Method used for measurement in wireless communication, and device

Abstract

Disclosed in the present application are a method used for measurement in wireless communication, and a device. The method comprises: receiving first signaling, the first signaling indicating a first measurement configuration and a first report configuration; triggering a first report on the basis of the first report configuration; and in response to the first report being triggered, executing the first measurement configuration. The present application can achieve a better measurement effect.

Description

A method and apparatus for measurement in wireless communication

[0001] The present application claims priority from the Chinese patent application No. 202410669481.9, filed on May 24, 2024, and entitled "A method and apparatus for measurement in wireless communication", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to a method for measurement in a cellular wireless communication system. BACKGROUND

[0003] The application scenarios of future wireless communication systems are increasingly diversified, and different application scenarios have different performance requirements for the system. In order to meet the different performance requirements of various application scenarios, it is decided at the 3GPP (3rd Generation Partner Project) RAN (Radio Access Network) #72 plenary meeting to study the New Radio (NR) (or Fifth Generation, 5G). The NR WI (Work Item) was approved at the 3GPP RAN #75 plenary meeting, and the standardization work of NR began.

[0004] In communication, whether it is LTE (Long Term Evolution) or 5G NR, it involves accurate reception of reliable information, optimized energy efficiency, determination of information effectiveness, flexible resource allocation, scalable system structure, efficient non-access layer information processing, low service interruption and drop rate, support for low power consumption, which is of great significance to the normal communication of base stations and user equipment, reasonable scheduling of resources, and balancing of system load. It can be said that it is the cornerstone of high throughput, meeting the communication needs of various services, improving spectrum utilization, and improving service quality. Whether it is eMBB (enhanced Mobile BroadBand), URLLC (Ultra Reliable Low Latency Communication) or eMTC (enhanced Machine Type Communication) is indispensable. At the same time, in IIoT (Industrial Internet of Things), in V2X (Vehicular to X), in Device to Device communication, in unlicensed spectrum communication, in user communication quality monitoring, in network planning optimization, in TN (Territerial Network) communication, in dual connectivity system, in wireless resource management and multi-antenna codebook selection, in signaling design, neighbor management, service management, and in beamforming, there are extensive demands. The transmission mode of information is divided into broadcast and unicast, and the two transmission modes are indispensable for 5G system because they are very helpful to meet the above requirements.

[0005] With the increasing complexity and scenarios of the system, higher requirements are put forward for reducing the interruption rate, reducing the delay, enhancing the reliability, enhancing the stability of the system, the flexibility of the service, and the power saving. At the same time, when designing the system, the compatibility between different systems and different versions also needs to be considered. SUMMARY

[0006] Researchers have found that terminals need to perform corresponding measurements according to measurement configurations. In the prior art, when a terminal receives a measurement configuration, it will perform such a configuration, which has disadvantages. Therefore, how to better control the execution of the first measurement configuration is a problem to be solved.

[0007] To solve the above problems, the present application provides a solution.

[0008] It should be noted that the embodiments in any node and the features in the embodiments of the present application can be applied to any other node without conflict. The embodiments and the features in the embodiments of the present application can be combined with each other without conflict. At the same time, the method proposed in the present application can also be used to solve other problems in communication, such as problems in NR evolution and 6G system.

[0009] As an embodiment, the explanation of the terminology in the present application refers to the definition of the specification agreement TS38 series of 3GPP.

[0010] As an embodiment, the explanation of the terminology in the present application refers to the definition of the specification agreement TS37 series of 3GPP.

[0011] The present application discloses a method used in a first node for wireless communication, comprising:

[0012] receiving first signaling, the first signaling indicating a first measurement configuration and a first reporting configuration;

[0013] triggering a first report according to the first reporting configuration; as a response to the first report being triggered, performing the first measurement configuration.

[0014] As an embodiment, the problem to be solved by the present application includes: how to control the execution of the first measurement configuration, and how to control the execution of the first measurement configuration through the first report.

[0015] As an embodiment, the benefits of the above method include: more flexibility, power saving, reducing the impact of measurement on communication, better support for specific measurement configurations, such as AI (artificial intelligence) based or AI required measurements.

[0016] Specifically, according to one aspect of the present application, the triggering of the first report includes sending the first report.

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

[0018] As an embodiment, the first reporting configuration is a measurement reporting configuration.

[0019] Specifically, according to one aspect of the present application, a second report is triggered according to a second reporting configuration; as a response to triggering the second report, the execution of the first measurement configuration is stopped.

[0020] 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.

[0021] Specifically, according to an aspect of the present application, the first measurement configuration is associated with an identification of an AI model.

[0022] Specifically, according to an aspect of the present application, only the second measurement configuration among the first measurement configuration and the second measurement configuration is associated with an identification of an AI model.

[0023] Specifically, according to an aspect of the present application, the execution of the first measurement configuration depends on a difference between a measurement result in the first report and a measurement result of a 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 value.

[0024] Among the first report configuration and the second measurement configuration, only one is associated with an AI model identification.

[0025] Specifically, according to an aspect of the present application, the execution of the first measurement configuration depends on a difference between a measurement result in the first report and a measurement result of a 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 value.

[0026] Among the first report configuration and the second measurement configuration, different AI model identifications are respectively associated.

[0027] Specifically, according to an aspect of the present application, the first node is an Internet of Things terminal.

[0028] Specifically, according to an aspect of the present application, the first node is a user equipment.

[0029] Specifically, according to an aspect of the present application, the first node is a vehicle-mounted terminal.

[0030] Specifically, according to an aspect of the present application, the first node is a mobile phone.

[0031] The present application discloses a first node used in wireless communication, comprising:

[0032] A first receiver receives a first signaling, the first signaling indicating a first measurement configuration and a first report configuration;

[0033] A first processor triggers a first report according to the first report configuration; in response to the triggering of the first report, the first processor executes the first measurement configuration.

[0034] As an embodiment, compared with the conventional scheme, the present application has the following advantages:

[0035] In wireless communication, channel changes rapidly, measurement plays an extremely important role for mobility management, for configuration of communication parameters, for reasonable allocation of resources, in order to achieve ideal performance, the network will indicate multiple measurement configurations to the terminal, in general, the more measurement results, for example, the more measurement configurations, the more conducive to master more accurate channel information, however, the more measurement configurations, it may have an impact on communication itself, for example, waste of resources, cause communication interruption, waste of power, and in certain cases, further indication of measurement configuration is not flexible enough, at the same time, it is not timely enough, the method proposed in the present application is beneficial to increase flexibility, reduce signaling overhead, avoid time delay when reconfiguring, and support continuous measurement.

[0036] In the network supporting artificial intelligence, artificial intelligence needs to rely on measurement results for input, or some functions of artificial intelligence, for example, prediction of channel and other wireless parameters, are configured in the form of measurement configuration, the method proposed in the present application is beneficial to start artificial intelligence at the right time, on the other hand, using artificial intelligence for channel prediction may have errors, even large errors, compared with traditional methods in some cases, therefore, it is necessary to re-enable traditional measurement, the method proposed in the present application is beneficial to activate traditional measurement in time in the case of inaccurate artificial intelligence prediction.

