Method and apparatus for wireless communication

Abstract

A method and apparatus for wireless communication are provided. The method comprises: a first device receiving a first reference signal, the first reference signal being sent through a first beam set; the first device performs performance monitoring on a first model according to a measurement result of the first beam set; wherein the first reference signal corresponds to a first monitoring instance, and the first beam set is determined according to a reasoning beam set of the first model; the first wave beam set comprises all wave beams in the reasoning wave beam set, or the first wave beam set comprises a part of wave beams in the reasoning wave beam set.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and more particularly, to a method and apparatus for wireless communication. Background Art

[0002] In certain beam management scenarios, terminal devices can use models to predict downlink transmission beams and report these predictions to network equipment. This model must be trained before it can perform inference and prediction. However, there is a certain time gap between the training and inference phases. If beam transmission conditions change, the model may encounter errors during inference. Therefore, how to monitor model performance is a pressing technical challenge. Summary of the Invention

[0003] The present application provides a method and apparatus for wireless communication. The following describes various aspects of the embodiments of the present application.

[0004] In a first aspect, a method for wireless communication is provided, including: a first device receives a first reference signal, and the first reference signal is sent through a first beam set; the first device performs performance monitoring on a first model according to a measurement result of the first beam set; wherein the first reference signal corresponds to a first monitoring instance, and the first beam set is determined according to an inference beam set of the first model; the first beam set includes all beams in the inference beam set, or the first beam set includes part of the beams in the inference beam set.

[0005] According to a second aspect, a method for wireless communication is provided, including: a second device sends a first reference signal, and the first reference signal is sent through a first beam set; wherein the first reference signal corresponds to a first monitoring instance, and the measurement result of the first beam set is used to perform performance monitoring on a first model, and the first beam set is determined according to an inference beam set of the first model; the first beam set includes all beams in the inference beam set, or the first beam set includes part of the beams in the inference beam set.

[0006] According to a third aspect, a device for wireless communication is provided, which is a first device and includes: a transceiver unit for receiving a first reference signal, which is sent through a first beam set; a processing unit for monitoring the performance of a first model based on a measurement result of the first beam set; wherein the first reference signal corresponds to a first monitoring instance, and the first beam set is determined based on an inference beam set of the first model; the first beam set includes all beams in the inference beam set, or the first beam set includes some beams in the inference beam set.

[0007] In a fourth aspect, a device for wireless communication is provided, which is a second device and includes: a transceiver unit, used to send a first reference signal, and the first reference signal is sent through a first beam set; wherein the first reference signal corresponds to a first monitoring instance, and the measurement result of the first beam set is used to perform performance monitoring on a first model, and the first beam set is determined based on an inference beam set of the first model; the first beam set includes all beams in the inference beam set, or the first beam set includes some beams in the inference beam set.

[0008] In a fifth aspect, a communication device is provided, comprising a memory and a processor, wherein the memory is used to store a program, and the processor is used to call the program in the memory to execute the method described in the first aspect or the second aspect.

[0009] In a sixth aspect, a device is provided, comprising a processor for calling a program from a memory to execute the method as described in the first aspect or the second aspect.

[0010] In a seventh aspect, a chip is provided, comprising a processor for calling a program from a memory so that a device equipped with the chip executes the method described in the first aspect or the second aspect.

[0011] In an eighth aspect, a computer-readable storage medium is provided, on which a program is stored, wherein the program enables a computer to execute the method as described in the first aspect or the second aspect.

[0012] In a ninth aspect, a computer program product is provided, comprising a program, wherein the program enables a computer to execute the method as described in the first aspect or the second aspect.

[0013] In a tenth aspect, a computer program is provided, which enables a computer to execute the method as described in the first aspect or the second aspect.

[0014] In the embodiments of the present application, a first device (e.g., a terminal device) may determine a first beam set based on the inference beam set of the first model, thereby monitoring the performance of the first model based on the measurement results of the first beam set. The first beam set may be the inference beam set or a subset of the inference beam set, thereby improving the accuracy of the first model through real-time performance testing of the inference beams. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a wireless communication system applied in the embodiments of the present application.

[0016] Figure 2 It is a schematic diagram of the model processing process applied in the embodiment of the present application.

[0017] Figure 3 This is a flow chart of a method for wireless communication provided in an embodiment of the present application.

[0018] Figure 4 yes Figure 3 A schematic diagram of a possible implementation of the method shown.

[0019] Figure 5 yes Figure 3 A schematic diagram of another possible implementation of the method shown.

[0020] Figure 6 yes Figure 3 A flowchart of a possible implementation of the method is shown.

[0021] Figure 7 yes Figure 3 Schematic diagram of a possible configuration of the first beam set in .

[0022] Figure 8 yes Figure 3 Schematic diagram of another possible configuration of the first beam set in .

[0023] Figure 9 yes Figure 3 Schematic diagram of another possible configuration of the first beam set in .

[0024] Figure 10 yes Figure 3 Schematic diagram of another possible configuration of the first beam set in .

[0025] Figure 11 yes Figure 3 Schematic diagram of another possible configuration of the first beam set in .

[0026] Figure 12 This is a schematic diagram of the structure of a device for wireless communication provided in an embodiment of the present application.

[0027] Figure 13 It is a structural diagram of another device for wireless communication provided in an embodiment of the present application.

[0028] Figure 14 It is a structural diagram of a wireless communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present application are described below with reference to the accompanying drawings.

[0030] Figure 1 FIG. 1 is a schematic diagram of the architecture of a wireless communication system 100 used in an embodiment of the present application. Figure 1As shown, wireless communication system 100 may include network device 110 and terminal device 120. Network device 110 may be a device that communicates with terminal device 120. Network device 110 may provide communication coverage for a specific geographical area and may communicate with terminal devices within the coverage area.

[0031] Figure 1 While one network device and two terminal devices are shown as an example, the wireless communication system 100 may optionally include multiple network devices, and each network device may include another number of terminal devices within its coverage area, without limitation. In other words, the wireless communication system may include one or more network devices, each of which may support wireless communication with one or more terminal devices.

[0032] In the embodiments of this application, Figure 1 The communication system shown may also include other network entities such as a mobility management entity (MME), an access and mobility management function (AMF), and a network controller, but this embodiment of the present application does not limit this.

[0033] It should be understood that the embodiments of the present application can be applied to various communication systems. For example, the embodiments of the present application can be applied to global system of mobile communication (GSM) system, code division multiple access (CDMA) system, wideband code division multiple access (WCDMA) system, general packet radio service (GPRS) system, long term evolution (LTE) system, advanced long term evolution (LTE-A) system, new radio (NR) system, NR system evolution system, LTE-based access to unlicensed spectrum (LTE-U) system on unlicensed spectrum, NR-based access to unlicensed spectrum (NR-U) system on unlicensed spectrum, universal mobile telecommunication system (UMTS), wireless local area network (WLAN) system, wireless fidelity (WiFi) system, and fifth generation communication (5G) system. The embodiments of the present application can also be applied to other communication systems, such as the sixth-generation (6G) mobile communication system, or future communication systems such as satellite communication systems.

[0034] Traditional communication systems support a limited number of connections and are easy to implement. However, with the development of communication technology, communication systems can not only support traditional cellular communications, but also support one or more other types of communications. For example, a communication system can support one or more of the following communications: device to device (D2D) communication, machine to machine (M2M) communication, machine type communication (MTC), enhanced machine type communication (eMTC), vehicle to vehicle (V2V) communication, and vehicle to everything (V2X) communication, etc. The embodiments of the present application can also be applied to communication systems that support the above-mentioned communication methods.

[0035] The communication system in the embodiment of the present application can be applied to a carrier aggregation (CA) scenario, a dual connectivity (DC) scenario, and a standalone (SA) networking scenario.

[0036] The communication system in the embodiments of the present application can be applied to unlicensed spectrum. The unlicensed spectrum can also be considered a shared spectrum. Alternatively, the communication system in the embodiments of the present application can also be applied to licensed spectrum. The licensed spectrum can also be considered a dedicated spectrum.

[0037] The embodiments of the present application can be applied to non-terrestrial network (NTN) systems. As an example, the NTN system can be a 4G-based NTN system, an NR-based NTN system, an Internet of Things (IoT)-based NTN system, or a narrowband Internet of Things (NB-IoT)-based NTN system.

[0038] The wireless communication system in the embodiments of the present application can utilize the following resources to support wireless communication with one or more communication devices: time resources (e.g., symbols, sub-time slots, time slots, subframes, frames, etc.) or frequency resources (e.g., subcarriers, carriers). Additionally, the wireless communication system can support wireless communication across various radio access technologies (RATs), including third generation (3G) radio access technology, fourth generation (4G) radio access technology, fifth generation (5G) radio access technology, and other suitable radio access technologies beyond 5G.

[0039] The terminal device in the embodiments of the present application may also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station (MS), mobile terminal (MT), remote station, remote terminal, mobile device, user terminal, terminal, user communication equipment, wireless communication equipment, user agent or user device, etc.

[0040] In some embodiments, the terminal device in the embodiments of the present application can be a device that provides voice and / or data connectivity to a user and can be used to connect people, objects, and machines, such as a handheld device with wireless connection capabilities, an in-vehicle device, etc. The terminal device in the embodiments of the present application can be a mobile phone, a tablet computer, a laptop computer, a PDA, a mobile internet device (MID), a wearable device, a virtual reality (VR) device, an augmented reality (AR) device, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical surgery, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, etc. Optionally, the UE can be used to act as a base station. For example, the UE can act as a scheduling entity that provides sidelink signals between UEs in V2X or D2D, etc. For example, a cellular phone and a car communicate with each other using sidelink signals. Cellular phones and smart home devices communicate with each other without relaying the communication signal through a base station.

[0041] In some embodiments, the terminal device may be a station (ST) in a WLAN. In some embodiments, the terminal device may be a cellular phone, a cordless phone, a session initiation protocol (SIP) phone, a wireless local loop (WLL) station, a personal digital assistant (PDA) device, a handheld device with wireless communication capabilities, a computing device or other processing device connected to a wireless modem, an in-vehicle device, a wearable device, a terminal device in a next-generation communication system (e.g., a NR system), or a terminal device in a future-evolved public land mobile network (PLMN) network.

[0042] The network device in the embodiments of the present application may be a device for communicating with a terminal device, and may also be referred to as an access network device or a radio access network device. The network device may be, for example, a base station. The network device in the embodiments of the present application may refer to a radio access network (RAN) node (or device) that connects a terminal device to a wireless network. A base station may broadly cover various names as follows, or be replaced with the following names, such as: NodeB, evolved NodeB (eNB), next generation NodeB (gNB), relay station, access point, transmitting and receiving point (TRP), transmitting point (TP), master station (MeNB), secondary station (SeNB), multi-standard radio (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), positioning node, network communication device, etc. A base station may be a macro base station, a micro base station, a relay node, a donor node, or the like, or a combination thereof. A base station may also refer to a communication module, modem, or chip used to be set in the aforementioned device or apparatus. A base station may also be a mobile switching center and a device that performs base station functions in D2D, V2X, and M2M communications, a network-side device in a 6G network, or a device that performs base station functions in future communication systems. A base station may support networks with the same or different access technologies. The embodiments of this application do not limit the specific technology and specific device form used by network devices.

[0043] Base stations can be fixed or mobile. For example, a helicopter or drone can be configured to act as a mobile base station, and one or more cells can move based on the location of the mobile base station. In other examples, a helicopter or drone can be configured to act as a device that communicates with another base station.

[0044] In some deployments, the network device in the embodiments of the present application may refer to a CU or a DU, or the network device includes a CU and a DU. The gNB may also include an AAU.

[0045] The network equipment and terminal devices can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; they can also be deployed in the air on aircraft, balloons, and satellites. The embodiments of this application do not limit the scenarios in which the network equipment and terminal devices are located.

[0046] It should be understood that all or part of the functions of the communication device in this application can also be implemented through software functions running on hardware, or through virtualization functions instantiated on a platform (such as a cloud platform).

[0047] In an embodiment of the present application, the network device can provide services for a cell, and the terminal device communicates with the network device through the transmission resources used by the cell (for example, frequency domain resources, or spectrum resources). The cell can be a cell corresponding to the network device (for example, a base station). The cell can belong to a macro base station or a base station corresponding to a small cell. The small cells here may include: metro cells, micro cells, pico cells, femto cells, etc. These small cells have the characteristics of small coverage and low transmission power, and are suitable for providing high-speed data transmission services.

[0048] It should be understood that the device with communication function in the network / system in the embodiment of the present application can be called a communication device. Figure 1 Taking the wireless communication system 100 shown as an example, the communication equipment may include a network device 110 and a terminal device 120 with communication functions, and may also include other devices in the wireless communication system 100, such as a network controller, a mobile management entity and other network entities, which is not limited in the embodiments of the present application.

