Information transmission methods and apparatuses, and devices, chip, storage medium, product and program

Abstract

Provided in the embodiments of the present application are information transmission methods and apparatuses, and devices, a chip, a storage medium, a product and a program. An information transmission method comprises: a terminal device receiving first related information of a first model, wherein the first model is used for executing measurement event prediction.

Description

Information transmission methods, devices, equipment, chips, storage media, products and programs Technical Field

[0001] This application relates to the field of communication technology, specifically to an information transmission method, apparatus, device, chip, storage medium, product, and program. Background Technology

[0002] Given the tremendous success of Artificial Intelligence (AI) and Machine Learning (ML) technologies in areas such as computer vision and natural language processing, the communications field has begun to explore the use of AI / ML technologies to seek new technical solutions to technical challenges that are limited by traditional methods.

[0003] In AI / ML mobility management, there is currently no clear method for performing measurement event prediction / inference. Summary of the Invention

[0004] This application provides an information transmission method, apparatus, device, chip, storage medium, product, and program.

[0005] Firstly, an information transmission method is provided, the method comprising:

[0006] The terminal device receives first relevant information from the first model, which is used to perform measurement event prediction.

[0007] Secondly, an information transmission method is provided, the method comprising:

[0008] The network device sends first relevant information about a first model, which is used to perform measurement event prediction.

[0009] Thirdly, a terminal device is provided, the terminal device comprising:

[0010] The first communication unit is configured to receive first relevant information from a first model, the first model being used to perform measurement event prediction.

[0011] Fourthly, a network device is provided, the network device comprising:

[0012] The second communication unit is configured to send first relevant information of the first model, which is used to perform measurement event prediction.

[0013] Fifthly, the terminal device provided in the embodiments of this application includes a processor and a memory. The memory is used to store computer programs, and the processor is used to call and run the computer programs stored in the memory, so that the terminal device performs the above-described information transmission method.

[0014] Sixthly, the network device provided in the embodiments of this application includes a processor and a memory. The memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory, so that the network device performs the above-described information transmission method.

[0015] Seventhly, the chip provided in the embodiments of this application is used to implement the above-described information transmission method.

[0016] Specifically, the chip includes a processor for retrieving and running a computer program from memory, causing a device equipped with the chip to perform the aforementioned information transmission method.

[0017] Eighthly, the computer-readable storage medium provided in the embodiments of this application is used to store a computer program that causes a computer to perform the above-described information transmission method.

[0018] Ninthly, the computer program product provided in the embodiments of this application includes computer program instructions that cause a computer to perform the above-described information transmission method.

[0019] In a tenth aspect, the computer program provided in the embodiments of this application, when run on a computer, causes the computer to perform the above-described information transmission method.

[0020] In the information transmission method provided in this application embodiment, the network device can send relevant information about a first model for performing measurement event prediction to the terminal device, informing the terminal device of the applicable first model. In this way, the terminal device can perform measurement event prediction based on the first model, which helps reduce the latency for the terminal device to obtain measurement events and also helps reduce the power consumption of the terminal device. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0022] Figure 1 is a schematic diagram of a communication system provided in an embodiment of this application;

[0023] Figure 2 is a schematic diagram of an RRM measurement model structure provided in an embodiment of this application;

[0024] Figure 3 is a schematic diagram of a time-domain Case A scenario provided in an embodiment of this application;

[0025] Figure 4 is a schematic diagram of a time-domain Case B scenario provided in an embodiment of this application;

[0026] Figure 5 is a schematic diagram of a frequency domain prediction scenario for measurement results provided in an embodiment of this application;

[0027] Figure 6 is a schematic diagram of a spatial prediction scenario for measurement results provided in an embodiment of this application;

[0028] Figure 7 is a schematic flowchart of an information transmission method provided in an embodiment of this application;

[0029] Figure 8 is a schematic flowchart of an information transmission method provided in an embodiment of this application;

[0030] Figure 9 is a schematic flowchart of an information transmission method provided in an embodiment of this application;

[0031] Figure 10 is a structural schematic diagram of a terminal device 1000 provided in an embodiment of this application;

[0032] Figure 11 is a structural schematic diagram of a network device 1100 provided in an embodiment of this application;

[0033] Figure 12 is a schematic structural diagram of a terminal device provided in an embodiment of this application;

[0034] Figure 13 is a schematic structural diagram of a network device provided in an embodiment of this application;

[0035] Figure 14 is a schematic structural diagram of a chip according to an embodiment of this application;

[0036] Figure 15 is a schematic block diagram of a communication system provided in an embodiment of this application. Detailed Implementation

[0037] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0038] The technical solutions of this application embodiment can be applied to various communication systems, such as: New Radio (NR) communication systems, Long Term Evolution (LTE) systems, LTE Time Division Duplex (TDD) systems, Universal Mobile Telecommunication System (UMTS), Internet of Things (IoT) systems, Narrow Band Internet of Things (NB-IoT) systems, enhanced Machine-Type Communications (eMTC) systems, or future communication systems, etc.

[0039] Figure 1 is a schematic diagram of a communication system applied in an embodiment of this application.

[0040] As shown in Figure 1, the communication system 100 may include a terminal device 110 and a network device 120. The network device 120 can communicate with the terminal device 110 via an air interface. Multi-service transmission is supported between the terminal device 110 and the network device 120.

[0041] It should be understood that the embodiments of this application are only illustrated by way of example with communication system 100, but the embodiments of this application are not limited thereto. That is to say, the technical solutions of the embodiments of this application can be applied to various communication systems, such as: Long Term Evolution (LTE) system, LTE Time Division Duplex (TDD), Universal Mobile Telecommunication System (UMTS), Internet of Things (IoT) system, Narrow Band Internet of Things (NB-IoT) system, enhanced Machine-Type Communications (eMTC) system, 5G communication system (also known as New Radio (NR) communication system), or future communication systems, etc.

[0042] In the communication system 100 shown in Figure 1, network device 120 may be an access network device that communicates with terminal device 110. The access network device can provide communication coverage for a specific geographical area and can communicate with terminal device 110 (e.g., UE) located within that coverage area.

[0043] The terminal device 110 in this application embodiment can be any terminal device, including but not limited to terminal devices that are connected to the network device 120 or other terminal devices via wired or wireless connections.

[0044] For example, the terminal device 110 can refer to an access terminal, user equipment (UE), user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent, or user device. The access terminal can be a cellular phone, cordless phone, Session Initiation Protocol (SIP) phone, IoT device, satellite handheld terminal, Wireless Local Loop (WLL) station, Personal Digital Assistant (PDA), handheld device with wireless communication capabilities, computing device or other processing device connected to a wireless modem, in-vehicle device, wearable device, terminal device in a 5G network, or terminal device in a future evolved network, etc.

[0045] The network device 120 in this embodiment can be a device for communicating with terminal devices. This network device can also be called an access network device or a radio access network device, such as a base station. In this embodiment, the network device can refer to a radio access network (RAN) node (or device), core network device, model monitoring and management device, or operation administration and maintenance (OAM) device that connects the terminal device to the wireless network.

[0046] It should be noted that the term "base station" can broadly encompass various names listed below, or be used interchangeably with them, such as: NodeB, evolved NodeB (eNB), next-generation NodeB (gNB), relay station, access point, transmitting and receiving point (TRP), transmitting point (RP), master station (MeNB), auxiliary station (SeNB), multi-mode radio (MSR) node, home base station, network controller, access node, primary 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), centralized unit-control plane (CU-CP), centralized unit-user plane (CU-UP), etc.

[0047] A base station can be a macro base station, a micro base station, a relay node, or the like, or a combination thereof. A base station can also refer to a communication module, modem, or chip installed within the aforementioned equipment or apparatus. A base station can also be a mobile switching center, or equipment that performs base station functions in device-to-device (D2D), vehicle-to-everything (V2X), and machine-to-machine (M2M) communications, network-side equipment in 6G networks, and equipment performing base station functions in future communication systems. Base stations can support networks using the same or different access technologies. The embodiments of this application do not limit the specific technologies or equipment forms used in the network equipment.

[0048] 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 depending on the location of the mobile base station. In other examples, a helicopter or drone can be configured as a device to communicate with another base station.

[0049] It should also be noted that core network equipment broadly covers, or replaces, various names listed below, such as: location management function (LMF) network element, network slice selection function (NSSF) network element, authentication server function (AUSF) network element, unified data management (UDM) network element, access and mobility management function (AMF) network element, session management function (SMF) network element, policy control function (PCF) network element, user plane function (UPF) network element, sensing function (SF) network element, network data analytics function (NWDAF) network element, and artificial intelligence (AI) function management entity, etc.

[0050] Network devices and terminal devices can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; and they can also be deployed in the air on airplanes, balloons, and satellites. This application does not limit the scenario in which the network devices and terminal devices are located.

[0051] It should be understood that all or part of the functions of the communication device in this application can also be implemented by software functions that run remotely on the hardware, or by virtualization functions instantiated on a platform (such as a cloud platform).

[0052] It should be noted that Figure 1 is merely an example illustrating the system to which this application applies. Of course, the method shown in the embodiments of this application can also be applied to other systems. Furthermore, the terms "system" and "network" are often used interchangeably in this document.

[0053] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0054] It should also be understood that the term "instruction" mentioned in the embodiments of this application can be a direct instruction, an indirect instruction, or an indication of a relationship. For example, A instructing B can mean that A directly instructs B, such as B being able to obtain information through A; it can also mean that A indirectly instructs B, such as A instructing C, so B can obtain information through C; or it can mean that there is a relationship between A and B.

[0055] It should also be understood that the term "correspondence" mentioned in the embodiments of this application may indicate a direct or indirect correspondence between the two, or an association between the two, or a relationship of instruction and being instructed, configuration and being configured, etc.

[0056] It should also be understood that the "predefined" or "predefined rules" mentioned in the embodiments of this application can be implemented by pre-storing corresponding codes, tables, or other means that can be used to indicate relevant information in the device (e.g., including terminal devices and network devices), and this application does not limit the specific implementation method. For example, predefined can refer to those defined in the protocol.

[0057] To facilitate understanding of the technical solutions of the embodiments of this application, the relevant technologies of the embodiments of this application are described below. The following relevant technologies are optional solutions and can be combined with the technical solutions of the embodiments of this application in any way, and they all fall within the protection scope of the embodiments of this application.

[0058] The following section mainly introduces Radio Resource Measurement (RRM) in mobility management.

[0059] The purpose of RRM measurement is to achieve radio resource management. In some implementations, terminal devices can perform RRM measurements on the Synchronization Signal Block (SSB) and / or Channel State Information Reference Signal (CSI-RS) sent by network devices to obtain RRM measurement results.

[0060] It should be noted that the RRM measurement results described above are used to indicate the communication quality of the terminal device. In some embodiments, the RRM measurement results may include one or more of the following measurements: signal amplitude (Srxlev), signal strength (Squal), reference signal received power (RSRP), reference signal received quality (RSRQ), signal to noise ratio (SNR), signal to interference plus noise ratio (SINR), received signal strength indication (RSSI), etc.

[0061] In the RRC_CONNECTED state, the UE measures one or more beams in a cell and averages the measurements to derive cell quality. During this process, the UE is configured to consider a subset of the detected beams. Filtering occurs at two distinct levels: at the physical layer for deriving beam quality, and then at the RRC layer for deriving cell quality from multiple beams. The method for deriving cell quality from beam measurements is the same for both serving and non-serving cells. If the gNB is configured to do this, the measurement report may contain the measurement results for X best beams.

[0062] Referring to Figure 2, which shows an RRM measurement model, RSRP is used as an example here. The relevant content also applies to RSRQ / SINR.

[0063] It should be noted that the K beams in Figure 2 correspond to the measurements of SSB and / or CSI-RS resources that the gNB configures for Layer 3 (L3) mobility and that are detected by the UE in Layer 1 (L1).

[0064] As can be seen from Figure 2, the RRM measurement model includes the following reference points:

[0065] A: L1-based beam-level measurement results (e.g., beam-level L1-RSRP).

