Method and apparatus used in node for wireless communication and artificial intelligence

Abstract

The present application discloses a method and apparatus used in a node for wireless communication and artificial intelligence. A first node receives a first information block, the first information block configuring a first RS set; performs measurement in the first RS set, the measurement or prediction for the first RS set being used to trigger a first measurement report; and sends the first measurement report, wherein whether the first measurement report comprises an information element in a first information element set depends on a trigger mode of the first measurement report, the trigger mode of the first measurement report is predictive or non-predictive, and the first information element set comprises at least one of the following: a first air interface resource set for the first node; a target of the prediction for the first measurement report; a confidence interval of the prediction for the first measurement report; a time window for the first measurement report; and a prediction mode of the first measurement report. The present application improves the probability of successful handover.

Description

A method and apparatus in a node for wireless communication and artificial intelligence TECHNICAL FIELD

[0001] The present application relates to a signal transmission method and device in a wireless communication system, in particular to a method and device of channel information. BACKGROUND

[0002] The application scenarios of future wireless communication systems are increasingly diversified. In order to meet the different performance requirements of different scenarios, 3GPP (3rd Generation Partner Project) is actively studying how to combine AI (Artificial Intelligence) / ML (Machine Learning) technology with mobile communication. The main application scenarios include network automation, optimization of resource allocation, and improvement of service continuity for mobile users, etc.

[0003] The mobility of user equipment (UE) is an important feature of wireless networks. In order to further enhance the mobility performance of the UE, the L1 / L2 triggered mobility (L1 / L2 Triggered Mobility, LTM) introduced in 3GPP Rel-18 (Release-18) is an important research direction to reduce latency, overhead and interruption time. Rel-19 will further study the support of inter-cell handover (HandOver, HO) across centralized units (Centralized Unit), and explore the feasibility of using AI / ML for beam prediction and UE mobility prediction.

[0004] Further, AI / ML can achieve load balancing and energy saving by analyzing and predicting resource state information such as physical resource block utilization of neighboring cells and serving cells, and number of active UEs. In addition, AI / ML can also optimize wireless resource management strategies by predicting the moving trajectory of the UE, so that the network can allocate resources and make handover decisions according to the expected moving path of the UE. AI / ML-based mobility management not only helps to improve the accuracy and efficiency of handover, but also reduces the interruption time during handover, and improves user experience. In the future, 3GPP will further deepen the application of AI / ML in mobility management, promote the deep integration of AI / ML and communication networks, and improve the intelligent level of the network, optimize mobility management, and improve the spectrum and energy efficiency, to provide users with more stable and efficient mobile communication services. SUMMARY

[0005] In the NR (New Radio) system, network-controlled mobility can be applied to the UE (User Equipment) in the RRC_CONNECTED state; specifically, the network configures the UE with measurement parameters and reporting parameters through higher layer signaling, the UE in the RRC_CONNECTED state performs measurement according to the measurement configuration, and sends a measurement report when the condition is met, and the base station receives the measurement report and decides whether to hand over the UE to other cells based on the measurement report; in the AI / ML scenario, the AI / ML model can predict the UE's moving path through massive data, helping the base station to make better resource allocation and handover decisions, so the enhancement of AI / ML-based mobility management is a problem worth studying.

[0006] To solve the above problems, a solution is disclosed in the present application. It should be noted that in the description of the above problems, the NR system is taken as an example, and the present application is also applicable to scenarios such as future 6G systems, achieving similar technical effects as the NR system; further, although the original intention of the present application is for AI / ML scenarios, the present application can also be applied to other non-AI / ML scenarios; further, a unified design scheme for different scenarios (such as other non-AI / ML scenarios, including but not limited to V2X (Vehicle to Everything), capacity enhancement system, near distance communication system, NTN (Non Terrestrial Network), IoT (Internet of Things), URLLC (Ultra Reliable Low Latency Communication) network, etc.) can also help to reduce hardware complexity and cost. In the case of no conflict, the embodiments in any node of the present application and the features in the embodiments can be applied to any other node. In the case of no conflict, the embodiments of the present application and the features in the embodiments can be arbitrarily combined with each other.

[0007] In particular, the explanation of the terminology, nouns, functions, variables in the present application (if not specially stated) can refer to the definitions in TS38 series, TS37 series in the technical standards (Technical Specification, TS) of 3GPP (the 3rd Generation Partnership Project). If necessary, TS38.211, TS38.212, TS38.213, TS38.214, TS38.215, TS38.300, TS38.304, TS38.305, TS38.321, TS38.331, TS37.355, TS38.423 in the technical standards of 3GPP can be referred to for the understanding of the present application.

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

[0009] As an embodiment, the explanation of the terminology in the present application refers to the definitions in the specification protocol TS37 series of 3GPP.

[0010] As an embodiment, the explanation of the terminology in the present application refers to the definitions in the specification protocol TS40 series of 3GPP.

[0011] As an embodiment, the explanation of the terminology in the present application refers to the definitions in the specification protocol TS39 series of 3GPP.