[0037] Report-driven measurement is beneficial to improve efficiency and reduce complexity.

[0038] It is beneficial to adapt to different AI models. BRIEF DESCRIPTION OF DRAWINGS

[0039] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments thereof, read in conjunction with the accompanying drawings:

[0040] Fig. 1 shows a schematic diagram of receiving first signaling, triggering first reporting according to a first reporting configuration, and performing a first measurement configuration according to one embodiment of the present application;

[0041] Fig. 2 shows a schematic diagram of a network architecture according to one embodiment of the present application;

[0042] Fig. 3 shows a schematic diagram of an embodiment of a wireless protocol architecture of user plane and control plane according to one embodiment of the present application;

[0043] Fig. 4 shows a schematic diagram of a first communication device and a second communication device according to one embodiment of the present application;

[0044] Fig. 5 shows a flowchart of wireless signal transmission according to one embodiment of the present application;

[0045] FIG. 6 illustrates a schematic diagram of RAN (Radio Access Network) domain AI / ML function deployment, according to an embodiment of the application;

[0046] FIG. 7 illustrates a schematic diagram of AI / ML function deployment of a UE, according to an embodiment of the application;

[0047] FIG. 8 illustrates a flowchart based on artificial intelligence or machine learning, according to an embodiment of the application;

[0048] FIG. 9 illustrates a flowchart of performing a first measurement configuration, according to an embodiment of the application;

[0049] FIG. 10 illustrates a schematic diagram of performing a difference between a measurement result in a first report and a measurement result of a third measurement configuration, according to an embodiment of the application;

[0050] FIG. 11 illustrates a schematic diagram of a processing device in a first node, according to an embodiment of the application. DETAILED DESCRIPTION

[0051] The technical solutions of the application will be further described in detail below with reference to the accompanying drawings. It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other arbitrarily without conflict.

[0052] Embodiment 1

[0053] Embodiment 1 illustrates a flowchart 100 of receiving first information, according to an embodiment of the application, as shown in FIG. 1. In FIG. 1, each block represents a step, and it is particularly emphasized that the order of the blocks in the figure does not represent the time sequence between the steps represented.

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

[0055] Wherein, the first signaling indicates the first measurement configuration and the first report 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 at the MAC sublayer can also be understood as or referred to as the any operation performed by the MAC entity.

[0059] As an embodiment, the lower layer when the operation is performed at the MAC sublayer is the physical layer.

[0060] As an embodiment, the higher layer when the operation is performed at the MAC sublayer includes the RLC sublayer, the RRC sublayer, the PDCP sublayer.

[0061] Typically, the higher layer when the operation is performed at the MAC sublayer is the RRC sublayer.

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

[0063] As an embodiment, the lower layer when the operation is performed at the RRC sublayer includes the physical layer, the MAC layer, the RLC sublayer, the PDCP sublayer.

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

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

[0066] As an embodiment, in the present application, if it is not specifically indicated that the operation is performed at the MAC sublayer, it is performed at the RRC sublayer.

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

[0068] As an embodiment, the access stratum (AS) includes a plurality of protocol layers, and the details can refer to embodiment 3.

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

[0070] As an embodiment, any parameter in the present application is either configured by the network or can be generated by the first node according to an internal algorithm, for example, 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 of the timer when it is not intervened.

[0073] As an embodiment, the values of any parameters in the present application, including but not limited to the values of the timers and the values of the counters, are limited unless specifically stated.

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

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

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

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

[0078] As one example, this application is directed to NR.

[0079] As one example, this application is directed to NR evolved wireless communication networks.

[0080] As one example, a serving cell refers to a cell in which a UE is camped on. Performing a cell search includes a UE searching for a suitable cell of a selected PLMN (Public Land Mobile Network) or SNPN (Stand-alone Non-Public Network) that provides available service, monitoring control channels of the suitable cell, which is defined as camping on a cell; that is, a camped-on cell, with respect to a UE, is a serving cell of the UE. Camping on a cell in RRC idle state or RRC inactive state has the following benefits: it enables the UE to receive system information from the PLMN or SNPN; if the UE wishes to establish an RRC connection or continue a suspended RRC connection after registration, the UE can do so by performing initial access on the control channels of the camped-on cell; it enables the UE to be paged by the network; it enables the UE to receive ETWS (Earthquake and Tsunami Warning System) and CMAS (Commercial Mobile Alert System) notifications.

[0081] As an embodiment, for a UE in RRC CONNECTED state without configured CA / DC (carrier aggregation / dual connectivity), there is only one serving cell including the primary cell. For a UE in RRC CONNECTED state with configured CA / DC (carrier aggregation / dual connectivity), serving cells refer to a set of cells including the special cell (SpCell) and all secondary cells. The primary cell (PCell) is the MCG (Master Cell Group) cell operating on the primary frequency, on which the UE performs the initial connection establishment procedure or initiates connection re-establishment. For dual connectivity operation, the special cell refers to the PCell of the MCG or the PSCell of the SCG (Secondary Cell Group); if not dual connectivity operation, the special cell refers to the PCell.

[0082] As an embodiment, the frequency on which the SCell (Secondary Cell) operates is a secondary frequency.

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

[0084] As an embodiment, the individual content of the information element is referred to as a field.

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

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

[0087] As an embodiment, MCG refers to a set of serving cells associated with the master node in MR-DC, including the SpCell, and optionally, one or more SCells.

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

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

[0090] As one embodiment, in MR-DC, the wireless access node that provides the UE with additional resources is a secondary node, which is not provided with a control plane connection to the core network.

[0091] As one embodiment, in MR-DC, the set of serving cells associated with the secondary node is a secondary cell group (SCG), which includes the SpCell and, optionally, one or more SCells.

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

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

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

[0095] As one embodiment, in RRC inactive state, CA is typically not used.

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

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

[0098] As one embodiment, L1 is Layer-1 or physical layer.

[0099] As one embodiment, L2 is Layer-2.

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

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

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

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

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

[0105] As one sub-em embodiment of this embodiment, the higher layer includes at least one of an RRC sub-layer, a NAS, an SDAP layer.

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

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

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

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

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

[0111] As one sub-em embodiment of this embodiment, the network is a radio access network (RAN).

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

[0113] As one sub-em embodiment of this embodiment, the network is a radio access network (RAN).

[0114] As one embodiment, the first node enters an RRC inactive state when the first node suspends an RRC connection with the network.

[0115] As one sub-em embodiment of this embodiment, the network is a radio access network (RAN).

[0116] As one embodiment, different functionalities are supported in different RRC states.

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

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

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

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

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

[0122] As one embodiment, the first signaling comprises RRC reconfiguration signaling.

[0123] As one embodiment, the first signaling comprises at least part of fields in RRC reconfiguration signaling.