[0066] It should be understood that point A represents the measurement results of the K gNB beams obtained by the UE, which are measurement results within the physical layer. Specifically, the UE performs physical layer measurement sampling according to the beam granularity, resulting in beam-level L1-RSRP.

[0067] A1: Beam-level measurement results based on L1 filtering (e.g., filtered beam-level L1-RSRP).

[0068] It should be understood that the UE can perform L1 filtering on the L1 beam-level measurement results obtained at reference point A, i.e., filtering within the physical layer. The specific filtering method depends on the implementation. How the measurement (input A and L1 filtering) is actually performed in the physical layer is not subject to standard constraints.

[0069] It should be noted that, generally, the standard specifies the length of the measurement period under a specific RRC configuration. The measurement period stipulates that the UE must perform at least one sampling, and the beam measurement results after L1 filtering must meet the performance requirements specified in 3GPP specification 38.133. The specific number of samplings for the UE at reference point A within one measurement period is specified. In the test case, 4 to 5 oversampling operations are generally used.

[0070] B: Cell-level measurement results based on L1 (e.g., cell-level L1-RSRP).

[0071] It should be understood that the UE can perform beam combining / selection operations on the beam measurement results obtained from reference point A1 within a certain cell to obtain cell-level L1-RSRP.

[0072] It should be noted that the beam combining / selection behavior is standardized, and the module's configuration is provided via RRC signaling. The reporting cycle for point B is equal to one measurement cycle for point A1.

[0073] C: Cell-level measurement results based on Layer 3 (e.g., cell-level L3-RSRP).

[0074] It should be understood that the UE can filter the measurement results provided by point B. In other words, the UE can obtain the L3 cell-level measurement results by sequentially filtering the L1 cell-level measurement results of a certain cell through L3 filtering.

[0075] It should be noted that the behavior of the L3 filter is standardized, and its configuration is provided via RRC signaling. The filter reporting cycle at point C is equal to one measurement cycle at point B.

[0076] D: Send measurement reports via wireless interface.

[0077] It should be understood that the UE performs a reporting standard evaluation; specifically, the UE checks whether actual measurement reporting is required at reference point D. The reporting standard is standardized, and its configuration is provided via RRC signaling.

[0078] E: Beam-level measurement results based on layer 3 (e.g., beam-level L3-RSRP).

[0079] It should be understood that the UE can filter the beam-level measurement results provided by reference point A1 based on L1 filtering to obtain beam-level measurement results based on layer 3 filtering.

[0080] It should be noted that the behavior of beam filters is standardized, and their configuration is provided via RRC signaling.

[0081] F: The filtered and reported portion is based on layer 3 beam-level measurement results.

[0082] It should be noted that the UE selects X layer 3-based beam-level measurement results from the K layer 3-based beam-level measurement results provided by reference point E.

[0083] It should also be noted that the beam selection behavior is standardized, and the configuration of this module is provided via RRC signaling.

[0084] The following section mainly introduces the AI / ML mobility management (mobility) project.

[0085] The AI / ML mobility project defines three sub-use cases: RRM measurement prediction, Radio Link Failure (RLF) / Handover Failure (HOF) prediction, and measurement event prediction.

[0086] For RRM measurement prediction, the model's input and output can be the A / A1 / B / C / E / F point information in the RRM measurement model shown in Figure 2. The input and output measurement results can come from the same cell, different cells at different frequency layers, or a group of cells. Prediction can be performed in the time domain, frequency domain, or between different frequencies (i.e., frequency domain).

[0087] For predicting measurement events, there are both indirect and direct prediction methods.

[0088] The indirect prediction method is based on the results of RRM measurement prediction, combined with the configuration parameters related to a certain measurement event in the network configuration, to predict the time and / or type of the measurement event (i.e. whether a certain measurement event will occur at a certain point in the future).

[0089] The direct prediction method is based on the input measurement results (including at least the serving cell and / or neighboring cells directly related to the event) to predict the time and / or type of the measurement event.

[0090] For HOF / RLF prediction, similar to measurement event prediction, either direct prediction or indirect prediction methods can be adopted. However, the input measurement results must include at least the serving cell and / or neighboring cells directly related to the event.

[0091] The following section introduces the RRM measurement and prediction scenario.

[0092] The RRM measurement and prediction scenarios mainly include the following four scenarios.

[0093] 1) First Measurement Result Time-Domain Prediction Scenario (or Time-Domain Case A Scenario): Using the measurement results obtained from historical time-domain locations, the measurement result at the time-domain location to be measured is predicted. For example, referring to the schematic diagram of Time-Domain Case A in Figure 3, the shaded area represents the actual measurement result (or measured result), and the unfilled area represents the predicted measurement result. That is, in the example described in Figure 3, the measurement results at time-domain locations 5 and 6 can be predicted using the measured results from historical time-domain locations 1 to 4.

[0094] 2) Second Measurement Result Time-Domain Prediction Scenario (or Time-Domain Case B Scenario): Using the measurement results from some historical time-domain locations, the measurement results for the time-domain location to be measured are predicted. For example, referring to the schematic diagram of the Time-Domain Case B scenario shown in Figure 4, the shaded area represents the actual measurement results (or measured results), and the unfilled area represents the predicted measurement results. That is, the measurement results for time-domain locations 2, 4, and 6 can be predicted using the measured results from historical time-domain locations 1, 3, and 5.

[0095] 3) Frequency Domain Prediction Scenario of Measurement Results: Using the measurement results of the acquired measured frequency points, the measurement results of one or more frequency points to be predicted are predicted. For example, referring to the schematic diagram of the frequency domain prediction scenario of measurement results shown in Figure 5, the shaded area represents the actual measured frequency points (or measured frequency points), and the unfilled area represents the frequency points to be predicted. That is, the measurement results of the measured frequency point (cell A) can be used to predict the measurement results of the frequency point to be predicted (cell B).

[0096] 4) Spatial Domain Prediction Scenario of Measurement Results: Using the measurement results of the acquired measured beams, the measurement results of one or more beams to be predicted are predicted. For example, referring to the schematic diagram of the spatial domain prediction scenario of measurement results shown in Figure 6, the shaded area represents the actually measured beam (or measured beam), and the unfilled area represents the beam to be predicted. In other words, the measurement results of the measured beams can be used to predict the measurement results of the beams to be predicted.

[0097] In practical applications, AI / ML mobility management projects mainly discuss beam management, and there is currently no disclosed information regarding measurement event prediction.

[0098] In view of this, this application provides an information transmission method, apparatus, device, chip, storage medium, product, and program. In this method, a network device can send relevant information about a first model for performing measurement event prediction to a terminal device, informing the terminal device of the applicable first model. Thus, the terminal device can perform measurement event prediction based on the first model, which helps reduce the latency for the terminal device to obtain measurement events and also helps reduce the power consumption of the terminal device.

[0099] To facilitate understanding of the technical solutions of the embodiments of this application, the technical solutions of this application are described in detail below through specific embodiments. The above-mentioned related technologies are optional solutions and can be arbitrarily combined with the technical solutions of the embodiments of this application, all of which fall within the protection scope of the embodiments of this application. The embodiments of this application include at least some of the following contents.

[0100] It should be noted that the model in the embodiments of this application can also be equivalent to or replaced by a function. Here, a function refers to the function that the model can perform. For example, the function in the embodiments of this application can be a measurement and prediction function.

[0101] It should also be noted that the model information in the embodiments of this application can also be referred to as model parameters, and the two are equivalent or interchangeable.

[0102] Figure 7 is a schematic flowchart of an information transmission method according to an embodiment of this application. This method can optionally be applied to the system shown in Figure 1, but is not limited thereto. The method may include at least some of the following:

[0103] S710, the network device sends the first relevant information of the first model, and the terminal device receives the first relevant information of the first model accordingly.

[0104] The first model is used to perform measurement event prediction.

[0105] It should be noted that the measurement event refers to the measurement event in the mobility management of terminal devices. For example, the measurement event can be one or more of events A1-A6, and / or one or more of events B1-B2. The embodiments of this application do not limit the measurement events.

[0106] It should also be noted that the measurement event prediction in the embodiments of this application can be either indirect measurement event prediction or direct measurement event prediction, and the embodiments of this application do not impose any restrictions on this.

[0107] Indirect measurement event prediction refers to predicting the time and / or type of a measurement event based on the results of RRM measurement prediction and the configuration parameters related to a specific measurement event configured in the network. Direct measurement event prediction refers to predicting the time and / or type of a measurement event based on the input measurement results (at least including the serving cell and / or neighboring cells directly related to the event).

[0108] It should be noted that the type of the measured event can be one or more of events A1-A6 or events B1-B2.

[0109] It should be understood that the first model in the embodiments of this application can be understood as a model used to perform measurement event prediction. The first relevant information may be a model describing the communication between the terminal device and the network device, or an applicable model used to perform measurement event prediction. Alternatively, the first relevant information may indicate the model required for communication between the terminal device and the network device, or an applicable model used to perform measurement event prediction.

[0110] In other words, network devices can send information about the model used to perform measurement event prediction to terminal devices. This allows the terminal devices to select a matching or applicable model based on the model-related information to perform measurement event prediction.

[0111] The information transmission method provided in this application allows a network device to send relevant information about a first model for performing measurement event prediction to a terminal device, informing the terminal device of the applicable first model. This enables the terminal device to perform measurement event prediction based on the first model, helping to reduce the latency for the terminal device to receive measurement events and also reducing the power consumption of the terminal device.

[0112] It should be noted that event prediction can include many different types.

[0113] In some embodiments, the type of measurement event prediction may include one or more of the following (1) to (4):

[0114] (1) First type of time-domain-based measurement event prediction;

[0115] (2) Second type of time-domain based measurement event prediction;

[0116] (3) Frequency domain-based measurement event prediction;

[0117] (4) Prediction of measurement events based on spatial domain.

[0118] For (1), the first type of time-domain-based measurement event prediction can refer to predicting measurement events in advance based on historical time-domain measurement results.

[0119] It should be noted that the first type of time-domain-based measurement event prediction can also be understood as predicting measurement events in advance.

[0120] In one implementation, the first type of time-domain-based measurement event prediction can be achieved using a direct prediction method. Specifically, it can directly predict the timing and / or type of future measurement events based on historical time-domain measurement results. For example, historical time-domain measurement results can be used as input to a first model, which then outputs the timing and / or type of future measurement events.

[0121] In another implementation, the first type of time-domain-based measurement event prediction can be achieved using an indirect prediction method. In other words, the first type of time-domain-based measurement event prediction can be achieved based on the time-domain prediction function of the first measurement result within the RRM prediction function. Specifically, RRM measurement prediction can be performed based on historical time-domain measurement results to predict time-domain measurement results for a future period, and then, based on these future time-domain measurement results, the timing and / or type of future measurement events can be predicted in advance.

[0122] It should be noted that the time-domain prediction function of the first measurement result can correspond to the time-domain Case A scenario shown in Figure 3. The relevant content of the time-domain Case A scenario can be referred to the description in the above embodiments, and will not be repeated here for the sake of brevity.

[0123] For (2), the second type of time-domain-based measurement event prediction can refer to predicting measurement events based on measurement results in part of the time domain.

[0124] It should be noted that the second type of time-domain-based measurement event prediction can also be understood as predicting measurement events while saving measurement intervals.

[0125] In one implementation, the second type of time-domain-based measurement event prediction can be achieved using a direct prediction method. Specifically, it can directly predict whether a measurement event will occur at the current moment and / or the type of measurement event that will occur, based on partial time-domain measurement results. For example, partial time-domain measurement results can be used as input to a first model, which then outputs whether a measurement event will occur at the current moment and / or the type of measurement event that will occur.

[0126] In another implementation, the second type of time-domain-based measurement event prediction can be achieved using an indirect prediction method. In other words, it can be implemented based on the second measurement result time-domain prediction function within the RRM prediction function. Specifically, RRM-related interpolation prediction can be performed based on measurement results at some time-domain locations to obtain measurement results at all time-domain locations. Then, based on the measurement results at all time-domain locations, it can be predicted whether a measurement event will occur at the current time and / or the type of measurement event that occurs.

