of the fourth beam and the third beam is less than the first offset value.

在一些实施例中，L1-SINR的difference可以是预测L1-SINR最强的波束的实际L1-SINR与实际L1-SINR最强的波束的实际L1-SINR的差值。In some embodiments, the L1-SINR difference may be a difference between the actual L1-SINR of the beam with the strongest predicted L1-SINR and the actual L1-SINR of the beam with the strongest actual L1-SINR.

在一些实施例中，Predicted L1-SINR difference可以是预测L1-SINR最强的波束的预测L1-SINR与预测L1-SINR最强的波束的实际L1-SINR的差值。In some embodiments, Predicted L1-SINR difference may be the difference between the predicted L1-SINR of the beam with the strongest predicted L1-SINR and the actual L1-SINR of the beam with the strongest predicted L1-SINR.

需要说明的是，上述L1-SINR也可以替换为L1-RSRP，或者结合L1-SINR和L1-RSRP进行上述处理。It should be noted that the above L1-SINR may also be replaced by L1-RSRP, or the above processing may be performed in combination with L1-SINR and L1-RSRP.

在一些实施例中，用于模型性能监测的计算performance metric的数据可以包括以下至少一项：In some embodiments, the data for calculating the performance metric used for model performance monitoring may include at least one of the following:

模型在终端设备侧时，终端设备需要上报模型输出的预测值，和相应的每个预测值的测量值，输出的预测值可以参照上述图2A所示实施例的描述，而测量值可以是对应每个输出值都有一个测量值；When the model is on the terminal device side, the terminal device needs to report the predicted value output by the model and the corresponding measured value of each predicted value. The output predicted value can refer to the description of the embodiment shown in FIG. 2A above, and the measured value can be a measured value corresponding to each output value;

模型在网络设备侧时，模型输出的值在网络设备侧，所以终端设备只需要上报与模型输出的预测值对应的每个值的测量值。此外，为了网络设备侧模型的输入，终端设备还需要上报模型的输入数据，但是模型的输入数据和用于模型性能监测的测量值可以在一个report(性能监测报告)里，也可以在不同的report里。When the model is on the network device side, the value of the model output is on the network device side, so the terminal device only needs to report the measured value of each value corresponding to the predicted value of the model output. In addition, for the input of the model on the network device side, the terminal device also needs to report the input data of the model, but the input data of the model and the measured values used for model performance monitoring can be in one report (performance monitoring report) or in different reports.

在一些实施例中，终端设备可以上报基于performance metric触发的event。In some embodiments, terminal devices can report events triggered by performance metrics.

网络设备配置event，比如，Top-1波束预测准确率低于80％时触发event1；Top-1波束预测准确率高于90％时触发event2；L1-SINR的值的difference低于1dB时触发event3；L1-SINR的值的difference高于3dB时触发event4……因此，终端设备可以基于终端设备侧模型输出的预测值和实际测量的测量值，来判断是否触发以及触发哪个event，之后上报相应的event ID，进一步也可以上报触发该event对应的performance metric的值。The network device configures events. For example, event 1 is triggered when the Top-1 beam prediction accuracy is lower than 80%; event 2 is triggered when the Top-1 beam prediction accuracy is higher than 90%; event 3 is triggered when the difference of the L1-SINR value is lower than 1dB; event 4 is triggered when the difference of the L1-SINR value is higher than 3dB... Therefore, the terminal device can determine whether to trigger and which event to trigger based on the predicted value output by the model on the terminal device side and the actual measured value, and then report the corresponding event ID, and further report the value of the performance metric corresponding to the triggering of the event.

在一些实施例中，终端设备可以基于终端设备侧模型的预测值和实际测量的测量值，进行判断，判断是否需要激活或去激活或切换AI模型或功能(以上AI模型性能监测，可以是基于模型或功能的性能监测)，并告知网络设备侧终端设备的决定。In some embodiments, the terminal device can make a judgment based on the predicted value of the model on the terminal device side and the actual measured value to determine whether it is necessary to activate or deactivate or switch the AI model or function (the above AI model performance monitoring can be based on the performance monitoring of the model or function), and inform the decision of the terminal device on the network device side.

在一些实施例中，若该模型处于激活状态，发现该模型性能差，则去激活。In some embodiments, if the model is in an activated state and it is found that the model has poor performance, it is deactivated.

在一些实施例中，若该模型处于非激活状态，发现该模型性能好，则激活。In some embodiments, if the model is in an inactive state and is found to have good performance, it is activated.