[0124] As one embodiment, the first signaling comprises RRC resume signaling.

[0125] As one embodiment, the first signaling comprises MeasConfig.

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

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

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

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

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

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

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

[0133] As one embodiment, the first measurement threshold controls the measurement of the first node on a non-serving cell.

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

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

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

[0137] As one embodiment, the method proposed in the present application is applicable to the measurement using AI assistance.

[0138] As one embodiment, the method proposed in the present application is applicable to the control of the measurement report using AI.

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

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

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

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

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

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

[0145] As one embodiment, the reporting configuration is a configuration of measurement reporting.

[0146] As one embodiment, the at least one reporting configuration comprised by the first measurement configuration does not comprise the first reporting configuration.

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

[0148] As one embodiment, the first measurement configuration comprises a configuration of measurement gaps.

[0149] As one embodiment, the first measurement configuration comprises a second measurement threshold, the second measurement threshold controlling measurement of non-serving cells.

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

[0151] As one sub-embodiment of this embodiment, the measurement of non-serving cells refers to layer 3 measurements.

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

[0153] As one sub-embodiment of this embodiment, the measurement of non-serving cells refers to layer 3 measurements.

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

[0155] As one embodiment, the first measurement configuration is identified by a first measurement identity.

[0156] As one embodiment, the measurement identity is Measld.

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

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

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

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

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

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

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

[0164] As one embodiment, the first measurement configuration includes at least a processing method of a measurement result.

[0165] As one embodiment, the first measurement configuration includes at least a generation method of a measurement result.

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

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

[0168] As one embodiment, the first measurement configuration includes at least a generation method of a measurement report.

[0169] As one embodiment, the first measurement configuration includes at least a triggering event of a measurement report.

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

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

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

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

[0174] As one embodiment, the first measurement configuration comprises at least whether a measurement report includes an identification of an AI model.

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

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

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

[0178] As one embodiment, the first reporting configuration is for Wifi.

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

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

[0181] As one 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 one embodiment, the above method has the advantage that effective coordination and balance can be made 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 one embodiment, the first reporting configuration includes a configuration for beam measurement.

[0184] As one embodiment, the meaning of triggering a first report according to the first reporting configuration is that the first report is generated according to the first reporting configuration.

[0185] As one embodiment, the meaning of triggering a first report according to the first reporting configuration is that the first report is transmitted according to the first reporting configuration.

[0186] As one embodiment, the meaning of triggering a first report according to the first reporting configuration is that the first report is generated and transmitted according to the first reporting configuration.

[0187] As one embodiment, the meaning of triggering a first report according to the first reporting configuration is that the first report is triggered according to the satisfaction of an event included in the first reporting configuration.

[0188] As an embodiment, the meaning that the first report is triggered according to the first reporting configuration is that the first report is generated according to a measurement configured by the first reporting configuration.

[0189] As an embodiment, the meaning that the first report is triggered according to the first reporting configuration is that the first report includes measurement results for a cell identified by a cell identity configured by the first reporting configuration.

[0190] As an embodiment, the meaning that the first report is triggered according to the first reporting configuration is that the first report is generated according to a reporting time configured by the first reporting configuration.

[0191] As an embodiment, the meaning that the first report is triggered according to the first reporting configuration is that the first report is generated according to a reporting accuracy configured by the first reporting 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 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 a Reference Signal Received Power (RSRP) of the serving cell.

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

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

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

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

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

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

[0204] As one embodiment, the first report indicates a reason for the first report being triggered.

[0205] As one embodiment, the meaning of performing the first measurement configuration in response to the first report being triggered comprises that the first measurement configuration is not performed when the first report is not triggered.

[0206] As one embodiment, the meaning of performing the first measurement configuration in response to the first report being triggered comprises that the first node does not perform measurements according to the first measurement configuration when the first report is not triggered.

[0207] As one embodiment, the meaning of performing the first measurement configuration in response to the first report being triggered comprises that the first measurement configuration is deactivated when the first report is not triggered.

[0208] As one embodiment, the meaning of performing the first measurement configuration in response to the first report being triggered comprises that the first measurement configuration is activated when the first report is triggered.

[0209] As one embodiment, the meaning of performing the first measurement configuration in response to the first report being triggered comprises that measurements are performed according to the first measurement configuration when the first report is triggered.

[0210] As one embodiment, the meaning of performing the first measurement configuration in response to the first report being triggered comprises that a measurement report is generated according to the first measurement configuration when the first report is triggered.

[0211] As one embodiment, the meaning of performing the first measurement configuration in response to the first report being triggered comprises that the first measurement configuration is applied when the first report is triggered.

[0212] As one embodiment, the performing the first measurement configuration comprises performing measurements on reference signal resources indicated by the first measurement configuration.

[0213] As one embodiment, the performing the first measurement configuration comprises performing measurements at times indicated by the first measurement configuration.

[0214] As one embodiment, the performing the first measurement configuration comprises performing measurements on a cell indicated by the first measurement configuration.

[0215] As one embodiment, the performing the first measurement configuration comprises using a measurement gap indicated by the first measurement configuration.

[0216] As one embodiment, the performing the first measurement configuration comprises performing measurements on a frequency indicated by the first measurement configuration.

[0217] As one embodiment, the performing the first measurement configuration comprises performing a reporting configuration comprised by the first measurement configuration.

[0218] As one embodiment, the performing of the first measurement configuration triggers stopping performing the first reporting configuration.

[0219] As one embodiment, the stopping of the performing of the first measurement configuration triggers performing the first reporting configuration.

[0220] As one embodiment, the above method has the advantage that flexible switching between the first measurement configuration and the first reporting configuration, or between the first measurement configuration and a measurement configuration associated with the first reporting configuration, is possible, and always guarantees that a valid and appropriate measurement is performed.

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

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

[0223] As one embodiment, the second measurement configuration is associated with a measurement identity different from a measurement identity associated with the first measurement configuration.

[0224] As one embodiment, the second measurement configuration comprises the first reporting configuration.

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

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

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

[0228] As one embodiment, the first measurement configuration comprises a third reporting configuration.

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

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

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

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

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

[0234] As an embodiment, the validity of the first measurement configuration comprises that the first measurement configuration is activated.

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

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

[0237] As an embodiment, the validity of the third reporting configuration depending on a report configured by the first reporting configuration being triggered means that the third reporting configuration is valid when the first report is triggered, and the third reporting configuration is not valid when the first report is not triggered.

[0238] As an embodiment, the validity of the third reporting configuration depending on a report configured by the first reporting configuration being triggered means that the third reporting configuration is valid when X measurement reports are triggered according to the first reporting configuration, where X is a positive integer.

[0239] As an embodiment, the validity of the first measurement configuration depending on a report configured by the first reporting configuration being triggered means that the first measurement configuration is valid when the first report is triggered, and the first measurement configuration is not valid when the first report is not triggered.

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

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

[0242] As an embodiment, the first signaling comprises a plurality of sub-signaling respectively indicating the first report configuration and the first measurement configuration.