[0127] It should be noted that the second measurement result time-domain prediction function corresponds to the time-domain case B scenario shown in Figure 4. The relevant description of the time-domain case B scenario can be found in the description in the above embodiments, and for the sake of brevity, it will not be repeated here.

[0128] For (3), frequency domain-based measurement event prediction refers to predicting measurement events based on measurement results in a portion of the frequency domain.

[0129] It should be noted that frequency domain-based measurement event prediction can be understood as predicting measurement events while saving measurement intervals.

[0130] In one implementation, frequency-domain-based measurement event prediction can be achieved using a direct prediction method. Specifically, it can directly predict whether a measurement event will occur at the current moment and / or the type of measurement event that will occur, based on measurement results in a portion of the frequency domain. For example, the measurement results in a portion of the frequency domain can be used as input to a first model, which then outputs whether a measurement event will occur at the current moment and / or the type of measurement event that will occur.

[0131] In another implementation, frequency-domain-based measurement event prediction can be achieved using an indirect prediction method. In other words, frequency-domain-based measurement event prediction can be achieved through the measurement result frequency-domain prediction function within the RRM prediction function. Specifically, the measurement results of the acquired measured frequency points can be used to predict the measurement results of one or more frequency points to be predicted. Then, based on the measurement results across all frequency points, it can be predicted whether a measurement event will occur at the current time and / or the type of measurement event that occurs.

[0132] It should be noted that the measurement result frequency domain prediction function can correspond to the measurement result frequency domain prediction scenario shown in Figure 5. The relevant description of the measurement result frequency domain prediction scenario can be referred to the description in the above embodiments. For the sake of brevity, it will not be repeated here.

[0133] For (4), spatial domain-based measurement event prediction refers to predicting measurement events based on the measurement results of a portion of the beam.

[0134] It should be noted that spatial domain-based measurement event prediction can be understood as predicting measurement events while saving on spatial beam measurements.

[0135] In one implementation, spatial domain-based measurement event prediction can be achieved using a direct prediction method. Specifically, based on the measurement results of a portion of the beam, it can be directly predicted whether a measurement event will occur at the current moment and / or the type of measurement event that will occur. For example, the measurement results of a portion of the beam can be used as input to a first model, and the first model can output whether a measurement event will occur at the current moment and / or the type of measurement event that will occur.

[0136] In another implementation, spatial-domain-based measurement event prediction can be achieved using an indirect prediction method. In other words, spatial-domain-based measurement event prediction can be achieved through the spatial prediction function of measurement results within the RRM prediction function. Specifically, the measurement results of the acquired measured beams can be used to predict the measurement results of one or more beams to be predicted. Then, based on the measurement results across all beams, it can be predicted whether a measurement event will occur at the current moment and / or the type of measurement event that occurs.

[0137] It should be noted that the measurement result spatial domain prediction function can correspond to the measurement result spatial domain prediction scenario shown in Figure 6. The relevant description of the measurement result spatial domain prediction scenario can be referred to the description in the above embodiments. For the sake of brevity, it will not be repeated here.

[0138] In one embodiment of this application, the network device may indicate, in the first relevant information, that a first model is used to perform measurement event prediction, and may also indicate the type of measurement event prediction performed by the first model. The type of measurement event prediction may be one or more of (1) to (4) described above.

[0139] It should be noted that there are several ways in which the network device instructs the first model to perform measurement event prediction, and the type of measurement event prediction performed by the first model. In one possible implementation, the network device can use two separate information cells, one indicating that the first model is used to perform measurement event prediction, and the other indicating the type of measurement event prediction performed by the first model. In another possible implementation, the network device can use a single information cell that directly indicates that the first model is used for a specific type of measurement event prediction.

[0140] The following sections introduce the two different implementation methods described above, using method #A and method #B.

[0141] Method #A: The first relevant information may include the first instruction information and the second instruction information.

[0142] The first indication information is used to instruct the first model to perform measurement event prediction, and the second indication information is used to instruct the measurement event prediction to be implemented based on the first RRM prediction function.

[0143] It should be understood that in mode #A, the network device can instruct the first model to perform measurement event prediction and the type of measurement event prediction performed by the first model through two information elements (i.e., the first indication information and the second indication information mentioned above).

[0144] Regarding the first instruction:

[0145] In one implementation, the first indication information is explicitly indicated by a first parameter. For example, the first parameter being a first value indicates that the model currently indicated is used to perform measurement event prediction; the first parameter being a second value indicates that the model currently indicated is used for other types of prediction.

[0146] Regarding the second instruction:

[0147] In this embodiment of the application, the second indication information can be understood as an indication of the RRM prediction function associated with the measurement event prediction (this is referred to as the first RRM prediction function in this embodiment of the application).

[0148] It should be noted that the first RRM function includes one or more of the following a) to d):

[0149] a) Time-domain prediction function for the first measurement result;

[0150] b) Time-domain prediction function for the second measurement result;

[0151] c) Frequency domain prediction function for measurement results;

[0152] d) Spatial prediction function for measurement results.

[0153] In item a), the first measurement result time domain prediction function represents the prediction of the measurement result of the time domain position to be predicted by using the measurement result of the historical time domain position obtained. This first measurement result time domain prediction function corresponds to the first measurement result time domain prediction scenario shown in Figure 3. For details, please refer to the description in the above embodiments. For the sake of brevity, it will not be repeated here.

[0154] The second measurement result time-domain prediction function in item b) represents the prediction of the measurement result of the time-domain position to be predicted by using the measurement results of some historical time-domain positions. This second measurement result time-domain prediction function corresponds to the second measurement result time-domain prediction scenario shown in Figure 4. For details, please refer to the description in the above embodiments. For the sake of brevity, it will not be repeated here.

[0155] The measurement result frequency domain prediction function in item c) represents the prediction of the measurement result of one or more frequency points to be predicted using the measurement results of the actual measured frequency points. This measurement result frequency domain prediction function corresponds to the measurement result frequency domain prediction scenario shown in Figure 5. For details, please refer to the description in the above embodiments. For the sake of brevity, it will not be repeated here.

[0156] The measurement result spatial prediction function in item d) represents the prediction of the measurement results of one or more beams to be predicted using the measurement results of the acquired measured beam. This measurement result spatial prediction function corresponds to the measurement result spatial prediction scenario shown in Figure 6. For details, please refer to the description in the above embodiments. For the sake of brevity, it will not be repeated here.

[0157] It should be noted that the first RRM prediction function may include one or more of the above a) to d).

[0158] In one implementation, the first RRM prediction function may include any one of a) to d) above. For example, the first RRM prediction function may include a first measurement result time-domain prediction function, or a second measurement result time-domain prediction function, or a measurement result frequency-domain prediction function, or a measurement result spatial-domain prediction function.

[0159] In another implementation, the first RRM prediction function may include any two of a) to d) above. For example, the first RRM prediction function may include a first measurement result time-domain prediction function and a second measurement result time-domain prediction function, or it may include a first measurement result time-domain prediction function and a measurement result frequency-domain prediction function, or it may include a first measurement result time-domain prediction function and a measurement result spatial-domain prediction function, or it may include a second measurement result time-domain prediction function and a measurement result frequency-domain prediction function, or it may include a second measurement result time-domain prediction function and a measurement result frequency-domain and spatial-domain prediction function, or it may include a measurement result frequency-domain prediction function and a measurement result spatial-domain prediction function.

[0160] In another implementation, the first RRM prediction function may include any three of the above a) to d). For example, the first RRM prediction function may include a first measurement result time-domain prediction function, a second measurement result time-domain prediction function, and a measurement result frequency-domain prediction function. Alternatively, the first RRM prediction function may include a first measurement result time-domain prediction function, a second measurement result time-domain prediction function, and a measurement result spatial-domain prediction function. Alternatively, it may include a first measurement result time-domain prediction function, a measurement result frequency-domain prediction function, and a measurement result spatial-domain prediction function. Alternatively, it may include a second measurement result time-domain prediction function, a measurement result frequency-domain prediction function, and a measurement result spatial-domain prediction function.

[0161] In another implementation, the first RRM prediction function may include the above a) to d), that is, the first RRM prediction function may include a first measurement result time domain prediction function, a second measurement result time domain prediction function, a measurement result frequency domain prediction function and a measurement result spatial domain prediction function.

[0162] In some embodiments, the second indication information can indicate the first RRM prediction function through a bitmap. The second indication information may include four bits, each of which can be associated with an RRM prediction function. When the bit associated with a certain RRM prediction function takes a first value, it can be characterized that the first RRM prediction function associated with the first model includes the RRM prediction function associated with that bit. For example, the first bit of the second indication information is associated with the first measurement result time-domain prediction function, the second bit is associated with the second measurement result time-domain prediction function, the third bit is associated with the measurement result frequency-domain prediction function, and the fourth bit is associated with the measurement result spatial-domain prediction function. When the second indication information is mapped to "1000", it indicates that the measurement event prediction is implemented based on the first measurement result time-domain prediction function; when the second indication information is mapped to "0100", it indicates that the measurement event prediction is implemented based on the second measurement result time-domain prediction function; when the second indication information is mapped to "0010", it indicates that the measurement event prediction is implemented based on the measurement result frequency-domain prediction function; and when the second indication information is mapped to "0001", it indicates that the measurement event prediction is implemented based on the measurement result spatial-domain prediction function. When the second indication information is mapped to "1010", the indication measurement event prediction is implemented based on the time domain prediction function and the frequency domain prediction function of the first measurement result. When the second indication information is mapped to "1011", the indication measurement event prediction is implemented based on the time domain prediction function, the frequency domain prediction function, and the time domain prediction function of the first measurement result, and so on. Examples will not be given here.

[0163] It should be noted that the network device instructs the measurement event prediction to be implemented based on the first RRM prediction function via the second indication information. This can be understood as the network device expecting the terminal device to perform measurement event prediction using an indirect prediction method. In other words, after receiving the first and second indication information, the terminal device can interpret them as indicating the use of an indirect prediction method for measurement event prediction, rather than RRM prediction. Specifically, the terminal device can implement indirect measurement event prediction based on the first RRM prediction function indicated by the second indication information.

[0164] It should be understood that the first RRM prediction function indicated by the second indication information can characterize the type of measurement event prediction to a certain extent; that is, the first RRM prediction function is related to the type of measurement event prediction. For example, when the first RRM prediction function includes a first measurement result time-domain prediction function, it can characterize the measurement event prediction as a first type of time-domain based measurement event prediction; when the first RRM prediction function includes a second measurement result time-domain prediction function, it can characterize the measurement event prediction as a second type of time-domain based measurement event prediction; when the first RRM prediction function includes a measurement result frequency-domain prediction function, it can characterize the measurement event prediction as a frequency-domain based measurement event prediction; when the first RRM prediction function includes a measurement result spatial-domain prediction function, it can characterize the measurement event prediction as a spatial-domain based measurement event prediction.

[0165] In embodiment #A of this application, measurement event prediction is combined with RRM prediction function through first and second indication information. The terminal device can flexibly predict measurement events based on the specific content of the RRM prediction function without directly relying on explicit measurement event prediction function instructions. This indirect approach makes the prediction process more flexible and better adaptable to different network environments and terminal device states.

[0166] Method #B: The first relevant information includes the third indication information, which is used to indicate that the first model is used for the prediction of the first type of measurement event.

[0167] It should be understood that in mode #B, the network device can instruct the first model to perform prediction of the first type of measurement event through a cell (i.e., the third indication information).

[0168] It should be noted that the first type can be one or more of the measurement event prediction types (1) to (4) mentioned above, and this application embodiment does not limit this.

[0169] It should also be noted that the third indication information indicates that the first model is used for the prediction of the first type of measurement event, which can be either indirect measurement event prediction or direct measurement event prediction. This application embodiment does not limit this.