[0012] As an embodiment, the explanation of the terminology in the present application refers to the definitions in the specification protocol Rel-17 version of 3GPP.

[0013] As an embodiment, the explanation of the terminology in the present application refers to the definitions in the specification protocol Rel-18 version of 3GPP.

[0014] As an embodiment, the explanation of the terminology in the present application refers to the definitions in the specification protocol Rel-19 version of 3GPP.

[0015] As an embodiment, the explanation of the terminology in the present application refers to the definitions in the specification protocol Rel-20 version of 3GPP.

[0016] The present application discloses a method in a first node used for wireless communication and artificial intelligence, comprising:

[0017] Receiving a first information block, the first information block configuring a first RS set; performing measurement in the first RS set, the measurement or prediction for the first RS set being used to trigger a first measurement report;

[0018] Sending the first measurement report;

[0019] Wherein, whether the first measurement report includes information units in a first information unit set depends on a triggering mode of the first measurement report, the triggering mode of the first measurement report being one of predicted and not predicted; the first information unit set includes at least one of the following:

[0020] - a first set of air interface resources for the first node;

[0021] - an object for which prediction for the first measurement report is made;

[0022] - a confidence interval of the prediction for the first measurement report;

[0023] - a time window for the first measurement report;

[0024] - a prediction mode of the first measurement report.

[0025] As an embodiment, the problem to be solved by the present application includes: how to improve the performance of Handover.

[0026] As an embodiment, the problem to be solved by the present application includes: how to improve the reliability of measurement reporting.

[0027] As an embodiment, the problem to be solved by the present application includes: information units included in measurement reporting based on AI / ML prediction.

[0028] As an embodiment, the problem to be solved by the present application includes: how to provide more information to the base station to improve the overall system performance in the scenario with AI / ML technology.

[0029] As an embodiment, the problem to be solved by the present application includes: in the traditional measurement report, only the measured cell and the corresponding measurement result are reported, and in the present scheme, the UE reports a first set of air interface resources based on prediction, because the reporting triggered based on AI / ML is earlier than the traditional reporting, and then the position of the UE after triggering the first measurement report is closer to the cell center than the traditional reporting, and then the first set of air interface resources needs to be reported to avoid interference and improve transmission quality.

[0030] As an embodiment, the characteristics of the above method include: in the present application, whether the first measurement report includes information units in the first information unit set depends on whether the first measurement report is predicted, thereby solving the above problem.

[0031] As an embodiment, the method has the feature that the UE indicates one or more of the following in the measurement report: predicted source cell and candidate cell, predicted set of radio resource configurations, confidence interval of the prediction result, trigger time of the time prediction, and model or prediction method used for the prediction.

[0032] As an embodiment, the method has the feature that the trigger mode of the first measurement report is based on AI / ML inference or only based on traditional reference signal measurement.

[0033] As an embodiment, the method has the feature that whether the first node triggers the first measurement report is based on AI / ML inference or only based on traditional reference signal measurement.

[0034] As an embodiment, the method has the feature that the first node generates channel state information of the serving cell and the neighbor cell based on AI / ML inference and determines whether to trigger the first measurement report based on the channel state information.

[0035] As an embodiment, the method has the feature that the first node determines whether to trigger the first measurement report based on the measurement result.

[0036] As an embodiment, the method has the benefit of supporting the integration of AI and communications, improving the adaptability and intelligence level of the communication system, and thus improving the performance, efficiency, and user experience of the communication system.

[0037] As an embodiment, the method has the benefit of enabling the base station to make a handover decision before the channel state of the serving cell deteriorates based on the predicted measurement report.

[0038] As an embodiment, the method has the benefit of reducing the handover latency, improving the probability of successful handover, and improving the performance of system handover.

[0039] As an embodiment, the method has the benefit of enabling the network to determine whether the measurement report is generated based on prediction based on whether the information units in the first set of information units are included in the measurement report, so that the base station has enough time to select an appropriate time point to send a handover command.

[0040] According to an aspect of the present application, the method has the feature that when the trigger mode of the first measurement report is prediction, the first measurement report includes the first set of information units; and when the trigger mode of the first measurement report is not prediction, the first measurement report does not include the first set of information units.

[0041] As an embodiment, the problem to be solved by the present application includes: information units included in measurement reporting based on AI / ML prediction.

[0042] As an embodiment, the feature of the above method includes: in the present application, whether the first information unit set is included in the first measurement report depends on whether the first measurement report is generated based on prediction, thereby solving the above problem.

[0043] As an embodiment, the feature of the above method includes: in the present application, the information units included in the measurement report generated based on prediction and not generated based on prediction are different.

[0044] As an embodiment, the benefit of the above method includes: the network can determine whether the measurement report is generated based on prediction based on whether the first information unit set is included in the measurement report, so that the base station has enough time to select an appropriate time point to send a handover command.

[0045] As an embodiment, the benefit of the above method includes: including the first information unit set in the measurement report generated based on prediction can help the base station to allocate resources more efficiently.

[0046] As an embodiment, the benefit of the above method includes: providing accurate network optimization suggestions, supporting scientific network planning and deployment.