[0049] Exemplarily, the wireless communication system may include one or more network communication devices, such as the base station described above. Each network communication device, such as a base station, may support wireless communication of one or more user communication devices (eg, terminal devices).

[0050] For ease of understanding, some relevant technical knowledge involved in the embodiments of this application is first introduced. The following related technologies can be combined with the technical solutions of the embodiments of this application as optional solutions, and they all fall within the scope of protection of the embodiments of this application. The embodiments of this application include at least part of the following contents.

[0051] In a wireless communication system, a terminal device can acquire a beam (also referred to as a spatial beam) to enable a wireless connection between the terminal device and a wireless network. For example, the terminal device can perform beam scanning on available beams transmitted by the wireless network and measure the properties of the beams. Beam properties include signal strength and signal quality. For example, after performing beam scanning, the terminal device can also perform beam refinement to achieve a set of potentially narrower beams for wireless connection to the wireless network. Beams can not only enable a wireless connection between the terminal device and the wireless network, but also achieve high directional accuracy and high signal quality for wireless signal transmission between the terminal device and the wireless network.

[0052] With the development of communication technology, research on artificial intelligence (AI) / machine learning (ML) technologies based on the air interface of communication systems (e.g., NR systems) has become one of the research directions. The goals of this research include exploring how to enhance the advantages of the air interface. For example, the relevant performance of the air interface can be enhanced by enhancing support for AI / ML algorithms. Another example is to reduce the complexity and / or overhead of the air interface by enhancing support for AI / ML algorithms.

[0053] Research into AI / ML technologies can also enhance beam management (BM) capabilities. For example, AI / ML enhancements related to beam management can reduce overhead and latency in beam measurement and reporting. For example, AI / ML models can be used to predict beams, improving air interface transmission efficiency.

[0054] The entire process of applying AI / ML models for enhancement includes model training, model inference, and model monitoring. During this process, the trained AI / ML model generates a set of outputs based on a set of inputs. The inputs can be a set of beam measurements, while the outputs can be a set of beams that are different from or larger than the inputs.

[0055] During model inference, the AI / ML model can predict the best beam from a set of different / larger beam groups based on a set of beam measurements.

[0056] In some embodiments, the AI / ML model may be located on the terminal device side or the terminal device may perform model training and / or model inference. This model may be referred to as a UE-side model. For example, the AI ​​model is located at the terminal device, and AI model training and / or inference using the AI ​​model may be performed at or by the terminal device to generate the optimal beam.

[0057] As an example, a terminal device can use the beams in Set B (also called Beam Group B) as input to an ML model. The ML model can predict the best beam in Set A (also called Beam Group A), which is not fully measured by the terminal device.

[0058] In the above example, Set B may be the beam set that the terminal device first measures, also known as the training beam set. Set B may also be multiple beams transmitted by a base station (e.g., a gNB). Each beam can correspond to a different direction or angle to cover multiple spatial directions. Each beam may also correspond to a measurement signal, used to obtain a measurement value, such as reference signal received power (RSRP). Set B provides preliminary environmental information and channel conditions for the model.

[0059] In the above example, Set A can be the beam group required for prediction during the inference phase, also known as the inference beam set. Set A typically contains more beams than Set B, or the beam directions may be more concentrated. The AI / ML model can use measurements from Set B to predict the optimal beam in Set A, improving data transmission efficiency.

[0060] Optionally, the beams of set A and set B can be in the same frequency range. The selection of set B can be given by the base station or determined by the terminal device itself. The relationship between set A and set B can be: set A and set B are different (set B is not a subset of set A), or set B is a subset of set A (set A and set B are different), or set A and set B are the same. For the first two, set B can be transmitted simultaneously in the measurement window and the prediction window, or it can be transmitted only in the measurement window. The last case can save the reference signal (RS) transmission overhead, and set B as the measurement resource can be transmitted only in the measurement window.

[0061] Optionally, 64 or even more beams can be used as the size of beam set A. For future-oriented networks, network devices will be able to transmit 64 narrower beams with higher directivity. More narrow beams can also scan a larger set A; for example, the number of beams in set A could be as high as 256.

[0062] Optionally, the network device can send multiple reference signals, such as the channel state information (CSI) reference signal (CSI-reference signal, CSI-RS), synchronization signal block (SSB), etc. It should be noted that SSB can also represent synchronization signal / physical broadcast channel block (SS / PBCHblock). SSB can include primary synchronization signal (PSS) and secondary synchronization signal (SSS).

[0063] Optionally, the terminal device estimates the channel quality of each beam by measuring the RSRP of the received CSI-RS / SSS.

[0064] Optionally, during the model training process, the AI / ML model can adjust its weights by minimizing the loss function so that the model can accurately predict the best beam in set A from the RSRP measurement results of set B.

[0065] In some embodiments, AI / ML can be located on a network device (such as a base station) or the network device can perform model training and / or model inference. This model can be referred to as a network-side model. For example, the AI ​​model is located at the base station, and AI model training and / or inference using the AI ​​model can be performed at or by the base station to generate the optimal beam.

[0066] In some embodiments, the network can fully control the data collection process for model training on the terminal device side, including the initiation, termination, and management of data collection and data transmission.

[0067] As an example, when a terminal device utilizes AI algorithms or ML to process beams detected and measured from a wireless network to infer other beams that may have higher strength and / or higher quality, the terminal device needs to ensure consistency between the training phase and the inference phase models.

[0068] Compared with other beam management technologies, through beam management that supports AI / ML technology, terminal devices can experience reduced latency, reduced overhead, reduced power consumption and improved signal quality based on beam prediction.

[0069] For ease of understanding, the following Figure 2, the entire process of model processing on the terminal device side is introduced. Figure 2 This is explained from the perspective of the interaction between the terminal device (such as UE) and the network (NW). The network side uses 4 beams as an example. Figure 2 It can be seen that the entire process may include a model training process, a model inference process, and a reporting process. The model training process includes steps S210 and S220, and the model inference process includes steps S230 and S240.

[0070] See also Figure 2 In step S210, the terminal device reports training-related information (UE report training-related information).

[0071] In step S220, the network side performs beam scanning based on four beams.

[0072] In step S230, the terminal device reports inference-related information (UEreport inference-relatedinformation).

[0073] In step S240, the network side selects two beams from four beams to perform beam scanning based on the report from the terminal device.

[0074] In step S250, the terminal device reports the optimal K beams (top-K beam report).

[0075] In step S260, the network side performs beam scanning according to the beam in the beam report. Figure 2 It can be seen that the two beams used for beam scanning by the network side in step S260 may be different from the two beams used for beam scanning in step S240.

[0076] In step S270, the terminal device sends a beam report so that the network side can determine the optimal beam.

[0077] In step S280, the network side sends a beam indication to the terminal device.

[0078] Combined with the above Figure 2 This section describes the model processing process when the model resides on the end device. To complete the entire process, the end device also needs to collect and analyze data to perform model training. The model inference process is primarily used for beam prediction.

[0079] In relevant scenarios, terminal devices can support beam prediction in the spatial domain and / or time domain, namely BM-Case 1 and / or BM-Case 2. Based on AI / ML enhancements, beam prediction in the spatial domain (BM-Case 1) and beam prediction in the time domain (BM-Case 2) can reduce terminal device overhead and lower beam measurement and reporting delays.

[0080] BM-Case 1 performs spatial-domain downlink (DL) beam prediction for Set A based on measurements from Set B. For BM-Case 1, measurements based on Set B of beams are used as model input to predict the Top-1 / Top-K beams in Set A.

[0081] BM-Case2 predicts the time-domain DL beams for Set A based on historical measurements of Set B. For BM-Case2, measurements based on Set B of beams at historic time instances are used as model input to predict the time-domain DL beams for Set A. The predictions for DL ​​Tx beams and DL Tx / Rx beams can also be used to evaluate prediction performance.

[0082] For BM-Case1 and BM-Case2, the terminal device can report the prediction results to the NW based on the output of the terminal device side model, or the NW can predict the Top-1 / Top-K beams based on the measurement report of the set B of the NW side model.

[0083] Combined with the above Figure 2 Model training and model inference are introduced. In order to ensure the correctness of model inference, model monitoring process needs to be performed.

[0084] AI / ML model monitoring during the model monitoring process serves at least the following purposes: model activation, deactivation, selection, switching, rollback, and update (including retraining). Model monitoring can also be referred to as the process of monitoring the inference performance of AI / ML models. There is always a time lag between model training and inference. When radio parameters or conditions in the network change, errors are more likely to occur during the model's inference phase. Therefore, the model needs to be continuously corrected and trained based on the results of model monitoring.

[0085] Illustratively, model monitoring is required in certain situations (eg before using the model in a new radio environment / condition / parameters).

[0086] During model monitoring, AI / ML model performance can be monitored through three performance monitoring methods. Model monitoring, also known as model monitoring or model monitoring, involves performance monitoring on the terminal equipment (UE), performance monitoring on the network equipment (gNB), and hybrid performance monitoring of the terminal and network equipment.

[0087] In some scenarios, since the terminal device sends an uplink beam and the network device sends a downlink beam, the terminal device and the network device may perform beam prediction and reporting based on the model, regardless of whether the model is on the terminal device side, the network device side, or both sides.

[0088] For performance monitoring on the terminal device side, configuration / signaling from the network device to the terminal device can be used for performance monitoring. For network device-side and hybrid performance monitoring, configuration / signals from the network device to the terminal device can be used for measurement and / or reporting configuration / signals to support performance testing of the model. In some embodiments, the selection of performance monitoring methods depends on various factors. These factors include, but are not limited to, device requirements, model location, service type, communication scenario, etc.

[0089] As mentioned earlier, there's a certain time gap between the training and inference phases, and changes in radio parameters and conditions can cause errors during the inference phase. Therefore, in order to promptly detect model issues, model performance monitoring becomes a pressing technical challenge.

[0090] For example, the selection of a data set for model performance monitoring and the timing of model monitoring are both crucial. In other words, data collection for model performance monitoring is crucial. This is because model monitoring may continue for an extended period, until AI-based beam management is finalized. The selection of a performance monitoring data set is crucial to ensure efficient data collection and accurate model monitoring. Therefore, selecting a performance monitoring data set for effective performance monitoring is crucial.

[0091] Based on this, an embodiment of the present application proposes a method for wireless communication. In this method, a first beam set for performance monitoring of a first model is determined based on an inference beam set of the first model. Since the first beam set is associated with the inference beam set, the first device (e.g., a terminal device) can promptly compare the prediction results of the inference beam set with the measurement results to ensure the accuracy of the model. It should be understood that the first model in the embodiment of the present application can be located on the terminal device side and / or the network device side.

[0092] The following combination Figure 3 The method for wireless communication proposed in the embodiments of the present application is described in detail. Figure 3It is introduced from the perspective of the interaction between the first device and the second device.

[0093] The first device may be any communication device that supports model performance monitoring. In some embodiments, the first device may be a terminal device. For example, the first device may be a UE with monitoring capabilities. In another example, the first device may include a terminal device and any processing device that supports performance monitoring. In some embodiments, the first device may be a network device. For example, the first device may be a base station.

[0094] In some embodiments, the first device supports enhanced functionality based on AI / ML operations. For example, the first device has enhanced beam management and / or performance monitoring capabilities based on AI / ML operations.

[0095] In some embodiments, a first model is deployed on the first device to implement beam prediction. When the second device is a network device, the first device performs DL beam prediction. When the second device is a terminal device, the first device performs side beam prediction.

[0096] As an example, the first model is a model that supports AI algorithms or ML, that is, the first model is an AI / ML model.

[0097] As an example, the beam prediction implemented by the first model may be the BM-Case1 described above, or may be BM-Case2, or may be other beam prediction types in the future, which are not limited here.

[0098] In some embodiments, a first model is deployed on the first device side. The first model may not be on the terminal device, but on a server that communicates with the terminal device. For example, the first model is on a server that directly communicates with the terminal device.

[0099] The second device can be any network device that communicates with the first device, or a terminal device that communicates with the first device. When the first device is a terminal device and is within the coverage area of ​​the network device, the second device can be a network device. When the first device is a network device, the second device can be a terminal device that communicates with the network device. In a sideline communication system, when the first device communicates with another terminal device, the second device can also be another terminal device.

[0100] In some embodiments, the second device can monitor the first device's processing of the first model. For example, the second device can determine the current training and inference phases of the first model based on reports sent by the first device, or can determine the performance monitoring results of the first model based on reports sent by the first device. In another example, the second device can assist the first device in monitoring the performance of the first model.

[0101] In some embodiments, the second device supports AI / ML operations. The first model can be deployed on the second device side.

[0102] In some embodiments, the second device may send multiple beams to the first device multiple times to facilitate the first device to perform measurements, and perform model training, model inference and / or model monitoring on the first model based on the measurement results.