[0170] In one implementation, the third indication information can be indicated by the second parameter. For example, the second parameter being a first value indicates that the first model is used to perform (1) the first type of time-domain-based measurement event prediction; the second parameter being a second value indicates that the first model is used to perform (2) the second type of time-domain-based measurement event prediction; the second parameter being a third value indicates that the first model is used to perform (3) the frequency-domain-based measurement event prediction; the second parameter being a fourth value indicates that the first model is used to perform (4) the spatial-domain-based measurement event prediction. The second parameter being a fifth value indicates that the first model is used to perform (1) the first type of time-domain-based measurement event prediction and (3) the frequency-domain-based measurement event prediction; the second parameter being a sixth value indicates that the first model is used to perform (1) the first type of time-domain-based measurement event prediction and (4) the spatial-domain-based measurement event prediction, and so on. Examples will not be provided here.

[0171] In another implementation, the third indication information can be indicated using a bitmap. The third indication information may include four bits, each of which can be associated with a measurement event prediction type. When the bit associated with a certain measurement event prediction type takes the first value, it can characterize the measurement event type associated with that specific information used by the first model.

[0172] For example, the first bit of the third indication information is associated with a first type of time-domain based measurement event prediction, the second bit is associated with a second type of time-domain based measurement event prediction, the third bit is associated with a frequency-domain based measurement event prediction, and the fourth bit is associated with a spatial-domain based measurement event prediction. When the third indication information is mapped to "1000", it indicates that the first model is used for the first type of time-domain based measurement event prediction; when the third indication information is mapped to "0100", it indicates that the first model is used for the second type of time-domain based measurement event prediction; when the third indication information is mapped to "0010", it indicates that the first model is used for frequency-domain based measurement event prediction; and when the third indication information is mapped to "0001", it indicates that the first model is used for spatial-domain based measurement event prediction.

[0173] In the embodiments of this application, compared with method #A, method #B provides a more direct indication of the applicable function and can reduce the amount of air interface signaling transmission, making it a more streamlined method.

[0174] In one embodiment of this application, the first relevant information in step S710 may further include fourth indication information, wherein the fourth indication information is used to indicate the first measurement task associated with the measurement event prediction.

[0175] It should be understood that in some embodiments, the network device may also instruct the terminal device to perform a measurement task of predicting measurement events (this is referred to as the first measurement task in this application embodiment).

[0176] It should be noted that the first measurement task can be a conventional event-triggered measurement task for a given measurement object (e.g., carrier, frequency point, or cell list). For example, the first measurement task can be indicated by a measurement identifier (measId); that is, the fourth indication information can be the measurement identifier (measId).

[0177] Simply put, the fourth indication information can indicate the measurement object to the terminal device during measurement event prediction. The measurement object can be a carrier, a frequency point, or a cell list, etc., and this application embodiment does not impose any limitations on this. In this way, the terminal device can more clearly understand the specific measurement configuration information and perform measurement event prediction based on the first measurement task indicated by the network device.

[0178] It should be noted that the number of first indication information includes one or more, or the number of first measurement tasks may include one or more. This application embodiment does not limit the number of first measurement tasks.

[0179] In some embodiments, the first relevant information further includes fifth indication information; wherein the fifth indication information is used to indicate that the first measurement task is implemented based on the second RRM prediction function.

[0180] It should be understood that, as an optional solution, when the network device instructs the terminal device to perform a first measurement task related to measurement event prediction, the network device may further instruct the RRM prediction function associated with the first measurement task (here referred to as the second RRM function). The second RRM prediction function associated with the first measurement task can be understood as enabling the execution of the first measurement task based on the second RRM prediction function, thereby achieving the prediction of measurement events.

[0181] It should be noted that the second RRM prediction function may include one or more of the following a) to d):

[0182] a) Time-domain prediction function for the first measurement result;

[0183] b) Time-domain prediction function for the second measurement result;

[0184] c) Frequency domain prediction function for measurement results;

[0185] d) Spatial prediction function for measurement results.

[0186] It should be noted that the relevant content of a) to d) can be referred to the description in the above embodiments, and will not be repeated here for the sake of brevity.

[0187] In one possible implementation, based on the above-mentioned method #A, the first relevant information sent by the network device may include first indication information, second indication information, fourth indication information and fifth indication information.

[0188] The first instruction information is used to instruct the first model to perform measurement event prediction, the second instruction information is used to instruct the measurement event prediction to be implemented based on the first RRM prediction function, the fourth instruction information instructs the first measurement task associated with the measurement event prediction, and the fifth instruction information instructs the first measurement task to be implemented based on the second RRM prediction function.

[0189] It should be understood that the second indication information, which indicates that the measurement event prediction is based on the first RRM prediction function, can be understood as a coarse-grained indication coupled with the overall measurement event prediction. The fifth indication information, which indicates that the first measurement task is based on the second RRM prediction function, can be understood as a finer-grained indication coupled with the first measurement task indicated by the fourth indication information.

[0190] In other words, in this implementation, the second RRM prediction function can be at least a portion of the prediction function in the first RRM prediction function. Or, the first RRM prediction function can include the second RRM prediction function.

[0191] For example, when the number of first measurement tasks includes multiple tasks, different first measurement tasks can be implemented through different RRM prediction functions. For example, the network device can send fourth indication information 1 and fifth indication information 1, as well as fourth indication information 2 and fifth indication information 2. Here, fourth indication information 1 indicates measID_1, fifth indication information 1 indicates that measurement event prediction on measID_1 is implemented based on time-domain case A, fourth indication information 2 indicates measID_2, and fifth indication information 2 can indicate that measurement event prediction on measID_2 is implemented based on time-domain case B.

[0192] In another possible implementation, based on the above method #B, the first relevant information sent by the network device may include a third indication information, a fourth indication information, and a fifth indication information.

[0193] The third indication information is used to indicate that the first model is used for the prediction of the first type of measurement event, the fourth indication information indicates the first measurement task associated with the measurement event prediction, and the fifth indication information indicates that the first measurement task is implemented based on the second RRM prediction function.

[0194] It should be noted that in this implementation, the second RRM function can be associated with the first type of measurement event prediction, or in other words, the second RRM function indicated by the fifth indication information can be consistent with the first type indicated by the third indication information.

[0195] For example, when the third instruction information indicates that the first model is used for prediction of a first type of time-domain-based measurement event, the second RRM prediction function can be the time-domain prediction function of the first measurement result (corresponding to Figure 3). When the third instruction information indicates that the first model is used for prediction of a second type of time-domain-based measurement event, the second RRM prediction function can be the time-domain prediction function of the second measurement result (corresponding to Figure 4). When the third instruction information indicates that the first model is used for prediction of a frequency-domain-based measurement event, the second RRM prediction function can be the frequency-domain prediction function of the measurement result (corresponding to Figure 5). When the fourth instruction information indicates that the first model is used for prediction of a spatial-domain-based measurement event, the second RRM prediction function can be the spatial-domain prediction function of the measurement result (corresponding to Figure 6).

[0196] In the information transmission method provided in the embodiments of this application, the first relevant information of the first model sent by the network device can indicate a traditional measurement task, which can make the terminal device more aware of its measurement configuration information, avoid conflicts with existing measurements, and also help improve the prediction performance and accuracy of the model.

[0197] In some embodiments, when the measurement event prediction is implemented based on the frequency domain prediction function of the measurement result, the first relevant information further includes a sixth indication information, which is used to indicate one or more frequency points to be predicted.

[0198] Referring to Figure 5, the frequency domain prediction function of the measurement result refers to using the measurement results of the acquired measured frequency points to predict the measurement results of one or more frequency points to be predicted.

[0199] It should be understood that when the first RRM prediction function includes the measurement result frequency domain prediction function, or the second RRM prediction function includes the measurement result frequency domain prediction function, the network device can further instruct the terminal device through the first relevant information of the first model to indicate one or more frequency points to be predicted associated with the model when performing measurement event prediction, so as to facilitate frequency domain prediction.

[0200] It should be noted that, in some embodiments, the sixth indication information can also be a measurement identifier (measId). The sixth indication information can be associated with the fourth indication information to jointly complete frequency domain-based measurement event prediction.

[0201] In one embodiment of this application, the first relevant information of the first model may further include all or part of the model information of the first model.

[0202] The model information of the first model may include one or more of the following:

[0203] The speed at which the first model is applicable;

[0204] Input data for the first model;

[0205] Output data of the first model.

[0206] It should be noted that the speed used in the first model can be a specific speed value, such as 50 km / h, or an applicable speed range, such as 20-40 km / h, 30-60 km / h, or 50-80 km / h. This application does not impose any limitations on this.

[0207] In some embodiments, the input data for the first model may include one or more of the following ① to ⑩:

[0208] ① Enter the length of the observation window (OW);

[0209] ② The time interval between measurement results within OW;

[0210] ③ The number of input data;

[0211] ④ The type of input data;

[0212] ⑤ Measurement reduction rate in the temporal domain (MRRT);

[0213] ⑥ Time-domain measurement reduction pattern;

[0214] ⑦ Measurement reduction rate in spatial domain (MRRS);

[0215] ⑧ Skipping pattern for reducing spatial measurements;

[0216] ⑨ Input the frequency information corresponding to the data;

[0217] ⑩ Frequency domain prediction direction.

[0218] For item ①, the length of OW can be the length of time, where the time unit can be milliseconds, seconds, minutes, hours, etc., and this application embodiment does not limit this.

[0219] For item ②, the time interval between measurement results within OW can be understood as the interval between any two adjacent measurement results within OW. For example, the interval between two adjacent measurement results is 40ms.

[0220] For item ③, the number of input data can be the maximum number.

[0221] For item ④, the input data type includes one or more of the following:

[0222] Layer 1 measurement results;

[0223] Layer 3 measurement results;

[0224] Community-level measurement results;

[0225] Beam-level measurement results;

[0226] Filtered measurement results;

[0227] Unfiltered measurement results.

[0228] In one possible implementation, the type of input data can be any combination of the different measurement results described above. For example, the type of input data can be cell-level measurement results based on Layer 1, or cell-level measurement results based on Layer 3, or beam-level measurement results based on Layer 1, or beam-level measurement results based on Layer 3, or cell-level measurement results filtered by Layer 1, or cell-level measurement results filtered by Layer 3, or beam-level measurement results filtered by Layer 1, or beam-level measurement results filtered by Layer 3.

[0229] It should be noted that the types of input data are not limited to the combinations of the examples above, and will not be listed one by one here.

[0230] For example, based on the RRM measurement model shown in Figure 2, the type of input data can be the measurement results of any one or more reference points A / A1 / B / C / E / F. That is, the type of input data can be one or more of the following: layer 1 beam-level measurement results, layer 1 filtered beam-level measurement results, layer 1 cell-level measurement results, layer 3 cell-level measurement results, layer 3 filtered beam-level measurement results, and a portion of layer 3 beam-level measurement results that are filtered and reported.

[0231] For items ⑤ and ⑥, if the first model is used to perform the second type of time-domain-based measurement event prediction (e.g., applied to the time-domain case B scenario shown in Figure 4), the input data of the first model may also include MRRT and specific reduction patterns.

[0232] For items ⑦ and ⑧, if the first model is used to perform spatial domain-based measurement event prediction (e.g., applied to the scenario shown in Figure 6), the input data of the first model may also include MRRS and specific reduction patterns.

[0233] For item ⑨, if the first model is used to perform frequency domain-based measurement event prediction (e.g., applied to the scenario shown in Figure 5), and when the model-related configuration does not contain frequency point information (e.g., does not contain fourth indication information and / or sixth indication information), the input data of the first model may also include the frequency point information corresponding to the input data.

[0234] It should be noted that frequency information may include frequency range (FR), carrier, band, band combination, etc., and this application embodiment does not limit this.

[0235] For item ⑩, the frequency domain prediction direction may include a first direction and / or a second direction. The first direction indicates that the frequency of the measured frequency point in the frequency domain prediction function of the measurement result is lower than the frequency of the frequency point to be predicted, and the second direction indicates that the frequency of the measured frequency point in the frequency domain prediction function of the measurement result is higher than the frequency of the frequency point to be predicted.

[0236] In some embodiments, the output data of the first model includes one or more of the following:

[0237] The length of the output data prediction window (PW);

[0238] The time interval between prediction results within PW;

[0239] The number of data points to output.

