Method and device used for measurement in wireless communication

Abstract

Disclosed are a method and device for measurement in wireless communication, comprising: receiving a first signaling indicating a first measurement configuration and a first report configuration; triggering a first report according to the first report configuration; in response to the first report being triggered, the first measurement configuration is performed. According to the invention, better measurement can be realized.

Description

Technical Field

[0001] The present application relates to a method for measurement in a cellular wireless communication system. Background Art

[0002] In the future, the application scenarios of wireless communication systems will become more and more diversified, and different application scenarios will put forward different performance requirements for the system. In order to meet the different performance requirements of various application scenarios, the 3GPP (3rd Generation Partner Project) RAN (Radio Access Network) #72 plenary meeting decided to study the new radio (NR) (or Fifth Generation, 5G) technology, and the NR WI (Work Item) was passed at the 3GPP RAN #75 plenary meeting, starting the standardization work on NR.

[0003] In communication, both LTE (Long Term Evolution) and 5G NR involve accurate reception of reliable information, optimized energy efficiency, determination of information validity, flexible resource allocation, scalable system structure, efficient non-access layer information processing, low service interruption and drop rate, and support for low power consumption. This is of great significance to the normal communication between base stations and user equipment, the reasonable scheduling of resources, and the balancing of system loads. It can be said to be the cornerstone of high throughput, meeting the communication needs of various services, improving spectrum utilization, and improving service quality. It is indispensable for eMBB (ehanced Mobile BroadBand), URLLC (Ultra Reliable Low Latency Communication) and eMTC (enhanced Machine Type Communication). At the same time, in IIoT (Industrial Internet of Things), V2X (Vehicular to X), device to device, unlicensed spectrum communication, user communication quality monitoring, network planning and optimization, TN (Territial Network), dual connectivity system, wireless resource management and multi-antenna codebook selection, signaling design, neighbor management, business management, and beamforming, there are extensive demands. Information transmission methods are divided into broadcast and unicast. Both transmission methods are essential for 5G systems because they are very helpful in meeting the above requirements.

[0004] As the scenarios and complexity of the system continue to increase, higher requirements are placed on reducing interruption rates, reducing latency, enhancing reliability, enhancing system stability, business flexibility, and power saving. At the same time, compatibility between different versions of different systems also needs to be considered during system design. Summary of the invention

[0005] The researchers found that the terminal needs to perform corresponding measurements according to the measurement configuration. In the prior art, when the terminal receives the measurement configuration, it will execute such configuration, which has disadvantages. Therefore, how to better control the execution of the first measurement configuration is a problem that needs to be solved.

[0006] In view of the above-mentioned problems, this application provides a solution.

[0007] It should be noted that, in the absence of conflict, the embodiments and features in any node of the present application can be applied to any other node. In the absence of conflict, the embodiments and features in the embodiments of the present application can be arbitrarily combined with each other. At the same time, the method proposed in the present application can also be used to solve other problems in communication, such as NR evolution and problems in 6G systems.

[0008] As an embodiment, the interpretation of the terminology in the present application refers to the definition of the 3GPP specification protocol TS38 series.

[0009] As an example, the interpretation of the terms in the present application refers to the definitions of the TS37 series of specification protocols of 3GPP.

[0010] The present application discloses a method in a first node used for wireless communication, comprising:

[0011] receiving first signaling, where the first signaling indicates a first measurement configuration and a first reporting configuration;

[0012] A first report is triggered according to the first reporting configuration; and in response to the first report being triggered, the first measurement configuration is performed.

[0013] As an embodiment, the problems to be solved by the present application include: how to control the execution of the first measurement configuration, and how to control the execution of the first measurement configuration through a first report.

[0014] As an embodiment, the benefits of the above method include: greater flexibility, power saving, reduced impact of measurements on communications, and better support for specific measurement configurations, such as measurements based on AI (artificial intelligence) or required by AI.

[0015] Specifically, according to one aspect of the present application, triggering the first report includes sending the first report.

[0016] As an embodiment, the first report is a measurement report.

[0017] As an embodiment, the first reporting configuration is a measurement reporting configuration.

[0018] Specifically, according to one aspect of the present application, a second report is triggered according to a second report configuration; and in response to triggering the second report, execution of the first measurement configuration is stopped.

[0019] Specifically, according to one aspect of the present application, when the first report is triggered, any measurement configuration in the first measurement configuration set is executed; when the first report is not triggered, any measurement configuration in the second measurement configuration set is executed.

[0020] Specifically, according to one aspect of the present application, the first measurement configuration is associated with an identifier of an AI model.

[0021] Specifically, according to one aspect of the present application, only the latter of the first measurement configuration and the second measurement configuration is associated with an identifier of an AI model.

[0022] Specifically, according to one aspect of the present application, the executing the first measurement configuration depends on the difference between the measurement result in the first report and the measurement result of the third measurement configuration, wherein the executing the first measurement configuration is triggered only when the difference between the measurement result in the first report and the measurement result of the third measurement configuration is greater than a first threshold;

[0023] Among them, only one of the first reporting configuration and the second measurement configuration is associated with the AI ​​model identifier.

[0024] Specifically, according to one aspect of the present application, the executing the first measurement configuration depends on the difference between the measurement result in the first report and the measurement result of the third measurement configuration, wherein the executing the first measurement configuration is triggered only when the difference between the measurement result in the first report and the measurement result of the third measurement configuration is greater than a first threshold;

[0025] The first reporting configuration and the second measurement configuration are respectively associated with different AI model identifiers.

[0026] Specifically, according to one aspect of the present application, the first node is an Internet of Things terminal.

[0027] Specifically, according to one aspect of the present application, the first node is a user equipment.

[0028] Specifically, according to one aspect of the present application, the first node is a vehicle-mounted terminal.

[0029] Specifically, according to one aspect of the present application, the first node is a mobile phone.

[0030] The present application discloses a first node used in wireless communication, comprising:

[0031] A first receiver receives a first signaling, where the first signaling indicates a first measurement configuration and a first reporting configuration;

[0032] The first processor triggers a first report according to the first report configuration; and executes the first measurement configuration in response to the triggering of the first report.

[0033] As an embodiment, compared with the traditional solution, this application has the following advantages:

[0034] In wireless communications, channels change rapidly, and measurement plays an extremely important role in mobility management, configuration of communication parameters, and reasonable allocation of resources. In order to achieve ideal performance, the network will indicate multiple measurement configurations to the terminal. Under normal circumstances, the richer the measurement results, such as the more measurement configurations, the more conducive it is to grasp more accurate channel information. However, the more measurement configurations, the more likely it is that the communication itself will be affected, such as wasting resources, causing communication interruptions, and wasting electricity. In certain circumstances, indicating further measurement configurations is not flexible enough and not timely enough. The method proposed in this application is conducive to increasing flexibility, reducing signaling overhead, avoiding delays during reconfiguration, and supporting continuous measurement.

[0035] In a network that supports artificial intelligence, artificial intelligence needs to rely on measurement results for input, or certain functions of artificial intelligence, such as the prediction of wireless parameters such as channels, are configured in the form of measurement configuration. The method proposed in this application is conducive to enabling artificial intelligence at the appropriate time. On the other hand, the use of artificial intelligence for channel prediction may, in some cases, result in errors or even large errors relative to traditional methods. Therefore, it is necessary to re-enable traditional measurements. The method proposed in this application is conducive to timely activation of traditional measurements when artificial intelligence predictions are inaccurate.

[0036] Report-driven measurement helps improve efficiency and reduce complexity.

[0037] It is helpful to adapt to different AI models. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0039] Figure 1 A schematic diagram showing receiving a first signaling, triggering a first report according to a first report configuration, and executing a first measurement configuration according to an embodiment of the present application is shown;

[0040] Figure 2 A schematic diagram of a network architecture according to an embodiment of the present application is shown;

[0041] Figure 3 A schematic diagram showing an embodiment of a wireless protocol architecture of a user plane and a control plane according to an embodiment of the present application;

[0042] Figure 4 A schematic diagram showing a first communication device and a second communication device according to an embodiment of the present application is shown;

[0043] Figure 5A flow chart showing wireless signal transmission according to an embodiment of the present application is shown;

[0044] Figure 6 A schematic diagram of RAN (Radio Access Network) domain (Domain) AI / ML function deployment according to an embodiment of the present application is shown;

[0045] Figure 7 A schematic diagram showing the deployment of AI / ML functions of a UE according to an embodiment of the present application is shown;

[0046] Figure 8 A flowchart based on artificial intelligence or machine learning according to an embodiment of the present application is shown;

[0047] Fig. 9 A flowchart of performing a first measurement configuration according to an embodiment of the present application is shown;

[0048] Fig.10 A schematic diagram showing a difference between a measurement result in a first report and a measurement result in a third measurement configuration according to an embodiment of the present application is shown;

[0049] Fig.11 The following is a schematic diagram illustrating a processing device used in a first node according to an embodiment of the present application. Implementation

[0051] The technical solution of the present application will be further described in detail below in conjunction with the accompanying drawings. It should be noted that, unless there is a conflict, the embodiments in the present application and the features in the embodiments can be combined with each other arbitrarily.

[0052] Example 1

[0053] Embodiment 1 illustrates a flowchart of receiving first information according to an embodiment of the present application, as shown in the attached Figure 1 Attached Figure 1 In the figure, each box represents a step. It should be emphasized that the order of the boxes in the figure does not represent the temporal relationship between the steps represented.

[0054] In Embodiment 1, the first node in the present application receives a first signaling in step 101; triggers a first report according to a first report configuration in step 102; and executes a first measurement configuration in step 103.

[0055] The first signaling indicates a first measurement configuration and a first reporting configuration.

[0056] As an embodiment, the first node is a UE (User Equipment).

[0057] As an embodiment, the first node is a terminal.

[0058] As an embodiment, any operation performed in the MAC sublayer may also be understood or referred to as any operation performed by the MAC entity.

[0059] As an embodiment, the lower layer at which the MAC sublayer performs operations is the physical layer.

[0060] As an embodiment, the higher layers when the MAC sublayer performs operations include the RLC sublayer, the RRC sublayer, and the PDCP sublayer.

[0061] Typically, the higher layer at which the MAC sublayer performs operations is the RRC sublayer.