在一些实施例中，参考信号资源配置信息可以包括setB和setA内的参考信号资源，若区分不同TRP，则参考信号资源配置信息可以包括setB1、setB2、setA1、setA2内的参考信号资源。若setB是setA的子集，则参考信号资源配置信息可以只包括setA的参考信号资源，若setBi是setAi的子集，则参考信号资源配置信息可以只包括setAi的参考信号资源。若第一AI模型的输入数据和输出数据是L1-SINR，则该参考信号资源配置信息还包括set B和set A对应的用于干扰测量的参考信号资源。In some embodiments, the reference signal resource configuration information may include reference signal resources in setB and setA. If different TRPs are distinguished, the reference signal resource configuration information may include reference signal resources in setB1, setB2, setA1, and setA2. If setB is a subset of setA, the reference signal resource configuration information may only include reference signal resources of setA. If setBi is a subset of setAi, the reference signal resource configuration information may only include reference signal resources of setAi. If the input data and output data of the first AI model are L1-SINR, the reference signal resource configuration information also includes reference signal resources for interference measurement corresponding to set B and set A.

在本公开的一些实施例中，提供一种通信系统，该通信系统可以包括网络设备和终端设备，其中，该网络设备可以执行本公开前述实施例中的由网络设备执行的模型性能监测方法；该终端设备可以执行本公开前述实施例中由终端设备执行的模型性能监测方法。In some embodiments of the present disclosure, a communication system is provided, which may include a network device and a terminal device, wherein the network device can execute the model performance monitoring method executed by the network device in the aforementioned embodiment of the present disclosure; and the terminal device can execute the model performance monitoring method executed by the terminal device in the aforementioned embodiment of the present disclosure.

本公开实施例还提出用于实现以上任一方法的装置，例如，提出一装置，上述装置包括用以实现以上任一方法中终端所执行的各步骤的单元或模块。再如，还提出另一装置，包括用以实现以上任一方法中网络设备(例如接入网设备、核心网功能节点、核心网设备等)所执行的各步骤的单元或模块。The embodiments of the present disclosure also propose a device for implementing any of the above methods, for example, a device is proposed, the above device includes a unit or module for implementing each step performed by the terminal in any of the above methods. For another example, another device is also proposed, including a unit or module for implementing each step performed by a network device (such as an access network device, a core network function node, a core network device, etc.) in any of the above methods.

应理解以上装置中各单元或模块的划分仅是一种逻辑功能的划分，在实际实现时可以全部或部分集成到一个物理实体上，也可以物理上分开。此外，装置中的单元或模块可以以处理器调用软件的形式实现：例如装置包括处理器，处理器与存储器连接，存储器中存储有指令，处理器调用存储器中存储的指令，以实现以上任一方法或实现上述装置各单元或模块的功能，其中处理器例如为通用处理器，例如中央处理单元(Central Processing Unit，CPU)或微处理器，存储器为装置内的存储器或装置外的存储器。或者，装置中的单元或模块可以以硬件电路的形式实现，可以通过对硬件电路的设计实现部分或全部单元或模块的功能，上述硬件电路可以理解为一个或多个处理器；例如，在一种实现中，上述硬件电路为专用集成电路 (Application-Specific Integrated Circuit，ASIC)，通过对电路内元件逻辑关系的设计，实现以上部分或全部单元或模块的功能；再如，在另一种实现中，上述硬件电路为可以通过可编程逻辑器件(Programmable Logic Device，PLD)实现，以现场可编程门阵列(Field Programmable Gate Array，FPGA)为例，其可以包括大量逻辑门电路，通过配置文件来配置逻辑门电路之间的连接关系，从而实现以上部分或全部单元或模块的功能。以上装置的所有单元或模块可以全部通过处理器调用软件的形式实现，或全部通过硬件电路的形式实现，或部分通过处理器调用软件的形式实现，剩余部分通过硬件电路的形式实现。It should be understood that the division of the various units or modules in the above devices is only a division of logical functions, and in actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. In addition, the units or modules in the device can be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, instructions are stored in the memory, and the processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above devices, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory inside the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all units or modules can be realized by designing the hardware circuits. The above hardware circuits can be understood as one or more processors; for example, in one implementation, the above hardware circuit is a dedicated integrated circuit. (Application-Specific Integrated Circuit, ASIC), by designing the logical relationship of components in the circuit, the functions of some or all of the above units or modules are realized; for example, in another implementation, the above hardware circuit can be realized by a programmable logic device (Programmable Logic Device, PLD), taking a field programmable gate array (Field Programmable Gate Array, FPGA) as an example, which can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured through a configuration file, so as to realize the functions of some or all of the above units or modules. All units or modules of the above device can be realized in the form of a processor calling software, or in the form of a hardware circuit, or in part by a processor calling software, and the rest by a hardware circuit.