[0240] It should be noted that the length of the prediction window (PW) can be a time period, where the time unit can be milliseconds, seconds, minutes, hours, etc., and this application embodiment does not impose any limitation on this. The time interval between measurement results within the PW can be understood as the interval between any two adjacent prediction results within the PW. For example, the interval between two adjacent prediction results is 80ms.

[0241] In this embodiment, the network device carries part or all of the model information of the first model in the first relevant information, which can explicitly indicate the model for predicting the measurement events that the network device expects or is applicable. Explicit model information indication helps to improve the predictive performance of the model and increase prediction accuracy.

[0242] In one embodiment of this application, referring to the second schematic flowchart of the information transmission method shown in FIG8, the information transmission method provided in this embodiment may further include the following steps:

[0243] S720 and terminal equipment determine the first model based on relevant information of the first model.

[0244] It should be understood that after receiving the first relevant information, the terminal device can determine the first model for performing measurement event prediction based on the first relevant information of the first model.

[0245] For example, the network device may pre-configure multiple models for performing measurement event prediction for the terminal device, or the terminal device may pre-store multiple models for performing measurement event prediction. When the terminal device receives relevant information about the first model, it can select a matching first model from the multiple models for performing measurement event prediction based on the first relevant information.

[0246] In some embodiments, referring to the flowchart shown in FIG8, the information transmission method provided in this application embodiment may further include the following steps:

[0247] S730: The terminal device sends a seventh indication message, and the network device receives the seventh indication message accordingly. The seventh indication message is used to indicate whether the first model is available.

[0248] It should be understood that after determining the first model, the terminal device can judge whether the determined first model is available based on the actual situation, and indicate to the network device whether the selected first model is available through the seventh indication information.

[0249] It should be noted that the seventh instruction information may be a response to the first relevant information of the first model sent by the network device in step S710.

[0250] In one implementation, the seventh indication information is explicitly indicated by the third parameter. For example, the third parameter being the first value indicates that the first model is available; the first parameter being the second value indicates that the first model is unavailable.

[0251] In some other implementations, the seventh indication information is implicitly indicated by the predefined location of the signaling carried in the protocol. That is, if the third parameter does not appear in the signaling, it indicates that the first model is unavailable; conversely, if the third parameter appears in the signaling, it indicates that the first model is available.

[0252] In some embodiments, referring to the flowchart shown in FIG8, the information transmission method provided in this application embodiment may further include the following steps:

[0253] S740, the terminal device sends the second related information of the first model, and the network device receives the second related information of the first model accordingly; wherein, the second related information includes other model information in the model information of the first model other than the first related information.

[0254] It should be understood that if the terminal device determines that the first model is available, it may further instruct the network device on more detailed model information (i.e., the second relevant information).

[0255] The second set of relevant information may include one or more of the following: the applicable speed of the first model, the input data of the first model, and the output data of the first model, as described above. It should be noted that the descriptions of the applicable speed, the input data, and the output data of the first model can be found in the descriptions above, and for the sake of brevity, will not be repeated here.

[0256] It should be noted that model information already included in the first relevant information does not need to be reported in the second relevant information.

[0257] It should also be noted that if the first relevant information already includes all the model information, the terminal device may not need to send the second relevant information. In other words, step S740 is an optional step, which is represented by a dashed arrow in Figure 8.

[0258] It should also be noted that when the terminal device determines that the first model is available, the terminal device can send the seventh indication information and the second related information of the first model through the same signaling, or it can send the seventh indication information and the second related information of the first model through different signaling. This application embodiment does not limit this.

[0259] In summary, through the information transmission method provided in this application embodiment, the network device can send relevant information about a first model for performing measurement event prediction to the terminal device, informing the terminal device of the applicable first model. In this way, the terminal device can perform measurement event prediction based on the first model, which helps reduce the latency for the terminal device to obtain measurement events and also helps reduce the power consumption of the terminal device.

[0260] The information transmission method provided in this application embodiment will be described in detail below with reference to specific application scenarios.

[0261] Referring to the flowchart shown in Figure 9, the information transmission method provided in this application embodiment may include the following steps:

[0262] S1: gNB indicates to UE an applicable function or model for supporting measurement event prediction, wherein the indication may include a portion of the model inference configuration.

[0263] S2: The UE selects the appropriate function or model according to the instruction and responds to the instruction to indicate that the function or model is available. The UE further indicates more detailed parameters in the response message.

[0264] In some embodiments, the indication of applicable functions or models includes at least one of the following options:

[0265] Option 1: In addition to indicating that the applicable function is used for measurement event prediction, the gNB also indicates the associated RRM prediction function, which the UE should interpret as being used for indirect measurement event prediction rather than RRM prediction.

[0266] Option 2: gNB directly indicates different measurement event prediction functions, including at least one of the following:

[0267] Predict measurement events in advance (measurement event prediction based on case A);

[0268] Predict measurement events while reducing measurement overhead (measurement event prediction based on case B);

[0269] Predict measurement events while saving measurement gaps (frequency domain-based measurement event prediction);

[0270] Predict measurement events while saving on spatial beam measurements (spatial measurement event prediction);

[0271] It should be noted that the difference between options 1 and 2 is whether it is indicated by two information cells or one information cell. Option 1 is a prediction of the measurement event + a specific RRM prediction function (2 information cells), while option 2 is a prediction of the measurement event based on the specific RRM prediction function (1 information cell).

[0272] In some embodiments, the indication of the applicable function or model includes the following:

[0273] A conventional event-triggered measurement task is used for a given carrier being measured, for example, including a measurement identifier of measId_A;

[0274] A new parameter (which can be the same as in option 2) indicates which RRM prediction function is used to predict the event. The RRM prediction function can include at least one of the following:

[0275] Time-domain-based advance measurement and prediction;

[0276] Measurements are reduced by predictive measurements performed in the time domain.

[0277] Measurements are reduced by predictive measurements, which are performed in the spatial domain.

[0278] Measurements are reduced by predictive measurements that span the frequency domain. In this case, relevant information about the measured carrier should also be configured, such as another measurement identifier associated with measId_A.

[0279] In some embodiments, the more detailed parameters further indicated by the UE in the response message in S2 may include at least one of the following:

[0280] • What speed or speed range is the model suitable for, for example, 30-60 km / h;

[0281] • Supported model input information includes at least one of the following: the length of the input data observation window (OW), the interval between measurement results within OW (e.g., the interval between two adjacent measurement results is 40ms), the number of model input data points, and which point (A / A1 / B / C) the model input information is (i.e., L1 / L3, cell-level / beam-level, filtered / non-filtered combinations). For case B and the spatial domain, it also includes the measurement reduction rate in the temporal domain (MRRT), the measurement reduction rate in the spatial domain (MRRS), and the specific measurement reduction pattern (skipping pattern). For the frequency domain, if not included in the model configuration, this can also include the input frequency (frequency range, frequency band, frequency band combination) and the prediction direction (e.g., low frequency to high frequency, or high frequency to low frequency).

[0282] • Supported model output information, including at least one of the following: the length of the output data prediction window (PW), the interval between prediction results within the PW (e.g., the interval between two adjacent prediction results is 80ms), and the number of model output data.

[0283] In some embodiments, the partial model inference configuration in step S2 may include some or all of the above parameters. Configurations already included in the partial model inference configuration do not need to be reported again in more detailed parameters.

[0284] Example 1: Indirect measurement event prediction based on RRM function.

[0285] • gNB Operation: The gNB sends an indication message to the UE, specifying the applicable function for measurement event prediction, and attaching the associated Radio Resource Management (RRM) function information (2 cells).

[0286] • UE Operation: Upon receiving the instruction, the UE analyzes the RRM function information and infers that this is a function used to indirectly support measurement event prediction. The UE selects the appropriate function based on its capabilities and indicates its availability to the gNB in ​​the response message. Simultaneously, the UE provides further detailed parameters in the response message, such as the model's applicability to a speed range of 30-60 km / h, the input data observation window length of 200 ms, the measurement result interval within the operating field (OW) of 40 ms, and the input information being L1 layer cell-level and filtered data.

[0287] Beneficial effects: By combining measurement event prediction with RRM functionality, the UE can flexibly predict measurement events based on the specific content of the RRM function, without directly relying on explicit measurement event prediction function instructions. This indirect approach makes the prediction process more flexible and better adaptable to different network environments and UE states.

[0288] Example 2: Predicting measurement events in advance.

[0289] • gNB Operation: The gNB sends an indication message to the UE, directly specifying that the applicable function is for prediction of measurement events based on time-domain advance measurement.

[0290] • UE Operation: Upon receiving the instruction, the UE selects the function for predicting measurement events in advance, based on the direct instruction from the gNB. The UE indicates to the gNB in ​​the response message that the function is available and further provides detailed parameters, such as the model's applicability to a speed range of 50-80 km / h. Additionally, the UE provides model output information, such as an output data prediction window length of 200 ms and a model output data count of 4.

[0291] Beneficial effects: Compared to Example 1, it provides more direct indication of applicable functions and reduces the amount of air interface signaling transmission, making it a more streamlined approach. However, the drawback is that the air interface signaling design becomes more complex and requires consideration of various combinations.

[0292] Example 3: Frequency Domain Measurement Event Prediction.

[0293] • gNB Operation: The gNB sends an indication message to the UE, specifying that the applicable function is for frequency domain measurement event prediction. The gNB configures a conventional event-triggered measurement task for a given carrier being measured, with the measurement identifier measId_2, and appends a new parameter indicating that the event is a frequency domain-based measurement prediction function. Simultaneously, the gNB also configures another measurement identifier measId_1 associated with measId_2 to provide relevant information about the carrier being predicted for frequency domain prediction.

[0294] • UE Operation: Upon receiving the instruction, the UE selects the frequency domain measurement event prediction function. In its response message, the UE indicates to the gNB that the function is available and provides further detailed parameters, such as the model's applicability to a speed range of 20-40 km / h, the number of model input data points being 5, and the input information being L3 layer cell-level, filtered data, etc. Additionally, the UE provides frequency domain-related input parameters, such as the input frequency range of 3.3 GHz-3.8 GHz and the prediction direction from low to high frequencies. Simultaneously, the UE provides model output information, such as the number of model output data points being 1.

[0295] Beneficial effects: Compared to Example 2, this method sends a conventional measurement task simultaneously with the indication of applicable capabilities, allowing the UE to more clearly understand its measurement configuration information and avoid conflicts with existing measurements. Explicit measurement and prediction identification also helps improve the model's predictive performance and increase prediction accuracy.

[0296] The preferred embodiments of this application have been described in detail above with reference to the accompanying drawings. However, this application is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this application, various simple modifications can be made to the technical solutions of this application, and these simple modifications all fall within the protection scope of this application. For example, the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this application will not describe the various possible combinations separately. Furthermore, various different embodiments of this application can also be arbitrarily combined, as long as they do not violate the spirit of this application, they should also be considered as the content disclosed in this application. Moreover, without conflict, the various embodiments and / or the technical features in the various embodiments described in this application can be arbitrarily combined with the prior art, and the resulting technical solutions should also fall within the protection scope of this application.

[0297] Figure 10 is a schematic block diagram of a terminal device 1000 according to an embodiment of the present application. The terminal device 1000 may include:

[0298] The first communication unit 1010 is configured to receive first relevant information from a first model, the first model being used to perform measurement event prediction.

[0299] In some embodiments, the first relevant information includes first indication information and second indication information;

[0300] The first indication information is used to instruct the first model to perform measurement event prediction.

[0301] The second indication information is used to indicate that the measurement event prediction is implemented based on the first RRM prediction function.

[0302] In some embodiments, the first relevant information includes third indication information, which is used to indicate that the first model is used for predicting a first type of measurement event.

[0303] In some embodiments, the first type of measurement event prediction includes one or more of the following:

[0304] The first type is time-domain-based prediction of measurement events;

[0305] The second type is time-domain-based prediction of measurement events;

[0306] Frequency domain-based measurement event prediction;

[0307] Spatial-domain-based measurement event prediction;

[0308] in,

[0309] The first type of time-domain-based measurement event prediction refers to predicting measurement events in advance based on historical time-domain measurement results;

[0310] The second type of time-domain-based measurement event prediction refers to predicting measurement events based on partial time-domain measurement results;

[0311] The frequency domain-based measurement event prediction refers to predicting measurement events based on measurement results in a portion of the frequency domain.