[0062] As an embodiment, MAC CE is the control signaling of the MAC layer, which has the characteristics of fast speed but lower reliability than RRC signaling. RRC signaling is more reliable but slower than MAC CE. RRC signaling cannot replace MAC CE, and MACCE cannot replace RRC signaling.

[0063] As an embodiment, the lower layers when the RRC sublayer performs operations include the physical layer, the MAC layer, the RLC sublayer, and the PDCP sublayer.

[0064] As an embodiment, the higher layer when the RRC sublayer performs an operation includes a non-access stratum.

[0065] As an embodiment, the higher layer signaling refers to RRC signaling or non-access stratum.

[0066] As an embodiment, in the present application, if it is not specifically stated that it is executed in the MAC sublayer, it is executed in the RRC sublayer.

[0067] As an embodiment, access layer security of the first node is activated.

[0068] As an embodiment, the access stratum (AS) includes multiple protocol layers, and the details can be referred to Embodiment 3.

[0069] As an embodiment, the first node is in an RRC connected state.

[0070] As an embodiment, any parameter in the present application may be either configured by the network or generated by the first node according to an internal algorithm, such as randomly.

[0071] As an embodiment, the values ​​of the timers in the present application are all limited and do not exceed 2560 milliseconds.

[0072] As an embodiment, the value of the timer is the running time when the timer is not interfered with.

[0073] As an example, the value of any parameter in the present application, including but not limited to the value of a timer and the value of a counter, is limited unless otherwise stated.

[0074] As a sub-embodiment of this embodiment, the upper limit of the value of any parameter in this application is 1024 times of 65536.

[0075] As a sub-embodiment of this embodiment, the upper limit of the value of any parameter in this application is 65536 or 65535.

[0076] As a sub-embodiment of this embodiment, the upper limit of the value of any parameter in this application is 1024.

[0077] As a sub-embodiment of this embodiment, the upper limit of the value of any parameter in this application is 640 or 320.

[0078] As an embodiment, the present application is directed to NR.

[0079] As an embodiment, the present application is directed to a NR evolved wireless communication network.

[0080] As an embodiment, the serving cell refers to the cell where the UE resides. Performing a cell search includes the UE searching for a suitable cell of the selected PLMN (Public Land Mobile Network) or SNPN (Stand-alone Non-Public Network), selecting the suitable cell to provide available services, and monitoring the control channel of the suitable cell. This process is defined as residing on the cell; that is, a residing cell is the serving cell of the UE relative to the UE. Residing on a cell in the RRC idle state or the RRC inactive state has the following benefits: it enables the UE to receive system messages from the PLMN or SNPN; after registration, if the UE wishes to establish an RRC connection or continue a suspended RRC connection, the UE can do so by performing initial access on the control channel of the residing cell; the network can page the UE; and the UE can receive ETWS (Earthquake and Tsunami Warning System) and CMAS (Commercial Mobile Alert System) notifications.

[0081] As an embodiment, for a UE in an RRC connected state that is not configured with CA / DC (carrier aggregation / dual connectivity), there is only one service cell including a primary cell. For a UE in an RRC connected state that is configured with CA / DC (carrier aggregation / dual connectivity), the service cell is used to indicate a set of cells including a special cell (SpCell) and all cells from the small cell. The primary cell (Primary Cell) is an MCG (Master Cell Group) cell that operates on the main frequency. The UE performs an initial connection establishment process or initiates connection reconstruction on the primary cell. For dual connection operations, the special cell refers to the PCell (Primary Cell) of the MCG or the PSCell (Primary SCGCell) of the SCG (Secondary Cell Group); if it is not a dual connection operation, the special cell refers to the PCell.

[0082] As an embodiment, the operating frequency of the SCell (Secondary Cell) is a secondary frequency.

[0083] As an embodiment, the first node is only configured with MCG.

[0084] As an example, individual contents of an information element are referred to as fields.

[0085] As an embodiment, MR-DC (Multi-Radio Dual Connectivity) refers to dual connectivity between E-UTRA and NR nodes, or dual connectivity between two NR nodes.

[0086] As an embodiment, in MR-DC, the wireless access node that provides the control plane connection to the core network is the master node, which may be a master eNB, a master ng-eNB, or a master gNB.

[0087] As an embodiment, MCG refers to a group of service cells associated with a master node in MR-DC, including SpCells, and may also, optionally, include one or more SCells.

[0088] As an embodiment, PCell is the SpCell of MCG.

[0089] As an embodiment, the PSCell is the SpCell of the SCG.

[0090] As an embodiment, in MR-DC, the control plane connection to the core network is not provided, and the radio access node that provides additional resources to the UE is a slave node. The slave node can be an en-gNB, a slave ng-eNB or a slave gNB.

[0091] As an embodiment, in MR-DC, a group of service cells associated with a slave node is a SCG (secondary cell group), including a SpCell and, optionally, one or more SCells.

[0092] As an embodiment, the SpCell is a PCell or the SpCell is a PSCell.

[0093] As an embodiment, only the former of the first cell and the second cell belongs to a first cell group, and the first cell group is one of the MCG or the SCG of the first node.

[0094] As an embodiment, in the RRC inactive state, DC is not used.

[0095] As an example, in the RRC inactive state, CA is typically not used.

[0096] As an embodiment, the RRC information block refers to an information block (information element) in an RRC message.

[0097] As an embodiment, SSB may be referred to as SS\PBCH, or SS block.

[0098] As an embodiment, L1 is layer 1 (Layer-1) or physical layer.

[0099] As an example, L2 is Layer-2

[0100] As an embodiment, the present application is directed to NR and NR evolved networks, such as 6G networks.

[0101] As an embodiment, one RRC information block may include one or more RRC information blocks.

[0102] As an embodiment, an RRC information block may not include any RRC information block, but only include at least one parameter.

[0103] As an embodiment, the radio bearer includes at least a signaling radio bearer and a data radio bearer.

[0104] As an embodiment, the radio bearer is a service or an interface of a service provided by the PDCP layer to a higher layer.

[0105] As a sub-embodiment of this embodiment, the higher layer includes one of the RRC sublayer, NAS, and SDAP layer.

[0106] As an embodiment, the signaling radio bearer is a service or an interface of services provided by PDCP to a higher layer.

[0107] As a sub-embodiment of this embodiment, the higher layer includes an RRC sublayer, at least the former in NAS.

[0108] As an embodiment, the data radio bearer is a service or an interface of services provided by PDCP to a higher layer.

[0109] As a sub-embodiment of this embodiment, the higher layer includes the SDAP layer, at least the former in NAS.

[0110] As an embodiment, after the first node establishes an RRC connection with the network, the first node enters an RRC connection state.

[0111] As a sub-embodiment of this embodiment, the network is a Radio Access Network (RAN).

[0112] As an embodiment, when the first node does not establish an RRC connection with the network, the first node is in an RRC idle state.

[0113] As a sub-embodiment of this embodiment, the network is a Radio Access Network (RAN).

[0114] As an embodiment, when the RRC connection established between the first node and the network is suspended, the first node enters an RRC inactive state.

[0115] As a sub-embodiment of this embodiment, the network is a Radio Access Network (RAN).

[0116] As an embodiment, different functions are supported in different RRC states.

[0117] As an embodiment, only very limited functions are supported in the non-RRC connected state.

[0118] As an embodiment, the non-RRC connected state is or includes an RRC idle state.

[0119] As an embodiment, the non-RRC connected state is or includes an RRC inactive state.

[0120] As an embodiment, the first node is not in limited service mode.

[0121] As an embodiment, the first signaling is RRC signaling.

[0122] As an embodiment, the first signaling includes RRC reconfiguration signaling.

[0123] As an embodiment, the first signaling includes at least part of the domain in the RRC reconfiguration signaling.

[0124] As an embodiment, the first signaling includes RRC recovery signaling.

[0125] As an embodiment, the first signaling includes MeasConfig.

[0126] As a sub-embodiment of this embodiment, the MeasConfig included in the first signaling indicates the first measurement configuration and the first reporting configuration.

[0127] As an embodiment, the first signaling indicates adding at least one measurement object.

[0128] As an embodiment, the measurement object is MeasObject.

[0129] As an embodiment, the first signaling indicates adding at least one reporting configuration.

[0130] As an embodiment, the first reporting configuration belongs to the at least one reporting configuration.

[0131] As an embodiment, the first signaling indicates adding at least one measurement identifier.

[0132] As an embodiment, the first signaling indicates a first measurement threshold.

[0133] As an embodiment, the first measurement threshold controls the measurement of non-serving cells by the first node.

[0134] As an embodiment, the method proposed in the present application is not limited to a serving cell or a non-serving cell.

[0135] As an embodiment, the method proposed in the present application is applicable to the measurement of a serving cell.

[0136] As an embodiment, the method proposed in the present application is applicable to measurements using AI.

[0137] As an embodiment, the method proposed in the present application is applicable to measurement assisted by AI.

[0138] As an embodiment, the method proposed in the present application is applicable to the control of measurement reports when AI is used.

[0139] As an embodiment, the first signaling configures a measurement gap.

[0140] As an embodiment, any measurement identifier of the at least one measurement identifier is associated with a measurement object and at least one reporting configuration.

[0141] As an embodiment, any measurement identifier of the at least one measurement identifier is associated with at least one measurement object and a reporting configuration.

[0142] As an embodiment, any measurement identifier of the at least one measurement identifier is associated with at least one measurement object and at least one reporting configuration.

[0143] As an embodiment, the first measurement configuration is or includes at least one measurement object.

[0144] As an embodiment, the first measurement configuration is or includes at least one reporting configuration.

[0145] As an embodiment, the report configuration is a configuration of a measurement report.

[0146] As an embodiment, the at least one reporting configuration included in the first measurement configuration does not include the first reporting configuration.

[0147] As an embodiment, the first measurement configuration only includes reporting configurations other than the first reporting configuration.

[0148] As an embodiment, the first measurement configuration includes a configuration of a measurement gap.

[0149] As an embodiment, the first measurement configuration includes a second measurement threshold, and the second measurement threshold controls the measurement of non-serving cells.

[0150] As an embodiment, the controlling the measurement of the non-serving cell includes: when the quality of the serving cell is greater than a threshold, not measuring the non-serving cell.

[0151] As a sub-embodiment of this embodiment, the measurement of the non-serving cell refers to layer 3 measurement.

[0152] As an embodiment, the controlling the measurement of the non-serving cell includes: when the quality of the serving cell is not greater than a threshold, measuring the non-serving cell.