在本公开实施例中，处理器是具有信号处理能力的电路，在一种实现中，处理器可以是具有指令读取与运行能力的电路，例如中央处理单元(Central Processing Unit，CPU)、微处理器、图形处理器(Graphics Processing Unit，GPU)(可以理解为微处理器)、或数字信号处理器(Digital Signal Processor，DSP)等；在另一种实现中，处理器可以通过硬件电路的逻辑关系实现一定功能，上述硬件电路的逻辑关系是固定的或可以重构的，例如处理器为专用集成电路(Application-Specific Integrated Circuit，ASIC)或可编程逻辑器件(Programmable Logic Device，PLD)实现的硬件电路,例如FPGA。在可重构的硬件电路中，处理器加载配置文档，实现硬件电路配置的过程，可以理解为处理器加载指令，以实现以上部分或全部单元或模块的功能的过程。此外，还可以是针对人工智能设计的硬件电路，其可以理解为ASIC，例如神经网络处理单元(Neural Network Processing Unit，NPU)、张量处理单元(Tensor Processing Unit，TPU)、深度学习处理单元(Deep learning Processing Unit，DPU)等。In the disclosed embodiments, the processor is a circuit with signal processing capability. In one implementation, the processor may be a circuit with instruction reading and running capability, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which may be understood as a microprocessor), or a digital signal processor (DSP); in another implementation, the processor may implement certain functions through the logical relationship of a hardware circuit, and the logical relationship of the above hardware circuit may be fixed or reconfigurable, such as a hardware circuit implemented by a processor such as an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the hardware circuit configuration may be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as ASIC, such as Neural Network Processing Unit (NPU), Tensor Processing Unit (TPU), Deep Learning Processing Unit (DPU), etc.

图6A是本公开实施例提出的一种终端设备的结构示意图。如图6A所示，该终端设备101可以包括收发模块6101、处理模块6102等中的至少一者。在一些实施例中，该收发模块6101，被配置为接收网络设备发送的第一信息，所述第一信息包括参考信号资源的配置，所述参考信号资源用于终端设备进行波束测量；处理模块6102，被配置为根据所述第一信息进行波束测量，得到波束测量结果；所述收发模块6101，还被配置为根据所述波束测量结果，向所述网络设备发送性能监测数据，所述性能监测数据用于确定第一AI模型的性能。可选地，该收发模块6101可以用于执行以上任一方法中终端设备101执行的发送和/或接收等通信步骤(例如步骤S2101、步骤S2105，但不限于此)中的至少一者，此处不再赘述。可选地，该处理模块6102可以用于执行以上任一方法中终端设备101执行的其他步骤(例如步骤S2102、步骤S2103、步骤S2104，但不限于此)中的至少一者，此处不再赘述。FIG6A is a schematic diagram of the structure of a terminal device proposed in an embodiment of the present disclosure. As shown in FIG6A , the terminal device 101 may include at least one of a transceiver module 6101, a processing module 6102, etc. In some embodiments, the transceiver module 6101 is configured to receive first information sent by a network device, wherein the first information includes the configuration of reference signal resources, and the reference signal resources are used for the terminal device to perform beam measurement; the processing module 6102 is configured to perform beam measurement according to the first information to obtain a beam measurement result; the transceiver module 6101 is also configured to send performance monitoring data to the network device according to the beam measurement result, and the performance monitoring data is used to determine the performance of the first AI model. Optionally, the transceiver module 6101 can be used to perform at least one of the communication steps such as sending and/or receiving performed by the terminal device 101 in any of the above methods (for example, step S2101, step S2105, but not limited to this), which will not be repeated here. Optionally, the processing module 6102 can be used to execute at least one of the other steps (such as step S2102, step S2103, step S2104, but not limited to these) performed by the terminal device 101 in any of the above methods, which will not be repeated here.

在一些实施例中，收发模块可以包括发送模块和/或接收模块，发送模块和接收模块可以是分离的，也可以集成在一起。可选地，收发模块可以与收发器相互替换。In some embodiments, the transceiver module may include a sending module and/or a receiving module, and the sending module and the receiving module may be separate or integrated. Optionally, the transceiver module may be interchangeable with the transceiver.