[0047] According to one aspect of the present application, the above method is characterized in that: the first set of air interface resources includes at least one of spatial resources, power resources or transmission directions; the first set of air interface resources is for a serving cell of the first node, or the first set of air interface resources is for a candidate cell of the first node.

[0048] As an embodiment, the feature of the above method includes: the present application supports the UE indicating to the base station the set of air interface resources to which the measurement report is directed.

[0049] As an embodiment, the feature of the above method includes: the present application supports the UE indicating to the base station the set of air interface resources of the prediction candidate.

[0050] As an embodiment, the benefit of the above method includes: AI-based measurement reporting can help the base station to allocate resources such as spectrum, power, etc. more efficiently, and improve the utilization rate of network resources.

[0051] As an embodiment, the benefit of the above method includes: effectively reducing inter-cell and same-frequency interference, improving signal quality and network performance.

[0052] As an embodiment, the benefit of the above method includes: improving cell coverage.

[0053] According to an aspect of the present application, the method is characterized in that the object for which the prediction is made for the first measurement report comprises a serving cell, a neighbor cell, or a serving cell and a neighbor cell.

[0054] As an embodiment, the method is characterized in that the UE indicates at least one of the predicted source cell and the candidate cell in the measurement report generated based on the prediction to the base station.

[0055] As an embodiment, the method is characterized in that the serving cell of the first node is associated with one MeasObjectNR IE, the neighbor cell of the first node is associated with another MeasObjectNR IE, and the neighbor cell for which the prediction is made for the first measurement report comprises a serving cell associated with the another MeasObjectNR IE.

[0056] As an embodiment, the method is characterized in that the method helps to improve the accuracy of network handover decision and optimize the selection of candidate cells.

[0057] As an embodiment, the method is characterized in that the method enhances the stability of the network, improves the service performance of the system, and improves the user experience.

[0058] As an embodiment, the method is characterized in that the method reduces unnecessary handover processes and reduces system interruption caused by handover.

[0059] According to an aspect of the present application, the method is characterized in that the first timer of the first node expires when the first node does not receive a response to the first measurement report in the time window for the first measurement report.

[0060] As an embodiment, the method is characterized in that the first timer is maintained at the RRC layer.

[0061] As an embodiment, the method is characterized in that the expiration value of the first timer is configured by the network.

[0062] As an embodiment, the method is characterized in that the expiration value of the first timer depends on the maximum prediction time window of the AI / ML model.

[0063] As an embodiment, the method is characterized in that the introduction of the first timer can guarantee the continuity of network services, the expiration of the first timer can trigger the next step of behavior, avoid unnecessary resource occupation and waiting time, and reduce system interruption caused by long waiting for handover commands.

[0064] As an embodiment, the benefits of the above method include that the UE maintaining the first timer allows the UE to monitor for a response to the first measurement report only during the time window when the first timer is running, which facilitates reducing resource consumption and power of the UE, and improving terminal endurance.

[0065] As an embodiment, the benefits of the above method include that the UE does not retransmit the first measurement report during the running time of the first timer, which avoids the UE sending redundant measurement reports in a short time, thereby reducing signaling overhead and improving the effectiveness and accuracy of the measurement report.

[0066] According to an aspect of the present application, the above method is characterized in that the value range of the confidence interval of the prediction for the first measurement report depends on at least one of the following:

[0067] - the number of objects to which the prediction for the first measurement report is directed;

[0068] - the prediction manner of the first measurement report.

[0069] As an embodiment, the features of the above method include that in the present application, the user equipment indicates the confidence interval of the prediction to the network through the measurement report, and the value of the confidence interval depends on the AI / ML model used to trigger the measurement report.

[0070] As an embodiment, the features of the above method include that in the present application, different triggering events or different AI / ML models have different confidence intervals.

[0071] As an embodiment, the benefits of the above method include that the performance characteristics of each model are better matched, and the reliability of the prediction result is improved.

[0072] As an embodiment, the benefits of the above method include that the model is better trained and optimized to adapt to different tasks.

[0073] As an embodiment, the benefits of the above method include that the risk and benefit of the AI / ML model prediction are balanced, and different confidence intervals under different numbers of predicted objects or different prediction manners help to improve the overall performance of the system and optimize the computing resources of AI / ML.

[0074] As an embodiment, the benefits of the above method include that the adaptability and flexibility of the model are optimized, the performance is optimized, and the risk is managed.

[0075] According to an aspect of the present application, the method is characterized in that the configuration for the first measurement report comprises a first parameter; and the value of the first parameter depends on the triggering manner of the first measurement report, or whether the first parameter is used to trigger the first measurement report depends on the triggering manner of the first measurement report.

[0076] As an embodiment, the problem to be solved by the present application includes the configuration of measurement report based on AI / ML prediction.

[0077] As an embodiment, the method is characterized in that the configuration for the measurement report in the present application comprises a parameter based on prediction to solve the above-mentioned problem.

[0078] As an embodiment, the method is characterized in that the present application supports the network to configure the measurement parameters and report parameters based on AI / ML prediction for the UE through higher layer signaling.