[0103] In the above embodiment, regardless of whether the first model is deployed on the terminal device side or the network device side, the first model can be one of multiple models deployed on that side. Multiple models can be used to predict the transmission beams of different scenarios or different beam transmitting devices.

[0104] See also Figure 3 In step S310, a first device receives a first reference signal. The first reference signal is sent via a first beam set.

[0105] The first reference signal is used by the first device to perform performance monitoring on the first model. As an example, the first reference signal is used by the first device to collect a data set for performance monitoring of the first model. This data set may also be referred to as a performance monitoring set.

[0106] In some embodiments, the first reference signal is at least one of dedicated reference signals related to performance monitoring. A dedicated reference signal may also be referred to as a dedicated reference signal, a performance monitoring reference signal, or an auxiliary reference signal. As an example, the first reference signal may be a dedicated reference signal. As an example, the first reference signal may be multiple dedicated reference signals.

[0107] As an example, the dedicated reference signal can be used to determine a performance parameter of the first model. The performance parameter of the first model can also be referred to as a key performance indicator (KPI) for performance monitoring. Optionally, the performance parameter of the first model can include a prediction accuracy of the first model and / or a prediction error of the first model.

[0108] The first reference signal corresponds to a first monitoring instance. The first reference signal is used by the first device to perform performance monitoring on the first model at its corresponding time instance. Therefore, the first monitoring instance may also be referred to as a first performance monitoring instance. As an embodiment, the first monitoring instance may be one or more monitoring instances among multiple monitoring instances that perform performance monitoring on the first model.

[0109] In some embodiments, the first monitoring instance may be one or more monitoring instances within a monitoring period of the first model. The monitoring period of the first model may be referred to as a first monitoring period. As an embodiment, the first monitoring period includes at least one monitoring instance. During the first monitoring period, the first device may receive and measure the reference signal and then generate a monitoring report based on the measurement results.

[0110] As an example, the first monitoring cycle includes a monitoring instance, and the first device performs performance monitoring on the monitoring instance.

[0111] As an example, the first monitoring cycle includes multiple monitoring instances, and the multiple monitoring instances correspond to reference signals at multiple time points respectively.

[0112] As an example, the first monitoring period is equal to or less than a reporting period of the monitoring report. When the reporting period is greater than the first monitoring period, the first device may send the monitoring report after performing multiple monitoring operations.

[0113] The first reference signal is transmitted via the first beam set. Alternatively, the first reference signal may be associated with the first beam set. The first beam set corresponds to the first monitoring instance. Therefore, the measurement results of the first beam set can serve as the monitoring dataset of the first model. Exemplarily, some or all beams in the first beam set can be used to transmit the first reference signal.

[0114] The first beam set is determined based on the inference beam set of the first model to ensure the efficiency of data collection and the accuracy of the monitoring model. The inference beam set is, for example, the set A mentioned above. When the first beam set is determined based on the inference beam set, the first device can directly compare the measurement results of the first beam set with the inference results of the first model. As an example, the first reference signal transmitted by the first beam set can span the entire set A or only cover a subset of the set A, so that the first device can compare the prediction about the set A (the inference target set) with the actual measurement of the beams from the set A.

[0115] In some embodiments, the inference beam set of the first model can be determined based on the first model. That is, the beams used for inference above and throughout the text can be determined based on the first model. As an example, the inference beam set of the first model can be determined based on specific first model features. The first model features are, for example, features related to model building and learning.

[0116] It should be noted that the inferred beam can be called a predicted beam, and the inferred beam set can also be called a predicted beam set.

[0117] In some embodiments, the first beam set includes all beams in the inference beam set. When performance monitoring is performed on the first model, the performance monitoring reference signal corresponding to each monitoring instance can be associated with the entire inference beam set. In this scenario, the performance monitoring dataset collected by the first device is equal to the dataset of the inference beam set.

[0118] As an embodiment, the first beam set is an inference beam set. When all beams in the inference beam set (set A) transmit the first reference signal, the first device may obtain measurement values ​​from the entire set A and define monitoring procedures and metrics accordingly.

[0119] For ease of understanding, the following Figure 4 A method in which the first beam set is an inference beam set is exemplarily described. Figure 4 The set B in is the training beam set of the first model, and the set A is the inference beam set of the first model. Figure 4 It can be seen that on the time axis, the training beam set of the first model is respectively at the time instance [t -3 , t0, t +3 ]; the inference beam set of the first model is respectively transmitted at the time instance [t -2 , t -1 , t +1 , t +2 , t +4 ] on the transmission; time instance [t -2 , t -1 , t +1 , t +2 , t +4 ] corresponds to multiple performance monitoring instances.

[0120] See also Figure 4 , at each time instance corresponding to the inference beam set, the inference beam set is the same as the first beam set of the performance monitoring instance. Optionally, at each time instance [t -2 , t -1 , t +1 , t +2 , t +4 ], the beam set of the first reference signal is the same as the inference beam set.

[0121] In some embodiments, the first beam set includes a subset of the beams in the inference beam set. In some scenarios, measuring all beams in the inference beam set can result in significant complexity and resource consumption. For example, for a large inference beam set (e.g., 128 beams or more), measuring the entire inference beam set in each monitoring instance can result in excessive power consumption on the first device. Therefore, in each performance monitoring instance, selecting only a subset of the beams in the inference beam set for measurement can reduce the measurement burden on the first device.

[0122] As an embodiment, some beams in the inference beam set may constitute a first beam subset. That is, when the first beam set only includes some beams in the inference beam set, the first beam set may also be referred to as a first beam subset of the inference beam set.

[0123] In the above embodiment, when the first beam set includes some beams, the some beams may be Top-K beams in the inference beam set.

[0124] In some embodiments, when the first beam set includes some beams in the inference beam set, multiple monitoring instances for performance monitoring may correspond to multiple beam subsets, respectively. The first beam subset may be any beam subset from the multiple beam subsets that corresponds to the first reference signal. As an example, the multiple monitoring instances may include a first monitoring instance and a second monitoring instance, where the first monitoring instance corresponds to the first beam subset and the second monitoring instance corresponds to the second beam subset.

[0125] As an example, at least one beam in the first beam subset and the second beam subset is the same. That is, some beams in the first beam subset and the beam subsets corresponding to other monitoring instances may overlap. For example, both the first beam subset and the second beam subset include beam 1 in the inference beam set. Beam 1 may be a beam with better signal quality in the inference beam set.

[0126] As an example, at least one beam in the first beam subset is different from that in the second beam subset. For example, all beams in the first beam subset are different from those in the second beam subset. In another example, some beams in the first beam subset are different from those in the second beam subset.

[0127] As an example, the first beam subset is dynamically selected. That is, the multiple beam subsets corresponding to the multiple monitoring instances are dynamically determined. Some beams of the monitoring beam subsets selected each time may overlap.

[0128] In some embodiments, when the first device selects a first beam subset from the inference beam set, the first beam subset may be determined based on first information. The first information may include information related to some or all beams in the inference beam set. The first information may include one or more of the following: beam priorities in the inference beam set, historical performance data of the beams in the inference beam set, perceived quality of the beams in the inference beam set, coverage areas of the beams in the inference beam set, and network load of the cell where the first device is located.

[0129] As an example, the first beam subset may be determined based on the priorities of some or all beams in the inference beam set. In other words, the first device may select the first beam subset based on the beam priorities.

[0130] As an example, the beam priority within an inferred beam set can be determined based on the beam's importance in the network, beam quality, and / or the amount of resources required to monitor the beam. When the beam priority is correlated with these factors, it can ensure that the first device properly selects the first beam set. For example, system resources can be used to monitor beams that have the greatest impact on network performance, reducing the monitoring burden on less important beams.

[0131] In the above example, the importance of a beam in the network can be determined based on the number of users or user levels covered by the beam.

[0132] In the above example, beam quality may include historical performance data. This historical performance data may include quality indicators such as the beam's historical RSRP, signal to interference plus noise ratio (SINR), and other parameters indicating beam quality, such as the ratio of the beam's RSRP to a relevant threshold.

[0133] In the above example, the beam priority of the first beam in the inference beam set satisfies one or more of the following conditions: the beam priority of the first beam is positively correlated with the importance of the first beam; the beam priority of the first beam is positively correlated with the quality of the first beam; the beam priority of the first beam is negatively correlated with the amount of resources for monitoring the first beam.

[0134] As an implementation method, the priority P of the i-th beam in the inference beam set is i Can be: P i =U i *Q i / D i Among them, U i is the importance of the beam; Q i Is the quality indicator of the beam; D i is the amount of resources required to monitor the i-th beam.

[0135] As an example, the first beam set can be determined based on historical performance data for some or all beams in the inferred beam set. For example, this historical performance data can be used to construct a statistical model of beam performance, and the first beam set can be selected based on the statistical model. The statistical model can also be used to predict which beams will experience performance changes in the future. Therefore, based on the statistical model based on historical performance data, the system can more effectively select beams to monitor.

[0136] In the above example, the statistical model can prioritize monitoring beams that are likely to experience significant performance fluctuations in the future, thereby improving monitoring effectiveness. A commonly used statistical model is the auto-regressive moving average (ARMA) model. The ARMA model can predict future beam states based on historical performance data.

[0137] As an implementation, the ARMA model can be expressed as: t =α1y t―1 +α2y t―2 +…+β1e t―1 +β2e t―2 +…; where y t It is the performance of the current beam (such as RSRP or SINR); t is the error term, which represents the difference between the observed value and the predicted value; α * and β * are model parameters obtained by fitting historical data.

[0138] As an example, the first beam set may be determined based on user perceived quality of experience (QoE) corresponding to some or all beams in the inference beam set. In other words, the first device may select a monitoring data set based on user perception for performance monitoring.

[0139] In the above example, the first device can prioritize monitoring beams or resources that have a greater impact on user experience. For example, if users in a certain area report degraded signal quality or application lag, the system can immediately select the relevant beams for monitoring. This approach directly optimizes user experience. As can be seen, the system can prioritize performance monitoring for beams where user experience is degraded.

[0140] As an example, the first beam set can be determined based on the coverage areas of some or all beams in the inferred beam set. The number of users and / or user levels within the beam coverage area can be used to determine beam priorities or directly used to determine the first beam set.

[0141] As an example, the first beam set can be determined based on the network load of the cell where the first device is located. The first beam set can be dynamically configured based on the current load. When the network load changes in the first monitoring instance, the first beam set can be adjusted in a timely manner.

[0142] As an example, the first beam set can also be determined based on one or more of the beam priority, historical performance data, perceived quality, coverage area, and cell network load in the first information.

[0143] In some embodiments, the beam subset used to send the reference signal for each monitoring instance can be dynamically adjusted based on the current network status and beam prediction requirements to ensure that the collected data is sufficiently timely and representative.

[0144] In some embodiments, the first beam subset determined based on the first information may be one of multiple beam subsets in the inference beam set. Multiple beam subsets may be determined in the inference beam set based on the first information. Different monitoring levels may be set for each of the multiple beam subsets. During performance monitoring, beam subsets with different monitoring levels are selected based on monitoring requirements.

[0145] For example, historical network data, user needs, or service types can be used to determine which beams or resources should be monitored first. For example, high-priority beams may cover key areas (such as high-density user areas or VIP user locations), while low-priority beams may be located in areas with low or stable loads and therefore receive lower monitoring levels.

[0146] In some embodiments, to fully cover all beams in the inference beam set, the first beam set can be determined by a polling mechanism associated with the inference beam set. The polling mechanism can divide all beams in the inference beam set into multiple beam subsets corresponding to multiple monitoring instances based on a polling method. Thus, to avoid the first device measuring the entire inference beam set (which may include a large number of beams) in each monitoring cycle, the inference beam set can be divided into multiple subsets, and all beams can be gradually covered in a polling method over multiple monitoring cycles.

[0147] As an example, the inference beam set is divided into several non-overlapping subsets S1, S2, ..., S n , where each subset S i Contains a portion of beam resources. For example, if the inference beam set contains 128 beams, it can be divided into 4 subsets, each containing 32 beams.

[0148] In the above embodiment, the multiple beam subsets may correspond to the same reference signal or different reference signals. The reference signals corresponding to the multiple beam subsets may cyclically span the inference beam set based on a polling mechanism.

[0149] In some embodiments, after the inference beam set is divided, a polling cycle can be defined, during which each beam subset is measured sequentially. Each beam subset can correspond to a monitoring instance, such as a first monitoring instance. Multiple monitoring instances including the first monitoring instance constitute a polling cycle, namely the first polling cycle. Thus, it can be seen that the polling mechanism can be used to monitor the performance of all beams in the inference beam set within the first polling cycle. In other words, when the first beam set only includes some of the beams in the inference beam set, all beams in the inference beam set can be monitored through the polling mechanism and multiple monitoring instances.