图6B是本公开实施例提出的一种网络设备的结构示意图。如图6B所示，该网络设备102可以包括：收发模块6201、处理模块6202等中的至少一者。在一些实施例中，该收发模块6201，被配置为向终端设备发送第一信息，所述第一信息包括参考信号资源的配置，所述参考信号资源用于终端设备进行波束测量；所述收发模块6201，还被配置为接收所述终端设备根据波束测量结果发送的性能监测数据，所述波束测量结果是所述终端设备根据所述第一信息进行波束测量得到的，所述性能监测数据用于确定第一AI模型的性能。可选地，该收发模块6201可以用于执行以上任一方法中网络设备102执行的发送和/或接收等通信步骤(例如步骤S2101、步骤S2105，但不限于此)中的至少一者，此处不再赘述。Figure 6B is a schematic diagram of the structure of a network device proposed in an embodiment of the present disclosure. As shown in Figure 6B, the network device 102 may include: at least one of a transceiver module 6201, a processing module 6202, etc. In some embodiments, the transceiver module 6201 is configured to send a first information to a terminal device, wherein the first information includes a configuration of a reference signal resource, and the reference signal resource is used for the terminal device to perform beam measurement; the transceiver module 6201 is also configured to receive performance monitoring data sent by the terminal device according to the beam measurement result, wherein the beam measurement result is obtained by the terminal device performing beam measurement according to the first information, and the performance monitoring data is used to determine the performance of the first AI model. Optionally, the transceiver module 6201 can be used to perform at least one of the communication steps such as sending and/or receiving performed by the network device 102 in any of the above methods (for example, step S2101, step S2105, but not limited to this), which will not be repeated here.

在一些实施例中，处理模块可以是一个模块，也可以包括多个子模块。可选地，上述多个子模块分别执行处理模块所需执行的全部或部分步骤。可选地，处理模块可以与处理器相互替换。In some embodiments, the processing module can be a module or include multiple submodules. Optionally, the multiple submodules respectively execute all or part of the steps required to be executed by the processing module. Optionally, the processing module can be replaced with the processor.

图7A是本公开实施例提出的通信设备7100的结构示意图。通信设备7100可以是网络设备(例如接入网设备、核心网设备等)，也可以是终端(例如用户设备等)，也可以是支持第一设备实现以上任一方法的芯片、芯片系统、或处理器等，还可以是支持终端实现以上任一方法的芯片、芯片系统、或处理器等。通信设备7100可用于实现上述方法实施例中描述的方法，具体可以参见上述方法实施例中的说明。FIG7A is a schematic diagram of the structure of a communication device 7100 proposed in an embodiment of the present disclosure. The communication device 7100 may be a network device (e.g., an access network device, a core network device, etc.), or a terminal (e.g., a user device, etc.), or a chip, a chip system, or a processor that supports the first device to implement any of the above methods, or a chip, a chip system, or a processor that supports the terminal to implement any of the above methods. The communication device 7100 may be used to implement the method described in the above method embodiment, and the details may refer to the description in the above method embodiment.

如图7A所示，通信设备7100包括一个或多个处理器7101。处理器7101可以是通用处理器或者专用处理器等，例如可以是基带处理器或中央处理器。基带处理器可以用于对通信协议以及通信数据进行处理，中央处理器可以用于对通信装置(如，基站、基带芯片，物联网设备、物联网设备芯片，DU或CU等)进行控制，执行程序，处理程序的数据。通信设备7100用于执行以上任一方法。As shown in FIG7A , the communication device 7100 includes one or more processors 7101. The processor 7101 may be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor may be used to process the communication protocol and the communication data, and the central processing unit may be used to control the communication device (such as a base station, a baseband chip, an IoT device, an IoT device chip, a DU or a CU, etc.), execute a program, and process the data of the program. The communication device 7100 is used to execute any of the above methods.

在一些实施例中，通信设备7100还包括用于存储指令的一个或多个存储器7102。可选地，全部或部分存储器7102也可以处于通信设备7100之外。In some embodiments, the communication device 7100 further includes one or more memories 7102 for storing instructions. Optionally, all or part of the memory 7102 may also be outside the communication device 7100.

在一些实施例中，通信设备7100还包括一个或多个收发器7103。在通信设备7100包括一个或多个收发器7103时，收发器7103执行上述方法中的发送和/或接收等通信步骤(例如步骤S2101、步骤S4101，但不限于此)中的至少一者，处理器7101执行其他步骤(例如步骤S2102，但不限于此)中的至少一者。In some embodiments, the communication device 7100 further includes one or more transceivers 7103. When the communication device 7100 includes one or more transceivers 7103, the transceiver 7103 performs at least one of the communication steps such as sending and/or receiving in the above method (for example, step S2101, step S4101, but not limited thereto), and the processor 7101 performs at least one of the other steps (for example, step S2102, but not limited thereto).

在一些实施例中，收发器可以包括接收器和/或发送器，接收器和发送器可以是分离的，也可以集成在一起。可选地，收发器、收发单元、收发机、收发电路等术语可以相互替换，发送器、发送单元、发送机、发送电路等术语可以相互替换，接收器、接收单元、接收机、接收电路等术语可以相互替换。In some embodiments, the transceiver may include a receiver and/or a transmitter, and the receiver and the transmitter may be separate or integrated in the device. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, etc. can be replaced with each other, the terms transmitter, transmission unit, transmitter, transmission circuit, etc. can be replaced with each other, and the terms receiver, receiving unit, receiver, receiving circuit, etc. can be replaced with each other.