[0312] The spatial domain-based measurement event prediction refers to predicting measurement events based on the measurement results of a portion of the beam.

[0313] In some embodiments, the first relevant information further includes fourth indication information.

[0314] The fourth indication information is used to indicate the first measurement task associated with the measurement event prediction.

[0315] In some embodiments, the first relevant information further includes fifth indication information;

[0316] The fifth indication information is used to indicate that the first measurement task is implemented based on the second RRM prediction function.

[0317] In some embodiments, the second RRM prediction function is at least a portion of the prediction function in the first RRM prediction function;

[0318] or,

[0319] The second RRM function is associated with the prediction of the first type of measurement event.

[0320] In some embodiments, the first RRM prediction function and / or the second RRM prediction function includes one or more of the following:

[0321] First measurement result time-domain prediction function;

[0322] Second measurement result time-domain prediction function;

[0323] Frequency domain prediction function for measurement results;

[0324] Spatial domain prediction function for measurement results;

[0325] The first measurement result time-domain prediction function represents the use of the acquired historical time-domain location measurement results to predict the measurement results of the time-domain location to be predicted;

[0326] The second measurement result time-domain prediction function characterizes the prediction of the measurement result of the time-domain position to be predicted by using the measurement results of some historical time-domain positions.

[0327] The frequency domain prediction function of the measurement results represents the ability to predict the measurement results of one or more frequency points to be predicted using the measurement results of the acquired measured frequency points.

[0328] The measurement result spatial prediction function represents the ability to predict the measurement results of one or more beams to be predicted using the acquired measurement results of the measured beams.

[0329] In some embodiments, when the measurement event prediction is implemented based on the measurement result frequency domain prediction function, the first related information further includes a sixth indication information, wherein the measurement result frequency domain prediction function characterizes the prediction of the measurement results of one or more frequency points to be predicted using the measurement results of the acquired measured frequency points;

[0330] The sixth indication information is used to indicate the one or more frequency points to be predicted.

[0331] In some embodiments, the first relevant information of the first model may also include all or part of the model information of the first model.

[0332] In some embodiments, the terminal device 1000 further includes a processing unit configured to determine the first model based on relevant information of the first model.

[0333] In some embodiments, the first communication unit 1010 is further configured to send a seventh indication message, the seventh indication message being used to indicate whether the first model is available.

[0334] In some embodiments, the first communication unit 1010 is further configured to send a second related information of the first model; the second related information includes other model information in the model information of the first model besides the first related information.

[0335] In some embodiments, the first relevant information, the second relevant information, or the model information of the first model includes one or more of the following:

[0336] The speed at which the first model is applicable;

[0337] The input data of the first model;

[0338] The output data of the first model.

[0339] In some embodiments, the input data of the first model includes one or more of the following:

[0340] Enter the length of the data observation window;

[0341] The time interval between measurement results within the input data observation window;

[0342] The number of input data;

[0343] The type of input data;

[0344] Time-domain measurement reduction MRRT;

[0345] Time-domain measurement reduction mode;

[0346] Airspace Measurement Reduction (MRRS);

[0347] Airspace measurement reduction mode;

[0348] Frequency point information corresponding to the input data;

[0349] Frequency domain prediction direction.

[0350] In some embodiments, the type of input data includes one or more of the following:

[0351] Layer 1 measurement results;

[0352] Layer 3 measurement results;

[0353] Community-level measurement results;

[0354] Beam-level measurement results;

[0355] Filtered measurement results;

[0356] Unfiltered measurement results.

[0357] In some embodiments, the frequency domain prediction direction includes a first direction and / or a second direction;

[0358] The first direction indicates that the frequency of the measured frequency point in the frequency domain prediction function of the measurement result is lower than the frequency of the frequency point to be predicted, and the second direction indicates that the frequency of the measured frequency point in the frequency domain prediction function of the measurement result is higher than the frequency of the frequency point to be predicted.

[0359] In some embodiments, the output data of the first model includes one or more of the following:

[0360] The length of the output data prediction window;

[0361] The interval between prediction results within the output data prediction window;

[0362] The number of data points to output.

[0363] Those skilled in the art should understand that the description of the terminal device in the embodiments of this application can be understood with reference to the description of the information transmission method in the embodiments of this application.

[0364] Figure 11 is a schematic block diagram of a network device 1100 according to an embodiment of the present application. The network device 1100 may include:

[0365] The second communication unit 1110 is configured to send first relevant information of the first model, which is used to perform measurement event prediction.

[0366] In some embodiments, the first relevant information includes first indication information and second indication information;

[0367] The first indication information is used to instruct the first model to perform measurement event prediction.

[0368] The second indication information is used to indicate that the measurement event prediction is implemented based on the first RRM prediction function.

[0369] In some embodiments, the first relevant information includes third indication information, which is used to indicate that the first model is used for predicting a first type of measurement event.

[0370] In some embodiments, the first type of measurement event prediction includes one or more of the following:

[0371] The first type is time-domain-based prediction of measurement events;

[0372] The second type is time-domain-based prediction of measurement events;

[0373] Frequency domain-based measurement event prediction;

[0374] Spatial-domain-based measurement event prediction;

[0375] Among them, the first type of time-domain-based measurement event prediction refers to predicting measurement events in advance based on historical time-domain measurement results;

[0376] The second type of time-domain-based measurement event prediction refers to predicting measurement events based on partial time-domain measurement results;

[0377] The frequency domain-based measurement event prediction refers to predicting measurement events based on measurement results in a portion of the frequency domain.

[0378] The spatial domain-based measurement event prediction refers to predicting measurement events based on the measurement results of a portion of the beam.

[0379] In some embodiments, the first related information further includes fourth indication information, which is used to indicate a first measurement task associated with the measurement event prediction.

[0380] In some embodiments, the first relevant information further includes fifth indication information; the fifth indication information is used to indicate that the first measurement task is implemented based on the second RRM prediction function.

[0381] In some embodiments, the second RRM prediction function is at least a portion of the prediction function in the first RRM prediction function; or, the second RRM function is associated with the prediction of a first type of measurement event.

[0382] In some embodiments, the first RRM prediction function and / or the second RRM prediction function includes one or more of the following:

[0383] First measurement result time-domain prediction function;

[0384] Second measurement result time-domain prediction function;

[0385] Frequency domain prediction function for measurement results;

[0386] Spatial domain prediction function for measurement results;

[0387] The first measurement result time-domain prediction function represents the use of the acquired historical time-domain location measurement results to predict the measurement results of the time-domain location to be predicted;

[0388] The second measurement result time-domain prediction function characterizes the prediction of the measurement result of the time-domain position to be predicted by using the measurement results of some historical time-domain positions.

[0389] The frequency domain prediction function of the measurement results represents the ability to predict the measurement results of one or more frequency points to be predicted using the measurement results of the acquired measured frequency points.

[0390] The measurement result spatial prediction function represents the ability to predict the measurement results of one or more beams to be predicted using the acquired measurement results of the measured beams.

[0391] In some embodiments, when the measurement event prediction is implemented based on the measurement result frequency domain prediction function, the first related information further includes a sixth indication information, wherein the measurement result frequency domain prediction function characterizes the prediction of the measurement results of one or more frequency points to be predicted using the measurement results of the acquired measured frequency points;

[0392] The sixth indication information is used to indicate the one or more frequency points to be predicted.

[0393] In some embodiments, the first relevant information of the first model may also include all or part of the model information of the first model.

[0394] In some embodiments, the second communication unit 1110 is further configured to receive seventh indication information, the seventh indication information being used to indicate whether the first model is available.

[0395] In some embodiments, the second communication unit 1110 is further configured to receive a second related information of the first model; the second related information includes other model information in the model information of the first model besides the first related information.

[0396] In some embodiments, the first relevant information, the second relevant information, or the model information of the first model includes one or more of the following:

[0397] The speed at which the first model is applicable;

[0398] The input data of the first model;

[0399] The output data of the first model.

[0400] In some embodiments, the input data of the first model includes one or more of the following:

[0401] Enter the length of the data observation window;

[0402] The time interval between measurement results within the input data observation window;

[0403] The number of input data;

[0404] The type of input data;

[0405] Time-domain measurement reduction MRRT;

[0406] Time-domain measurement reduction mode;

[0407] Airspace Measurement Reduction (MRRS);

[0408] Airspace measurement reduction mode;

[0409] Frequency point information corresponding to the input data;

[0410] Frequency domain prediction direction.

[0411] In some embodiments, the type of input data includes one or more of the following:

[0412] Layer 1 measurement results;

[0413] Layer 3 measurement results;

[0414] Community-level measurement results;

[0415] Beam-level measurement results;

[0416] Filtered measurement results;

[0417] Unfiltered measurement results.

[0418] In some embodiments, the frequency domain prediction direction includes a first direction and / or a second direction;

[0419] The first direction indicates that the frequency of the measured frequency point in the frequency domain prediction function of the measurement result is lower than the frequency of the frequency point to be predicted, and the second direction indicates that the frequency of the measured frequency point in the frequency domain prediction function of the measurement result is higher than the frequency of the frequency point to be predicted.

[0420] In some embodiments, the output data of the first model includes one or more of the following:

[0421] The length of the output data prediction window;

[0422] The interval between prediction results within the output data prediction window;

[0423] The number of data points to output.

[0424] Those skilled in the art should understand that the description of the network device in the embodiments of this application can be understood with reference to the description of the information transmission method in the embodiments of this application.

[0425] Figure 12 is a schematic structural diagram of a terminal device provided in an embodiment of this application. The terminal device 1200 shown in Figure 12 includes a processor 1210, which can call and run computer programs from memory to enable the terminal device 1200 to implement the methods in the embodiments of this application.

[0426] Optionally, as shown in FIG12, the terminal device 1200 may further include a memory 1220. The processor 1210 may retrieve and run computer programs from the memory 1220 to implement the methods in the embodiments of this application.

[0427] The memory 1220 can be a separate device independent of the processor 1210, or it can be integrated into the processor 1210.

[0428] Optionally, as shown in FIG12, the terminal device 1200 may further include a transceiver 1230, and the processor 1210 may control the transceiver 1230 to communicate with other devices. Specifically, it may send information or data to other devices or receive information or data sent by other devices.

[0429] The transceiver 1230 may include a transmitter and a receiver. The transceiver 1230 may further include an antenna, and the number of antennas may be one or more.

[0430] It should be noted that the terminal device 1200 may specifically be the terminal device in the embodiments of this application, and the terminal device 1200 may implement the corresponding processes implemented by the terminal device in the various methods of the embodiments of this application. For the sake of brevity, it will not be described in detail here.

[0431] Figure 13 is a schematic structural diagram of a network device provided in an embodiment of this application. The network device 1300 shown in Figure 13 includes a processor 1310, which can call and run computer programs from memory to enable the network device 1300 to implement the methods in the embodiments of this application.

[0432] Optionally, as shown in FIG13, the network device 1300 may further include a memory 1320. The processor 1310 may retrieve and run computer programs from the memory 1320 to implement the methods described in the embodiments of this application.

[0433] The memory 1320 can be a separate device independent of the processor 1310, or it can be integrated into the processor 1310.

[0434] Optionally, as shown in FIG13, the network device 1300 may further include a transceiver 1330, and the processor 1310 may control the transceiver 1330 to communicate with other devices. Specifically, it may send information or data to other devices or receive information or data sent by other devices.

[0435] The transceiver 1330 may include a transmitter and a receiver. The transceiver 1330 may further include an antenna, and the number of antennas may be one or more.

[0436] It should be noted that the network device 1300 may specifically be the network device in the embodiments of this application, and the network device 1300 may implement the corresponding processes implemented by the network device in the various methods of the embodiments of this application. For the sake of brevity, it will not be described in detail here.