[0243] As an embodiment, a measurement configuration field comprised by the first signaling indicates the first report configuration and the first measurement configuration.

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

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

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

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

[0248] As an embodiment, the AI identification comprises an AI model identification.

[0249] As an embodiment, the AI identification comprises an AI configuration identification.

[0250] As an embodiment, the first signaling indicates a first time window, and the validity of the first measurement configuration depends on that the first report is triggered in a meaning that the first measurement configuration is valid within the first time window when the report configured by the first measurement configuration is triggered.

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

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

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

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

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

[0256] As an embodiment, the second measurement configuration set comprises 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 above method has the advantage that two measurement configuration sets can be flexibly controlled according to different situations.

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

[0261] As an embodiment, the meaning that the first measurement configuration is associated with an identity of an AI model includes that the first measurement configuration is configured with AI.

[0262] As an embodiment, the meaning that the first measurement configuration is associated with an identity of an AI model includes that the first measurement configuration is configured with AI measurement.

[0263] As an embodiment, the meaning that the first measurement configuration is associated with an identity of an AI model includes that the first measurement configuration is configured with measurement for AI.

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

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

[0266] As an embodiment, the meaning that the first measurement configuration is associated with an identity of an AI model includes that 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 comprises a measurement report generated according to the reporting configuration included in the first measurement configuration.

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

[0269] As an embodiment, the above method has the advantage that AI can be better supported.

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

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

[0272] As one embodiment, the implication that only the latter of the first measurement configuration and the second measurement configuration is associated with an identity of an AI model includes that the first measurement configuration is based on a legacy measurement.

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

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

[0275] As one embodiment, the implication that only the latter of the first measurement configuration and the second measurement configuration is associated with an identity of an AI model includes that the second measurement configuration is configured with AI.

[0276] As one embodiment, the implication that only the latter of the first measurement configuration and the second measurement configuration is associated with an identity of an AI model includes that the second measurement configuration is configured with AI measurement.

[0277] As one embodiment, the implication that only the latter of the first measurement configuration and the second measurement configuration is associated with an identity of an AI model includes that the second measurement configuration is configured with measurement for AI.

[0278] As one embodiment, the implication that only the latter of the first measurement configuration and the second measurement configuration is associated with an identity of an AI model includes that the measurement result obtained according to the second measurement configuration is an input of AI.

[0279] As one embodiment, the implication that only the latter of the first measurement configuration and the second measurement configuration is associated with an identity of an AI model includes that the measurement report generated according to the second measurement configuration is an input of AI.

[0280] As one embodiment, the implication that only the latter of the first measurement configuration and the second measurement configuration is associated with an identity of an AI model includes that the 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 comprises: 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 comprises: 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 the AI-based measurement has a problem, it can be timely fallback to the traditional measurement, while avoiding the problems such as power consumption, resource occupation, etc. when using the traditional measurement at the same time.

[0284] Embodiment 2

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

[0286] FIG. 2 illustrates a diagram of a network architecture 200 for 5G NR, LTE (Long-Term Evolution), and LTE-A (Long-Term Evolution Advanced) systems. The 5G NR or LTE network architecture 200 can be referred to as a 5GS (5G System) / EPS (Evolved Packet System) 200 or some other suitable terminology. The 5GS / EPS 200 can include one or more UEs (User Equipment) 201, NG-RAN (Next Generation Radio Access Network) 202, 5GC (5G Core Network, 5G Core Network) / EPC (Evolved Packet Core) 210, HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and Internet services 230. The 5GS / EPS can interconnect with other access networks, but these entities / interfaces are not shown for simplicity. As shown, the 5GS / EPS provides packet-switched services, however, one of ordinary skill 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. The NG-RAN includes an NR NodeB (gNB) 203 and other NR NodeBs (gNBs) 204. The gNB 203 provides user and control plane protocol terminations toward the UE 201. The gNB 203 can be connected to other gNBs 204 via an Xn interface (e.g., backhaul). The gNB 203 can 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 (Transmit Receive Point), or some other suitable terminology. The gNB 203 provides access to the 5GC / EPC 210 for the UE 201. Examples of UEs 201 include a cellular phone, a smart phone, a session initiation protocol (SIP) phone, a laptop, a personal digital assistant (PDA), a satellite radio, a non-tethered radio, a satellite mobile communication, a global positioning system, a multimedia device, a video device, a digital audio player (e.g., MP3 player), a camera, a game console, a drone, a vehicle, an automobile, a wearable device, or any other similar functional device.A person of ordinary skill in the art can also refer to the 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 terminology. The gNB 203 is connected by an S1 / NG interface to the 5GC / EPC 210. The 5GC / EPC 210 includes a MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MME / AMF / SMF 214, a S-GW (Service Gateway) / UPF (User Plane Function) 212, and a P-GW (Packet Date Network Gateway) / UPF 213. The MME / AMF / SMF 211 is a control node that handles signaling between the UE 201 and the 5GC / EPC 210. Generally, the MME / AMF / SMF 211 provides bearer and connection management. All user IP (Internet Protocal) packets are transferred through the S-GW / UPF 212, which itself is connected to the P-GW / UPF 213. The P-GW provides UE IP address allocation as well as other functions. The P-GW / UPF 213 is connected to Internet services 230. The Internet services 230 include operator corresponding Internet protocol services, which can specifically include the Internet, an intranet, an IMS (IP Multimedia Subsystem), and a packet switched streaming service.

[0287] As one embodiment, the first node in the present application is the UE 201.

[0288] As one embodiment, the base station of the second node in the present application is the gNB 203.

[0289] As one embodiment, the wireless link from the UE 201 to the NR Node B is an uplink.

[0290] As one embodiment, the wireless link from the NR Node B to the UE 201 is a downlink.

[0291] As one embodiment, the UE 201 is a mobile phone.

[0292] As one example, the UE 201 is a special purpose device or special equipment having communication functionality.

[0293] As one example, the gNB 203 is a Micro Cell base station.

[0294] As one example, the gNB 203 is a Pico Cell base station.

[0295] As one example, the gNB 203 is a base station used in a home network.

[0296] As one example, the gNB 203 is a base station used in a private network.