在一些实施例中，通信设备7100可以包括一个或多个接口电路。可选地，接口电路与存储器7102连接，接口电路可用于从存储器7102或其他装置接收信号，可用于向存储器7102或其他装置发送信号。例如，接口电路可读取存储器7102中存储的指令，并将该指令发送给处理器7101。In some embodiments, the communication device 7100 may include one or more interface circuits. Optionally, the interface circuit is connected to the memory 7102, and the interface circuit can be used to receive signals from the memory 7102 or other devices, and can be used to send signals to the memory 7102 or other devices. For example, the interface circuit can read the instructions stored in the memory 7102 and send the instructions to the processor 7101.

以上实施例描述中的通信设备7100可以是第一设备或者物联网设备，但本公开中描述的通信设备7100的范围并不限于此，通信设备7100的结构可以不受图7A的限制。通信设备可以是独立的设备或者可以是较大设备的一部分。例如所述通信设备可以是：1)独立的集成电路IC，或芯片，或，芯片系统或子系统；(2)具有一个或多个IC的集合，可选地，上述IC集合也可以包括用于存储数据，程序的存储部件；(3)ASIC，例如调制解调器(Modem)；(4)可嵌入在其他设备内的模块；(5)接收机、物联网设备、智能物联网设备、蜂窝电话、无线设备、手持机、移动单元、车载设备、第一设备、云设备、人工智能设备等等；(6)其他等等。The communication device 7100 described in the above embodiments may be a first device or an IoT device, but the scope of the communication device 7100 described in the present disclosure is not limited thereto, and the structure of the communication device 7100 may not be limited by FIG. 7A. The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, an IoT device, an intelligent IoT device, a cellular phone, a wireless device, a handheld device, a mobile unit, a vehicle-mounted device, a first device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

图7B是本公开实施例提出的芯片7200的结构示意图。对于通信设备7100可以是芯片或芯片系统的情况，可以参见图7B所示的芯片7200的结构示意图，但不限于此。7B is a schematic diagram of the structure of a chip 7200 provided in an embodiment of the present disclosure. In the case where the communication device 7100 may be a chip or a chip system, reference may be made to the schematic diagram of the structure of the chip 7200 shown in FIG. 7B , but the present disclosure is not limited thereto.

芯片7200包括一个或多个处理器7201，芯片7200用于执行以上任一方法。The chip 7200 includes one or more processors 7201, and the chip 7200 is used to execute any of the above methods.

在一些实施例中，芯片7200还包括一个或多个接口电路7203。可选地，接口电路7203与存储器7202连接，接口电路7203可以用于从存储器7202或其他装置接收信号，接口电路7203可用于向存储器7202或其他装置发送信号。例如，接口电路7203可读取存储器7202中存储的指令，并将该指令发送给处理器7201。In some embodiments, the chip 7200 further includes one or more interface circuits 7203. Optionally, the interface circuit 7203 is connected to the memory 7202, and the interface circuit 7203 can be used to receive signals from the memory 7202 or other devices, and the interface circuit 7203 can be used to send signals to the memory 7202 or other devices. For example, the interface circuit 7203 can read instructions stored in the memory 7202 and send the instructions to the processor 7201.

在一些实施例中，接口电路7203执行上述方法中的发送和/或接收等通信步骤(例如步骤S2101、步骤S4101，但不限于此)中的至少一者，处理器7201执行其他步骤(例如步骤S2102，但不限于此)中的至少一者。In some embodiments, the interface circuit 7203 executes at least one of the communication steps such as sending and/or receiving in the above method (for example, step S2101, step S4101, but not limited to this), and the processor 7201 executes at least one of the other steps (for example, step S2102, but not limited to this).

在一些实施例中，接口电路、接口、收发管脚、收发器等术语可以相互替换。In some embodiments, terms such as interface circuit, interface, transceiver pin, and transceiver may be used interchangeably.

在一些实施例中，芯片7200还包括用于存储指令的一个或多个存储器7202。可选地，全部或部分存储器7202可以处于芯片7200之外。In some embodiments, the chip 7200 further includes one or more memories 7202 for storing instructions. Alternatively, all or part of the memory 7202 may be outside the chip 7200.

本公开实施例还提出存储介质，上述存储介质上存储有指令，当上述指令在通信设备7100上运行时，使得通信设备7100执行以上任一方法。可选地，上述存储介质是电子存储介质。可选地，上述存储介质是计算机可读存储介质，但不限于此，其也可以是其他装置可读的存储介质。可选地，上述存储介质可以是非暂时性(non-transitory)存储介质，但不限于此，其也可以是暂时性存储介质。The embodiment of the present disclosure also proposes a storage medium, on which instructions are stored, and when the instructions are executed on the communication device 7100, the communication device 7100 executes any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited to this, and it can also be a storage medium readable by other devices. Optionally, the storage medium can be a non-transitory storage medium, but is not limited to this, and it can also be a temporary storage medium.