[0437] Figure 14 is a schematic structural diagram of a chip according to an embodiment of this application. The chip 1400 shown in Figure 14 includes a processor 1410, which can call and run computer programs from memory to implement the methods in the embodiments of this application.

[0438] Optionally, as shown in FIG14, chip 1400 may further include memory 1420. Processor 1410 may retrieve and run computer programs from memory 1420 to implement the methods in the embodiments of this application.

[0439] The memory 1420 can be a separate device independent of the processor 1410, or it can be integrated into the processor 1410.

[0440] Optionally, the chip 1400 may also include an input interface 1430. The processor 1410 can control the input interface 1430 to communicate with other devices or chips; specifically, it can acquire information or data sent by other devices or chips.

[0441] Optionally, the chip 1400 may also include an output interface 1440. The processor 1410 can control the output interface 1440 to communicate with other devices or chips, specifically, to output information or data to other devices or chips.

[0442] Optionally, the chip can be applied to the network device in the embodiments of this application, and the chip can implement the corresponding processes implemented by the network device in the various methods of the embodiments of this application. For the sake of brevity, it will not be described in detail here.

[0443] Optionally, the chip can be applied to the terminal device in the embodiments of this application, and the chip can implement the corresponding processes implemented by the first terminal device in the various methods of the embodiments of this application. For the sake of brevity, it will not be described in detail here.

[0444] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0445] This application also provides a computer storage medium that stores one or more programs, which can be executed by one or more processors to implement the methods in this application.

[0446] Figure 15 is a schematic block diagram of a communication system provided in an embodiment of this application. As shown in Figure 15, the communication system 1500 includes a network device 1510 and a terminal device 1520.

[0447] The network device 1510 can be used to implement the corresponding functions implemented by the network device in the above method, and the terminal device 1520 can be used to implement the corresponding functions implemented by the terminal device in the above method. For the sake of brevity, they will not be described in detail here.

[0448] It should be understood that the processor in the embodiments of this application may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor described above can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0449] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0450] It should be understood that the above-described memory is exemplary and not a limiting description. For example, the memory in the embodiments of this application may also be static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DR RAM), etc. That is to say, the memory in the embodiments of this application is intended to include, but is not limited to, these and any other suitable types of memory.

[0451] This application also provides a computer-readable storage medium for storing computer programs.

[0452] Optionally, the computer-readable storage medium can be applied to the network device in the embodiments of this application, and the computer program causes the computer to execute the corresponding processes implemented by the network device in the various methods of the embodiments of this application. For the sake of brevity, it will not be described in detail here.

[0453] Optionally, the computer-readable storage medium can be applied to the terminal device in the embodiments of this application, and the computer program causes the computer to execute the corresponding processes implemented by the terminal device in the various methods of the embodiments of this application. For the sake of brevity, it will not be described in detail here.

[0454] This application also provides a computer program product, including computer program instructions.

[0455] Optionally, the computer program product can be applied to the network device in the embodiments of this application, and the computer program instructions cause the computer to execute the corresponding processes implemented by the network device in the various methods of the embodiments of this application. For the sake of brevity, they will not be described in detail here.

[0456] Optionally, the computer program product can be applied to the terminal device in the embodiments of this application, and the computer program instructions cause the computer to execute the corresponding processes implemented by the terminal device in the various methods of the embodiments of this application. For the sake of brevity, they will not be described in detail here.

[0457] This application also provides a computer program.

[0458] Optionally, the computer program can be applied to the network device in the embodiments of this application. When the computer program is run on a computer, it causes the computer to execute the corresponding processes implemented by the network device in the various methods of the embodiments of this application. For the sake of brevity, it will not be described in detail here.

[0459] Optionally, the computer program can be applied to the terminal device in the embodiments of this application. When the computer program is run on the computer, it causes the computer to execute the corresponding processes implemented by the terminal device in the various methods of the embodiments of this application. For the sake of brevity, it will not be described in detail here.

[0460] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0461] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0462] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0463] The units described as separate components may or may not be physically separate. 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0464] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0465] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0466] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An information transmission method, the method comprising: The terminal device receives first relevant information from the first model, which is used to perform measurement event prediction.

2. The method according to claim 1, wherein, The first relevant information includes first indication information and second indication information; The first indication information is used to instruct the first model to perform measurement event prediction. The second indication information is used to indicate that the measurement event prediction is implemented based on the first RRM prediction function.

3. The method according to claim 1, wherein, The first relevant information includes third indication information, which is used to indicate that the first model is used for predicting a first type of measurement event.

4. The method of claim 3, wherein, The first type of measurement event prediction includes one or more of the following: The first type is time-domain-based prediction of measurement events; The second type is time-domain-based prediction of measurement events; Frequency domain-based measurement event prediction; Spatial-domain-based measurement event prediction; Specifically, the first type of time-domain-based measurement event prediction refers to predicting measurement events in advance based on historical time-domain measurement results; the second type of time-domain-based measurement event prediction refers to predicting measurement events based on partial time-domain measurement results; the frequency-domain-based measurement event prediction refers to predicting measurement events based on partial frequency-domain measurement results; and the spatial-domain-based measurement event prediction refers to predicting measurement events based on partial beam measurement results.

5. The method according to any one of claims 1-4, wherein, The first relevant information also includes fourth indication information. The fourth indication information is used to indicate the first measurement task associated with the measurement event prediction.

6. The method according to claim 5, wherein, The first relevant information also includes the fifth indication information; The fifth indication information is used to indicate that the first measurement task is implemented based on the second RRM prediction function.

7. The method according to claim 6, wherein, The second RRM prediction function is at least a portion of the prediction function in the first RRM prediction function; or, The second RRM function is associated with the prediction of the first type of measurement event.

8. The method of any one of claims 2, 5-7, wherein, The first RRM prediction function and / or the second RRM prediction function include one or more of the following: First measurement result time-domain prediction function; Second measurement result time-domain prediction function; Frequency domain prediction function for measurement results; Spatial domain prediction function for measurement results; Specifically, the first measurement result time-domain prediction function represents the prediction of the measurement result of the time-domain position to be predicted using the measurement results of the acquired historical time-domain positions; the second measurement result time-domain prediction function represents the prediction of the measurement result of the time-domain position to be predicted using the measurement results of a portion of the acquired historical time-domain positions; the measurement result frequency-domain prediction function represents the prediction of the measurement result of one or more frequency points to be predicted using the measurement results of the acquired measured frequency points; and the measurement result spatial-domain prediction function represents the prediction of the measurement result of one or more beams to be predicted using the measurement results of the acquired measured beams.

9. The method according to any one of claims 1-8, wherein, When the measurement event prediction is implemented based on the frequency domain prediction function of the measurement result, the first related information also includes a sixth indication information, wherein the frequency domain prediction function of the measurement result represents the prediction of the measurement result of one or more frequency points to be predicted using the measurement result of the acquired measured frequency points; The sixth indication information is used to indicate the one or more frequency points to be predicted.

10. The method according to any one of claims 1-9, wherein, The first relevant information of the first model also includes all or part of the model information of the first model.

11. The method of any one of claims 1-10, wherein, Also includes: The terminal device determines the first model based on the relevant information of the first model.

12. The method of claim 11, wherein, Also includes: The terminal device sends a seventh indication message, which is used to indicate whether the first model is available.

13. The method of claim 11 or 12, wherein, Also includes: The terminal device sends a second related information about the first model; the second related information includes other model information in the model information of the first model besides the first related information.

14. The method of any one of claims 1-13, wherein, The first relevant information, the second relevant information, or the model information of the first model includes one or more of the following: The applicable speed for the first model; the input data for the first model; the output data for the first model.

15. The method of claim 14, wherein, The input data for the first model includes one or more of the following: The length of the input data observation window; the time interval between measurement results within the input data observation window; the number of input data; the type of input data; the time-domain measurement reduction factor (MRRT); the time-domain measurement reduction mode; the spatial measurement reduction factor (MRRS); the spatial measurement reduction mode; the frequency information corresponding to the input data; and the frequency domain prediction direction.

16. The method of claim 15, wherein, The type of input data includes one or more of the following: Layer 1 measurement results; Layer 3 measurement results; Cell-level measurement results; Beam-level measurement results; Filtered measurement results; Unfiltered measurement results.

17. The method of claim 15 or 16, wherein, The frequency domain prediction direction includes a first direction and / or a second direction; The first direction indicates that the frequency of the measured frequency point in the frequency domain prediction function of the measurement result is lower than the frequency of the frequency point to be predicted, and the second direction indicates that the frequency of the measured frequency point in the frequency domain prediction function of the measurement result is higher than the frequency of the frequency point to be predicted.

18. The method of any one of claims 15-17, wherein, The output data of the first model includes one or more of the following: The length of the output data prediction window; the interval between prediction results within the output data prediction window; the number of output data.

19. An information transmission method, the method comprising: The network device sends first relevant information about a first model, which is used to perform measurement event prediction.

20. The method of claim 19, wherein, The first relevant information includes first indication information and second indication information; the first indication information is used to indicate that the first model is used to perform measurement event prediction, and the second indication information is used to indicate that the measurement event prediction is implemented based on the first RRM prediction function.

21. The method of claim 19, wherein, The first relevant information includes third indication information, which is used to indicate that the first model is used for predicting a first type of measurement event.

22. The method of claim 21, wherein, The first type of measurement event prediction includes one or more of the following: The first type is time-domain based measurement event prediction; the second type is time-domain based measurement event prediction; frequency-domain based measurement event prediction; spatial-domain based measurement event prediction. Specifically, the first type of time-domain-based measurement event prediction refers to predicting measurement events in advance based on historical time-domain measurement results; the second type of time-domain-based measurement event prediction refers to predicting measurement events based on partial time-domain measurement results; the frequency-domain-based measurement event prediction refers to predicting measurement events based on partial frequency-domain measurement results; and the spatial-domain-based measurement event prediction refers to predicting measurement events based on partial beam measurement results.

23. The method of any one of claims 19-22, wherein, The first related information also includes fourth indication information, which is used to indicate the first measurement task associated with the measurement event prediction.

24. The method of claim 23, wherein, The first relevant information also includes a fifth indication information; the fifth indication information is used to indicate that the first measurement task is implemented based on the second RRM prediction function.

25. The method of claim 24, wherein, The second RRM prediction function is at least a portion of the prediction function in the first RRM prediction function; or, the second RRM function is associated with the prediction of the first type of measurement event.

26. The method of any one of claims 20, 23-25, wherein, The first RRM prediction function and / or the second RRM prediction function include one or more of the following: First measurement result time-domain prediction function; second measurement result time-domain prediction function; measurement result frequency-domain prediction function; measurement result spatial-domain prediction function; Specifically, the first measurement result time-domain prediction function represents the prediction of the measurement result of the time-domain position to be predicted using the measurement results of the acquired historical time-domain positions; the second measurement result time-domain prediction function represents the prediction of the measurement result of the time-domain position to be predicted using the measurement results of a portion of the acquired historical time-domain positions; the measurement result frequency-domain prediction function represents the prediction of the measurement result of one or more frequency points to be predicted using the measurement results of the acquired measured frequency points; and the measurement result spatial-domain prediction function represents the prediction of the measurement result of one or more beams to be predicted using the measurement results of the acquired measured beams.

27. The method according to any one of claims 19-26, wherein, When the measurement event prediction is implemented based on the frequency domain prediction function of the measurement result, the first related information also includes a sixth indication information, wherein the frequency domain prediction function of the measurement result represents the prediction of the measurement result of one or more frequency points to be predicted using the measurement result of the acquired measured frequency points; The sixth indication information is used to indicate the one or more frequency points to be predicted.

28. The method of any one of claims 19-27, wherein, The first relevant information of the first model also includes all or part of the model information of the first model.

29. The method of any one of claims 19-28, wherein, Also includes: The network device receives a seventh indication message, which is used to indicate whether the first model is available.

30. The method of claim 29, wherein, Also includes: The network device receives the second relevant information of the first model; the second relevant information includes other model information in the model information of the first model besides the first relevant information.

31. The method of any one of claims 19-30, wherein, The first relevant information, the second relevant information, or the model information of the first model includes one or more of the following: The applicable speed for the first model; the input data for the first model; the output data for the first model.