[0297] Embodiment 3

[0298] Embodiment 3 shows a diagram of an embodiment of a radio protocol architecture for a user plane and a control plane according to the present application, as shown in FIG. 3. FIG. 3 is a diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300, which shows the radio protocol architecture for the control plane 300 between a first node (UE, gNB) and a second node (gNB, UE), or two UEs, in three layers: Layer 1, Layer 2, and Layer 3. Layer 1 (LI layer) is the lowest layer and implements various PHY (Physical layer) signal processing functions. The LI layer will be referred to as the PHY 301 herein. Layer 2 (L2 layer) 305 is above the PHY 301 and is responsible for the link between the first node and the second node, as well as between two UEs, through the PHY 301. The L2 layer 305 includes a MAC (Medium Access Control) sublayer 302, a RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, which are terminated at the second node. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security functions, such as ciphering of the data packets, and packet head compression, as well as handover support for the first node between the second nodes. 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 the various radio resources (e.g., resource blocks) in one cell among the UEs. 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 layers using RRC signaling between the second node and the first node. The PC5-S (PC5 Signaling Protocol) sublayer 307 is responsible for handling the signaling protocol for the PC5 interface. The radio protocol architecture for the user plane 350 includes Layer 1 (LI layer) and Layer 2 (L2 layer), which are generally the same as the corresponding layers and sublayers in the control plane 300 for the PHY 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 for the first node and the second node in the user plane 350, 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 a SDAP (Service Data Adaptation Protocol) sublayer 356, which is responsible for the mapping between a QoS flow and a data radio bearer (DRB) to support the diversity of services. SRBs can be seen as services or interfaces provided by the PDCP layer to higher layers, such as the RRC sublayer. In the NR system, SRBs include SRB1, SRB2, SRB3, which are used to transmit different types of control signaling. SRBs are bearers between the UE and the access network for transmitting control signaling including RRC signaling between the UE and the access network. SRB1 is of particular significance to the UE, and each UE establishes an RRC connection after SRB1 is established, which is used to transmit RRC signaling. Most signaling is transmitted through SRB1, and if SRB1 is interrupted or cannot be used, the UE must perform RRC reestablishment. SRB2 is generally used only to transmit NAS signaling or signaling related to security. The UE can not configure SRB3. Except for emergency services, the UE must establish an RRC connection with the network to perform subsequent communication. Although not shown, the first node can have several upper layers above the L2 layer 355. In addition, there is also a network layer (for example, an IP layer) that terminates at the P-GW on the network side and an application layer that terminates at the other end of the connection (for example, a remote UE, a server, etc.). The protocol layers can also be referred to as protocol sublayers. FIG. 3 shows a general protocol layer structure, and the nodes used in the present application can lack some protocol layers.

[0299] As an embodiment, the wireless protocol architecture in FIG. 3 is applicable to the first node in the present application.

[0300] As an embodiment, the wireless protocol architecture in FIG. 3 is applicable to the second node in the present application.

[0301] As an embodiment, the first signaling in the present application is generated at the RRC sublayer 306 or the NAS.

[0302] As an embodiment, the first report in the present application is generated at the RRC sublayer 306 or the MAC sublayer 302 or the PHY 301.

[0303] As an embodiment, the second report in the present application is generated at the RRC sublayer 306 or the MAC sublayer 302 or the PHY 301.

[0304] Embodiment 4

[0305] Figure 4 is a block diagram of a first communication device 450 and a second communication device 410 in communication with each other in an access network, in accordance with one embodiment of the present application.

[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 optionally, 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 receive processor 470, a transmit processor 416, and optionally, a multi-antenna receive processor 472, a multi-antenna transmit processor 471, a transmitter / receiver 418, and an antenna 420.

[0308] In transmissions from the second communication device 410 to the first communication device 450, upper layer packets from the core network are provided to the controller / processor 475 at the second communication device 410. The controller / processor 475 implements functionality of the L2 layer. In transmissions from the second communication device 410 to the first communication device 450, the controller / processor 475 provides header compression, ciphering, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocation for the first communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for retransmission of lost packets, and signaling to the first communication device 450. The transmit processor 416 and the multiple antenna transmit processor 471 implement various signal processing functions for the LI layer (i.e., physical layer). The transmit processor 416 implements coding and interleaving to facilitate forward error correction (FEC) at the second communication device 410, and mapping of coded bits to modulation symbols 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 multiple antenna transmit processor 471 performs digital spatial pre-coding of the coded and modulated symbols, including codebook-based and non-codebook-based pre-coding, and beamforming processing, to generate one or more spatial streams. The transmit processor 416 then maps to each spatial stream to the subcarriers, multiplexes the stream with reference signals (e.g., pilot) in the time and / or frequency domain, and then performs an inverse fast Fourier transform (IFFT) to generate a time-domain multicarrier symbol stream for the physical channel. The multiple antenna transmit processor 471 then performs transmit analog pre-coding / beamforming operations on the time-domain multicarrier symbol stream. Each transmitter 418 converts the baseband multicarrier symbol stream provided by the multiple antenna transmit processor 471 into a radio frequency stream, and then provides the radio frequency stream to the corresponding antenna 420.

[0309] In transmissions 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 respective antenna 452. Each receiver 454 recovers information modulated onto an RF carrier and provides the recovered information at baseband as a stream of symbols to a receive processor 456. The receive processor 456 and a multiple access receiver processor 458 implement various signal processing functions of the Ll layer. The multiple access receiver processor 458 performs receive analog precoding / beamforming operations on the baseband multiple access symbol streams from the receivers 454. The receive processor 456 converts the baseband multiple access symbol streams from the time-domain to the frequency domain using a Fast Fourier Transform (FFT). In the frequency domain, the physical layer data signals and the reference signals are demultiplexed from the received symbol streams, with the reference signals to be used for channel estimation and the data signals to be recovered after multiple access detection in the multiple access receiver processor 458 for any spatial streams destined for the first communication device 450. The symbols on each spatial stream are demodulated and recovered by the receive processor 456 and used to generate soft decisions. The receive processor 456 then decodes and de-interleaves 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 a controller / processor 459. The controller / processor 459 implements the functions of the L2 layer. The controller / processor 459 can be associated with a memory 460 that stores program codes and data. The memory 460 can be referred to as a computer-readable medium. In transmissions from the second communication device 410 to the first communication device 450, the controller / processor 459 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover 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 can also be provided to the 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 a controller / processor 459. The data source 467 represents all protocol layers above the L2 layer. Similar to the transmit 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, ciphering, packet segmentation and reordering, and multiplexing between logical and transport channels based on radio resource allocations, implements L2 layer functionality for the user plane and control plane. The controller / processor 459 is also responsible for error detection, retransmission of lost packets, and signaling to the second communication device 410. A transmit processor 468 performs modulation mapping, channel coding processing, and a multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based precoding and non-codebook-based precoding, and beamforming processing, and then the transmit processor 468 modulates the resulting spatial streams into multi-carrier / single-carrier symbol streams, which are then provided to different antennas 452 via transmitters 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 signal, and then provides the radio frequency signal to the antenna 452.

[0311] In the transmission from the first communication device 450 to the second communication device 410, the functions at the second communication device 410 are similar to the receive functions 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 respective antenna 420, converts the received radio frequency signal into a baseband signal, and provides the baseband signal to a multi-antenna receive processor 472 and a receive processor 470. The receive processor 470 and the multi-antenna receive processor 472 collectively implement the functionality of the L1 layer. A controller / processor 475 implements the functionality of the L2 layer. The controller / processor 475 can be associated with a memory 476 that stores 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 demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover upper layer data packets from the UE 450. Upper layer data packets from the controller / processor 475 can be provided to a core network.