本公开实施例还提出程序产品，上述程序产品被通信设备7100执行时，使得通信设备7100执行以上任一方法。可选地，上述程序产品可以是计算机程序产品。The embodiment of the present disclosure also provides a program product, and when the program product is executed by the communication device 7100, the communication device 7100 executes any of the above methods. Optionally, the program product may be a computer program product.

本公开实施例还提出计算机程序，当其在计算机上运行时，使得计算机执行以上任一方法。 The embodiment of the present disclosure also provides a computer program, which, when executed on a computer, enables the computer to execute any of the above methods.

Claims

A model performance monitoring method, characterized in that the method comprises:

Receiving first information sent by a network device, where the first information includes configuration of a reference signal resource, where the reference signal resource is used by the terminal device to perform beam measurement;

Performing beam measurement according to the first information to obtain a beam measurement result;

Based on the beam measurement result, performance monitoring data is sent to the network device, and the performance monitoring data is used to determine the performance of the first AI model.

The method according to claim 1, wherein the first AI model is a model for performing beam prediction, and the first information includes at least one of the following:

a first reference signal resource set, wherein the reference signal resources in the first reference signal resource set correspond to the beam to be measured;

a second reference signal resource set, wherein the reference signal resources in the second reference signal resource set correspond to beams to be predicted;

a third reference signal resource set, wherein the third reference signal resource set includes resources used for interference measurement;

The relationship between the first reference signal resource set and the second reference signal resource set.

The method according to claim 2, wherein the first AI model is a model for performing spatial beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

The first reference signal resource set is a subset of the second reference signal resource set;

The beam corresponding to the first reference signal resource set is a wide beam, the beam corresponding to the second reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set are the same.

The method according to claim 2, wherein the first AI model is a model for performing time domain beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

The first reference signal resource set is the same as the second reference signal resource set;

The method according to any one of claims 2 to 4, characterized in that

The first reference signal resource set includes multiple fourth reference signal resource sets, and different fourth reference signal resource sets correspond to different transceiver points TRP;

The second reference signal resource set includes multiple fifth reference signal resource sets, and different fifth reference signal resource sets correspond to different TRPs.

The method according to claim 5, characterized in that the first AI model is a model for performing spatial beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

The fourth reference signal resource set is a subset of the fifth reference signal resource set;

The beam corresponding to the fourth reference signal resource set is a wide beam, the beam corresponding to the fifth reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the fourth reference signal resource set and the fifth reference signal resource set are the same.

The method according to claim 5, characterized in that the first AI model is a model for performing time domain beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

The fourth reference signal resource set is the same as the fifth reference signal resource set;

The method according to any one of claims 2 to 7, wherein the performance monitoring data includes at least one of the following:

The performance value of the first AI model;

first data, the first data including at least one of the following: input data of the first AI model, output data of the first AI model, and measurement data corresponding to the output data, where the output data is data output by the first AI model according to the input data;

A specified event, wherein the specified event is triggered based on a comparison result between a performance value of the first AI model and a first threshold value or a first offset value;

First operation information, where the first operation information is used to indicate a management operation to be performed on the first AI model, where the management operation includes any one of the following: activating the first AI model, deactivating the first AI model, switching the first AI model, and not using the AI model.

The method according to claim 8, characterized in that the performance value includes at least one of the following:

Beam prediction accuracy;

A beam pair prediction accuracy rate, wherein the beam pair includes a beam pair that the terminal device can simultaneously receive and/or simultaneously transmit;

A beam quality difference, where the beam quality difference is a difference between a measured beam quality of a first beam and a measured beam quality of a second beam, where the first beam is a beam with the strongest predicted beam quality and the second beam is a beam with the strongest measured beam quality;

A predicted beam quality difference is a difference between a predicted beam quality of the first beam and a measured beam quality of the first beam.

The method according to claim 9, characterized in that

The beam prediction accuracy rate is the accuracy rate of including an actual optimal beam in at least one predicted beam.

The method according to claim 9 or 10, characterized in that

The beam pair prediction accuracy rate is an accuracy rate of including an actual optimal beam pair in the predicted at least one beam pair.