[0153] As a sub-embodiment of this embodiment, the measurement of the non-serving cell refers to layer 3 measurement.

[0154] As an embodiment, the first node performs measurement according to the first measurement configuration.

[0155] As an embodiment, the first measurement configuration is identified by a first measurement identifier.

[0156] As an embodiment, the measurement identifier is MeasId.

[0157] As an embodiment, the measurement identifier associated with the first reporting configuration is different from the measurement identifier associated with the first measurement configuration.

[0158] As an embodiment, the first measurement configuration is not for NR.

[0159] As an embodiment, the first measurement configuration includes a 5G evolution or 6G measurement object.

[0160] As an embodiment, the first measurement configuration includes at least: an identifier of a cell to be measured.

[0161] As an embodiment, the first measurement configuration includes at least: a frequency to be measured.

[0162] As an embodiment, the first measurement configuration includes at least: a time to be measured.

[0163] As an embodiment, the first measurement configuration includes at least: a reference signal configuration to be measured.

[0164] As an embodiment, the first measurement configuration includes at least: a method for processing the measurement result.

[0165] As an embodiment, the first measurement configuration includes at least: a method for generating a measurement result.

[0166] As an embodiment, the first measurement configuration includes at least: the quantity of the measurement result.

[0167] As an embodiment, the first measurement configuration includes at least: configuration of a first timer.

[0168] As an embodiment, the first measurement configuration includes at least: a method for generating a measurement report.

[0169] As an embodiment, the first measurement configuration includes at least: a triggering event for a measurement report.

[0170] As an embodiment, the first measurement configuration includes at least: the content of the corresponding measurement report.

[0171] As an embodiment, the first measurement configuration includes at least: the number of measurement reports.

[0172] As an embodiment, the first measurement configuration includes at least: accuracy of measurement report.

[0173] As an embodiment, the first measurement configuration includes at least: the time of the measurement report.

[0174] As an embodiment, the first measurement configuration includes at least: whether the measurement report includes an identifier of the AI ​​model.

[0175] As an embodiment, the first measurement configuration is not executed immediately after being received.

[0176] As an embodiment, the first measurement configuration is not automatically executed after being received.

[0177] As an embodiment, the first reporting configuration is for a sensor.

[0178] As an embodiment, the first report configuration is for Wifi.

[0179] As an embodiment, the first reporting configuration is for a non-3GPP network.

[0180] As an embodiment, the first report configuration is for a 3GPP network.

[0181] As an embodiment, one of the first measurement configuration and the first reporting configuration is for a non-3GPP network, and the other is for a 3GPP network.

[0182] As an embodiment, the advantage of the above method is that it can effectively coordinate and balance between non-3GPP and 3GPP networks, which is conducive to ensuring the continuity of communication and avoiding mutual interference when communicating with different networks.

[0183] As an embodiment, the first reporting configuration includes a configuration for beam measurement.

[0184] As an embodiment, triggering the first report according to the first report configuration means: generating the first report according to the first report configuration.

[0185] As an embodiment, triggering the first report according to the first report configuration means: sending the first report according to the first report configuration.

[0186] As an embodiment, triggering the first report according to the first report configuration means: generating and sending the first report according to the first report configuration.

[0187] As an embodiment, triggering the first report according to the first report configuration means: triggering the first report according to the satisfaction of an event included in the first report configuration.

[0188] As an embodiment, triggering the first report according to the first report configuration means: generating the first report according to the measurement quantity configured by the first report configuration.

[0189] As an embodiment, triggering the first report according to the first report configuration means that the first report includes measurement results for a cell identified by a cell identifier configured according to the first report configuration.

[0190] As an embodiment, triggering the first report according to the first report configuration means: generating the first report according to the report time configured by the first report configuration.

[0191] As an embodiment, triggering the first report according to the first report configuration means: generating the first report according to the report accuracy configured by the first report configuration.

[0192] As an embodiment, the first report is a measurement report.

[0193] As an embodiment, the first report is an RRC message.

[0194] As an embodiment, the RRC message is a measurementreport.

[0195] As an embodiment, the first report includes at least one measurement result.

[0196] As an embodiment, the at least one measurement result includes a measurement result of a serving cell.

[0197] As an embodiment, the measurement result of the serving cell includes RSRP (Reference Signal Received Power) of the serving cell.

[0198] As an embodiment, the measurement result of the serving cell includes the measurement result of the beam of the serving cell.

[0199] As an embodiment, the at least one measurement result includes a measurement result of a neighboring cell.

[0200] As an embodiment, the measurement result of the neighboring cell includes RSRP (Reference Signal Received Power) of the neighboring cell.

[0201] As an embodiment, the measurement result of the neighboring cell includes the measurement result of the beam of the neighboring cell.

[0202] As an embodiment, the at least one measurement result includes a predicted measurement result.

[0203] As an embodiment, the at least one measurement result includes an AI-based measurement result.

[0204] As an embodiment, the first report indicates the reason why the first report is triggered.

[0205] As an embodiment, the meaning of executing the first measurement configuration as a response to the first report being triggered includes: when the first report is not triggered, the first measurement configuration is not executed.

[0206] As an embodiment, the meaning of executing the first measurement configuration as a response to the first report being triggered includes: when the first report is not triggered, the first node does not perform measurements according to the first measurement configuration.

[0207] As an embodiment, the meaning of executing the first measurement configuration as a response to the first report being triggered includes: when the first report is not triggered, the first measurement configuration is deactivated.

[0208] As an embodiment, the meaning of executing the first measurement configuration in response to the first report being triggered includes: when the first report is triggered, the first measurement configuration is activated.

[0209] As an embodiment, the meaning of executing the first measurement configuration as a response to the first report being triggered includes: the first report is triggered, and measurement is performed according to the first measurement configuration.

[0210] As an embodiment, the meaning of executing the first measurement configuration as a response to the first report being triggered includes: the first report is triggered, and a measurement report is generated according to the first measurement configuration.

[0211] As an embodiment, the meaning of executing the first measurement configuration in response to the first report being triggered includes: the first report is triggered, and the first measurement configuration is applied.

[0212] As an embodiment, executing the first measurement configuration includes: performing measurement on a reference signal resource indicated by the first measurement configuration.

[0213] As an embodiment, executing the first measurement configuration includes: performing measurement at a time indicated by the first measurement configuration.

[0214] As an embodiment, executing the first measurement configuration includes: performing measurement on a cell indicated in the first measurement configuration.

[0215] As an embodiment, executing the first measurement configuration includes: using a measurement gap indicated by the first measurement configuration.

[0216] As an embodiment, executing the first measurement configuration includes: performing measurement at a frequency indicated by the first measurement configuration.

[0217] As an embodiment, the executing the first measurement configuration includes: executing the reporting configuration included in the first measurement configuration.

[0218] As an embodiment, the execution of the first measurement configuration triggers stopping the execution of the first reporting configuration.

[0219] As an embodiment, stopping execution of the first measurement configuration triggers execution of the first reporting configuration.

[0220] As an embodiment, the advantage of the above method is that it can flexibly switch between the first measurement configuration and the first reporting configuration, or flexibly switch between the first measurement configuration and the measurement configuration associated with the first reporting configuration, and always ensure that effective and appropriate measurements are performed.

[0221] As an embodiment, the measurement on which the first report depends is configured by a measurement configuration other than the first measurement configuration.

[0222] As an embodiment, the measurement configuration other than the first measurement configuration is a second measurement configuration.

[0223] As an embodiment, the measurement identifier associated with the second measurement configuration is different from the measurement identifier associated with the first measurement configuration.

[0224] As an embodiment, the second measurement configuration includes the first reporting configuration.

[0225] As an embodiment, the second measurement configuration is associated with the first reporting configuration.

[0226] As an embodiment, the measurement configuration corresponding to the first reporting configuration is the second measurement configuration.

[0227] As an embodiment, the measurement configuration corresponding to the first reporting configuration is not the first measurement configuration.

[0228] As an embodiment, the first measurement configuration includes a third reporting configuration.

[0229] As an embodiment, the first signaling is NAS signaling.

[0230] As an embodiment, the first report configuration and the second report configuration are configured by the core network.

[0231] As an embodiment, one of the first reporting configuration and the second reporting configuration is configured by the RAN, and the other is configured by the core network.

[0232] As an embodiment, the first report configuration is RAN configuration, and the second report configuration is core network configuration.

[0233] As an embodiment, the effectiveness of the first measurement configuration depends on the report configured by the first reporting configuration being triggered.

[0234] As an embodiment, the meaning of the effectiveness of the first measurement configuration includes: the first measurement configuration is activated.

[0235] As an embodiment, the effectiveness of the third report configuration depends on the report configured by the first report configuration being triggered.

[0236] As an embodiment, the effectiveness of the third reporting configuration means that the second reporting configuration is activated.

[0237] As an embodiment, the effectiveness of the third report configuration depends on the report configured by the first report configuration being triggered, which means that when the first report is triggered, the third report configuration takes effect; when the first report is not triggered, the third report configuration does not take effect.

[0238] As an embodiment, the effectiveness of the third report configuration depends on the report configured by the first report configuration being triggered, which means that when X measurement reports are triggered according to the first report configuration, the third report configuration takes effect, where X is a positive integer.

[0239] As an embodiment, the effectiveness of the first measurement configuration depends on the report configured by the first report configuration being triggered, which means that when the first report is triggered, the first measurement configuration takes effect; when the first report is not triggered, the first measurement configuration is not effective.

[0240] As an embodiment, the effectiveness of the first measurement configuration depends on the report configured by the first reporting configuration being triggered, which means that when X measurement reports are triggered according to the first reporting configuration, the first measurement configuration takes effect, where X is a positive integer.

[0241] As an embodiment, the failure of the first measurement configuration depends on that a triggering condition of the report configured by the first reporting configuration is no longer satisfied.

[0242] As an embodiment, the first signaling includes multiple sub-signals, and the multiple sub-signals respectively indicate the first reporting configuration and the first measurement configuration.

[0243] As an embodiment, the measurement configuration field included in the first signaling indicates the first reporting configuration and the first measurement configuration.

[0244] As an embodiment, the first report configuration is associated with a first AI identifier.

[0245] As an embodiment, the first measurement configuration is associated with a second AI identifier.

[0246] As an embodiment, only one of the first reporting configuration and the third reporting configuration is associated with the first AI identifier.