[0312] As one embodiment, the first communication device 450 comprises: at least one processor and at least one memory including a computer program code; the at least one memory and the computer program code are configured to, with the at least one processor, cause the first communication device 450 to perform at least the following: receive a first signaling, the first signaling indicating a first measurement configuration and a first reporting configuration; trigger a first reporting according to the first reporting configuration; perform the first measurement configuration in response to the first reporting being triggered.

[0313] As one embodiment, the first communication device 450 comprises: a memory storing a computer program code, the computer program code, when executed by at least one processor, causing actions comprising: receiving a first signaling, the first signaling indicating a first measurement configuration and a first reporting configuration; triggering a first reporting according to the first reporting configuration; performing the first measurement configuration in response to the first reporting being triggered.

[0314] As one embodiment, the first communication device 450 corresponds to a first node in the present application.

[0315] As one embodiment, the second communication device 410 corresponds to a second node in the present application.

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

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

[0318] As one embodiment, the first communication device 450 is a relay.

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

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

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

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

[0323] Embodiment 5

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

[0325] For the first node U01, the first signaling is received in step S5101; the first report is triggered according to the first report configuration in step S5102; the first report is sent in step S5103; the first measurement configuration is performed in step S5104; and the second report is sent in step S5105.

[0326] For the second node U02, the first signaling is sent in step S5201; the first report is received in step S5202; and the second report is received in step S5203.

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

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

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

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

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

[0332] As an embodiment, the second node U02 is the first cell or a 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 embodiment, the numbering order of the steps shown in FIG. 5 is the time sequence.

[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 earlier than step S5102.

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

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

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

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

[0342] As one embodiment, step S5103 is performed when the first report triggered according to the first report configuration includes generating the first report.

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

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

[0345] As one embodiment, when step S5103 is performed, step S5102 generates the first report carrying or corresponding RRC message.

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

[0347] As one embodiment, the benefit of sending the first report includes facilitating the network to master the situation of the first node U01, for example, the network to understand that the first measurement configuration is performed, thereby facilitating the optimization of the network.

[0348] As one embodiment, step S5103 is not performed.

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

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

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

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

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

[0354] As one sub embodiment of the embodiment, the first report comprises a prediction of a radio link failure.

[0355] As one sub embodiment of the embodiment, the first report comprises a prediction of a handover or a handover failure.

[0356] As one sub embodiment of the embodiment, the first report comprises a prediction of a motion state.

[0357] As one sub embodiment of the embodiment, the first report comprises a prediction of a motion route.

[0358] As one embodiment, benefits of the above method include: by triggering the execution of the first measurement configuration through the first report, it is beneficial to support very complex trigger mechanisms, even unquantifiable trigger mechanisms, better support AI, and better support nonlinear systems.

[0359] As one embodiment, step S5103 can be delayed to be executed.

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

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

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

[0363] As one embodiment, the delay execution comprises execution after step S5104.

[0364] As one embodiment, the delay execution comprises execution after a given time.

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

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

[0367] As one embodiment, the delay execution comprises execution after at least once.

[0368] As one embodiment, benefits of delaying the execution of step S5103 include: resource can be saved.

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

[0370] As an embodiment, the execution of the first measurement configuration comprises performing a corresponding measurement.

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

[0372] As an embodiment, the first measurement configuration comprises a third report configuration.

[0373] As an embodiment, the execution of the first measurement configuration comprises performing a measurement according to the first measurement configuration.

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

[0375] As an embodiment, the benefit of sending the second report comprises that the network can be aware of the first node U01 stopping the execution of the first measurement configuration, which is beneficial for network optimization.

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

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

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

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

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

[0381] As an embodiment, the benefit of the above method comprises 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 report 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 report configuration is a measurement configuration other than the second measurement configuration.

[0384] As one embodiment, the measurement configuration associated with or corresponding to the reporting configuration relied by the second reporting is a measurement configuration other than the first measurement configuration.

[0385] As one embodiment, the measurement configuration associated with or corresponding to the reporting configuration relied by the second reporting is a measurement configuration other than the second measurement configuration.

[0386] Embodiment 6

[0387] Embodiment 6 illustrates a schematic diagram of RAN (Radio Access Network) domain AI / ML function deployment according to one embodiment of the present application, as shown in FIG. 6. The gNB in Embodiment 8 can be replaced by, for example, eNB, or 6G base station, and the like network device.

[0388] The AI / ML related functions include ML training function (also referred to as AI training, or AI / ML training), ML testing function, ML inference function (also referred to as AI inference, or AI / ML inference), and the like. The ML training function, the ML testing function, and the ML inference function can be deployed independently, or can be co-located. The deployment of the AI / ML related functions can be implemented through software, such as downloading and / or running of executable files; or can be implemented through software combined with hardware, such as acceleration of specific computing units through hardware to improve operation speed or save power consumption.

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

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

[0391] Similarly, the ML test function can also be deployed in the cross-domain management system or the domain-specific management system.

[0392] In embodiment 6, the RAN domain ML training function 1402 is located in the RAN domain management function 1403; and the ML inference function is located in the base station, i.e., the AI / ML inference function 1404 is located in the gNB 1405, the AI / ML inference function 1406 is located in the gNB 1407, and so on.

[0393] In FIG. 6, the management of the ML inference functions of multiple base stations is completed by the RAN domain management function 1403, i.e., data interaction is performed with the RAN domain MnS (Management Service) consumer / cross-domain management 1401 (as shown by the dashed arrow in FIG. 6).

[0394] Optionally, the management of the ML inference function can also be completed by the base station itself, i.e., each base station can independently perform data interaction with the RAN domain MnS consumer / cross-domain management 1401.

[0395] It should be noted that embodiment 6 is only a non-limiting implementation; optionally, the ML training function of the RAN domain can also be deployed in the base station; or optionally, part of the base stations deploy the ML inference function and the ML training function of the RAN domain, and part of the base stations only deploy the ML inference function.

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

[0397] As an example, the second processor includes one AL / ML inference function in FIG. 6, i.e., 1404 or 1406.

[0398] As an example, one AL / ML inference function in FIG. 6 performs ML training according to the target precoding vector; the target reference signal is different from the first reference signal; any reference signal in the multiple reference signals except the first reference signal is associated to a precoding vector, wherein the target precoding vector is associated to the target reference signal.

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

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

[0401] As one embodiment, the execution of the first measurement configuration relies on at least one function in FIG. 6.

[0402] As one embodiment, the first report relies on training, the first measurement configuration does not rely on training, or, the first report does not rely on training, the first measurement configuration relies on training.

[0403] Embodiment 7

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

[0405] The RAN domain ML training function 1505 in FIG. 7 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 inference function 1506; the AI / ML inference function 1506 uses a ML model (also referred to as an AI model) for inference; one ML model usually undergoes training before being used for AI / ML inference.