[0150] In the above embodiment, different portions of the inference beam set are selected for monitoring in different performance monitoring instances, ensuring that the entire inference beam set is progressively covered over multiple monitoring cycles. This approach reduces the measurement complexity of each monitoring instance and ensures data integrity through continuous monitoring.

[0151] In the above embodiment, based on the polling mechanism, the performance monitoring of the first model may cover different beam subsets in the inference beam set within a certain time interval. The time interval may be referred to as the duration of a polling cycle.

[0152] As an example, the first beam set is a first beam subset of the inference beam set. The first beam subset is determined according to a polling mechanism of the inference beam set. The first beam subset can be one of a set of non-overlapping beam subsets defined for the inference beam set. Multiple non-overlapping beam subsets are used to implement polling monitoring of the entire inference beam set at multiple time instances.

[0153] As an example, the first polling cycle includes multiple monitoring instances, the multiple monitoring instances include the first monitoring instance, the multiple monitoring instances respectively correspond to multiple beam subsets including the first beam subset, and the beams in any two beam subsets among the multiple beam subsets are different.

[0154] As an implementation method, it is assumed that multiple beams in the inference beam set can be divided into n beam subsets based on a polling mechanism. The beam subset selected for each monitoring instance can be: S i ={B i,1 ,B i,2 ,…,B i,ki}, i=1,2,…,n; where S i is the i-th beam subset; ki is the number of beams in the i-th beam subset; B i,j is the jth beam in the i-th beam subset.

[0155] For ease of understanding, the following Figure 5 The method for determining the first beam set according to the polling mechanism is exemplified. Figure 4 same, Figure 5 The set B in is the training beam set of the first model, and the set A is the inference beam set of the first model. Similarly, on the time axis, the training beam set of the first model is respectively at the time instance [t -3 , t0, t +3 ]; the inference beam set of the first model is respectively transmitted at the time instance [t -2 , t -1 , t +1 , t +2 , t +4 ] on the transmission; time instance [t -2 , t -1 , t +1 , t +2 , t +4 ] corresponds to multiple performance monitoring instances.

[0156] and Figure 4 The difference is that at each time instance corresponding to the inference beam set, the beam subset for performance monitoring is only the beams filled with shadows. In other words, the inference beam set is larger than the beam set for performance monitoring instances.

[0157] Continue to see Figure 4 , time instance [t -2 , t -1 , t +1 , t +2 , t +4 ] can be used as a polling cycle to cover the 9 beams in the entire inference beam set. Among them, the time instance [t -2 , t -1 , t +1 , t +2 ] The corresponding beam subsets include 2 beams, time instance t +4 The corresponding beam subset includes the remaining 1 beam.

[0158] In some embodiments, the first polling period may be greater than or equal to the first monitoring period. For example, when the first monitoring period includes one monitoring instance, the first polling period is greater than the first monitoring period. For another example, when the first monitoring period includes multiple monitoring instances, the first polling period may be equal to the first monitoring period.

[0159] As an example, the duration of the first polling period is a positive integer multiple of the duration of the first monitoring period. The first monitoring period may include at least one monitoring instance. Optionally, the positive integer multiple may be determined based on the configuration of RS resources and the size of the CSI reporting period.

[0160] As an example, the number of monitoring cycles included in the first polling cycle is the number of beam subsets divided by the inference beam set. Figure 4 In the example of , beam subset S1 is measured in the first monitoring cycle, beam subset S2 is measured in the second monitoring cycle, and so on, until all beam subsets in the inference beam set are covered, and a new round of polling is restarted.

[0161] In some embodiments, the value of the first polling period can be dynamically adjusted. When the first polling period is determined based on one or more pieces of information, the value of the first polling period can be adjusted based on changes in the information. The one or more pieces of information used to determine the first polling period can include: a moving speed of the first device; a network load of a cell where the first device is located; and a maximum polling period of the cell where the first device is located.

[0162] As an example, the first polling period is linearly related to the moving speed of the first device. When the moving speed of the first device increases, the duration of the first polling period may be shortened to ensure that the channel status can be updated in a timely manner.

[0163] As an example, the first polling period is linearly correlated with the network load of the cell where the first device is located. When the network load of the cell where the first device is located increases, the duration of the first polling period may be shortened to accommodate more frequent data reporting requirements.

[0164] As an example, the first polling cycle is related to the longest polling cycle of the cell where the first device is located. The first polling cycle is less than or equal to the longest polling cycle. For example, the first polling cycle can be set to 80% of the longest polling cycle.

[0165] As an example, the first polling cycle is also related to the monitoring cycle of the first model or the reporting cycle of the monitoring report. The duration of the first polling cycle is an integer multiple of the duration of the first monitoring cycle, or the duration of the first polling cycle is an integer multiple of the duration of the reporting cycle.

[0166] As an example, the first polling period can be dynamically adjusted based on the moving speed of the first device, the network load, and the longest polling period. For example, the value of the first polling period is the value obtained by subtracting the corresponding durations of the moving speed and the network load from the longest polling period.

[0167] As an implementation, the first polling period T may be: T=T max ―α*v―β*L; where α and β are weight coefficients, T max is the longest polling cycle, v represents the impact of mobile speed on the first polling cycle, and L represents the impact of network load on the first polling cycle.

[0168] Optionally, T and T maxBoth represent the length of the polling cycle, in seconds. max The value may be pre-configured by the network. For example, the network may determine the value based on the cell size and whether the cell covers a high-speed mobile terminal device.

[0169] Optionally, two weight coefficients may be used to balance the impact of the first device's moving speed and the network load on the first polling period.

[0170] Optionally, v may convert the moving speed of the first device into a time length that affects the first polling period.

[0171] Optionally, L may convert the current load of the cell into a duration affecting the first polling cycle, which may be converted using the current network congestion rate.

[0172] In some embodiments, the number of beams in each beam subset, including the first beam subset, can also be dynamically adjusted in the polling mechanism. As an example, by combining the dynamic adjustment of the first polling period T and the beam subset size S, a joint optimization model can be derived to ensure maximum network performance while meeting the requirements of different scenarios.

[0173] As an implementation method, assuming that all beams in the inference beam set are gradually covered in multiple cycles, the total delay T for covering all beams is delay It can be expressed as: Where: N is the total number of beams in the inference beam set; S is the size of the dynamically adjusted subset; T monitor for each monitoring cycle.

[0174] In the above implementation, the network (NW) can dynamically adjust S and T so that T delay Minimize it as much as possible while ensuring monitoring accuracy to achieve performance optimization.

[0175] Combined with the above Figure 4 and Figure 5 Two methods for determining the first beam subset are described. Regardless of whether the first beam subset is determined based on a polling mechanism, the number of beams in the first beam subset can be dynamically adjusted to meet actual needs. To improve the monitoring accuracy of the first model, the number of beams in the first beam subset during dynamic adjustment can be related to one or more of the following information: the prediction accuracy of the first model; the network load of the cell where the first device is located; and the minimum number of beams in the first beam subset.

[0176] As an example, the number of beams in the first beam subset is linearly correlated with the prediction accuracy of the first model. When the prediction accuracy requirement is higher, the number of beams can be increased to obtain more beam data, thereby improving the prediction accuracy.

[0177] As an example, the number of beams in the first beam subset is linearly related to the network load of the cell where the first device is located. When the network load increases, the number of beams can be reduced to reduce the amount of measurement data and reduce network pressure.

[0178] As an example, the number of beams in the first beam subset is related to a minimum number of beams in the first beam subset. The minimum number of beams may be network-configured. The number of beams in the first beam subset is greater than or equal to the minimum number of beams.

[0179] As an example, the number of beams in the first beam subset can be dynamically adjusted based on the prediction accuracy of the first model, the cell network load, and the minimum number of beams. For example, the number of beams in the first beam subset can be the sum of the minimum number of beams and a parameter corresponding to the prediction accuracy, minus a parameter corresponding to the network load.

[0180] As an implementation, when the first beam subset is the i-th beam subset among the multiple beam subsets, i is an integer greater than or equal to 1, and the number of beams in the first beam subset S is i It can be: Among them, γ and δ are weight coefficients, is the minimum number of beams, E represents the impact of prediction accuracy on the number of beams, and L represents the impact of network load on the number of beams.

[0181] Optionally, E can convert the prediction accuracy requirement into relevant parameters, such as a target threshold of prediction accuracy, between 0 and 1.

[0182] Optionally, L can convert the current load of the cell into relevant parameters that affect the number of beams, which can be converted using the current network congestion rate.

[0183] Optionally, S i It can also indicate the beam subset size for each CSI reporting period, that is, the number of beams measured in each reporting period.

[0184] In some embodiments, the first reference signal may be directly sent by the second device or sent by the second device upon request. As an example, the first device may send a first request to the second device to request the first reference signal for performance monitoring.

[0185] In some embodiments, the dedicated resource set for the first beam set or the first reference signal is determined based on a dedicated CSI reporting configuration. When configuring the dedicated resource set for monitoring and the reporting configuration for monitoring in the dedicated CSI reporting configuration for monitoring, it is necessary to identify a connection between resource sets (RSs) for monitoring. The first device may also measure the monitored beam based on this connection and report the measurement results.

[0186] As an example, the dedicated resource set for performance monitoring is an RS resource set configured by the network, which may also be referred to as a monitoring RS resource set.

[0187] As an example, the current beam measurement and reporting framework allows configuring an RS resource set for a first device. The reporting configuration may also include a CSI-RS resource set corresponding to the first beam set (the inferred beam set or a portion of the inferred beam set).

[0188] As an example, the first beam set can be associated with the same identity (ID) as the inference beam set or the training beam set of the first model to facilitate measurement by the first device. When the associated IDs (associated IDs) of the inference beam set and the training beam set are the same, the first beam set is associated with the same ID as the inference beam set and the training beam set. That is, the first associated identifier corresponding to the first beam set is the same as the associated identifier of the inference beam set and / or the training beam set of the first model. For example, set A, set B, and the monitoring set are configured with the same associated ID. This will be discussed later in conjunction with Figures 6 to 8 An exemplary description is given.

[0189] As an implementation method, within the CSI framework, an association ID can be sent and tagged together with the CSI reference signal (CSI-RS), allowing the AI / ML model to use the same association ID to determine beam selection during the measurement, inference, and monitoring phases. For example, the association ID can be indicated by the CSI-RS resource index. Each CSI-RS resource has its own unique ID, and the AI / ML model can use this index to identify the CSI-RS measurement values ​​associated with different beam directions.

[0190] As an example, an association ID is set for the inference beam set and the training beam set, and an association ID is not set for the first beam set.

[0191] In some embodiments, when a polling method (ie, a polling mechanism) is used to monitor the inference beam set, resource configuration and reporting configuration for the first beam set need to consider the polling period to cover all beams in the inference beam set.

[0192] As an example, when the first beam set is one of multiple beam subsets determined by the inference beam set based on the polling mechanism, the CSI report configuration needs to ensure effective coverage of all beams in the inference beam set when configuring the CSI report and RS resources, while maintaining the resource efficiency of monitoring. For example, all configurations within a polling cycle are completed through multiple csi-ReportConfigs. For another example, in the configuration of RS resources, it is necessary to ensure that the resources of each beam subset are reasonably allocated for the polling mechanism to support effective measurement.

[0193] As an example, multiple beam subsets including the first beam set correspond one-to-one to multiple resource configurations in the first polling cycle. Each resource configuration can be used to configure an RS resource set. Thus, multiple beam subsets can correspond one-to-one to multiple RS resource sets. For example, an independent RS resource set is configured for each beam subset Si to ensure that the first device can measure the beams in the beam subset within a polling cycle. This will be discussed later in conjunction with Figure 9 An exemplary description is given.

[0194] As an implementation method, each beam subset Si in the inference beam set can be configured with an independent CSI-RS resource or a demodulation reference signal (DMRS) of the physical downlink control channel (PDCCH) for performance monitoring within a polling cycle. In each beam subset, key beams can also be selected as measurement targets for RS resources to ensure that the reported Top-K beam information can fully reflect the overall performance of the beam subset. Optionally, beams that have performed well historically or beams with higher signal strength in the current environment can be prioritized for measurement.

[0195] In some embodiments, the network can dynamically adjust RS resource configuration based on real-time demand to improve RS resource configuration flexibility. For example, in high mobility scenarios, the network can shorten the polling period and increase RS resource density to improve measurement timeliness and accuracy.

[0196] As an example, multiple beam subsets including the first beam set correspond to M resource configurations, where M is the number of reporting cycles in the first polling cycle. For example, when the first polling cycle includes two reporting cycles, resources of multiple beam subsets can be configured simultaneously through one resource configuration. In other words, resources are configured for multiple beam subsets at the same time. Figure 10 An exemplary description is given.