[0247] As an embodiment, the AI ​​includes AI and ML (machine learning).

[0248] As an embodiment, the AI ​​identifier includes an AI model identifier.

[0249] As an embodiment, the AI ​​identifier includes an AI configuration identifier.

[0250] As an embodiment, the first signaling indicates a first time window, and the effectiveness of the first measurement configuration depends on the triggering of the first report, which means that when the report configured by the first measurement configuration is triggered, the first measurement configuration takes effect within the first time window.

[0251] As an embodiment, outside the first time window, the first measurement configuration is invalid.

[0252] As an embodiment, outside the first time window, the first node does not execute the first measurement configuration.

[0253] As an embodiment, when the first report is triggered, any measurement configuration in the first measurement configuration set is executed; when the first report is not triggered, any measurement configuration in the second measurement configuration set is executed.

[0254] As an embodiment, the first measurement configuration set includes the first measurement configuration.

[0255] As an embodiment, the measurement object of any measurement configuration in the first measurement configuration set is associated with the third reporting configuration.

[0256] As an embodiment, the second measurement configuration set includes the second measurement configuration.

[0257] As an embodiment, the second measurement configuration indicates at least one measurement object.

[0258] As an embodiment, the measurement object indicated by any measurement configuration in the second measurement configuration set is associated with the first reporting configuration.

[0259] As an embodiment, the advantage of the above method is that the two measurement configuration sets can be flexibly controlled according to different situations.

[0260] As an embodiment, the first measurement configuration is associated with an identifier of an AI model.

[0261] As an embodiment, the meaning of the first measurement configuration being associated with an identifier of an AI model includes: the first measurement configuration is for configuring the AI.

[0262] As an embodiment, the meaning of the first measurement configuration being associated with an identifier of an AI model includes: the first measurement configuration is for configuring AI measurement.

[0263] As an embodiment, the meaning of the first measurement configuration being associated with an identifier of an AI model includes: the first measurement configuration is a measurement configured for use by the AI.

[0264] As an embodiment, the meaning of the identifier of an AI model being associated with the first measurement configuration includes: a measurement result obtained according to the first measurement configuration is an input of the AI.

[0265] As an embodiment, the meaning of the identifier of an AI model associated with the first measurement configuration includes: the measurement report generated according to the first measurement configuration is the input of the AI.

[0266] As an embodiment, the meaning of the identifier of an AI model associated with the first measurement configuration includes: the measurement report generated according to the first measurement configuration is predictive.

[0267] As an embodiment, the measurement report generated according to the first measurement configuration includes: a measurement report generated according to a report configuration included in the first measurement configuration.

[0268] As an embodiment, the measurement report generated according to the first measurement configuration includes: a measurement report triggered according to a report configuration included in the first measurement configuration.

[0269] As an embodiment, the benefits of the above method include: better support for AI.

[0270] As an embodiment, only the latter of the first measurement configuration and the second measurement configuration is associated with an identifier of an AI model.

[0271] As an embodiment, the meaning that only the latter of the first measurement configuration and the second measurement configuration is associated with an AI model includes: the first measurement configuration is not associated with an AI model.

[0272] As an embodiment, the meaning that only the latter of the first measurement configuration and the second measurement configuration is associated with an identifier of an AI model includes: the first measurement configuration is based on traditional measurement.

[0273] As an embodiment, the meaning that only the latter of the first measurement configuration and the second measurement configuration is associated with an identifier of an AI model includes: the measurement result generated by the first measurement configuration is not predictive.

[0274] As an embodiment, the meaning that only the latter of the first measurement configuration and the second measurement configuration is associated with an identifier of an AI model includes: the measurement result generated by the first measurement configuration is not an input of the AI.

[0275] As an embodiment, the meaning that only the latter of the first measurement configuration and the second measurement configuration is associated with an identifier of an AI model includes: the second measurement configuration is configured for AI.

[0276] As an embodiment, the meaning that only the latter of the first measurement configuration and the second measurement configuration is associated with an identifier of an AI model includes: the second measurement configuration is for configuring AI measurement.

[0277] As an embodiment, the meaning that only the latter of the first measurement configuration and the second measurement configuration is associated with an identifier of an AI model includes: the second measurement configuration is a measurement configured for use by the AI.

[0278] As an embodiment, the meaning of associating only the latter of the first measurement configuration and the second measurement configuration with an identifier of an AI model includes: the measurement result obtained according to the second measurement configuration is the input of the AI.

[0279] As an embodiment, the meaning of associating only the latter of the first measurement configuration and the second measurement configuration with an identifier of an AI model includes: the measurement report generated according to the second measurement configuration is an input of the AI.

[0280] As an embodiment, the meaning of associating only the latter of the first measurement configuration and the second measurement configuration with an identifier of an AI model includes: a measurement report generated according to the second measurement configuration is predictive.

[0281] As an embodiment, the measurement report generated according to the second measurement configuration includes: a measurement report generated according to a report configuration included in the second measurement configuration.

[0282] As an embodiment, the measurement report generated according to the first measurement configuration includes: a measurement report triggered according to a report configuration included in the second measurement configuration.

[0283] As an embodiment, the benefits of the above method include: when problems occur in AI-based measurements, it is possible to promptly fall back to traditional measurements, while avoiding problems such as power consumption and resource occupation when using traditional measurements at the same time.

[0284] Example 2

[0285] Embodiment 2 illustrates a schematic diagram of a network architecture according to the present application, as shown in the attached Figure 2 shown.

[0286] Attached Figure 2A diagram of a network architecture 200 for 5G NR, LTE (Long-Term Evolution) and LTE-A (Long-Term Evolution Advanced) systems is illustrated. The 5G NR or LTE network architecture 200 may be referred to as 5GS (5G System) / EPS (Evolved Packet System) 200 or some other suitable terminology. The 5GS / EPS 200 may include one or more UEs (User Equipment) 201, NG-RAN (Next Generation Radio Access Network) 202, 5GC (5G Core Network) / EPC (Evolved Packet Core) 210, HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and Internet services 230. The 5GS / EPS may be interconnected with other access networks, but these entities / interfaces are not shown for simplicity. As shown, 5GS / EPS provides packet-switched services, however, those skilled in the art will readily appreciate that the various concepts presented throughout this application can be extended to networks providing circuit-switched services or other cellular networks. NG-RAN includes NR Node B (gNB) 203 and other gNBs 204. gNB 203 provides user and control plane protocol terminations toward UE 201. gNB 203 can be connected to other gNBs 204 via an Xn interface (e.g., backhaul). gNB 203 may also be referred to as a base station, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS), an extended service set (ESS), a TRP (transmitting receiving node), or some other suitable term. gNB 203 provides an access point to 5GC / EPC 210 for UE 201. Examples of UE 201 include cellular phones, smart phones, session initiation protocol (SIP) phones, laptop computers, personal digital assistants (PDAs), satellite radios, non-terrestrial base station communications, satellite mobile communications, global positioning systems, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, drones, aircraft, narrowband Internet of Things devices, machine type communication devices, land vehicles, cars, wearable devices, or any other similar functional devices. Those skilled in the art may also refer to UE 201 as a mobile station, a subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a mobile device, a wireless device, a wireless communication device, a remote device, a mobile subscriber station, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, a user agent, a mobile client, a client, or some other suitable term.gNB203 is connected to 5GC / EPC210 via the S1 / NG interface. 5GC / EPC210 includes MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MME / AMF / SMF214, S-GW (Service Gateway) / UPF (User Plane Function) 212, and P-GW (Packet Data Network Gateway) / UPF213. MME / AMF / SMF211 is the control node that handles signaling between UE201 and 5GC / EPC210. ​​In general, MME / AMF / SMF211 provides bearer and connection management. All user IP (Internet Protocol) packets are transmitted through S-GW / UPF212, which itself is connected to P-GW / UPF213. P-GW provides UE IP address allocation and other functions. P-GW / UPF213 is connected to Internet service 230. Internet service 230 includes operator-corresponding Internet protocol services, which may specifically include Internet, Intranet, IMS (IP Multimedia Subsystem) and packet-switched streaming services.

[0287] As an embodiment, the first node in the present application is UE201.

[0288] As an embodiment, the base station of the second node in the present application is gNB203.

[0289] As an embodiment, the wireless link from the UE201 to the NR Node B is an uplink.

[0290] As an embodiment, the wireless link from the NR Node B to UE201 is a downlink.

[0291] As an embodiment, the UE 201 includes a mobile phone.

[0292] As an embodiment, the UE201 is a dedicated device or special device with communication function.

[0293] As an embodiment, the gNB203 is a micro cell base station.

[0294] As an embodiment, the gNB203 is a pico cell base station.

[0295] As an embodiment, the gNB203 is a base station used in a home network.

[0296] As an embodiment, the gNB203 is a base station used in a private network.