32. The method of claim 31, wherein, The input data for the first model includes one or more of the following: The length of the input data observation window; the time interval between measurement results within the input data observation window; the number of input data; the type of input data; the time-domain measurement reduction factor (MRRT); the time-domain measurement reduction mode; the spatial measurement reduction factor (MRRS); the spatial measurement reduction mode; the frequency information corresponding to the input data; and the frequency domain prediction direction.

33. The method of claim 32, wherein, The type of input data includes one or more of the following: Layer 1 measurement results; Layer 3 measurement results; Cell-level measurement results; Beam-level measurement results; Filtered measurement results; Unfiltered measurement results.

34. The method of claim 32 or 33, wherein, The frequency domain prediction direction includes a first direction and / or a second direction; The first direction indicates that the frequency of the measured frequency point in the frequency domain prediction function of the measurement result is lower than the frequency of the frequency point to be predicted, and the second direction indicates that the frequency of the measured frequency point in the frequency domain prediction function of the measurement result is higher than the frequency of the frequency point to be predicted.

35. The method of any one of claims 19-34, wherein, The output data of the first model includes one or more of the following: The length of the output data prediction window; the interval between prediction results within the output data prediction window; the number of output data.

36. A terminal device, the terminal device comprising: The first communication unit is configured to receive first relevant information from a first model, the first model being used to perform measurement event prediction.

37. The terminal device according to claim 36, wherein, The first relevant information includes first indication information and second indication information; The first indication information is used to instruct the first model to perform measurement event prediction. The second indication information is used to indicate that the measurement event prediction is implemented based on the first RRM prediction function.

38. The terminal device according to claim 36, wherein, The first relevant information includes third indication information, which is used to indicate that the first model is used for predicting a first type of measurement event.

39. The terminal device of claim 38, wherein, The first type of measurement event prediction includes one or more of the following: The first type is time-domain based measurement event prediction; the second type is time-domain based measurement event prediction; frequency-domain based measurement event prediction; spatial-domain based measurement event prediction. Specifically, the first type of time-domain-based measurement event prediction refers to predicting measurement events in advance based on historical time-domain measurement results; the second type of time-domain-based measurement event prediction refers to predicting measurement events based on partial time-domain measurement results; the frequency-domain-based measurement event prediction refers to predicting measurement events based on partial frequency-domain measurement results; and the spatial-domain-based measurement event prediction refers to predicting measurement events based on partial beam measurement results.

40. The terminal device according to any one of claims 36-39, wherein, The first relevant information also includes fourth indication information. The fourth indication information is used to indicate the first measurement task associated with the measurement event prediction.

41. The terminal device according to claim 10, wherein, The first relevant information also includes the fifth indication information; The fifth indication information is used to indicate that the first measurement task is implemented based on the second RRM prediction function.

42. The terminal device according to claim 41, wherein, The second RRM prediction function is at least a portion of the prediction function in the first RRM prediction function; or, The second RRM function is associated with the prediction of the first type of measurement event.

43. The terminal device of any of claims 37, 40-42, wherein, The first RRM prediction function and / or the second RRM prediction function include one or more of the following: First measurement result time-domain prediction function; second measurement result time-domain prediction function; measurement result frequency-domain prediction function; measurement result spatial-domain prediction function; Specifically, the first measurement result time-domain prediction function represents the prediction of the measurement result of the time-domain position to be predicted using the measurement results of the acquired historical time-domain positions; the second measurement result time-domain prediction function represents the prediction of the measurement result of the time-domain position to be predicted using the measurement results of a portion of the acquired historical time-domain positions; the measurement result frequency-domain prediction function represents the prediction of the measurement result of one or more frequency points to be predicted using the measurement results of the acquired measured frequency points; and the measurement result spatial-domain prediction function represents the prediction of the measurement result of one or more beams to be predicted using the measurement results of the acquired measured beams.

44. The terminal device according to any one of claims 36-43, wherein, When the measurement event prediction is implemented based on the frequency domain prediction function of the measurement result, the first related information also includes a sixth indication information, wherein the frequency domain prediction function of the measurement result represents the prediction of the measurement result of one or more frequency points to be predicted using the measurement result of the acquired measured frequency points; The sixth indication information is used to indicate the one or more frequency points to be predicted.

45. The terminal device according to any one of claims 36-44, wherein, The first relevant information of the first model also includes all or part of the model information of the first model.

46. The terminal device of any of claims 36-45, wherein, The terminal device also includes a processing unit; The processing unit is configured to determine the first model based on relevant information of the first model.

47. The terminal device of claim 46, wherein, The first communication unit is further configured to send a seventh indication message, which is used to indicate whether the first model is available.

48. The terminal device according to claim 46 or 47, wherein, The first communication unit is further configured to send a second related information of the first model; the second related information includes other model information in the model information of the first model besides the first related information.

49. The terminal device of any one of claims 36-48, wherein, The first relevant information, the second relevant information, or the model information of the first model includes one or more of the following: The applicable speed for the first model; the input data for the first model; the output data for the first model.

50. The terminal device of claim 49, wherein, The input data for the first model includes one or more of the following: The length of the input data observation window; the time interval between measurement results within the input data observation window; the number of input data; the type of input data; the time-domain measurement reduction factor (MRRT); the time-domain measurement reduction mode; the spatial measurement reduction factor (MRRS); the spatial measurement reduction mode; the frequency information corresponding to the input data; and the frequency domain prediction direction.

51. The terminal device of claim 50, wherein, The type of input data includes one or more of the following: Layer 1 measurement results; Layer 3 measurement results; Cell-level measurement results; Beam-level measurement results; Filtered measurement results; Unfiltered measurement results.

52. The terminal device of claim 50 or 51, wherein, The frequency domain prediction direction includes a first direction and / or a second direction; the first direction indicates that the frequency of the measured frequency point in the frequency domain prediction function of the measurement result is lower than the frequency of the frequency point to be predicted, and the second direction indicates that the frequency of the measured frequency point in the frequency domain prediction function of the measurement result is higher than the frequency of the frequency point to be predicted.

53. The terminal device of any of claims 50-52, wherein, The output data of the first model includes one or more of the following: the length of the output data prediction window; the interval of the prediction results within the output data prediction window; and the number of output data.

54. A network device, the network device comprising: The second communication unit is configured to send first relevant information of the first model, which is used to perform measurement event prediction.

55. The network device of claim 54, wherein, The first relevant information includes first indication information and second indication information; the first indication information is used to indicate that the first model is used to perform measurement event prediction, and the second indication information is used to indicate that the measurement event prediction is implemented based on the first RRM prediction function.

56. The network device of claim 54, wherein, The first relevant information includes third indication information, which is used to indicate that the first model is used for predicting a first type of measurement event.

57. The network device of claim 56, wherein, The first type of measurement event prediction includes one or more of the following: The first type is time-domain based measurement event prediction; the second type is time-domain based measurement event prediction; frequency-domain based measurement event prediction; spatial-domain based measurement event prediction. Specifically, the first type of time-domain-based measurement event prediction refers to predicting measurement events in advance based on historical time-domain measurement results; the second type of time-domain-based measurement event prediction refers to predicting measurement events based on partial time-domain measurement results; the frequency-domain-based measurement event prediction refers to predicting measurement events based on partial frequency-domain measurement results; and the spatial-domain-based measurement event prediction refers to predicting measurement events based on partial beam measurement results.

58. The network device of any of claims 54-57, wherein, The first relevant information also includes fourth indication information. The fourth indication information is used to indicate the first measurement task associated with the measurement event prediction.

59. The network device of claim 58, wherein, The first relevant information also includes a fifth indication information; the fifth indication information is used to indicate that the first measurement task is implemented based on the second RRM prediction function.

60. The network device of claim 59, wherein, The second RRM prediction function is at least a portion of the prediction function in the first RRM prediction function; or, the second RRM function is associated with the prediction of the first type of measurement event.

61. The network device of any of claims 55, 58-30, wherein, The first RRM prediction function and / or the second RRM prediction function include one or more of the following: First measurement result time-domain prediction function; second measurement result time-domain prediction function; measurement result frequency-domain prediction function; measurement result spatial-domain prediction function; Specifically, the first measurement result time-domain prediction function represents the prediction of the measurement result of the time-domain position to be predicted using the measurement results of the acquired historical time-domain positions; the second measurement result time-domain prediction function represents the prediction of the measurement result of the time-domain position to be predicted using the measurement results of a portion of the acquired historical time-domain positions; the measurement result frequency-domain prediction function represents the prediction of the measurement result of one or more frequency points to be predicted using the measurement results of the acquired measured frequency points; and the measurement result spatial-domain prediction function represents the prediction of the measurement result of one or more beams to be predicted using the measurement results of the acquired measured beams.

62. The network device according to any one of claims 54-61, wherein, When the measurement event prediction is implemented based on the frequency domain prediction function of the measurement result, the first related information also includes a sixth indication information, wherein the frequency domain prediction function of the measurement result represents the prediction of the measurement result of one or more frequency points to be predicted using the measurement result of the acquired measured frequency points; The sixth indication information is used to indicate the one or more frequency points to be predicted.

63. The network device according to any one of claims 54-62, wherein, The first relevant information of the first model also includes all or part of the model information of the first model.

64. The network device of any of claims 54-63, wherein, The second communication unit is further configured to receive a seventh indication message, the seventh indication message being used to indicate whether the first model is available.

65. The network device of claim 64, wherein, The second communication unit is further configured to receive second related information of the first model; the second related information includes other model information in the model information of the first model besides the first related information.

66. The network device of any of claims 54-65, wherein, The first relevant information, the second relevant information, or the model information of the first model includes one or more of the following: The applicable speed for the first model; the input data for the first model; the output data for the first model.

67. The network device of claim 66, wherein, The input data for the first model includes one or more of the following: The length of the input data observation window; the time interval between measurement results within the input data observation window; the number of input data; the type of input data; the time-domain measurement reduction factor (MRRT); the time-domain measurement reduction mode; the spatial measurement reduction factor (MRRS); the spatial measurement reduction mode; the frequency information corresponding to the input data; and the frequency domain prediction direction.

68. The network device of claim 67, wherein, The type of input data includes one or more of the following: Layer 1 measurement results; Layer 3 measurement results; Cell-level measurement results; Beam-level measurement results; Filtered measurement results; Unfiltered measurement results.

69. The network device of claim 67 or 68, wherein, The frequency domain prediction direction includes a first direction and / or a second direction; the first direction indicates that the frequency of the measured frequency point in the frequency domain prediction function of the measurement result is lower than the frequency of the frequency point to be predicted, and the second direction indicates that the frequency of the measured frequency point in the frequency domain prediction function of the measurement result is higher than the frequency of the frequency point to be predicted.

70. The network device of any of claims 54-69, wherein, The output data of the first model includes one or more of the following: The length of the output data prediction window; the interval between prediction results within the output data prediction window; the number of output data.

71. A terminal device, comprising: A processor and a memory, the memory being used to store a computer program, the processor being used to invoke and run the computer program stored in the memory, so that a terminal device performs the method as described in any one of claims 1 to 18.

72. A network device, comprising: A processor and a memory, the memory for storing a computer program, the processor for calling and running the computer program stored in the memory to cause the network device to perform the method as described in any one of claims 19 to 35.

73. A chip, the chip comprising: A processor for retrieving and running a computer program from memory, causing a device having the chip mounted to perform the method as described in any one of claims 1 to 18, or to perform the method as described in any one of claims 19 to 35.

74. A computer-readable storage medium storing a computer program that, when executed by at least one processor, implements the method as claimed in any one of claims 1 to 18, or the method as claimed in any one of claims 19 to 35.

75. A computer program product comprising a computer storage medium storing a computer program, the computer program comprising instructions executable by at least one processor, wherein the instructions, when executed by the at least one processor, implement the method of any one of claims 1 to 18, or the method of any one of claims 19 to 35.

76. A computer program that causes a computer to perform the method as claimed in any one of claims 1 to 18, or to perform the method as claimed in any one of claims 19 to 35.

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