[0197] In some embodiments, AI / ML models can predict future locations based on a user's historical movement trajectory, dynamically adjusting the CSI reporting cycle and subset size. For example, if a user is predicted to enter a high-mobility area, RS resources are densely configured, and multiple monitoring subsets are configured in a single resource configuration, the polling cycle can be shortened in advance.

[0198] In some embodiments, the AI / ML model can dynamically adjust subsequent polling configurations based on real-time feedback of CSI report results and AI / ML algorithms. For example, if the beam signal strength of the current subset is found to fluctuate significantly, the subset can be expanded in the next cycle to ensure that more beams are covered. By dynamically adjusting the polling cycle length, CSI report triggering mechanism, and subset size, the network can flexibly optimize the CSI report configuration under different network conditions and user needs, achieving efficient and accurate performance monitoring. This dynamic adjustment strategy ensures that all beams in the inference beam set are fully covered, while optimizing resource utilization and meeting performance requirements in different scenarios.

[0199] Continue to see Figure 3 In step S320, the first device monitors the performance of the first model according to the measurement result of the first beam set.

[0200] The measurement results of the first beam set may include directly measured parameters of the first beam set and may also include performance parameters of the first model determined based on the measured parameters. Directly measured parameters include RSRP, etc. The performance parameters are as described above and are not further described.

[0201] In some embodiments, the performance parameters of the first model can be determined by comparing the prediction results with the actual measurement results. These results can be the Top-1 or Top-K beams in the inference beam set, or parameters such as RSRP difference. For example, when the first beam set is an inference beam set or a plurality of beam subsets including the first beam set cover the inference beam set, by comparing the predicted Top-K beams with the Top-K beams determined by measurement, it can be determined whether there is a problem with the first model. In other words, when the RS of performance monitoring covers the entire inference beam set, the inference error can be measured by comparing the Top-1 / Top-K beams predicted by the model and the actual optimal beam based on the measurement. For example, by comparing the prediction results with the measured values ​​of the resource set / resources obtained by performance monitoring, the prediction accuracy of the Top-1 or Top-K beam can be obtained.

[0202] As an example, whether the first model is accurate is determined based on L1-RSRP difference information between actual L1-RSRP measurement values ​​of one or more predicted optimal beams and L1-RSRP measurement values ​​from resource sets / resources used for monitoring.

[0203] As an example, the prediction accuracy of the top-1 beam with margin can be evaluated by measuring the RSRP difference between the predicted beam and the optimal beam. When the RSRP difference is less than a set threshold, the prediction is considered successful and the first model is accurate.

[0204] As an example, the best beam among the Top-K predicted beams is used when the highest measured L1-RSRP of the Top-K predicted beams is within a margin of the highest measured L1-RSRP of the beams in the first beam set. This scenario can occur after the first device measures the Top-K predicted beams and finds the best beam with the highest L1-RSRP. As long as the L1-RSRP of the best beam among the Top-K predicted beams is within a set or calculated margin compared to the L1-RSRP of the best beam in the first beam set, the performance degradation caused by using the predicted Top-K beam or the best beam compared to the actual best beam is considered to be tolerable, which is interpreted as a success event.

[0205] As an example, the performance parameter of the first model may be represented by a monitoring margin of the first model. For example, the monitoring margin of the first model may be determined based on an actual number of monitoring times for performance monitoring and a quality difference between the inference beam set and a corresponding beam in the first beam set.

[0206] As an implementation approach, the monitoring margin may be: Among them, N is the actual monitoring times, ε is the margin threshold, RSRP(b i ) is the predicted RSRP of beam b in the inference beam set during the i-th monitoring, is the measured RSRP of beam b in the first beam set during the i-th monitoring.

[0207] Optionally, ε is a preset margin threshold, which can be used to determine whether the RSRP difference between the predicted beam and the actual beam is acceptable.

[0208] Optionally, 1(·) is an indicator function. When the difference between the predicted result and the actual measurement result is within a preset margin range, 1 is returned; otherwise, 0 is returned. Multiple optimal predicted beams can use the same margin value or have different margin sizes.

[0209] Optionally, beam b may be the best predicted beam or multiple better beams in the first beam set.

[0210] In some embodiments, the first beam set includes the predicted Top-K beams in the inference beam set. Assume that the total number of monitoring instances is N instance , the number of monitoring instances actually monitored is N monitor, the performance monitoring accuracy related to Top-K beam prediction is N monitor / N instance .

[0211] In some embodiments, after performing performance monitoring, the first device may further send a monitoring report of the first model to the second device. For example, for network device-side performance monitoring for beam prediction at the first device, the network may need to configure / indicate a set of RS resources as monitoring RS resources, and the first device may measure these monitoring RS resources and send a monitoring report to the network.

[0212] In some embodiments, the monitoring report may include performance parameters of the first model. As described above, the performance parameters of the first model include the prediction accuracy of the first model and / or the prediction error of the first model. For example, based on the reported measurement values ​​corresponding to the monitored RS resources and the inference-related reports, performance indicators or related KPIs (e.g., beam prediction accuracy, RSRP difference, etc.) can be calculated. The calculated performance indicators or related KPIs can be used to evaluate the operability of CSI reports related to beam prediction.

[0213] As an example, the CSI reporting framework can be used to configure a monitoring RS resource set and obtain a beam measurement report corresponding to the monitoring RS resource set. The beam measurement report may include a CSI-RS resource indicator (CRI) or parameters such as RSRP and SINR for the measured RS resources.

[0214] As an example, the network can use different CSI reports (monitoring reports) to obtain beam measurements / reports for monitoring RS resource sets within the CSI reporting framework. The report content may include the L1-RSRP and RS index of the Top-K beams in the monitoring RS group.

[0215] In some embodiments, the first device can send monitoring reports based on different CSI reporting mechanisms to support network-side performance monitoring. As an example, the network can use different time behavior configurations for the CSI reporting framework associated with the monitoring report. These time behavior configurations include aperiodic (AP), periodic (P), or semi-persistent (SP). Correspondingly, the first device can configure and send periodic, aperiodic, and semi-persistent beam reports.

[0216] As an example, based on the relevant L1-CSI reporting framework, monitoring reports can be sent using periodic, non-periodic and semi-persistent signal reporting mechanisms to achieve accuracy in model monitoring or reporting of Top-K beam measurements.

[0217] In some embodiments, the monitoring report of the first model is configured via a first CSI reporting configuration. As an example, the first CSI reporting configuration may be the same as the CSI reporting configuration corresponding to the inference beam set. As an example, the first CSI reporting configuration may be different from the CSI reporting configuration corresponding to the inference beam set.

[0218] As an example, when the first beam set is an inference beam set, the first CSI report configuration is the same as the CSI report configuration corresponding to the inference beam set. In other words, the monitoring RS resource set is equal to the resource set of the inference beam set. In this scenario, the same RS resource set is used for model inference and model monitoring.

[0219] As an example, when the first beam set is a subset of the inference beam set, the first CSI reporting configuration is the same as the CSI reporting configuration corresponding to the inference beam set. That is, the first beam set only includes some of the beams in the inference beam set. In this scenario, the same CSI reporting configuration is used for both model monitoring and model inference.

[0220] As an example, when the first beam set is an inference beam set, the first CSI report configuration is different from the CSI report configuration corresponding to the inference beam set. In other words, the monitoring RS resource set and the resource set of the inference beam set are configured / indicated separately.

[0221] As an example, when the first beam set is a beam subset of the inference beam set, the first CSI report configuration is different from the CSI report configuration corresponding to the inference beam set.

[0222] As an example, regardless of whether the first beam set is a subset of the inference beam set or is equal to the inference beam set, the first CSI report configuration and the CSI report configuration of the inference beam set can be configured separately, or the same CSI report configuration can be used.

[0223] As an example, the first CSI report configuration also considers the configuration and indication of the monitoring RS resource set in different scenarios. The monitoring RS resource set corresponds to the first beam set. The settings of the first beam set can be configured in an explicit or implicit manner. For example, when the first beam set is a subset of the inference beam set, the network can configure / indicate the CSI-RS resource set corresponding to the beam subset of the inference beam set as the monitoring RS resource set, and further configure the timeline and reporting amount in the monitoring RS resource set.

[0224] In some embodiments, the first device may send a monitoring report of the first model based on a reporting period for performance monitoring. The reporting period based on performance monitoring may be determined based on different CSI reporting mechanisms.

[0225] As an example, the reporting period can be the same as the monitoring period. Within a polling cycle, each reporting period can measure only a subset of beams, significantly reducing the measurement burden on the first device and conserving resources. Over multiple reporting periods, the first device can gradually poll all beams in the inference beam set to ensure that all beams are measured. As previously mentioned, the network can dynamically adjust the polling period length and subset size based on network conditions to ensure effective performance monitoring in different scenarios.

[0226] In some embodiments, in scenarios where performance monitoring is performed based on a polling mechanism, the first configuration of the monitoring report may configure a beam subset corresponding to the polling mechanism and support phased monitoring reporting. Optionally, the first configuration may include one or more of the trigger type of the monitoring report, a beam subset indication, and the content of the monitoring report.

[0227] As an example, when the first configuration is a CSI report configuration based on a CSI framework, the first configuration may include a CSI report trigger type, a beam subset indication, and CSI report content. The three contents are specifically as follows.

[0228] For CSI report triggering type, periodic, aperiodic or semi-persistent CSI report triggering mechanism can be used to ensure that the first device can regularly report monitoring data. CSI reporting of corresponding beam subsets is activated in different polling periods.

[0229] For beam subset indication, the network can configure multiple CSI reports in each polling cycle to specify the beam subset that currently needs to be measured. For example, the network can configure beam subset S1 as the measurement target for CSI report in the first monitoring cycle in each polling cycle, and then configure subset S2 in the next monitoring cycle, such as Figure 9 shown.

[0230] The CSI report content can include the L1-RSRP values ​​and RS indices of the top-K beams in the beam subset. This allows the network to obtain the best-performing beam in each beam subset and calculate the model prediction accuracy based on this information.

[0231] Combined with the above Figures 3 to 5 This section introduces how to determine the first beam set for performance monitoring. As mentioned above, the first beam set can be configured with the same association ID as the training beam set and the inference beam set. Figures 6 to 8 A method for configuring the first beam set association ID is exemplified.

[0232] Figures 6 to 8The beam sets and associated IDs in the are configured through the CSI report configuration (csi-ReportConfig). In the entire AI / ML-based beam management process, the model training, model inference and model detection (monitoring) processes can configure the associated ID based on the CSI-RS resource index. Exemplarily, in the CSI framework, the inference beam set and the training beam set are configured as different CSI resource sets in csi-ResourceConfig, but will be associated with an ID. Among them, the resource set of the inference beam set is configured through csi-ResourceSetA, the resource set of the training beam set is configured through csi-ResourceSetB, and the resource set of the monitoring set (first beam set) is configured through csi-ResourcemonitorSet. The following introduces three different configuration methods in conjunction with the accompanying drawings.

[0233] See also Figure 6 In csi-ReportConfig, the inference beam set, training beam set, and first beam set corresponding to different CSI resource sets are configured in the same CSI resource configuration (csi-ResourceConfig). The three beam sets share the same association ID, which is also included in the CSI resource configuration.

[0234] and Figure 6 In comparison, although Figure 7 The inference beam set, training beam set, first beam set, and association ID in the same CSI resource configuration are also in the same CSI resource configuration, but the inference beam set, training beam set, and first beam set correspond to different CSI resource configuration indexes (csi-ResourceConfigId). In other words, the inference beam set, training beam set, and first beam set are configured separately based on different CSI resource configuration indexes.

[0235] and Figure 7 In comparison, although Figure 8 The inference beam set, training beam set, and first beam set in are also configured separately, but the association IDs corresponding to different beam sets are also configured in different CSI resource configuration indexes. Figure 8 In the method shown, since the correlation ID is configured in different csi-ReportConfigIds, it can meet the scenario of different correlation IDs. In addition, even if the correlation ID is different, no additional csi-ResourceConfig is required, which is more beneficial in terms of report configuration overhead.

[0236] exist Figure 7 and Figure 8In the example, each csi-ReportConfigId is constructed in a csi-ReportConfig. It should be noted that different csi-ReportConfigIds can also be configured in different csi-ReportConfigs.

[0237] As can be seen from the above, when multiple beam subsets are generated based on the polling mechanism for performance monitoring, the RS resource set can correspond to multiple beam subsets one by one, or can be flexibly configured according to actual conditions. Figure 6 As an example, the CSI framework in Figure 9 and Figure 10 The two configuration modes are respectively described with examples.

[0238] See also Figure 9 ,In the first polling cycle, the inference beam set is divided into n beam subsets, namely sets S1, S2,…, S n The CSI resource configuration for the first polling cycle includes n csi-ReportConfigs. The n csi-ReportConfigs correspond one-to-one to the n beam subsets.