[0297] Example 3

[0298] Embodiment 3 shows a schematic diagram of an embodiment of a wireless protocol architecture of a user plane and a control plane according to the present application, as shown in the attached Figure 3 shown. Figure 3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300, Figure 3The radio protocol architecture of the control plane 300 for a first node (UE, gNB) and a second node (gNB, UE), or between two UEs is shown in three layers: Layer 1, Layer 2, and Layer 3. Layer 1 (L1 layer) is the lowest layer and implements various PHY (physical layer) signal processing functions. The L1 layer will be referred to as PHY301 herein. Layer 2 (L2 layer) 305 is above PHY301 and is responsible for the link between the first node and the second node and the two UEs through PHY301. The L2 layer 305 includes a MAC (Medium Access Control) sublayer 302, an RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, which terminate at the second node. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security by encrypting data packets, and provides support for inter-zone mobility of the first node between the second node. The RLC sublayer 303 provides segmentation and reassembly of upper layer data packets, retransmission of lost data packets, and reordering of data packets to compensate for out-of-order reception due to HARQ. The MAC sublayer 302 provides multiplexing between logical and transport channels. The MAC sublayer 302 is also responsible for allocating various radio resources (e.g., resource blocks) in a cell between the first nodes. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in layer 3 (L3 layer) in the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and configuring the lower layer using RRC signaling between the second node and the first node. The PC5-S (PC5 Signaling Protocol) sublayer 307 is responsible for processing the signaling protocol of the PC5 interface. The radio protocol architecture of the user plane 350 includes Layer 1 (L1 layer) and Layer 2 (L2 layer). The radio protocol architecture for the first node and the second node in the user plane 350 is substantially the same as the corresponding layers and sublayers in the control plane 300 for the physical layer 351, the PDCP sublayer 354 in the L2 layer 355, the RLC sublayer 353 in the L2 layer 355, and the MAC sublayer 352 in the L2 layer 355, but the PDCP sublayer 354 also provides header compression for upper layer data packets to reduce radio transmission overhead.The L2 layer 355 in the user plane 350 also includes an SDAP (Service Data Adaptation Protocol) sublayer 356, which is responsible for mapping between QoS flows and data radio bearers (DRBs) to support the diversity of services. SRB can be regarded as a service or interface provided by the PDCP layer to a higher layer, such as the RRC sublayer. In the NR system, SRBs include SRB1, SRB2, and SRB3, which are used to transmit different types of control signaling. SRB is a bearer between the UE and the access network, and is used to transmit control signaling including RRC signaling between the UE and the access network. SRB1 has special significance for the UE. After each UE establishes an RRC connection, there will be SRB1 for transmitting RRC signaling. Most of the signaling is transmitted through SRB1. If SRB1 is interrupted or cannot be used, the UE must perform RRC reconstruction. SRB2 is generally only used to transmit NAS signaling or signaling related to security. The UE may not configure SRB3. Except for emergency services, the UE must establish an RRC connection with the network for subsequent communications. Although not shown, the first node may have several upper layers above the L2 layer 355. In addition, it also includes a network layer (e.g., IP layer) terminated at the P-GW on the network side and an application layer terminated at the other end of the connection (e.g., remote UE, server, etc.). The protocol layer may also be referred to as a protocol sublayer. Attached. Figure 3 What is shown is a general protocol layer structure, and the nodes used in this application may lack some protocol layers.

[0299] As an example, Figure 3 The wireless protocol architecture in is applicable to the first node in this application.

[0300] As an example, Figure 3 The wireless protocol architecture in is applicable to the second node in this application.

[0301] As an embodiment, the first signaling in the present application is generated in RRC306 or NAS.

[0302] As an embodiment, the first report in the present application is generated in RRC306 or MAC302 or PHY301.

[0303] As an embodiment, the second report in the present application is generated in RRC306 or MAC302 or PHY301.

[0304] Example 4

[0305] Embodiment 4 shows a schematic diagram of a first communication device and a second communication device according to an embodiment of the present application, as shown in the attached figure. Figure 4 shown. Figure 4 is a block diagram of a first communication device 450 and a second communication device 410 communicating with each other in an access network.

[0306] The first communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmit processor 468, a receive processor 456, and may optionally also include a multi-antenna transmit processor 457, a multi-antenna receive processor 458, a transmitter / receiver 454 and an antenna 452.

[0307] The second communication device 410 includes a controller / processor 475 , a memory 476 , a receiving processor 470 , a transmitting processor 416 , and may optionally also include a multi-antenna receiving processor 472 , a multi-antenna transmitting processor 471 , a transmitter / receiver 418 and an antenna 420 .

[0308] In the transmission from the second communication device 410 to the first communication device 450, at the second communication device 410, the upper layer data packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements the functionality of the L2 (Layer-2) layer. In the transmission from the second communication device 410 to the first communication device 450, the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocation to the first communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for the retransmission of lost packets and signaling to the first communication device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for the L1 layer (i.e., the physical layer). The transmit processor 416 implements coding and interleaving to facilitate forward error correction (FEC) at the second communication device 410, as well as mapping of signal constellations based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-phase shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The multi-antenna transmit processor 471 performs digital spatial precoding, including codebook-based precoding and non-codebook-based precoding, and beamforming processing on the coded and modulated symbols to generate one or more spatial streams. The transmit processor 416 then maps each spatial stream to a subcarrier, multiplexes it with a reference signal (e.g., a pilot) in the time domain and / or frequency domain, and then uses an inverse fast Fourier transform (IFFT) to generate a physical channel carrying a time-domain multi-carrier symbol stream. The multi-antenna transmit processor 471 then performs a transmit analog precoding / beamforming operation on the time-domain multi-carrier symbol stream. Each transmitter 418 converts the baseband multi-carrier symbol stream provided by the multi-antenna transmit processor 471 into a radio frequency stream, and then provides it to a different antenna 420.

[0309] In the transmission from the second communication device 410 to the first communication device 450, at the first communication device 450, each receiver 454 receives a signal through its corresponding antenna 452. Each receiver 454 recovers the information modulated onto the RF carrier and converts the RF stream into a baseband multi-carrier symbol stream and provides it to the receiving processor 456. The receiving processor 456 and the multi-antenna receiving processor 458 implement various signal processing functions of the L1 layer. The multi-antenna receiving processor 458 performs a receiving analog precoding / beamforming operation on the baseband multi-carrier symbol stream from the receiver 454. The receiving processor 456 uses a fast Fourier transform (FFT) to convert the baseband multi-carrier symbol stream after the receiving analog precoding / beamforming operation from the time domain to the frequency domain. In the frequency domain, the physical layer data signal and the reference signal are demultiplexed by the receiving processor 456, wherein the reference signal will be used for channel estimation, and the data signal is recovered after multi-antenna detection in the multi-antenna receiving processor 458 to any spatial stream destined for the first communication device 450. The symbols on each spatial stream are demodulated and recovered in the receiving processor 456, and soft decisions are generated. The receiving processor 456 then decodes and deinterleaves the soft decisions to recover the upper layer data and control signals transmitted by the second communication device 410 on the physical channel. The upper layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements the functions of the L2 layer. The controller / processor 459 may be associated with a memory 460 that stores program codes and data. The memory 460 may be referred to as a computer-readable medium. In the transmission from the second communication device 410 to the second communication device 450, the controller / processor 459 provides multiplexing between transport and logical channels, packet reassembly, decryption, header decompression, and control signal processing to recover the upper layer data packets from the core network. The upper layer data packets are then provided to all protocol layers above the L2 layer. Various control signals may also be provided to L3 for L3 processing.

[0310] In the transmission from the first communication device 450 to the second communication device 410, at the first communication device 450, a data source 467 is used to provide upper layer data packets to the controller / processor 459. The data source 467 represents all protocol layers above the L2 layer. Similar to the transmission function at the second communication device 410 described in the transmission from the second communication device 410 to the first communication device 450, the controller / processor 459 implements header compression, encryption, packet segmentation and reordering, and multiplexing between logical and transport channels based on radio resource allocation, and implements L2 layer functions for user plane and control plane. The controller / processor 459 is also responsible for the retransmission of lost packets and signaling to the second communication device 410. The transmit processor 468 performs modulation mapping and channel coding processing, and the multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based precoding and non-codebook-based precoding, and beamforming processing. Then, the transmit processor 468 modulates the generated spatial stream into a multi-carrier / single-carrier symbol stream, which is then provided to different antennas 452 via the transmitter 454 after analog precoding / beamforming operations in the multi-antenna transmit processor 457. Each transmitter 454 first converts the baseband symbol stream provided by the multi-antenna transmit processor 457 into a radio frequency symbol stream, and then provides it to the antenna 452.

[0311] In the transmission from the first communication device 450 to the second communication device 410, the function at the second communication device 410 is similar to the reception function at the first communication device 450 described in the transmission from the second communication device 410 to the first communication device 450. Each receiver 418 receives a radio frequency signal through its corresponding antenna 420, converts the received radio frequency signal into a baseband signal, and provides the baseband signal to the multi-antenna reception processor 472 and the reception processor 470. The reception processor 470 and the multi-antenna reception processor 472 jointly implement the functions of the L1 layer. The controller / processor 475 implements the L2 layer functions. The controller / processor 475 can be associated with a memory 476 storing program codes and data. The memory 476 can be referred to as a computer-readable medium. In the transmission from the first communication device 450 to the second communication device 410, the controller / processor 475 provides multiplexing between transport and logical channels, packet reassembly, decryption, header decompression, control signal processing to recover the upper layer data packets from the UE 450. Upper layer packets from controller / processor 475 may be provided to the core network.

[0312] As an embodiment, the first communication device 450 apparatus includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used together with the at least one processor, and the first communication device 450 apparatus at least: receives a first signaling, the first signaling indicating a first measurement configuration and a first report configuration; triggers a first report according to the first report configuration; and executes the first measurement configuration in response to the first report being triggered.

[0313] As an embodiment, the first communication device 450 includes: a memory storing a computer-readable instruction program, wherein the computer-readable instruction program generates actions when executed by at least one processor, and the actions include: receiving a first signaling, wherein the first signaling indicates a first measurement configuration and a first report configuration; triggering a first report according to the first report configuration; and executing the first measurement configuration in response to the first report being triggered.

[0314] As an embodiment, the first communication device 450 corresponds to the first node in this application.

[0315] As an embodiment, the second communication device 410 corresponds to the second node in this application.

[0316] As an embodiment, the first communication device 450 is a UE.

[0317] As an embodiment, the first communication device 450 is a mobile phone.

[0318] As an embodiment, the second communication device 450 is a relay.

[0319] As an embodiment, the second communication device 410 is a base station.

[0320] As an embodiment, the receiver 454 (including the antenna 452), the receiving processor 456 and the controller / processor 459 are used to receive the first signaling in the present application.

[0321] As an embodiment, the transmitter 454 (including the antenna 452), the transmit processor 468 and the controller / processor 459 are used to send the first report in the present application.

[0322] As an embodiment, the transmitter 454 (including the antenna 452), the transmit processor 468 and the controller / processor 459 are used to send the second report in the present application.

[0323] Example 5

[0324] Embodiment 5 illustrates a wireless signal transmission flow chart according to an embodiment of the present application, as shown in the attached Figure 5 Attached Figure 5 In the example, U01 corresponds to the first node of the present application. It is particularly noted that the order in this example does not limit the signal transmission order and implementation order in the present application, wherein the steps in F51 and F52 are optional.

[0325] for First node U01 , receiving a first signaling in step S5101; triggering a first report according to a first report configuration in step S5102; sending a first report in step S5103; executing a first measurement configuration in step S5104; and sending a second report in step S5105.

[0326] for Second node U02 , sending a first signaling in step S5201; receiving a first report in step S5202; and receiving a second report in step S5203.

[0327] In Embodiment 5, the first signaling indicates a first measurement configuration and a first report configuration; the first node U01 executes step S5104 in response to the first report being triggered.