[0407] As one embodiment, the UE function 1504 includes a RAN domain ML training function 1505, which runs training data through a ML model to derive a related loss, and adjusts parameters of the ML model based on the calculated loss; 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 the air interface resources caused by the reporting of training data; however, the above embodiments put higher requirements on the processing capability of the UE side.

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

[0410] Optionally, the UE function 1504 further includes an AI / ML deployment function (not included in FIG. 7) for loading ML models and data.

[0411] As an embodiment, the first node indicates whether the ML training function (RAN domain or CN domain) is supported through capability reporting, which 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 an MnS (Management Service) producer that provides 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 indicated by the double-headed arrow 1507).

[0414] Optionally, the UE function 1504 is an MnS (Management Service) consumer that 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 indicated by the double-headed arrow 1507).

[0415] As an embodiment, the first channel information in the present application is obtained through inference of the AI / ML inference 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 in the plurality of reference signals other than the first reference signal is associated to a precoding vector, wherein the target precoding vector is associated to the target reference signal.

[0417] As an embodiment, the first processor includes an AL / ML inference function 1506 in FIG. 7.

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

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

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

[0421] As one embodiment, the generating or sending of the first report uses at least one function in FIG. 7.

[0422] As one embodiment, the performing of the first measurement configuration uses at least one function in FIG. 7.

[0423] As one embodiment, the performing of the first measurement configuration relies on at least one function in FIG. 7.

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

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

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

[0427] As one embodiment, the first report relies on training, the first measurement configuration does not rely on training, or, the first report does not rely on training, the first measurement configuration relies on training.

[0428] Embodiment 8

[0429] Embodiment 8 illustrates an AI / ML based diagram according to one embodiment of the present application, as shown in FIG. 8.

[0430] FIG. 8 includes a third operation, a fourth operation, a fifth operation, a sixth operation, and a seventh operation. In embodiment 11, the third operation and the fourth operation belong to a first stage, the fifth operation belongs to a second stage, the sixth operation belongs to a third stage, and the seventh operation belongs to a fourth stage. In FIG. 8, the line with arrow indicates the order of the flow.

[0431] As one 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 comprises a training phase, the second phase comprises an emulation phase, the third phase comprises a deployment phase, and the fourth phase comprises an inference phase.

[0433] As an embodiment, the first phase comprises AI / ML model training.

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

[0435] As an embodiment, the AI / ML model training comprises initial training and re-training of one or a set 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 comprises AI / ML entity validation.

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

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

[0440] As an embodiment, if the result of AI / ML entity validation does not meet the expectation, the AI / ML model will be re-trained.

[0441] As an embodiment, the AI / ML testing comprises testing the validated AI / ML entity to evaluate the performance of the trained AI / ML model.

[0442] As an embodiment, if the result of AI / ML testing meets the expectation, the AI / ML entity proceeds to the next phase; otherwise, the AI / ML model will be re-trained.

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

[0444] As an embodiment, the second phase comprises AI / ML emulation, which performs inference of the AI / ML entity in an emulation environment.

[0445] As one embodiment, the AI / ML simulation estimates the performance of AI / ML entity inference in a simulation environment before the AI / ML entity is used.

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

[0447] As one embodiment, the third stage includes AI / ML entity loading for obtaining a trained AI / ML entity for a desired AI / ML inference function.

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

[0449] As one embodiment, the third stage is not needed when the training function and the inference function are co-located.

[0450] As one embodiment, the fourth stage includes AI / ML inference.

[0451] As one embodiment, the generation or sending of the first report uses at least one operation in FIG. 8.

[0452] As one embodiment, the first report uses inference.

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

[0454] As one embodiment, the execution of the first measurement configuration uses at least one operation in FIG. 8.

[0455] As one embodiment, the execution of the first measurement configuration relies on at least one operation in FIG. 8.

[0456] As one embodiment, the first report relies on training, the first measurement configuration does not rely on training, or the first report does not rely on training, the first measurement configuration relies on training.

[0457] Embodiment 9

[0458] Embodiment 9 illustrates an example of performing a first measurement configuration according to one embodiment of the present application, as shown in FIG. 9.

[0459] The rectangles in FIG. 9 represent the time periods when the first measurement configuration is performed, the different widths of the rectangles indicate that the lengths of the time periods when the first measurement configuration is performed can be different, and the different intervals between the rectangles indicate that the intervals of the time periods when the first measurement configuration is performed can be different.

[0460] As one embodiment, the method presented in this application does not limit the first measurement configuration to be performed multiple times.

[0461] As one embodiment, the first measurement configuration can be performed multiple times.

[0462] As one embodiment, the first measurement configuration is performed for at least one time period.

[0463] As one embodiment, the first measurement configuration is performed for multiple time periods.

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

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

[0466] As one embodiment, the multiple time periods are aperiodic.

[0467] As one embodiment, the time gap is greater than 0.

[0468] As one embodiment, the time gap is greater than one measurement period.

[0469] As one embodiment, the time gap is greater than one reporting period.

[0470] As one embodiment, the time gap is greater than the period of the first reporting.

[0471] As one embodiment, the performance of the first measurement configuration can be aperiodic.

[0472] As one embodiment, the time of the performance of the first measurement configuration can be non-equal length.

[0473] As one embodiment, although the measurements of the first node can be periodic or have a certain time gap, the performance of the first measurement configuration for one time period means that the first measurement configuration is active or activated for the one time period.

[0474] As one embodiment, although the measurements of the first node can be periodic or have a certain time gap, the performance of the first measurement configuration for one time period means that the first measurement configuration is active or activated for the one time period.

[0475] As one embodiment, although the measurements of the first node can be periodic or have a certain time gap, the performance of the first measurement configuration for one time period means that the first measurement configuration is active or activated for the one time period.

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

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

[0478] As one embodiment, although the measurement of the first node can be periodic or have a certain time interval, the meaning of the execution of the first measurement configuration within a time period is that the first measurement configuration is not effective or deactivated outside the at least one time period.

[0479] As one embodiment, the start of each time period in FIG. 8 depends on the report triggered according to the first report configuration.

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

[0481] As one embodiment, the meaning of the start of each time period in FIG. 8 depending on the report triggered according to the first report configuration is that when a report is triggered according to the first report configuration, a time period in FIG. 8 starts.

[0482] As one embodiment, the end of any time period in FIG. 8 depends on the report triggered according to the second report configuration.

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

[0484] As one embodiment, the meaning of the end of any time period in FIG. 8 depending on the report triggered according to the second report configuration includes that when a report is triggered according to the second report configuration, a time period in FIG. 8 ends.

[0485] As one embodiment, the benefit of the above method includes that the execution of the first measurement configuration is more flexible.

[0486] As one embodiment, the first report configuration and the second report configuration correspond to different measurement configurations.

[0487] As one embodiment, the first report configuration and the second report configuration correspond to different measurement identities.

[0488] Embodiment 10

[0489] Embodiment 10 illustrates a schematic diagram of performing a difference between a measurement result in a first report and a measurement result of a third measurement configuration according to a first measurement configuration, as shown in FIG. 10.