[0239] and Figure 9 compared to, Figure 10 The inference beam set in is also divided into n beam subsets, but the first polling period is determined by two reporting periods. In this scenario, two csi-ReportConfigs are required to configure resources for multiple monitoring subsets respectively. Figure 10 , the two csi-ReportConfigs correspond to the beam subset sets {S1,…,S k}, {S k+1 ,S k+2 ,…,S n}.

[0240] Optionally, Figure 10 The two csi-ReportConfigs in the example can be two beam subset sets {S1,…,S k}, {S k+1 ,S k+2 ,…,S n}Set a corresponding RS to improve the accuracy of performance monitoring.

[0241] Combined with the above Figures 3 to 10The determination method of the first beam set for performance monitoring and the related configuration are introduced. Among them, the first beam set is used to send a dedicated RS for performance monitoring; the related configuration includes the configuration of RS resources and monitoring reports. In order to more clearly introduce the application of the embodiment of the present application. The following takes the first device as UE and the second device as base station (eNB) as an example, combined with Figure 11 An example is introduced in which a first device requests a dedicated reference signal from a second device and sends a monitoring report.

[0242] In step S1110, the UE sends UE capability indication information to the base station. For example, the UE sends its own AI / ML capability indication (UE AI / ML capacity indication) to the base station.

[0243] In step S1120, the UE sends a request (first request) to the base station to request a dedicated RS for performance monitoring (request for dedicated RS for performance monitoring) to perform performance detection on the model.

[0244] In step S1130 , the base station sends a dedicated RS (dedicated RS for transmission) based on the request of the UE to support performance detection.

[0245] In step S1140, the UE calculates monitoring KPIs or determines occurrence of events based on the received performance RS.

[0246] In step S1150, the UE sends KPIs monitoring results or triggering event information (information about monitoring KPIs or event occurrence) to the base station according to the performance monitoring and evaluation results.

[0247] In step S1160, the base station receives KPI detection result information from the UE to evaluate model performance (evaluate AI / ML performance). Optionally, the base station can perform performance evaluation and auxiliary monitoring on the UE-side model, or it can perform performance evaluation on its own model. Conversely, during model monitoring, the UE can perform performance evaluation on its own model, or it can perform auxiliary monitoring on the base station-side model.

[0248] In step S1170, based on the evaluation results, the base station notifies the UE of information about AI / ML and life cycle management (LCM) operations at the UE. This information covers aspects such as the model's status, configuration, monitoring, and updates. This information ensures that the UE maintains optimal performance and consistency during model usage.

[0249] In step S1180, the UE performs LCM operation (LCM operation at UE) according to the LCM information from the base station, activating, generalizing, switching, falling back or updating the AM / ML model (AI / ML model activation / deactivation / switch / fallback) on the UE side.

[0250] In step S1190, the UE notifies the base station of relevant information about the executed LCM operation at the UE according to the updated result, for example, relevant results after the LCM is implemented.

[0251] Depend on Figure 11 It can be seen that the embodiments of the present application can be applied to monitoring model performance by terminal devices and network devices. The transmission of LCM information can facilitate terminal devices and network devices to timely understand the status of related models and improve the accuracy of the models.

[0252] Combined with the above Figures 1 to 11 , describes the method embodiment of the present application in detail. Figures 12 to 14 , the device embodiment of the present application is described in detail. It should be understood that the description of the device embodiment corresponds to the description of the method embodiment, so for parts not described in detail, reference can be made to the previous method embodiment.

[0253] Figure 12 1 is a schematic block diagram of an apparatus for wireless communication according to an embodiment of the present application. The apparatus 1200 may be any of the first devices described above. The first device may be a terminal device. Figure 12 The device 1200 shown includes a transceiver unit 1210 and a processing unit 1220 .

[0254] The transceiver unit 1210 may be configured to receive a first reference signal, where the first reference signal is sent via a first beam set.

[0255] Processing unit 1220 can be used to monitor the performance of the first model based on the measurement results of the first beam set; wherein, the first reference signal corresponds to the first monitoring instance, and the first beam set is determined based on the inference beam set of the first model; the first beam set includes all beams in the inference beam set, or the first beam set includes part of the beams in the inference beam set.

[0256] Optionally, the first beam set is a first beam subset of the inference beam set, and the first beam subset is determined based on first information, and the first information includes one or more of the following information: beam priority in the inference beam set; historical performance data of the beams in the inference beam set; perceived quality of the beams in the inference beam set; coverage area of ​​the beams in the inference beam set; and network load of the cell where the first device is located.

[0257] Optionally, the first beam subset is determined according to the beam priority, and the beam priority of the first beam in the inferred beam set satisfies one or more of the following conditions: the beam priority of the first beam is positively correlated with the importance of the first beam; the beam priority of the first beam is positively correlated with the quality of the first beam; the beam priority of the first beam is negatively correlated with the amount of resources for monitoring the first beam.

[0258] Optionally, the performance monitoring includes multiple monitoring instances, the multiple monitoring instances include a first monitoring instance and a second monitoring instance, the second monitoring instance corresponds to a second beam subset, and at least one beam in the first beam subset is the same as that in the second beam subset.

[0259] Optionally, the first beam set is a first beam subset of the inference beam set, and the first beam subset is determined according to a polling mechanism related to the inference beam set, and the polling mechanism is used to monitor the performance of all beams in the inference beam set within a first polling cycle.

[0260] Optionally, the first polling cycle includes multiple monitoring instances, the multiple monitoring instances include the first monitoring instance, the multiple monitoring instances respectively correspond to multiple beam subsets including the first beam subset, and the beams in any two beam subsets among the multiple beam subsets are different.

[0261] Optionally, the duration of the first polling cycle is a positive integer multiple of the duration of the first monitoring cycle, and the first monitoring cycle includes at least one monitoring instance.

[0262] Optionally, the first polling cycle is related to one or more of the following information: a moving speed of the first device; a network load of a cell where the first device is located; and a longest polling cycle of the cell where the first device is located.

[0263] Optionally, the first polling period is: T = T max ―α*v―β*L; where α and β are weight coefficients, T maxis the longest polling cycle, v represents the impact of mobile speed on the first polling cycle, and L represents the impact of network load on the first polling cycle.

[0264] Optionally, the number of beams in the first beam subset is related to one or more of the following information: prediction accuracy of the first model; network load of the cell where the first device is located; and the minimum number of beams in the first beam subset.

[0265] Optionally, when the first beam subset is the i-th beam subset among the multiple beam subsets, i is an integer greater than or equal to 1, and the number of beams in the first beam subset is: Among them, γ and δ are weight coefficients, is the minimum number of beams, E represents the impact of prediction accuracy on the number of beams, and L represents the impact of network load on the number of beams.

[0266] Optionally, the first beam set corresponds to a first association identifier, and the first association identifier is the same as an association identifier of the inference beam set and / or the training beam set of the first model.

[0267] Optionally, the first beam set is one of multiple beam subsets determined by the inference beam set based on the polling mechanism; the multiple beam subsets correspond one-to-one to the multiple resource configurations within the first polling cycle, or the multiple beam subsets correspond to M resource configurations, where M is the number of reporting cycles within the first polling cycle.

[0268] Optionally, the transceiver unit 1210 is further used to send a first request to the second device, where the first request is used to request a first reference signal; wherein the first reference signal is at least one of multiple dedicated reference signals, and the dedicated reference signal is used to monitor the performance of the first model.

[0269] Optionally, the transceiver unit 1210 is also used to send a monitoring report of the first model based on the reporting period of performance monitoring; wherein the monitoring report includes performance parameters of the first model, and the performance parameters of the first model include the prediction accuracy of the first model and / or the prediction error of the first model.

[0270] Optionally, the monitoring report of the first model is configured through a first CSI report configuration; the first CSI report configuration is the same as the CSI report configuration corresponding to the inference beam set, or the first CSI report configuration is different from the CSI report configuration corresponding to the inference beam set.

[0271] Optionally, the performance parameter of the first model is represented by a monitoring margin of the first model, and the monitoring margin is determined according to an actual monitoring number of performance monitoring and a quality difference between the inference beam set and a corresponding beam in the first beam set.

[0272] Optionally, monitor the margin as: Among them, N is the actual monitoring times, ε is the margin threshold, RSRP(b i ) is the predicted RSRP of beam b in the inference beam set during the i-th monitoring, is the measured RSRP of beam b in the first beam set during the i-th monitoring.

[0273] Optionally, the first model is an artificial intelligence or machine learning model.

[0274] Figure 13 1300 is a schematic block diagram of another apparatus for wireless communication according to an embodiment of the present application. The apparatus 1300 may be any second device described above. The second device may be a network device or a terminal device. Figure 13 The illustrated apparatus 1300 includes a transceiver unit 1310 .

[0275] The transceiver unit 1310 can be used to send a first reference signal, which is sent through a first beam set; wherein the first reference signal corresponds to a first monitoring instance, the measurement result of the first beam set is used to monitor the performance of the first model, and the first beam set is determined based on the inference beam set of the first model; the first beam set includes all beams in the inference beam set, or the first beam set includes some beams in the inference beam set.

[0276] Optionally, the first beam set is a first beam subset of the inference beam set, and the first beam subset is determined based on first information, and the first information includes one or more of the following information: beam priority in the inference beam set; historical performance data of the beams in the inference beam set; perceived quality of the beams in the inference beam set; coverage area of ​​the beams in the inference beam set; and network load of the cell corresponding to the second device.

[0277] Optionally, the first beam subset is determined according to the beam priority, and the beam priority of the first beam in the inferred beam set satisfies one or more of the following conditions: the beam priority of the first beam is positively correlated with the importance of the first beam; the beam priority of the first beam is positively correlated with the quality of the first beam; the beam priority of the first beam is negatively correlated with the amount of resources for monitoring the first beam.

[0278] Optionally, the performance monitoring includes multiple monitoring instances, the multiple monitoring instances include a first monitoring instance and a second monitoring instance, the second monitoring instance corresponds to a second beam subset, and at least one beam in the first beam subset is the same as that in the second beam subset.

[0279] Optionally, the first beam set is a first beam subset of the inference beam set, and the first beam subset is determined according to a polling mechanism related to the inference beam set, and the polling mechanism is used to monitor the performance of all beams in the inference beam set within a first polling cycle.

[0280] Optionally, the first polling cycle includes multiple monitoring instances, the multiple monitoring instances include the first monitoring instance, the multiple monitoring instances respectively correspond to multiple beam subsets including the first beam subset, and the beams in any two beam subsets among the multiple beam subsets are different.

[0281] Optionally, the duration of the first polling cycle is a positive integer multiple of the duration of the first monitoring cycle, and the first monitoring cycle includes at least one monitoring instance.

[0282] Optionally, the first polling cycle is related to one or more of the following information: a moving speed of the first device receiving the first reference signal; a network load of a cell corresponding to the second device; and a longest polling cycle of the cell corresponding to the second device.

[0283] Optionally, the first polling period is: T = T max ―α*v―β*L; where α and β are weight coefficients, T max is the longest polling cycle, v represents the impact of mobile speed on the first polling cycle, and L represents the impact of network load on the first polling cycle.

[0284] Optionally, the number of beams in the first beam subset is related to one or more of the following information: prediction accuracy of the first model; network load of the cell corresponding to the second device; and the minimum number of beams in the first beam subset.

[0285] Optionally, when the first beam subset is the i-th beam subset among the multiple beam subsets, i is an integer greater than or equal to 1, and the number of beams in the first beam subset is: Among them, γ and δ are weight coefficients, is the minimum number of beams, E represents the impact of prediction accuracy on the number of beams, and L represents the impact of network load on the number of beams.

[0286] Optionally, the first beam set corresponds to a first association identifier, and the first association identifier is the same as an association identifier of the inference beam set and / or the training beam set of the first model.

[0287] Optionally, the first beam set is one of multiple beam subsets determined by the inference beam set based on the polling mechanism; the multiple beam subsets correspond one-to-one to the multiple resource configurations within the first polling cycle, or the multiple beam subsets correspond to M resource configurations, where M is the number of reporting cycles within the first polling cycle.

[0288] Optionally, the transceiver unit 1310 is further used to receive a first request sent by the first device, where the first request is used to request a first reference signal; wherein the first reference signal is at least one of multiple dedicated reference signals, and the dedicated reference signal is used to monitor the performance of the first model.

[0289] Optionally, the transceiver unit 1310 is also used to receive a monitoring report of the first model based on a reporting period of performance monitoring; wherein the monitoring report includes performance parameters of the first model, and the performance parameters of the first model include the prediction accuracy of the first model and / or the prediction error of the first model.