[0328] As an embodiment, the second node U02 is a base station corresponding to the PCell of the first node U01.

[0329] As an embodiment, the second node U02 is the service cell of the first node U01 or the base station corresponding to the service cell.

[0330] As an embodiment, the second node U02 belongs to a cellular network.

[0331] As an embodiment, the second node U02 corresponds to the source cell.

[0332] As an embodiment, the second signaling U02 is the first cell or the base station corresponding to the first cell.

[0333] As an embodiment, the second node U02 is a measurement control node or an AI control node of the first node U01.

[0334] As an example, Figure 5 The order in which the steps are numbered is the chronological order.

[0335] As an embodiment, the first signaling is RRC signaling.

[0336] As an embodiment, the first signaling is unicast.

[0337] As an embodiment, step S5101 is performed earlier than step S5102.

[0338] As an embodiment, step S5101 is performed earlier than step S5104.

[0339] As an embodiment, step S5102 is performed earlier than step S5103.

[0340] As an embodiment, step S5104 is performed earlier than step S5105.

[0341] As an embodiment, step S5102 is performed earlier than step S5104.

[0342] As an embodiment, when triggering the first report according to the first report configuration includes generating a first report, step S5103 is executed.

[0343] As a sub-embodiment of this embodiment, the first report includes an RRC message.

[0344] As a sub-embodiment of this embodiment, the first report includes MeasurementReport.

[0345] As an embodiment, when step S5103 is executed, step S5102 generates an RRC message carried or corresponding to the first report.

[0346] As a sub-embodiment of this embodiment, the RRC message includes MeasurementReport.

[0347] As an embodiment, the benefits of sending the first report include: it helps the network to understand the situation of the first node U01, for example, it helps the network to understand that the first measurement configuration is executed, thereby facilitating network optimization.

[0348] As an embodiment, step S5103 is not executed.

[0349] As a sub-embodiment of this embodiment, the first report is used to trigger step S5104.

[0350] As a sub-embodiment of this embodiment, step S5102 is used to trigger step S5104.

[0351] As a sub-embodiment of this embodiment, the first report is the execution condition of step S5104.

[0352] As a sub-embodiment of this embodiment, the first report includes measurement or prediction results generated by AI.

[0353] As a sub-embodiment of this embodiment, the first report includes measurement results or prediction results of multiple cells.

[0354] As a sub-embodiment of this embodiment, the first report includes a prediction of wireless link failure.

[0355] As a sub-embodiment of this embodiment, the first report includes a prediction of switching or switching failure.

[0356] As a sub-embodiment of this embodiment, the first report includes a prediction of the motion state.

[0357] As a sub-embodiment of this embodiment, the first report includes a prediction of the movement route.

[0358] As an embodiment, the benefits of the above method include: executing the first measurement configuration through the first report trigger is conducive to supporting very complex trigger mechanisms, even non-quantifiable trigger mechanisms, and can better support AI and nonlinear systems.

[0359] As an embodiment, step S5103 may be executed delayed.

[0360] As an embodiment, the first node U01 determines whether to delay execution of step S5103 according to channel quality.

[0361] As an embodiment, when the channel quality of the first node U01 is worse than the first quality threshold, the first node U01 does not delay executing step S5103; when the channel quality of the first node U01 is not worse than the first quality threshold, the first node U01 delays executing step S5103.

[0362] As an embodiment, the second node U02 configures the first quality threshold.

[0363] As an embodiment, the delayed execution includes executing after step S5104.

[0364] As an embodiment, the delayed execution includes executing after delaying for a given time.

[0365] As a sub-embodiment of this embodiment, the second node U02 configures the given time.

[0366] As a sub-embodiment of this embodiment, the first node U01 determines the given time by itself.

[0367] As an embodiment, the delayed execution includes executing after delaying at least once.

[0368] As an embodiment, the benefits of delaying the execution of step S5103 include: saving resources.

[0369] As an embodiment, step S5103 and step S5104 may be performed simultaneously.

[0370] As an embodiment, the execution of the first measurement configuration includes executing corresponding measurements.

[0371] As an embodiment, the first node U01 triggers a second report according to the second report configuration; and stops executing the first measurement configuration in response to triggering the second report.

[0372] As an embodiment, the first measurement configuration includes a third reporting configuration.

[0373] As an embodiment, executing the first measurement configuration includes performing measurement according to the first measurement configuration.

[0374] As an embodiment, the first node U01 sends a third report, and the third report carries the measurement result of the measurement performed according to the first measurement configuration.

[0375] As an embodiment, as an embodiment, the benefits of sending the second report include that it can let the network know that the first node U01 stops executing the first measurement configuration, which is beneficial to network optimization.

[0376] As an embodiment, the second report includes at least one measurement result.

[0377] As an embodiment, the report configuration on which the second report depends is different from the cell targeted by the first report configuration.

[0378] As an embodiment, the reporting configuration on which the second report depends is different from the frequency targeted by the first reporting configuration.

[0379] As an embodiment, the report configuration on which the second report depends is different from the reference signal resource targeted by the first report configuration.

[0380] As an embodiment, the reporting configuration on which the second report depends is different from the accuracy indicated by the first reporting configuration.

[0381] As an embodiment, the above method has the advantage that the execution of the first measurement configuration and the stopping of the execution of the first measurement configuration can be controlled more flexibly.

[0382] As an embodiment, the measurement configuration associated with or corresponding to the second reporting configuration is a measurement configuration other than the first measurement configuration.

[0383] As an embodiment, the measurement configuration associated with or corresponding to the second reporting configuration is a measurement configuration other than the second measurement configuration.

[0384] As an embodiment, the measurement configuration associated with or corresponding to the reporting configuration on which the second report depends is a measurement configuration other than the first measurement configuration.

[0385] As an embodiment, the measurement configuration associated with or corresponding to the reporting configuration on which the second report depends is a measurement configuration other than the second measurement configuration.

[0386] Example 6

[0387] Embodiment 6 illustrates a schematic diagram of RAN (Radio Access Network) domain (Domain) AI / ML function deployment according to an embodiment of the present application, as shown in the attached Figure 6 The gNB in ​​Example 8 may be replaced by a network device such as an eNB or a 6G base station.

[0388] AI / ML related functions include ML training function (also called AI training, or AI / ML training), ML testing function, ML reasoning (also called AI reasoning, or AI / ML reasoning) function, etc. ML training function, ML testing function, and ML reasoning function can be deployed independently or co-located. The deployment of AI / ML related functions can be achieved through software, such as downloading and / or running executable files; it can also be achieved through a combination of software and hardware, such as accelerating specific computing units through hardware to increase computing speed or save power consumption.

[0389] The ML training function can be deployed in a cross-domain management system or a domain-specific management system; the domain-specific management system is used to manage the RAN domain or the CN (Core Network) domain. For example, the ML training function for MDA (Management Data Analytics) can be deployed in MDAF (MDA function); ML training for network data analysis can be deployed in NWDAF (Network Data Analytics Function), that is, the ML training function is MTLF (Model Training logical function).

[0390] The ML reasoning function can also be deployed in a cross-domain management system or a domain-specific management system; for example, the ML reasoning function is MDAF, or the ML reasoning function is AnLF (Analytics logical function) located in NWDAF.

[0391] Similarly, ML testing capabilities can also be deployed in cross-domain management systems, or domain-specific management systems.

[0392] In Example 6, the RAN domain ML training function 1402 is located in the RAN domain management function 1403; and the ML reasoning function is located in the base station, that is, the AI / ML reasoning function 1404 is located in gNB1405, the AI / ML reasoning function 1406 is located in gNB1407, and so on.

[0393] Attached Figure 6 In the example, the management of the ML inference functions of multiple base stations is completed by the RAN domain management function 1403, that is, data interaction with the RAN domain MnS (Management Service) consumer / cross-domain management 1401 (see the attached Figure 6 ).

[0394] Optionally, the management of the ML reasoning function may also be performed by the base station itself, that is, each base station may independently interact with the RAN domain MnS consumer / cross-domain management 1401 for data.

[0395] It should be noted that Example 6 is merely a non-restrictive implementation method; optionally, the ML training function of the RAN domain may also be deployed in the base station; or optionally, some base stations deploy the ML reasoning function and the ML training function of the RAN domain, while some base stations only deploy the ML reasoning function.

[0396] As an embodiment, a gNB (or base station) in Embodiment 6 is the second node of the present application.

[0397] As an embodiment, the second processor includes an attached Figure 6 An AL / ML reasoning function, namely 1404 or 1406.

[0398] As an example, Figure 6 An AL / ML inference function performs ML training based on the target precoding vector; the target reference signal is different from the first reference signal; any reference signal among the multiple reference signals except the first reference signal is associated with a precoding vector, wherein the target precoding vector is associated with the target reference signal.

[0399] As an embodiment, the generation or sending of the first report uses an attached Figure 6 at least one function in .

[0400] As an embodiment, the execution of the first measurement configuration uses an additional Figure 6 at least one function in .

[0401] As an embodiment, the execution of the first measurement configuration depends on the attached Figure 6 at least one function in .

[0402] As an embodiment, the first report depends on training, and the first measurement configuration does not depend on training, or the first report does not depend on training, and the first measurement configuration depends on training.

[0403] Example 7

[0404] Embodiment 7 illustrates a schematic diagram of the AI / ML function deployment of a UE according to an embodiment of the present application, as shown in the attached Figure 7 shown.

[0405] Attached Figure 7 The RAN domain ML training functionality 1505 is optional.

[0406] The UE function 1504 is deployed in the first node of the present application, and the UE function 1504 includes an AI / ML reasoning function 1506; the AI / ML reasoning function 1506 uses an ML model (also called an AI model) for reasoning; an ML model is usually trained before being used for AI / ML reasoning.

[0407] As an embodiment, the UE function 1504 includes a RAN domain ML training function 1505, and the RAN domain ML training function 1505 runs the training data through the ML model to derive relevant losses, and adjusts the parameters of the ML model based on the calculated losses; the ML training includes at least one of ML initial training, ML re-training, and reinforcement learning.

[0408] The above embodiments can reduce the complexity of the base station, or save air interface resources caused by reporting training data; however, the above embodiments place high demands on the processing capability of the UE side.

[0409] Optionally, the UE function 1504 also includes a CN domain ML training function ( Figure 7 not included).

[0410] Optionally, the UE function 1504 also includes an AI / ML deployment function: Figure 7 Not included, used to load ML models and data.