[0079] As an embodiment, the method is characterized in that the present application supports the UE or the network to optimize the measurement parameters and report parameters based on AI / ML prediction.

[0080] As an embodiment, the method is characterized in that the above-mentioned method has the benefits of optimizing the measurement parameters and report parameters.

[0081] As an embodiment, the method is characterized in that the above-mentioned method has the benefits of AI / ML specific measurement parameters and report parameters, which are conducive to improving the accuracy of triggering measurement report.

[0082] As an embodiment, the method is characterized in that the above-mentioned method has the benefits of improving the intelligent level of the network, optimizing the resource utilization, and improving the user experience.

[0083] According to an aspect of the present application, the method is characterized in that the first node is a user equipment.

[0084] According to an aspect of the present application, the method is characterized in that the first node is a relay node.

[0085] According to an aspect of the present application, the method is characterized in that the first node has AI / ML capability.

[0086] According to an aspect of the present application, the method is characterized in that the first node supports AI / ML based measurement prediction.

[0087] According to an aspect of the present application, the method is characterized in that the first node supports AI / ML based measurement report.

[0088] The application discloses a method used in a second node for wireless communication and artificial intelligence, comprising:

[0089] sending a first information block, the first information block configuring a first RS set;

[0090] receiving a first measurement report;

[0091] wherein the sender of the first measurement report is a first node, the first node performs measurement in the first RS set, and the measurement or prediction for the first RS set is used to trigger the first measurement report; whether the first measurement report includes information units in a first information unit set depends on the triggering mode of the first measurement report, the triggering mode of the first measurement report is one of prediction and non-prediction; the first information unit set includes at least one of the following:

[0092] a first set of air interface resources for the first node;

[0093] an object for which prediction is made for the first measurement report;

[0094] a confidence interval of prediction for the first measurement report;

[0095] a time window for the first measurement report;

[0096] a prediction mode of the first measurement report.

[0097] According to one aspect of the application, the above method is characterized in that when the triggering mode of the first measurement report is prediction, the first measurement report includes the first information unit set; when the triggering mode of the first measurement report is not prediction, the first measurement report does not include the first information unit set.

[0098] According to one aspect of the application, the above method is characterized in that the first set of air interface resources includes at least one of spatial resources, power resources or transmission directions; the first set of air interface resources is for a serving cell of the first node, or the first set of air interface resources is for a candidate cell of the first node.

[0099] According to one aspect of the application, the above method is characterized in that the object for which the prediction for the first measurement report includes a serving cell, a neighbor cell or a serving cell and a neighbor cell.

[0100] According to one aspect of the application, the above method is characterized in that when the first node does not receive a response for the first measurement report in the time window for the first measurement report, a first timer of the first node expires.

[0101] According to an aspect of the present application, the above method is characterized in that the range of values of the confidence interval of the prediction for the first measurement report depends on at least one of:

[0102] - the number of objects for which the prediction for the first measurement report is made;

[0103] - the manner of the prediction of the first measurement report.

[0104] According to an aspect of the present application, the above method is characterized in that the configuration for the first measurement report includes a first parameter; the value of the first parameter depends on the manner of triggering of the first measurement report, or whether the first parameter is used to trigger the first measurement report depends on the manner of triggering of the first measurement report.

[0105] According to an aspect of the present application, the above method is characterized in that the second node is a base station.

[0106] According to an aspect of the present application, the above method is characterized in that the second node is a user equipment.

[0107] According to an aspect of the present application, the above method is characterized in that the second node is a TRP.

[0108] The present application discloses a device of a first node used for wireless communication and artificial intelligence, comprising:

[0109] a first receiver, receiving a first information block, the first information block configuring a first RS set; performing measurement in the first RS set, the measurement or prediction for the first RS set being used to trigger a first measurement report;

[0110] a first transmitter, transmitting the first measurement report;

[0111] wherein whether information units in a first information unit set are included in the first measurement report depends on the manner of triggering of the first measurement report, the manner of triggering of the first measurement report being one of predicted and not predicted; the first information unit set including at least one of:

[0112] - a first set of air interface resources for the first node;

[0113] - objects for which the prediction for the first measurement report is made;

[0114] - a confidence interval of the prediction for the first measurement report;

[0115] - a time window for the first measurement report;

[0116] - a prediction manner of the first measurement report.

[0117] The application discloses a device of a second node used for wireless communication and artificial intelligence, comprising:

[0118] a second transmitter, which transmits a first information block, the first information block configuring a first RS set;

[0119] a second receiver, which receives a first measurement report;

[0120] The sender of the first measurement report is a first node, the first node performs measurement in the first RS set, and the measurement or prediction of the first RS set is used to trigger the first measurement report; whether the first measurement report includes information units in a first information unit set depends on a triggering manner of the first measurement report, the triggering manner of the first measurement report is one of prediction and non-prediction; the first information unit set includes at least one of the following:

[0121] - a first set of air interface resources for the first node;

[0122] - an object to which prediction for the first measurement report is directed;

[0123] - a confidence interval of the prediction for the first measurement report;

[0124] - a time window for the first measurement report;

[0125] - a prediction manner of the first measurement report.