The method according to any one of claims 9 to 11, characterized in that the designated event comprises at least one of the following: a first event, a second event, a third event, a fourth event, a fifth event, a sixth event, a seventh event, and an eighth event;

The sending the performance monitoring data to the network device according to the beam measurement result includes at least one of the following:

Determine, according to the beam measurement result, that the beam prediction accuracy is less than a first accuracy threshold, and send the first event to the network device;

Determine, according to the beam measurement result, that the beam prediction accuracy is greater than a second accuracy threshold, and send the second event to the network device;

Determine, according to the beam measurement result, that the beam pair prediction accuracy is less than a third accuracy threshold, and send the third event to the network device;

Determine, according to the beam measurement result, that the beam pair prediction accuracy is greater than a fourth accuracy threshold, and send the fourth event to the network device;

Determine, according to the beam measurement result, that the beam quality difference is less than a first difference threshold, and send the fifth event to the network device;

Determine, according to the beam measurement result, that the beam quality difference is greater than a second difference threshold, and send the sixth event to the network device;

Determine, according to the beam measurement result, that the predicted beam quality difference is less than a third difference threshold, and send the seventh event to the network device;

Determine, according to the beam measurement result, that the predicted beam quality difference is greater than a fourth difference threshold, and send the eighth event to the network device.

The method according to any one of claims 8 to 12, characterized in that the sending the performance monitoring data to the network device according to the beam measurement result comprises:

Determine the output data and the measurement data corresponding to the output data according to the beam measurement result;

determining the first operation information according to the output data and the measurement data corresponding to the output data;

The first operation information is sent to the network device.

The method according to claim 13, characterized in that the determining the first operation information according to the output data and the measurement data corresponding to the output data comprises:

The first AI model is in an inactive state, and it is determined that the performance of the first AI model meets the performance requirement according to the output data and the measurement data corresponding to the output data, and the first operation information is determined to activate the first AI model; or

The first AI model is in an activated state. It is determined according to the output data and the measurement data corresponding to the output data that the performance of the first AI model does not meet the performance requirement, and the first operation information is determined to be to deactivate the first AI model.

The method according to any one of claims 8 to 14, characterized in that the first AI model is used to perform spatial beam prediction The input data includes at least one of the following:

beam qualities of the N beams corresponding to the first reference signal resource set, the beam qualities comprising layer 1 reference signal received power L1-RSRP or layer 1 signal to interference plus noise ratio L1-SINR, where N is a positive integer;

identifiers of reference signal resources corresponding to the N beams corresponding to the first reference signal resource set;

The second information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device and can be received and/or sent simultaneously.

The method according to any one of claims 8 to 15, characterized in that the first AI model is a model for performing spatial beam prediction, and the output data includes at least one of the following:

at least one group;

identifiers of two reference signal resources corresponding to each group, wherein the reference signal resources are reference signal resources in the second reference signal resource set;

a beam quality corresponding to an identifier of each of the reference signal resources;

at least one third beam;

an identifier of a reference signal resource corresponding to each of the third beams, wherein the reference signal resource is a reference signal resource in the second reference signal resource set;

a beam quality corresponding to each of the third beams;

The third information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and/or sent simultaneously.

The method according to any one of claims 8 to 14, characterized in that the first AI model is a model for performing time-domain beam prediction, and the input data includes at least one of the following:

at least one historical time;

beam quality of N beams corresponding to the first reference signal resource set corresponding to each of the historical times, the beam quality comprising L1-RSRP or L1-SINR, where N is a positive integer;

identifiers of reference signal resources corresponding to the N beams corresponding to the first reference signal resource set corresponding to each of the historical times;

The fourth information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and/or sent simultaneously.

The method according to any one of claims 8 to 14, characterized in that the first AI model is a model for performing time-domain beam prediction, and the output data includes at least one of the following:

At least one future time, where the future time is a beam corresponding time for beam prediction by the first AI model;

at least one group corresponding to each of the future times;

identifiers of two reference signal resources corresponding to each group corresponding to each of the future times, wherein the reference signal resources are reference signal resources in the second reference signal resource set;

a beam quality corresponding to an identifier of each of the reference signal resources corresponding to each of the future times;

at least one fourth beam corresponding to each of the future times;

an identifier of a reference signal resource corresponding to each of the fourth beams corresponding to each of the future times, wherein the reference signal resource is a reference signal resource in the second reference signal resource set;

The beam quality corresponding to each of the fourth beams at each of the future times;

The fifth information is used to indicate that the beams contained in at least one group corresponding to at least one future time in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and/or sent simultaneously.

Sending first information to a terminal device, where the first information includes configuration of a reference signal resource, where the reference signal resource is used by the terminal device to perform beam measurement;

Receive performance monitoring data sent by the terminal device according to the beam measurement result, where the beam measurement result is obtained by the terminal device performing beam measurement according to the first information, and the performance monitoring data is used to determine the performance of the first AI model.