[0411] As an embodiment, the first node indicates whether the ML training function is supported (RAN domain or CN domain) through capability reporting, and the capability reporting is RRC signaling, or NAS (Non-Access Stratum) signaling.

[0412] As an embodiment, the ML model and related metadata are loaded by the first node from a network device or a remote server.

[0413] Optionally, the UE function 1504 is a MnS (Management Service) producer, providing data to the CN domain MnF (Management Function) 1501, and / or the RAN domain MnF 1502, and / or the cross-domain management system 1503 for management or analysis (as shown by the double arrow 1507).

[0414] Optionally, the UE function 1504 is an MnS consumer, which loads data from the CN domain MnF (Management Function) 1501, and / or the RAN domain MnF 1502, and / or the cross-domain management system 1503 for AI / ML related management, such as management data requests, ML model activation, and / or ML training, etc. (as shown by the double arrow 1507).

[0415] As an embodiment, the first channel information in the present application is obtained through reasoning by the AI / ML reasoning function 1506 .

[0416] As an embodiment, the RAN domain ML training function 1505 performs ML training according to a target precoding vector; wherein the target reference signal is different from the first reference signal; any reference signal among the multiple reference signals except the first reference signal is associated with a precoding vector, wherein the target precoding vector is associated with the target reference signal.

[0417] As an embodiment, the first processor includes an attached Figure 7 An AL / ML reasoning function 1506 in.

[0418] As an embodiment, the ML model is based on a neural network.

[0419] As an embodiment, the ML model is based on CNN (Conventional Neural Networks).

[0420] As an embodiment, the ML model is based on the Transformer architecture.

[0421] As an embodiment, the generation or sending of the first report uses an attached Figure 7 at least one function in .

[0422] As an embodiment, the execution of the first measurement configuration uses an additional Figure 7 at least one function in .

[0423] As an embodiment, the execution of the first measurement configuration depends on the attached Figure 7 at least one function in .

[0424] As an embodiment, the at least one function is the function in 1504.

[0425] As an embodiment, the at least one function is the function in 1505.

[0426] As an embodiment, the at least one function is the function in 1506.

[0427] As an embodiment, the first report depends on training, and the first measurement configuration does not depend on training, or the first report does not depend on training, and the first measurement configuration depends on training.

[0428] Example 8

[0429] Embodiment 8 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of the present application, as shown in the attached Figure 8 shown.

[0430] Attached Figure 8 Including a third operation, a fourth operation, a fifth operation, a sixth operation and a seventh operation. In Example 11, the third operation and the fourth operation belong to the first stage, the fifth operation belongs to the second stage, the sixth operation belongs to the third stage, and the seventh operation belongs to the fourth stage. Figure 8 In the diagram, the lines with arrows indicate the sequence of the processes.

[0431] As an embodiment, the third operation includes AI / ML training, the fourth operation includes AI / ML testing, the fifth operation includes AI / ML emulation, the sixth operation includes AI / ML entity loading, and the seventh operation includes AI / ML inference.

[0432] As an embodiment, the first phase includes a training phase (training phase), the second phase includes a simulation phase (emulation phase), the third phase includes a deployment phase (deployment phase), and the fourth phase includes an inference phase (emulation phase).

[0433] As an embodiment, the first stage includes AI / ML model training.

[0434] As an embodiment, the first stage includes AI / ML model training and AI / ML testing.

[0435] As an embodiment, the AI / ML model training includes initial training and re-training of one or a group of AI / ML entities.

[0436] As an embodiment, the AI / ML model training relies on training data.

[0437] As an embodiment, the AI / ML model training includes AI / ML entity validation.

[0438] As an embodiment, the AI / ML entity verification is used to evaluate the performance of the AI / ML entity.

[0439] As an embodiment, the AI / ML entity verification relies on verification data.

[0440] As an example, if the result of AI / ML entity verification does not meet expectations, the AI / ML model will be retrained.

[0441] As an embodiment, the AI / ML testing includes testing the verified AI / ML entity to estimate the performance of the trained AI / ML model.

[0442] As an example, if the result of the AI / ML test meets expectations, the AI / ML entity proceeds to the next stage; otherwise the AI / ML model will be retrained.

[0443] As an embodiment, the AI / ML testing relies on test data.

[0444] As an embodiment, the second stage includes AI / ML simulation, which performs inference of AI / ML entities in a simulation environment.

[0445] As an embodiment, the AI / ML simulation estimates the performance of AI / ML entity reasoning in a simulation environment before using the AI / ML entity.

[0446] As an embodiment, the second stage is optional.

[0447] As an embodiment, the third stage includes AI / ML entity loading, and the AI / ML entity loading is to obtain the trained AI / ML entity to obtain the desired AI / ML reasoning function.

[0448] As an embodiment, the third stage is optional.

[0449] As an example, when the training function and the inference function are co-located, the third stage is no longer required.

[0450] As an embodiment, the fourth stage includes AI / ML reasoning.

[0451] As an embodiment, the generation or sending of the first report uses an attached Figure 8 At least one operation in .

[0452] As an embodiment, the first report uses reasoning.

[0453] As a sub-embodiment of this embodiment, at least one or all of the measurement results included in the first report are obtained by inference.

[0454] As an embodiment, the execution of the first measurement configuration uses an additional Figure 8 At least one operation in .

[0455] As an embodiment, the execution of the first measurement configuration depends on the attached Figure 8 At least one operation in .

[0456] As an embodiment, the first report depends on training, and the first measurement configuration does not depend on training, or the first report does not depend on training, and the first measurement configuration depends on training.

[0457] Example 9

[0458] Embodiment 9 illustrates a schematic diagram of executing a first measurement configuration according to an embodiment of the present application, as shown in the attached figure. Fig. 9 shown.

[0459] Attached Fig. 9 The rectangle in the figure represents the time period during which the first measurement configuration is executed. Different widths of the rectangles indicate that the lengths of the time period during which the first measurement configuration is executed may be different. Different intervals between the rectangles indicate that the intervals of the time period during which the first measurement configuration is executed may be different.

[0460] As an embodiment, the method proposed in the present application does not limit the first measurement configuration to be executed multiple times.

[0461] As an embodiment, the first measurement configuration may be executed multiple times.

[0462] As an embodiment, the first measurement configuration is executed within at least one time period.

[0463] As an embodiment, the first measurement configuration is executed within multiple time periods.

[0464] As an embodiment, there is a time interval between adjacent time periods in the multiple time periods.

[0465] As an embodiment, there is a time interval between at least one pair of adjacent time periods in the multiple time periods.

[0466] As an embodiment, the multiple time periods are non-periodic.

[0467] As an embodiment, the time interval is greater than 0.

[0468] As an embodiment, the time interval is greater than a measurement cycle.

[0469] As an embodiment, the time interval is greater than a reporting period.

[0470] As an embodiment, the time interval is greater than the period of the first report.

[0471] As an embodiment, the execution of the first measurement configuration may be non-periodic.

[0472] As an embodiment, the execution time of the first measurement configuration may be of unequal length.

[0473] As an embodiment, although the measurement of the first node may be periodic or have a certain time interval, the execution of the first measurement configuration within a time period means that the first measurement configuration is active or activated within the time period.

[0474] As an embodiment, although the measurement of the first node may be periodic or have a certain time interval, the execution of the first measurement configuration within a time period means that the first measurement configuration is active or activated within the time period.

[0475] As an embodiment, although the measurement of the first node may be periodic or have a certain time interval, the execution of the first measurement configuration within a time period means that the first measurement configuration is effective within the time period.

[0476] As an embodiment, although the measurement of the first node may be periodic or have a certain time interval, the execution of the first measurement configuration within a time period means that the first node is required to perform measurement within the time period according to the first measurement configuration.

[0477] As an embodiment, although the measurement of the first node may be periodic or have a certain time interval, the execution of the first measurement configuration within a time period means that the first node is not required to perform measurement according to the first measurement configuration outside the at least one time period.

[0478] As an embodiment, although the measurement of the first node may be periodic or have a certain time interval, the execution of the first measurement configuration within a time period means that the first measurement configuration is ineffective or deactivated outside the at least one time period.

[0479] As an example, Figure 8 The start of each time period in depends on the report triggered according to the first reporting configuration.

[0480] As an embodiment, the first report is the report triggered according to the first report configuration.

[0481] As an example, Figure 8 The start of each time period in the report depends on the report triggered according to the first report configuration. Figure 8 A period in the middle begins.

[0482] As an example, Figure 8 The end of any time period in depends on the report triggered according to the second report configuration.

[0483] As an embodiment, the second report is the report triggered according to the second report configuration.

[0484] As an example, Figure 8 The end of any time period in the report depends on the report triggered according to the second report configuration, including: when the report is triggered according to the second report configuration, the attached Figure 8 A time period ends.

[0485] As an embodiment, the benefits of the above method include: the execution of the first measurement configuration is more flexible.

[0486] As an embodiment, the measurement configurations corresponding to the first reporting configuration and the second reporting configuration are different.

[0487] As an embodiment, the measurement identifiers corresponding to the first reporting configuration and the second reporting configuration are different.

[0488] Example 10

[0489] Embodiment 10 illustrates a schematic diagram of executing the first measurement configuration according to an embodiment of the present application depending on the difference between the measurement result in the first report and the measurement result in the third measurement configuration, as shown in the attached figure. Fig.10 shown.

[0490] As an embodiment, the execution of the first measurement configuration depends on the difference between the measurement result in the first report and the measurement result of the third measurement configuration, wherein the execution of the first measurement configuration is triggered only when the difference between the measurement result in the first report and the measurement result of the third measurement configuration is greater than a first threshold.

[0491] As an embodiment, only one of the first reporting configuration and the third measurement configuration is associated with an AI model identifier.

[0492] As an embodiment, the meaning that only one of the first reporting configuration and the third measurement configuration is associated with the AI ​​model identifier includes: the first reporting configuration is associated with the AI ​​model identifier; and the third measurement configuration is not associated with the AI ​​model identifier.

[0493] As an embodiment, the meaning that only one of the first reporting configuration and the third measurement configuration is associated with the AI ​​model identifier includes: the first reporting configuration is not associated with the AI ​​model identifier; and the third measurement configuration is associated with the AI ​​model identifier.

[0494] As an embodiment, the first report configuration being associated with the AI ​​model identifier means that: the first report configuration includes a configuration for AI.