[0290] Optionally, the monitoring report of the first model is configured through a first CSI report configuration; the first CSI report configuration is the same as the CSI report configuration corresponding to the inference beam set, or the first CSI report configuration is different from the CSI report configuration corresponding to the inference beam set.

[0291] Optionally, the performance parameter of the first model is represented by a monitoring margin of the first model, and the monitoring margin is determined according to an actual monitoring number of performance monitoring and a quality difference between the inference beam set and a corresponding beam in the first beam set.

[0292] Optionally, monitor the margin as: Among them, N is the actual monitoring times, ε is the margin threshold, RSRP(b i ) is the predicted RSRP of beam b in the inference beam set during the i-th monitoring, is the measured RSRP of beam b in the first beam set during the i-th monitoring.

[0293] Optionally, the first model is an artificial intelligence or machine learning model.

[0294] Figure 14 Shown is a schematic structural diagram of a communication device according to an embodiment of the present application. Figure 14 The dotted line in the figure indicates that the unit or module is optional. The apparatus 1400 can be used to implement the method described in the above method embodiment. The apparatus 1400 can be a chip, a terminal device, or a network device.

[0295] The device 1400 may include one or more processors 1410. The processor 1410 may support the device 1400 to implement the method described in the above method embodiment. The processor 1410 may be a general-purpose processor or a special-purpose processor. For example, the processor may be a central processing unit (CPU). Alternatively, the processor may be another general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0296] The apparatus 1400 may further include one or more memories 1420. The memories 1420 store programs that can be executed by the processor 1410, causing the processor 1410 to perform the methods described in the above method embodiments. The memories 1420 may be independent of the processor 1410 or integrated into the processor 1410.

[0297] The apparatus 1400 may further include a transceiver 1430. The processor 1410 may communicate with other devices or chips via the transceiver 1430. For example, the processor 1410 may transmit and receive data with other devices or chips via the transceiver 1430.

[0298] The present application also provides a computer-readable storage medium for storing a program. The computer-readable storage medium can be applied to a terminal device or network device provided in the present application, and the program enables a computer to execute the method performed by the terminal device or network device in each embodiment of the present application.

[0299] The computer-readable storage medium may be any available medium that can be read by a computer or a data storage device such as a server or data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, or a magnetic tape), an optical medium (e.g., a digital versatile disc (DVD)), or a semiconductor medium (e.g., a solid state drive (SSD)).

[0300] The present application also provides a computer program product. The computer program product includes a program. The computer program product can be applied to a terminal device or network device provided in the present application, and the program causes a computer to execute the method performed by the terminal device or network device in each embodiment of the present application.

[0301] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0302] The present application also provides a computer program that can be applied to a terminal device or network device provided in the present application, and enables a computer to execute the method performed by the terminal or network device in each embodiment of the present application.

[0303] The terms "system" and "network" in this application can be used interchangeably. In addition, the terms used in this application are only used to explain the specific embodiments of this application and are not intended to limit this application. The terms "first", "second", "third" and "fourth" in the specification and claims of this application and the accompanying drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. In the embodiments of this application, determining B based on A does not mean determining B based solely on A, but B can also be determined based on A and / or other information.

[0304] In the embodiments of this application, the term "indication" may refer to a direct indication, an indirect indication, or an indication of an association. For example, "A indicates B" may refer to a direct indication of B, e.g., B can obtain information through A; it may refer to an indirect indication of B, e.g., A indicates C, e.g., B can obtain information through C; or it may refer to an association between A and B.

[0305] In the embodiments of the present application, the term "corresponding" may indicate a direct or indirect correspondence between the two, or an association relationship between the two, or a relationship between indication and indication, configuration and configuration, etc.

[0306] In the embodiments of the present application, "pre-definition" or "pre-configuration" may be implemented by pre-storing corresponding codes, tables, or other methods that can be used to indicate relevant information in a device (e.g., including a terminal device and a network device). The present application does not limit the specific implementation method. For example, pre-definition may refer to a definition in a protocol.

[0307] In the embodiments of this application, the term "and / or" is simply a description of the association relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0308] In the embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0309] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0310] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0311] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0312] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for wireless communication, characterized in that: include: The first device receives a first reference signal, where the first reference signal is sent through a first beam set; The first device performs performance monitoring on the first model according to the measurement result of the first beam set; The first reference signal corresponds to a first monitoring instance, and the first beam set is determined according to the inference beam set of the first model; the first beam set includes all beams in the inference beam set, or the first beam set includes part of the beams in the inference beam set.

2. The method according to claim 1, characterized in that The first beam set is a first beam subset of the inference beam set, and the first beam subset is determined according to first information, where the first information includes one or more of the following information: beam priorities in the inference beam set; historical performance data of beams in the inference beam set; the perceived quality of beams in the inference beam set; coverage areas of the beams in the inference beam set; The network load of the cell where the first device is located.

3. The method according to claim 2, characterized in that The first beam subset is determined according to the beam priority, and the beam priority of the first beam in the inference beam set satisfies one or more of the following conditions: The beam priority of the first beam is positively correlated with the importance of the first beam; The beam priority of the first beam is positively correlated with the quality of the first beam; The beam priority of the first beam is negatively correlated with the amount of resources monitoring the first beam.

4. The method according to claim 2 or 3, characterized in that: The performance monitoring includes multiple monitoring instances, the multiple monitoring instances include the first monitoring instance and a second monitoring instance, the second monitoring instance corresponds to a second beam subset, and the first beam subset is the same as at least one beam in the second beam subset.

5. The method according to claim 1, characterized in that The first beam set is a first beam subset of the inference beam set, and the first beam subset is determined according to a polling mechanism related to the inference beam set, and the polling mechanism is used to monitor the performance of all beams in the inference beam set within a first polling cycle.

6. The method according to claim 5, characterized in that The first polling cycle includes multiple monitoring instances, the multiple monitoring instances include the first monitoring instance, the multiple monitoring instances respectively correspond to multiple beam subsets including the first beam subset, and beams in any two beam subsets among the multiple beam subsets are different.

7. The method according to claim 5 or 6, characterized in that: The duration of the first polling cycle is a positive integer multiple of the duration of the first monitoring cycle, and the first monitoring cycle includes at least one monitoring instance.

8. The method according to claim 5 or 6, characterized in that: The first polling cycle is related to one or more of the following information: a moving speed of the first device; a network load of a cell where the first device is located; The longest polling period of the cell where the first device is located.

9. The method according to claim 8, characterized in that The first polling cycle is: T=T max ―α*v―β*L; Among them, α and β are weight coefficients, T max is the longest polling cycle, v represents the influence of the moving speed on the first polling cycle, and L represents the influence of the network load on the first polling cycle.

10. The method according to any one of claims 2 to 9, characterized in that: The number of beams in the first beam subset is related to one or more of the following information: the prediction accuracy of the first model; a network load of a cell where the first device is located; The minimum number of beams of the first beam subset.

11. The method according to claim 10, characterized in that When the first beam subset is the i-th beam subset among the multiple beam subsets, i is an integer greater than or equal to 1, and the number of beams in the first beam subset is: Among them, γ and δ are weight coefficients, is the minimum number of beams, E represents the influence of the prediction accuracy on the number of beams, and L represents the influence of the network load on the number of beams.

12. The method according to any one of claims 1 to 11, characterized in that The first beam set corresponds to a first association identifier, and the first association identifier is the same as an association identifier of the inference beam set and / or the training beam set of the first model.

13. The method according to claim 12, characterized in that The first beam set is one of multiple beam subsets determined by the inference beam set based on a polling mechanism; the multiple beam subsets correspond one-to-one to multiple resource configurations within the first polling cycle, or the multiple beam subsets correspond to M resource configurations, where M is the number of reporting cycles within the first polling cycle.

14. The method according to any one of claims 1 to 13, characterized in that The method further comprises: The first device sends a first request to the second device, where the first request is used to request the first reference signal; The first reference signal is at least one of a plurality of dedicated reference signals, and the dedicated reference signal is used to monitor the performance of the first model.

15. The method according to any one of claims 1 to 14, characterized in that The method further comprises: The first device sends a monitoring report of the first model based on a reporting period of the performance monitoring; The monitoring report includes performance parameters of the first model, and the performance parameters of the first model include prediction accuracy of the first model and / or prediction error of the first model.

16. The method according to claim 15, characterized in that The monitoring report of the first model is configured through a first channel state information CSI report configuration; the first CSI report configuration is the same as the CSI report configuration corresponding to the inference beam set, or the first CSI report configuration is different from the CSI report configuration corresponding to the inference beam set.

17. The method according to any one of claims 1 to 16, characterized in that The performance parameter of the first model is represented by a monitoring margin of the first model, and the monitoring margin is determined according to an actual monitoring number of the performance monitoring and a quality difference between the inference beam set and a corresponding beam in the first beam set.

18. The method according to claim 17, characterized in that The monitoring margin is: Wherein, N is the actual monitoring times, ε is the margin threshold, RSRP(b i ) is the predicted reference signal received power RSRP of beam b in the inference beam set during the i-th monitoring, is the measured RSRP of beam b in the first beam set during the i-th monitoring.

19. The method according to any one of claims 1 to 18, characterized in that The first model is an artificial intelligence or machine learning model.

20. A method for wireless communication, characterized in that: include: The second device sends a first reference signal, where the first reference signal is sent through a first beam set; The first reference signal corresponds to a first monitoring instance, the measurement result of the first beam set is used to perform performance monitoring on a first model, and the first beam set is determined according to an inference beam set of the first model; the first beam set includes all beams in the inference beam set, or the first beam set includes part of the beams in the inference beam set.

21. The method according to claim 20, characterized in that The first beam set is a first beam subset of the inference beam set, and the first beam subset is determined according to first information, where the first information includes one or more of the following information: beam priorities in the inference beam set; historical performance data of beams in the inference beam set; the perceived quality of beams in the inference beam set; coverage areas of the beams in the inference beam set; The second device corresponds to a network load of a cell.

22. The method according to claim 21, characterized in that The first beam subset is determined according to the beam priority, and the beam priority of the first beam in the inference beam set satisfies one or more of the following conditions: The beam priority of the first beam is positively correlated with the importance of the first beam; The beam priority of the first beam is positively correlated with the quality of the first beam; The beam priority of the first beam is negatively correlated with the amount of resources monitoring the first beam.

23. The method according to claim 21 or 22, characterized in that The performance monitoring includes multiple monitoring instances, the multiple monitoring instances include the first monitoring instance and a second monitoring instance, the second monitoring instance corresponds to a second beam subset, and the first beam subset is the same as at least one beam in the second beam subset.

24. The method according to claim 20, characterized in that The first beam set is a first beam subset of the inference beam set, and the first beam subset is determined according to a polling mechanism related to the inference beam set, and the polling mechanism is used to monitor the performance of all beams in the inference beam set within a first polling cycle.

25. The method according to claim 24, characterized in that The first polling cycle includes multiple monitoring instances, the multiple monitoring instances include the first monitoring instance, the multiple monitoring instances respectively correspond to multiple beam subsets including the first beam subset, and beams in any two beam subsets among the multiple beam subsets are different.

26. The method according to claim 24 or 25, characterized in that The duration of the first polling cycle is a positive integer multiple of the duration of the first monitoring cycle, and the first monitoring cycle includes at least one monitoring instance.

27. The method according to claim 24 or 25, characterized in that The first polling cycle is related to one or more of the following information: a moving speed of a first device receiving the first reference signal; a network load of a cell corresponding to the second device; The longest polling period of the cell corresponding to the second device.

28. The method according to claim 27, characterized in that The first polling cycle is: T=T max ―α*v―β*L; Among them, α and β are weight coefficients, T max is the longest polling cycle, v represents the influence of the moving speed on the first polling cycle, and L represents the influence of the network load on the first polling cycle.

29. The method according to any one of claims 21 to 28, characterized in that The number of beams in the first beam subset is related to one or more of the following information: the prediction accuracy of the first model; a network load of a cell corresponding to the second device; The minimum number of beams of the first beam subset.

30. The method according to claim 29, characterized in that When the first beam subset is the i-th beam subset among the multiple beam subsets, i is an integer greater than or equal to 1, and the number of beams in the first beam subset is: Among them, γ and δ are weight coefficients, is the minimum number of beams, E represents the influence of the prediction accuracy on the number of beams, and L represents the influence of the network load on the number of beams.

31. The method according to any one of claims 20 to 30, characterized in that The first beam set corresponds to a first association identifier, and the first association identifier is the same as an association identifier of the inference beam set and / or the training beam set of the first model.

32. The method according to claim 31, characterized in that The first beam set is one of multiple beam subsets determined by the inference beam set based on a polling mechanism; the multiple beam subsets correspond one-to-one to multiple resource configurations within the first polling cycle, or the multiple beam subsets correspond to M resource configurations, where M is the number of reporting cycles within the first polling cycle.