[0126] As an embodiment, compared with a conventional scheme, the application has the following advantages which are not limited to:

[0127] The application supports the fusion of AI and communication, improves the adaptability and intelligent level of a communication system, and further improves the performance, efficiency and user experience of the communication system.

[0128] The terminal reports a predicted event in advance, so that the base station can make a handover decision before the channel state of the serving cell deteriorates according to the predicted measurement report.

[0129] The application reduces the time delay of HO, improves the probability of successful HO, and improves the performance of system HO. BRIEF DESCRIPTION OF DRAWINGS

[0130] Other features, objects and advantages of the application will become more apparent after reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0131] Figure 1 illustrates a flow diagram of transmissions by a first node, according to one embodiment of the application;

[0132] Figure 2 illustrates a schematic diagram of a network architecture, according to one embodiment of the application;

[0133] Figure 3 illustrates a schematic diagram of an embodiment of a radio protocol architecture for the user and control planes, according to one embodiment of the application;

[0134] Figure 4 illustrates a schematic diagram of a first communication device and a second communication device, according to one embodiment of the application;

[0135] Figure 5 illustrates a flow diagram of transmissions between a first node and a second node, according to one embodiment of the application;

[0136] Figure 6 illustrates a schematic diagram of a first set of air interface resources, according to one embodiment of the application;

[0137] Figure 7 illustrates a schematic diagram of a first timer, according to one embodiment of the application;

[0138] Figure 8 illustrates a schematic diagram of a case of a serving cell and a neighbor cell, according to one embodiment of the application;

[0139] Figure 9 illustrates a schematic diagram of a first parameter, according to one embodiment of the application;

[0140] Figure 10 illustrates a schematic diagram of RAN domain AI / ML function deployment, according to one embodiment of the application;

[0141] Figure 11 illustrates a schematic diagram of AI / ML function deployment for a UE, according to one embodiment of the application;

[0142] Figure 12 illustrates a schematic diagram of an artificial intelligence or machine learning based processing system, according to one embodiment of the application;

[0143] Figure 13 illustrates a schematic diagram of an artificial intelligence or machine learning based, according to one embodiment of the application;

[0144] Figure 14 illustrates a structural block diagram of a processing apparatus for use in a first node, according to one embodiment of the application;

[0145] Figure 15 illustrates a structural block diagram of a processing apparatus for use in a second node, according to one embodiment of the application. DETAILED DESCRIPTION

[0146] The technical solutions of the present application will be further described in detail below with reference to the drawings. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other in any manner without conflict. Based on performance, flexibility, complexity, overhead, and compatibility, etc., the person skilled in the art is motivated to combine the embodiments in different drawings without conflict, including but not limited to the embodiments in FIG. 1 and the embodiments in FIG. 5-FIG. 15, the embodiments in FIG. 5 and the embodiments in FIG. 6-FIG. 15, etc.

[0147] Embodiment 1

[0148] Embodiment 1 illustrates a flowchart of a first node transmitting according to an embodiment of the present application, as shown in FIG. 1. In FIG. 1, each block represents a step. In particular, the order of the steps in the blocks does not represent a specific time sequence between the steps.

[0149] The first node receives a first information block in step 101, the first information block configuring a first RS set; performs measurement in the first RS set in step 102, the measurement or prediction for the first RS set being used to trigger a first measurement report; and transmits the first measurement report in step 103.

[0150] In embodiment 1, whether the first information unit in the first measurement report includes the information unit in the first information unit set depends on the triggering mode of the first measurement report, the triggering mode of the first measurement report being one of predicted and not predicted; and the first information unit set includes at least one of the following:

[0151] - a first set of air interface resources for the first node;

[0152] - an object to which the prediction for the first measurement report is directed;

[0153] - a confidence interval of the prediction for the first measurement report;

[0154] - a time window for the first measurement report;

[0155] - a prediction mode of the first measurement report.

[0156] As an embodiment, the first node is the first node in the present application.

[0157] As an embodiment, the first information unit set includes at least one of a first set of air interface resources for the first node, an object to which the prediction for the first measurement report is directed, a confidence interval of the prediction for the first measurement report, a time window for the first measurement report, or a prediction mode of the first measurement report.

[0158] As an embodiment, the RS refers to Reference Signal.

[0159] As an embodiment, the first node receives the first information block, and the first information block configures the first RS set.

[0160] As an embodiment, the first information block is transmitted by higher layer signaling.

[0161] As an embodiment, the first information block is carried by RRC (Radio Resource Control) layer signaling.

[0162] As an embodiment, the first information block is transmitted by RRC signaling.

[0163] As an embodiment, the first information block is transmitted by RRC message.

[0164] As an embodiment, the first information block includes RRC layer signaling.

[0165] As an embodiment, the first information block includes one or more RRC IEs (Information Elements).

[0166] As an embodiment, the first information block includes one or more fields in one RRC IE.