[0495] As an embodiment, the first report configuration associated with the AI ​​model identifier means that: the first report configuration includes a configuration for generating a measurement report for AI.

[0496] As an embodiment, the first report configuration associated with the AI ​​model identifier means that the measurement report triggered by the first report configuration is generated by AI.

[0497] As an embodiment, the first report configuration associated with the AI ​​model identifier means that the measurement report triggered by the first report configuration is used for AI.

[0498] As an embodiment, the first report configuration is not associated with the AI ​​model identifier, which means that the measurement report triggered by the first report configuration is not generated by AI.

[0499] As an embodiment, the first report configuration is not associated with the AI ​​model identifier, which means that the first report configuration is a traditional measurement report configuration.

[0500] As an embodiment, the first report configuration is not associated with an AI model identifier, which means that the first report configuration is not configured with AI.

[0501] As an embodiment, the first report configuration is not associated with an AI model identifier, which means that the first report configuration is not configured with AI to generate a measurement report.

[0502] As an embodiment, the above method has the following advantages: whether to execute the first measurement configuration can be controlled according to the measurement results of multiple measurement configurations, which is more accurate, more flexible and less complex.

[0503] As an embodiment, the above method has the advantage that it can determine whether to execute the first measurement configuration according to the error of the measurement results obtained by different reporting configurations, which is conducive to obtaining more accurate measurement results and avoiding accidents.

[0504] As an embodiment, the first threshold is network configured.

[0505] As an embodiment, the first threshold is predicted by AI.

[0506] As an embodiment, the first threshold is determined by the first node itself.

[0507] As an embodiment, the first threshold is determined according to computer simulation.

[0508] As an embodiment, the measurement result in the first report and the measurement result of the third measurement configuration are both for the same cell.

[0509] As an embodiment, the measurement result in the first report and the measurement result of the third measurement configuration are both of the same reference signal resource.

[0510] As an embodiment, the same reference signal resource includes one of SSB or CSI-RS (channel state information-reference signal).

[0511] As an embodiment, the above method has the advantage of being able to better calibrate the measurement results.

[0512] As an embodiment, the measurement result in the first report and the measurement result of the third measurement configuration are both for different reference signal resources.

[0513] As an embodiment, the different reference signal resources are SSB or CSI-RS respectively.

[0514] As an embodiment, the advantage of the above method is that it can control the execution of the first measurement configuration more comprehensively and flexibly, the configuration of CSI-RS is very flexible, the measurement of SSB can better reflect the quality of a cell, and the above method can compare the quality of different beams to obtain more accurate information.

[0515] As an embodiment, the first reporting configuration and the third measurement configuration are respectively associated with different AI model identifiers.

[0516] As an embodiment, the first reporting configuration and the third measurement configuration are respectively associated with different AI model identifiers, which means that the first reporting configuration and the third measurement configuration are respectively associated with different AI models.

[0517] As an embodiment, the first reporting configuration and the third measurement configuration are respectively associated with different AI model identifiers, which means that the reporting configurations included in the first reporting configuration and the third measurement configuration are respectively associated with different AI model identifiers.

[0518] As an embodiment, the first reporting configuration and the third measurement configuration are respectively associated with different AI model identifiers, which means that: the measurement configuration to which the first reporting configuration belongs and the third measurement configuration are respectively associated with different AI model identifiers.

[0519] As an embodiment, the meaning of associating different AI model identifiers includes: using different AI models to output measurement results.

[0520] As an embodiment, the output measurement result includes an inferred or predicted measurement result.

[0521] As an embodiment, one of the associated different AI model identifiers is a UE-based AI model, and the other is a network-based AI model.

[0522] As an embodiment, the benefits of the above method include: being conducive to comprehensive utilization of different AI models and better controlling and indicating the first measurement configuration.

[0523] Embodiment 11

[0524] Embodiment 11 illustrates a structural block diagram of a processing device used in a first node according to an embodiment of the present application; Fig.11 As shown in the attached Fig.11 In the embodiment, the processing device 1100 in the first node includes a first receiver 1101, a first transmitter 1102 and a first processor 1103.

[0525] In embodiment 11, a first receiver 1101 receives a first signaling, where the first signaling indicates a first measurement configuration and a first reporting configuration;

[0526] The first processor 1103 triggers a first report according to the first report configuration; and executes the first measurement configuration in response to the triggering of the first report.

[0527] As an embodiment, triggering the first report includes sending the first report.

[0528] As an embodiment, the first receiver 1101 performs measurement according to the second measurement configuration and generates a first measurement result, and the first report includes the first measurement result.

[0529] As an embodiment, the first processor 1103 triggers a second report according to a second report configuration; and stops executing the first measurement configuration in response to triggering the second report.

[0530] As an embodiment, when the first report is triggered, any measurement configuration in the first measurement configuration set is executed; when the first report is not triggered, any measurement configuration in the second measurement configuration set is executed.

[0531] As an embodiment, the first measurement configuration is associated with an identifier of an AI model.

[0532] As an embodiment, only the latter of the first measurement configuration and the second measurement configuration is associated with an identifier of an AI model.

[0533] As an embodiment, the execution of the first measurement configuration depends on a difference between a measurement result in the first report and a measurement result in the third measurement configuration, wherein the execution of the first measurement configuration is triggered only when the difference between the measurement result in the first report and the measurement result in the third measurement configuration is greater than a first threshold;

[0534] Among them, only one of the first reporting configuration and the third measurement configuration is associated with the AI ​​model identifier.

[0535] As an embodiment, the execution of the first measurement configuration depends on a difference between a measurement result in the first report and a measurement result in the third measurement configuration, wherein the execution of the first measurement configuration is triggered only when the difference between the measurement result in the first report and the measurement result in the third measurement configuration is greater than a first threshold;

[0536] The first reporting configuration and the third measurement configuration are respectively associated with different AI model identifiers.

[0537] As an embodiment, the first node is a user equipment (UE).

[0538] As an embodiment, the first node is a mobile phone.

[0539] As an embodiment, the first node is a communication device supporting low latency.

[0540] As an embodiment, the first node is an industrial communication device.

[0541] As an embodiment, the first node is an Internet of Things terminal or an industrial Internet of Things terminal.

[0542] As an embodiment, the first receiver 1101 includes at least one of the antenna 452, receiver 454, receiving processor 456, multi-antenna receiving processor 458, controller / processor 459, memory 460, or data source 467 in Example 4.

[0543] As an embodiment, the first transmitter 1102 includes at least one of the antenna 452, transmitter 454, transmit processor 468, multi-antenna transmit processor 457, controller / processor 459, memory 460, or data source 467 in Embodiment 4.

[0544] A person of ordinary skill in the art can understand that all or part of the steps in the above method can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a hard disk or an optical disk. Optionally, all or part of the steps in the above embodiment can also be implemented using one or more integrated circuits. Accordingly, each module unit in the above embodiment can be implemented in the form of hardware or in the form of a software function module, and the present application is not limited to any specific form of software and hardware combination. The user equipment, terminal and UE in the present application include but are not limited to drones, communication modules on drones, remote-controlled aircraft, aircraft, small aircraft, mobile phones, tablet computers, notebooks, vehicle-mounted communication equipment, wireless sensors, Internet cards, Internet of Things terminals, RFID terminals, NB-IoT terminals, MTC (Machine Type Communication, machine type communication) terminals, eMTC (enhanced MTC, enhanced MTC) terminals, data cards, Internet cards, vehicle-mounted communication equipment, low-cost mobile phones, low-cost tablet computers, satellite communication equipment, ship communication equipment, NTN user equipment and other wireless communication equipment. The base stations or system equipment in this application include but are not limited to macrocell base stations, microcell base stations, home base stations, relay base stations, gNB (NR Node B) NR Node B, TRP (Transmitter Receiver Point), NTN base stations, satellite equipment, flight platform equipment and other wireless communication equipment.

[0545] The present invention may be implemented in other specified forms without departing from its core or essential features. Therefore, the embodiments disclosed herein should be considered as illustrative rather than restrictive in any way. The scope of the invention is determined by the appended claims rather than the preceding description, and all modifications within their equivalent meanings and regions are considered to be included therein.

Claims

1. A first node used for measurement in wireless communication, wherein: include: A first receiver receives a first signaling, where the first signaling indicates a first measurement configuration and a first reporting configuration; a first processor, triggering a first report according to the first report configuration; In response to the first report being triggered, the first measurement configuration is performed.

2. The first node according to claim 1, characterized in that: The triggering of the first report includes sending the first report.

3. The first node according to claim 1 or 2, characterized in that: include: The first receiver performs measurement according to the second measurement configuration and generates a first measurement result, and the first report includes the first measurement result.

4. The first node according to any one of claims 1 to 3, characterized in that: include: The first processor triggers a second report according to a second report configuration; In response to triggering the second report, execution of the first measurement configuration is stopped.

5. The first node according to any one of claims 1 to 4, characterized in that: When the first report is triggered, any measurement configuration in the first measurement configuration set is executed; when the first report is not triggered, any measurement configuration in the second measurement configuration set is executed.

6. The first node according to any one of claims 1 to 5, characterized in that: The first measurement configuration is associated with an identifier of an AI model.

7. The first node according to claim 5, characterized in that: Only the latter of the first measurement configuration and the second measurement configuration is associated with an identifier of an AI model.

8. The first node according to any one of claims 1 to 7, characterized in that: The executing the first measurement configuration depends on a difference between a measurement result in the first report and a measurement result in the third measurement configuration, wherein the executing the first measurement configuration is triggered only when the difference between the measurement result in the first report and the measurement result in the third measurement configuration is greater than a first threshold; Among them, only one of the first reporting configuration and the third measurement configuration is associated with the AI ​​model identifier.

9. The first node according to any one of claims 1 to 7, characterized in that: The executing the first measurement configuration depends on a difference between a measurement result in the first report and a measurement result in the third measurement configuration, wherein the executing the first measurement configuration is triggered only when the difference between the measurement result in the first report and the measurement result in the third measurement configuration is greater than a first threshold; The first reporting configuration and the third measurement configuration are respectively associated with different AI model identifiers.

10. A method in a first node for measuring in wireless communication, wherein: include: receiving first signaling, where the first signaling indicates a first measurement configuration and a first reporting configuration; triggering a first report according to the first report configuration; In response to the first report being triggered, the first measurement configuration is performed.

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