33. The method according to any one of claims 20 to 32, characterized in that The method further comprises: The second device receives a first request sent by the first device, where the first request is used to request the first reference signal; The first reference signal is at least one of a plurality of dedicated reference signals, and the dedicated reference signal is used to monitor the performance of the first model.

34. The method according to any one of claims 20 to 33, characterized in that The method further comprises: The second device receives the monitoring report of the first model based on the reporting period of the performance monitoring; The monitoring report includes performance parameters of the first model, and the performance parameters of the first model include prediction accuracy of the first model and / or prediction error of the first model.

35. The method according to claim 34, characterized in that The monitoring report of the first model is configured through a first channel state information CSI report configuration; the first CSI report configuration is the same as the CSI report configuration corresponding to the inference beam set, or the first CSI report configuration is different from the CSI report configuration corresponding to the inference beam set.

36. The method according to any one of claims 20 to 35, characterized in that The performance parameter of the first model is represented by a monitoring margin of the first model, and the monitoring margin is determined according to an actual monitoring number of the performance monitoring and a quality difference between the inference beam set and a corresponding beam in the first beam set.

37. The method according to claim 36, characterized in that The monitoring margin is: Wherein, N is the actual monitoring times, ε is the margin threshold, RSRP(b i ) is the predicted reference signal received power RSRP of beam b in the inference beam set during the i-th monitoring, is the measured RSRP of beam b in the first beam set during the i-th monitoring.

38. The method according to any one of claims 20 to 37, characterized in that The first model is an artificial intelligence or machine learning model.

39. A device for wireless communication, characterized in that: The device is a first device, and the device includes: A transceiver unit, configured to receive a first reference signal, where the first reference signal is sent via a first beam set; a processing unit, configured to monitor the performance of the first model according to the measurement result of the first beam set; The first reference signal corresponds to a first monitoring instance, and the first beam set is determined according to the inference beam set of the first model; the first beam set includes all beams in the inference beam set, or the first beam set includes part of the beams in the inference beam set.

40. The device according to claim 39, characterized in that The first beam set is a first beam subset of the inference beam set, and the first beam subset is determined according to first information, where the first information includes one or more of the following information: beam priorities in the inference beam set; historical performance data of beams in the inference beam set; the perceived quality of beams in the inference beam set; coverage areas of the beams in the inference beam set; The network load of the cell where the first device is located.

41. The device according to claim 40, characterized in that The first beam subset is determined according to the beam priority, and the beam priority of the first beam in the inference beam set satisfies one or more of the following conditions: The beam priority of the first beam is positively correlated with the importance of the first beam; The beam priority of the first beam is positively correlated with the quality of the first beam; The beam priority of the first beam is negatively correlated with the amount of resources monitoring the first beam.

42. The device according to claim 40 or 41, characterized in that The performance monitoring includes multiple monitoring instances, the multiple monitoring instances include the first monitoring instance and a second monitoring instance, the second monitoring instance corresponds to a second beam subset, and the first beam subset is the same as at least one beam in the second beam subset.

43. The device according to claim 39, characterized in that The first beam set is a first beam subset of the inference beam set, and the first beam subset is determined according to a polling mechanism related to the inference beam set, and the polling mechanism is used to monitor the performance of all beams in the inference beam set within a first polling cycle.

44. The device according to claim 43, characterized in that The first polling cycle includes multiple monitoring instances, the multiple monitoring instances include the first monitoring instance, the multiple monitoring instances respectively correspond to multiple beam subsets including the first beam subset, and beams in any two beam subsets among the multiple beam subsets are different.

45. The device according to claim 43 or 44, characterized in that The duration of the first polling cycle is a positive integer multiple of the duration of the first monitoring cycle, and the first monitoring cycle includes at least one monitoring instance.

46. ​​The device according to claim 43 or 44, characterized in that The first polling cycle is related to one or more of the following information: a moving speed of the first device; a network load of a cell where the first device is located; The longest polling period of the cell where the first device is located.

47. The device according to claim 46, characterized in that The first polling cycle is: T=T max ―α*v―β*L; Among them, α and β are weight coefficients, T max is the longest polling cycle, v represents the influence of the moving speed on the first polling cycle, and L represents the influence of the network load on the first polling cycle.

48. The device according to any one of claims 40 to 47, characterized in that The number of beams in the first beam subset is related to one or more of the following information: the prediction accuracy of the first model; a network load of a cell where the first device is located; The minimum number of beams of the first beam subset.

49. The device according to claim 48, characterized in that When the first beam subset is the i-th beam subset among the multiple beam subsets, i is an integer greater than or equal to 1, and the number of beams in the first beam subset is: Among them, γ and δ are weight coefficients, is the minimum number of beams, E represents the influence of the prediction accuracy on the number of beams, and L represents the influence of the network load on the number of beams.

50. The device according to any one of claims 39 to 49, characterized in that The first beam set corresponds to a first association identifier, and the first association identifier is the same as an association identifier of the inference beam set and / or the training beam set of the first model.

51. The device according to claim 50, characterized in that The first beam set is one of multiple beam subsets determined by the inference beam set based on a polling mechanism; the multiple beam subsets correspond one-to-one to multiple resource configurations within the first polling cycle, or the multiple beam subsets correspond to M resource configurations, where M is the number of reporting cycles within the first polling cycle.

52. The device according to any one of claims 39 to 51, characterized in that The transceiver unit is further used to send a first request to the second device, where the first request is used to request the first reference signal; wherein the first reference signal is at least one of a plurality of dedicated reference signals, and the dedicated reference signal is used to monitor the performance of the first model.

53. The device according to any one of claims 39 to 52, characterized in that The transceiver unit is also used to send a monitoring report of the first model based on the reporting period of the performance monitoring; wherein the monitoring report includes the performance parameters of the first model, and the performance parameters of the first model include the prediction accuracy of the first model and / or the prediction error of the first model.

54. The device according to claim 53, characterized in that The monitoring report of the first model is configured through a first channel state information CSI report configuration; the first CSI report configuration is the same as the CSI report configuration corresponding to the inference beam set, or the first CSI report configuration is different from the CSI report configuration corresponding to the inference beam set.

55. The device according to any one of claims 39 to 54, characterized in that The performance parameter of the first model is represented by a monitoring margin of the first model, and the monitoring margin is determined according to an actual monitoring number of the performance monitoring and a quality difference between the inference beam set and a corresponding beam in the first beam set.

56. The device according to claim 55, characterized in that The monitoring margin is: Wherein, N is the actual monitoring times, ε is the margin threshold, RSRP(b i ) is the predicted reference signal received power RSRP of beam b in the inference beam set during the i-th monitoring, is the measured RSRP of beam b in the first beam set during the i-th monitoring.

57. The device according to any one of claims 39 to 56, characterized in that The first model is an artificial intelligence or machine learning model.

58. An apparatus for wireless communication, characterized in that: The device is a second apparatus, and the device includes: A transceiver unit, configured to send a first reference signal, where the first reference signal is sent through a first beam set; The first reference signal corresponds to a first monitoring instance, the measurement result of the first beam set is used to perform performance monitoring on a first model, and the first beam set is determined according to an inference beam set of the first model; the first beam set includes all beams in the inference beam set, or the first beam set includes part of the beams in the inference beam set.

59. The device according to claim 58, characterized in that The first beam set is a first beam subset of the inference beam set, and the first beam subset is determined according to first information, where the first information includes one or more of the following information: beam priorities in the inference beam set; historical performance data of beams in the inference beam set; the perceived quality of beams in the inference beam set; coverage areas of the beams in the inference beam set; The second device corresponds to a network load of a cell.

60. The device according to claim 59, characterized in that The first beam subset is determined according to the beam priority, and the beam priority of the first beam in the inference beam set satisfies one or more of the following conditions: The beam priority of the first beam is positively correlated with the importance of the first beam; The beam priority of the first beam is positively correlated with the quality of the first beam; The beam priority of the first beam is negatively correlated with the amount of resources monitoring the first beam.

61. The device according to claim 59 or 60, characterized in that The performance monitoring includes multiple monitoring instances, the multiple monitoring instances include the first monitoring instance and a second monitoring instance, the second monitoring instance corresponds to a second beam subset, and the first beam subset is the same as at least one beam in the second beam subset.

62. The device according to claim 58, characterized in that The first beam set is a first beam subset of the inference beam set, and the first beam subset is determined according to a polling mechanism related to the inference beam set, and the polling mechanism is used to monitor the performance of all beams in the inference beam set within a first polling cycle.

63. The device according to claim 62, characterized in that The first polling cycle includes multiple monitoring instances, the multiple monitoring instances include the first monitoring instance, the multiple monitoring instances respectively correspond to multiple beam subsets including the first beam subset, and beams in any two beam subsets among the multiple beam subsets are different.

64. The device according to claim 62 or 63, characterized in that The duration of the first polling cycle is a positive integer multiple of the duration of the first monitoring cycle, and the first monitoring cycle includes at least one monitoring instance.

65. The device according to claim 62 or 63, characterized in that The first polling cycle is related to one or more of the following information: a moving speed of a first device receiving the first reference signal; a network load of a cell corresponding to the second device; The longest polling period of the cell corresponding to the second device.

66. The device according to claim 65, characterized in that The first polling cycle is: T=T max ―α*v―β*L; Among them, α and β are weight coefficients, T max is the longest polling cycle, v represents the influence of the moving speed on the first polling cycle, and L represents the influence of the network load on the first polling cycle.

67. The device according to any one of claims 59 to 66, characterized in that The number of beams in the first beam subset is related to one or more of the following information: the prediction accuracy of the first model; a network load of a cell corresponding to the second device; The minimum number of beams of the first beam subset.

68. The device according to claim 67, characterized in that When the first beam subset is the i-th beam subset among the multiple beam subsets, i is an integer greater than or equal to 1, and the number of beams in the first beam subset is: Among them, γ and δ are weight coefficients, is the minimum number of beams, E represents the influence of the prediction accuracy on the number of beams, and L represents the influence of the network load on the number of beams.

69. The device according to any one of claims 58 to 68, characterized in that The first beam set corresponds to a first association identifier, and the first association identifier is the same as an association identifier of the inference beam set and / or the training beam set of the first model.

70. The device according to claim 69, characterized in that The first beam set is one of multiple beam subsets determined by the inference beam set based on a polling mechanism; the multiple beam subsets correspond one-to-one to multiple resource configurations within the first polling cycle, or the multiple beam subsets correspond to M resource configurations, where M is the number of reporting cycles within the first polling cycle.

71. The device according to any one of claims 58 to 70, characterized in that The transceiver unit is also used to receive a first request sent by a first device, where the first request is used to request the first reference signal; wherein the first reference signal is at least one of a plurality of dedicated reference signals, and the dedicated reference signal is used to monitor the performance of the first model.

72. The device according to any one of claims 58 to 71, characterized in that The transceiver unit is also used to receive a monitoring report of the first model based on the reporting period of the performance monitoring; wherein the monitoring report includes performance parameters of the first model, and the performance parameters of the first model include the prediction accuracy of the first model and / or the prediction error of the first model.

73. The device according to claim 72, characterized in that The monitoring report of the first model is configured through a first channel state information CSI report configuration; the first CSI report configuration is the same as the CSI report configuration corresponding to the inference beam set, or the first CSI report configuration is different from the CSI report configuration corresponding to the inference beam set.

74. The device according to any one of claims 58 to 73, characterized in that The performance parameter of the first model is represented by a monitoring margin of the first model, and the monitoring margin is determined according to an actual monitoring number of the performance monitoring and a quality difference between the inference beam set and a corresponding beam in the first beam set.

75. The device according to claim 74, characterized in that The monitoring margin is: Wherein, N is the actual monitoring times, ε is the margin threshold, RSRP(b i ) is the predicted reference signal received power RSRP of beam b in the inference beam set during the i-th monitoring, is the measured RSRP of beam b in the first beam set during the i-th monitoring.

76. The device according to any one of claims 58 to 75, characterized in that The first model is an artificial intelligence or machine learning model.

77. A communication device, characterized in that: It comprises a memory and a processor, wherein the memory is used to store a program, and the processor is used to call the program in the memory to execute the method as described in any one of claims 1-38.

78. A device, characterized in that Comprising a processor, configured to call a program from a memory to execute a method as claimed in any one of claims 1 to 38.

79. A chip, characterized in that: It comprises a processor, which is used to call a program from a memory so that a device equipped with the chip executes a method as claimed in any one of claims 1 to 38.

80. A computer-readable storage medium, characterized in that A program is stored thereon, the program causing a computer to execute the method according to any one of claims 1 to 38.

81. A computer program product, characterized in that The method comprises a program which causes a computer to execute the method according to any one of claims 1 to 38.

82. A computer program, characterized in that The computer program causes a computer to execute the method according to any one of claims 1 to 38.

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