[0167] As an embodiment, the first information block includes one or more fields in each of a plurality of RRC IEs.

[0168] As an embodiment, the first RS set occupies one or more RS resources.

[0169] As an embodiment, the first RS set includes a plurality of RSs in one RS resource.

[0170] As an embodiment, the first RS set includes at least one RS in each of a plurality of RS resources.

[0171] As an embodiment, the first RS set includes SSB.

[0172] As an embodiment, the SSB in the present application refers to Synchronization Signal Block.

[0173] As an embodiment, the SSB in the present application refers to: SS (Synchronization Signal) / PBCH (Physical Broadcast CHannel) block, synchronization signal / physical broadcast channel block.

[0174] Typically, the receiving occasion (occasion) of PBCH, PSS (Primary Synchronization Signal, primary synchronization signal) and SSS (Secondary Synchronization Signal, secondary synchronization signal) is in consecutive symbols, and forms an SS / PBCH block.

[0175] As an embodiment, the first RS set includes CSI-RS (Channel State Information-Reference Signal, channel state information reference signal).

[0176] As an embodiment, the first RS set includes PRS (Positioning Reference Signal, positioning reference signal).

[0177] As an embodiment, the first RS set includes PTRS (Phase-Tracking Reference Signal, phase tracking reference signal).

[0178] As an embodiment, any RS included in the first RS set is CSI-RS or SSB.

[0179] As an embodiment, any RS included in the first RS set is CSI-RS.

[0180] As an embodiment, any RS included in the first RS set is SSB.

[0181] As an embodiment, when the signal included in the first RS set is CSI-RS or SSB, the above method has good forward compatibility; however, in order to adapt to the performance requirements of future wireless networks such as 6G, the signal included in the first RS set may also be other types of RS to better meet the performance requirements of measurement reporting.

[0182] As an embodiment, the first RS set corresponds to at least one measurement object.

[0183] As an embodiment, any RS included in the first RS set corresponds to a beam.

[0184] As one embodiment, the first information block includes one or more fields in an RRCResume message.

[0185] As one sub-embodiment of this embodiment, the first information block includes an RRCResume-IEs field.

[0186] As one embodiment, the first information block includes one or more fields in an RRCReconfiguration message.

[0187] As one sub-embodiment of this embodiment, the first information block includes an RRCReconfiguration-IEs field.

[0188] As one embodiment, the first information block includes one or more fields in a MeasConfig IE.

[0189] As one embodiment, the first information block includes one or more fields in a MeasObjectToAddModList IE.

[0190] As one embodiment, the first information block includes one or more fields in a MeasObjectNR IE.

[0191] As one embodiment, the first information block includes one or more fields in a SSB-ToMeasure IE.

[0192] As one embodiment, the first information block includes one or more fields in a CSI-RS-ResourceConfigMobility IE.

[0193] As one embodiment, the first information block includes one or more fields in a ReportConfigToAddModList IE.

[0194] As one embodiment, the first information block includes one or more fields in a ReportConfigNR IE.

[0195] As one embodiment, the first information block includes one or more fields in a MeasIdToAddModList IE.

[0196] As one embodiment, the first information block includes a ServingCellConfig IE.

[0197] As one embodiment, the first information block includes one or more fields in a ServingCellConfig IE.

[0198] As one embodiment, the first information block includes a CSI-MeasConfig IE.

[0199] As one embodiment, the first information block includes one or more fields in the CSI-MeasConfig IE.

[0200] As one embodiment, the first information block includes a CSI-SemiPersistentOnPUSCH-TriggerStateList IE.

[0201] As one embodiment, the first information block includes one or more fields in the CSI-SemiPersistentOnPUSCH-TriggerStateList IE.

[0202] As one embodiment, the first information block includes a CSI-SemiPersistentOnPUSCH-TriggerState IE.

[0203] As one embodiment, the first information block includes one or more fields in the CSI-SemiPersistentOnPUSCH-TriggerState IE.

[0204] As one embodiment, the first information block includes a CSI-ReportSubConfigTriggerList IE.

[0205] As one embodiment, the first information block includes one or more fields in the CSI-ReportSubConfigTriggerList IE.

[0206] As one embodiment, the first information block includes a CSI-ReportConfig IE.

[0207] As one embodiment, the first information block includes one or more fields in the CSI-ReportConfig IE.

[0208] As one embodiment, the first information block includes a CSI-ReportSubConfig IE.

[0209] As one embodiment, the first information block includes one or more fields in the CSI-ReportSubConfig IE.

[0210] As one embodiment, the first information block includes one or more fields in the CSI-AperiodicTriggerStateList IE.

[0211] As one embodiment, the first information block includes one or more fields in the CSI-IM-Resource IE.

[0212] As one embodiment, the first information block includes one or more fields in the CSI-IM-ResourceSet IE.

[0213] As one embodiment, the first information block includes one or more fields in the CSI-ResourceConfig IE.

[0214] As one embodiment, the first information block includes one or more fields in the CSI-RS-ResourceConfigMobility IE.

[0215] As one embodiment, the first information block includes one or more fields in the NZP-CSI-RS-Resource IE.