[0490] As an embodiment, the performing the difference between the measurement result in the first report and the measurement result of the third measurement configuration according to 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 report configuration and the third measurement configuration is associated with an AI model identification.

[0492] As an embodiment, the meaning that only one of the first report configuration and the third measurement configuration is associated with an AI model identification includes that the first report configuration is associated with an AI model identification, and the third measurement configuration is not associated with an AI model identification.

[0493] As an embodiment, the meaning that only one of the first report configuration and the third measurement configuration is associated with an AI model identification includes that the first report configuration is not associated with an AI model identification, and the third measurement configuration is associated with an AI model identification.

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

[0495] As an embodiment, the first report configuration being associated with an AI model identification means that the first report configuration includes a configuration for AI to generate a measurement report.

[0496] As an embodiment, the first report configuration being associated with an AI model identification means that a measurement report triggered according to the first report configuration is AI generated.

[0497] As an embodiment, the first report configuration being associated with an AI model identification means that a measurement report triggered according to the first report configuration is for AI.

[0498] As an embodiment, the first report configuration not being associated with an AI model identification means that a measurement report triggered according to the first report configuration is not AI generated.

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

[0500] As an embodiment, the first reporting configuration not being associated with the AI model identifier means that the first reporting configuration does not configure AI.

[0501] As an embodiment, the first reporting configuration not being associated with the AI model identifier means that the first reporting configuration does not configure AI-generated measurement report.

[0502] As an embodiment, the above method has the advantage of being able to control whether to perform the first measurement configuration according to the measurement results of multiple measurement configurations, being more accurate, being more flexible, and having lower complexity.

[0503] As an embodiment, the above method has the advantage of being able to determine whether to perform the first measurement configuration according to the error of the measurement results obtained by different reporting configurations, being conducive to obtaining more accurate measurement results, and avoiding errors.

[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 of 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 correct 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 above method has the advantages that: the execution of the first measurement configuration can be more comprehensive and flexible, the configuration of the CSI-RS is very flexible, the measurement on the SSB can better reflect the quality of a cell, the above method can compare the quality of different beams and master 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 being respectively associated with different AI model identifiers 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 being respectively associated with different AI model identifiers 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 being respectively associated with different AI model identifiers means that the measurement configurations to which the first reporting configuration and the third measurement configuration belong are respectively associated with different AI model identifiers.

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

[0520] As an embodiment, the output measurement results include inference or prediction measurement results.

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

[0522] As an embodiment, the above method has the advantages that: it is conducive to comprehensive utilization of different AI models and better control of the indication of the first measurement configuration.

[0523] Embodiment 11

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

[0525] In embodiment 11, the first receiver 1101 receives first signaling, and 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 performs the first measurement configuration in response to the first report being triggered.

[0527] As an embodiment, the triggering the first report comprises transmitting the first report.

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

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

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

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

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

[0533] As an embodiment, the performing the first measurement configuration depends on a difference between a measurement result in the first report and a measurement result of a third measurement configuration, wherein the performing 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 value.

[0534] As an embodiment, only one of the first report configuration and the third measurement configuration is associated with an identification of an AI model.

[0535] As an embodiment, the performing the first measurement configuration depends on a difference between a measurement result in the first report and a measurement result of a third measurement configuration, wherein the performing 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 value.

[0536] As an embodiment, the first report configuration and the third measurement configuration are respectively associated with different identifications of AI models.

[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 low latency enabled communication device.

[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 comprises at least one of the antenna 452, the receiver 454, the receive processor 456, the multi-antenna receive processor 458, the controller / processor 459, the memory 460, or the data source 467 in embodiment 4.

[0543] As an embodiment, the first transmitter 1102 comprises at least one of the antenna 452, the transmitter 454, the transmit processor 468, the multi-antenna transmit processor 457, the controller / processor 459, the memory 460, or the 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 instructed by a program to relevant hardware, 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, etc. Alternatively, all or part of the steps of the above embodiments can also be implemented using one or more integrated circuits. Correspondingly, each module unit in the above embodiments 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 combination of software and hardware. The user equipment, terminal and UE in the present application include but are not limited to unmanned aerial vehicles, communication modules on unmanned aerial vehicles, remote control aircrafts, aircrafts, small aircrafts, mobile phones, tablet computers, notebooks, vehicle-mounted communication devices, wireless sensors, network cards, Internet of Things terminals, RFID terminals, NB-IoT terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, network cards, vehicle-mounted communication devices, low-cost mobile phones, low-cost tablet computers, satellite communication devices, ship communication devices, NTN user equipment, and other wireless communication devices. The base station or system equipment in the present application includes but is not limited to macro cellular base stations, micro cellular base stations, home base stations, relay base stations, gNB (NR NodeB) NR NodeB, TRP (Transmitter Receiver Point), NTN base stations, satellite devices, flight platform devices, and other wireless communication devices.

[0545] This application can be implemented in other specific forms without departing from its core or essential characteristics. Accordingly, the presently disclosed embodiments are to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are to be embraced therein.

Claims

A first node used for measurement in wireless communication, wherein, Comprising: a first receiver configured to receive a first signaling, the first signaling indicating a first measurement configuration and a first reporting configuration; a first processor configured to trigger a first report according to the first reporting configuration; in response to the first report being triggered, perform the first measurement configuration. The first node of claim 1, wherein: the triggering the first report comprises sending the first report. The first node according to claim 1 or 2, characterized in that, Comprising: the first receiver configured to perform a measurement according to a second measurement configuration and generate a first measurement result, the first report comprising the first measurement result. The first node according to any one of claims 1 to 3, characterized in that Comprising: a first processor configured to trigger a first report according to the first reporting configuration; in response to the first report being triggered, perform the first measurement configuration. The first node of any of claims 1-4, wherein: any measurement configuration in a first measurement configuration set is performed when the first report is triggered; and any measurement configuration in a second measurement configuration set is performed when the first report is not triggered. The first node of any of claims 1-5, wherein: the first measurement configuration is associated with an identification of an AI model. The first node of claim 5, wherein: only the second measurement configuration of the first measurement configuration and the second measurement configuration is associated with an identification of an AI model. The first node of any of claims 1-7, wherein: the performing the first measurement configuration is dependent on a difference between a measurement result in the first report and a measurement result of a third measurement configuration, wherein the performing 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 value; wherein only one of the first reporting configuration and the third measurement configuration is associated with an AI model identification. The first node of any of claims 1-7, wherein: the performing the first measurement configuration is dependent on a difference between a measurement result in the first report and a measurement result of a third measurement configuration, wherein the performing 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 value; wherein the first reporting configuration and the third measurement configuration are respectively associated with different AI model identifications. A method in a first node used for measurement in wireless communication, wherein Comprising: receiving a first signaling, the first signaling indicating a first measurement configuration and a first reporting configuration; triggering a first report according to the first reporting configuration; in response to the first report being triggered, performing the first measurement configuration.

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