The method according to claim 19, wherein the first AI model is a model for performing beam prediction, and the first information includes at least one of the following:

The method according to claim 20, characterized in that the first AI model is a model for performing spatial beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

The method according to claim 20, characterized in that the first AI model is a model for performing time domain beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

The method according to any one of claims 20 to 22, characterized in that

The method according to claim 23, characterized in that the first AI model is a model for performing spatial beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

The method according to claim 23, characterized in that the first AI model is a model for performing time domain beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

The method according to any one of claims 20 to 25, characterized in that the performance monitoring data includes at least one of the following:

The performance value of the first AI model;

First operation information, where the first operation information is used to indicate a management operation to be performed on the first AI model, where the management operation includes activating the first AI model, deactivating the first AI model, switching the first AI model, and not using the AI model.

The method according to claim 26, characterized in that the performance value includes at least one of the following:

Beam prediction accuracy;

The method according to claim 27, characterized in that

The method according to claim 27 or 28, characterized in that

The beam pair prediction accuracy rate includes an accuracy rate of including an actual optimal beam pair in the predicted at least one beam pair.

The method according to any one of claims 27 to 29, characterized in that the designated event comprises at least one of the following: a first event, a second event, a third event, a fourth event, a fifth event, a sixth event, a seventh event, and an eighth event;

The receiving performance monitoring data sent by the terminal device according to the beam measurement result includes at least one of the following:

Receiving the first event sent by the terminal device, where the first event is triggered when the terminal device determines, based on the beam measurement result, that the beam prediction accuracy is less than a first accuracy threshold;

receiving the second event sent by the terminal device, where the second event is triggered when the terminal device determines, based on the beam measurement result, that the beam prediction accuracy is greater than a second accuracy threshold;

receiving the third event sent by the terminal device, where the third event is triggered when the terminal device determines, based on the beam measurement result, that the prediction accuracy of the beam pair is less than a third accuracy threshold;

receiving the fourth event sent by the terminal device, where the fourth event is triggered when the terminal device determines, based on the beam measurement result, that the beam pair prediction accuracy is greater than a fourth accuracy threshold;

receiving the fifth event sent by the terminal device, where the fifth event is triggered when the terminal device determines, according to the beam measurement result, that the beam quality difference is less than a first difference threshold;

receiving the sixth event sent by the terminal device, where the sixth event is triggered when the terminal device determines, according to the beam measurement result, that the beam quality difference is greater than a second difference threshold;

receiving the seventh event sent by the terminal device, where the seventh event is triggered when the terminal device determines, based on the beam measurement result, that the predicted beam quality difference is less than a third difference threshold;

Receive the eighth event sent by the terminal device, where the eighth event is triggered when the terminal device determines, based on the beam measurement result, that the predicted beam quality difference is greater than a fourth difference threshold.

The method according to any one of claims 26 to 30, characterized in that

The first operation information is determined by the terminal device according to the output data and measurement data corresponding to the output data.

The method according to any one of claims 26 to 31, characterized in that the first AI model is a model for performing spatial beam prediction, and the input data includes at least one of the following:

The method according to any one of claims 26 to 32, characterized in that the first AI model is a model for performing spatial beam prediction, and the output data includes at least one of the following:

at least one group;

at least one third beam;

a beam quality corresponding to each of the third beams;

The method according to any one of claims 26 to 31, characterized in that the first AI model is a model for performing time-domain beam prediction, and the input data includes at least one of the following:

at least one historical time;

The method according to any one of claims 26 to 31, characterized in that the first AI model is a model for performing time domain beam prediction, and the output data includes at least one of the following:

multiple future times, where the future times are times corresponding to beams predicted by the first AI model;

at least one group corresponding to each of the future times;

at least one fourth beam corresponding to each of the future times;

a beam quality corresponding to each of the fourth beams corresponding to each of the future time;

A terminal device, comprising:

A transceiver module is configured to receive first information sent by a network device, where the first information includes a configuration of a reference signal resource, where the reference signal resource is used by the terminal device to perform beam measurement;

A processing module, configured to perform beam measurement according to the first information to obtain a beam measurement result;

The transceiver module is also configured to send performance monitoring data to the network device based on the beam measurement result, and the performance monitoring data is used to determine the performance of the first AI model.

A network device, comprising:

A transceiver module is configured to send first information to a terminal device, where the first information includes a configuration of a reference signal resource, where the reference signal resource is used by the terminal device to perform beam measurement;

The transceiver module is also configured to receive performance monitoring data sent by the terminal device according to the beam measurement result, where the beam measurement result is obtained by the terminal device performing beam measurement according to the first information, and the performance monitoring data is used to determine the performance of the first AI model.

A communication device, characterized in that it comprises:

one or more processors;

The communication device is used to execute the communication method described in any one of claims 1 to 18 or claims 19 to 35.

A storage medium storing instructions, characterized in that when the instructions are executed on a terminal device, the terminal device executes a communication method as described in any one of claims 1 to 18, or when the instructions are executed on a network device, the network device executes a communication method as described in any one of claims 19 to 35.