[0216] As one embodiment, the first information block includes one or more fields in the NZP-CSI-RS-ResourceSet IE.

[0217] As one embodiment, the first information block includes the CSI-ReportConfig IE.

[0218] As one embodiment, the first information block includes the CSI-ReportSubConfig IE.

[0219] As one embodiment, the name of the RRC signaling used to transmit the first information block includes CSI.

[0220] As one embodiment, the name of the RRC signaling used to transmit the first information block includes CSI-RS.

[0221] As one embodiment, the name of the RRC signaling used to transmit the first information block includes Report.

[0222] As one embodiment, the name of the RRC signaling used to transmit the first information block includes Config.

[0223] As one embodiment, the name of the RRC signaling used to transmit the first information block includes Meas.

[0224] As one embodiment, the name of the RRC signaling used to transmit the first information block includes Mobility.

[0225] As an embodiment, the name of the RRC signaling used to transmit the first information block comprises prediction.

[0226] As an embodiment, the name of the RRC signaling used to transmit the first information block comprises AIorML.

[0227] As an embodiment, the first information block configuring the first RS set comprises: the first information block indicating the first RS set.

[0228] As an embodiment, the first information block configuring a reference signal type (RS type) of any RS in the first RS set.

[0229] As an embodiment, the first information block configuring any RS in the first RS set is a CSI-RS or an SSB.

[0230] As an embodiment, the first information block configuring a time domain resource occupied by a RS in the first RS set.

[0231] As an embodiment, the first information block configuring a frequency domain resource occupied by a RS in the first RS set.

[0232] As an embodiment, the first information block configuring a spatial transmission parameter corresponding to a RS in the first RS set.

[0233] As an embodiment, the first information block configuring a SCS (SubCarrier Spacing) adopted by a RS in the first RS set.

[0234] As an embodiment, the first information block configuring a TCI (Transmission Configuration Indication) corresponding to a RS in the first RS set.

[0235] As an embodiment, the first information block configuring the first RS set for mobility.

[0236] As an embodiment, the first information block configuring the first RS set for prediction.

[0237] As an embodiment, the first information block configuring the first RS set for AI (Artificial Intelligence) / ML (Machine Learning).

[0238] As an embodiment, the first information block configures the first set of RSs for LCM (Life Cycle Management).

[0239] As an embodiment, the LCM in the present disclosure comprises LCM of AI / ML model, which comprises one or more of model training, model inference, model monitoring, model selection, model update.

[0240] As an embodiment, the first node performs measurement in the first set of RSs.

[0241] As an embodiment, the measurement performed in the first set of RSs comprises measurement for RRM (Radio Resource Management).

[0242] As an embodiment, the measurement performed in the first set of RSs comprises measurement for RLM (Radio Link Monitoring).

[0243] As an embodiment, the measurement performed in the first set of RSs comprises measurement for HO (HandOver).

[0244] As an embodiment, the measurement performed in the first set of RSs comprises measurement for RLF (Radio Link Failure).

[0245] As an embodiment, the measurement performed in the first set of RSs comprises measurement for Inter-cell.

[0246] As an embodiment, the measurement performed in the first set of RSs comprises measurement for Intra-cell.

[0247] As an embodiment, the measurement performed in the first set of RSs comprises measurement for Inter-frequency.

[0248] As an embodiment, the measurement performed in the first set of RSs comprises measurement for Intra-frequency.

[0249] As an embodiment, the measurement in the first set of RS comprises measurement for BM (Beam Management).

[0250] As an embodiment, the measurement in the first set of RS comprises measurement for CSI (Channel State Information).

[0251] As an embodiment, the measurement in the first set of RS comprises L1 (Layer 1) measurement.

[0252] As an embodiment, the measurement in the first set of RS comprises L3 (Layer) measurement.

[0253] As an embodiment, the measurement in the first set of RS comprises beam level measurement.

[0254] As an embodiment, the measurement in the first set of RS comprises cell level measurement.

[0255] As an embodiment, the first node transmits the first measurement report.

[0256] As an embodiment, the first measurement report is carried by a baseband signal.

[0257] As an embodiment, the first measurement report is carried by a radio frequency signal.

[0258] As an embodiment, the first measurement report is carried by a wireless signal.

[0259] As an embodiment, the first measurement report is transmitted by UCI (Uplink Control Information).

[0260] As an embodiment, the first measurement report is transmitted by MAC CE (Medium Access Control layer Control Element).

[0261] As an embodiment, the first measurement report is transmitted by RRC message.

[0262] As an embodiment, the first measurement report comprises one or more RRC messages.

[0263] As one embodiment, the first measurement report comprises one or more fields in an RRC message.

[0264] As one embodiment, the first measurement report comprises one or more fields in an RRC message.

[0265] As one embodiment, the first measurement report comprises a Measurement Report.

[0266] As one embodiment, the first measurement report comprises a Measurement Report message.

[0267] As one embodiment, the first measurement report comprises a MeasResults IE.

[0268] As one embodiment, the first measurement report comprises one or more fields of a MeasResults IE.

[0269] As one embodiment, the first measurement report is transmitted over a PUSCH (Physical Uplink Shared CHannel).

[0270] As one embodiment, the first node uses measurements or predictions for the first set of RSs to trigger the first measurement report.

[0271] As one embodiment, measurements for the first set of RSs are used to trigger the first measurement report.

[0272] As one embodiment, measurements for the first set of RSs are used to trigger transmission of the first measurement report.

[0273] As one embodiment, the measurements for the first set of RSs comprise measurement results obtained from the measurements for the first set of RSs.

[0274] As one embodiment, the measurements for the first set of RSs comprise measurement results generated from the measurements for the first set of RSs.

[0275] As one embodiment, the measurements for the first set of RSs comprise measurement results calculated from the measurements for the first set of RSs.

[0276] As one embodiment, the measurements for the first set of RSs comprise measurement results calculated from the measurements for the first set of RSs.

[0277] As one embodiment, the measurement for the first set of RSs comprises beam level measurement results generated for the measurement for the first set of RSs.

[0278] As one embodiment, the measurement for the first set of RSs comprises cell level measurement results generated for the measurement for the first set of RSs.

[0279] As one embodiment, the measurement for the first set of RSs comprises L1 measurement results generated for the measurement for the first set of RSs.

[0280] As one embodiment, the measurement for the first set of RSs comprises L3 measurement results generated for the measurement for the first set of RSs.

[0281] As one embodiment, the prediction for the first set of RSs is used to trigger the first measurement report.

[0282] As one embodiment, the prediction for the first set of RSs is used to trigger the sending of the first measurement report.

[0283] As one embodiment, the prediction for the first set of RSs comprises measurement results predicted for the measurement for the first set of RSs.

[0284] As one embodiment, the prediction for the first set of RSs comprises cell level prediction results predicted for beam level measurement results generated for the measurement for the first set of RSs.

[0285] As one embodiment, the prediction for the first set of RSs comprises L3 prediction results predicted for L1 measurement results generated for the measurement for the first set of RSs.

[0286] As one embodiment, the prediction for the first set of RSs comprises current prediction results predicted for historical measurement results generated for historical measurement for the first set of RSs.

[0287] As one embodiment, the meaning of prediction in this application comprises inference.

[0288] As one embodiment, the meaning of prediction in this application comprises being generated based on AI / ML.

[0289] As one embodiment, whether an information element in the first set of information elements is included in the first measurement report depends on the triggering manner of the first measurement report.

[0290] As an embodiment, the first measurement report includes the information elements in the first information element set when the trigger manner of the first measurement report is predicted, and the first measurement report does not include the information elements in the first information element set when the trigger manner of the first measurement report is not predicted.

[0291] As an embodiment, whether the first measurement report includes the first information element set depends on the trigger manner of the first measurement report.

[0292] As an embodiment, the first measurement report includes the first information element set when the trigger manner of the first measurement report is predicted, and the first measurement report does not include the first information element set when the trigger manner of the first measurement report is not predicted.

[0293] As an embodiment, the trigger manner of the first measurement report is one of predicted and not predicted.

[0294] As an embodiment, the trigger manner of the first measurement report is based on prediction.

[0295] As an embodiment, the trigger manner of the first measurement report being predicted means that it is predicted that the first measurement report is triggered.

[0296] As an embodiment, the trigger manner of the first measurement report being predicted means that it is predicted that the condition of the event corresponding to the first measurement report is met.

[0297] As an embodiment, the trigger manner of the first measurement report being predicted means that it is predicted that the measurement result for judging that the condition of the event corresponding to the first measurement report is met.

[0298] As an embodiment, the trigger manner of the first measurement report being predicted means that the parameter for judging that the condition of the event corresponding to the first measurement report is met is based on prediction.

[0299] As an embodiment, the trigger manner of the first measurement report being predicted means that the measurement result carried by the first measurement report includes a predicted result.

[0300] As an embodiment, the trigger manner of the first measurement report is not based on prediction.

[0301] As an embodiment, the trigger manner of the first measurement report being not predicted means that it is not predicted that the first measurement report is triggered.

[0302] As one embodiment, the first measurement report is triggered in a non-predictive manner includes that a condition of an event corresponding to the first measurement report is satisfied based on a measurement result.

[0303] As one embodiment, the first measurement report is triggered in a non-predictive manner includes that a condition of an event corresponding to the first measurement report is satisfied based on a measurement result.

[0304] As one embodiment, the first measurement report is triggered in a non-predictive manner includes that a condition of an event corresponding to the first measurement report is satisfied based on a measurement result.

[0305] As one embodiment, the first measurement report is triggered in a non-predictive manner includes that the first measurement report is triggered based on a protocol of a Release 19 (Rel-19) or a previous Release.

[0306] As one embodiment, the first measurement report is triggered in a non-predictive manner includes that a condition of an event corresponding to the first measurement report is satisfied based on a measurement result.

[0307] As one embodiment, the first set of information elements includes a first set of air interface resources for the first node.

[0308] As one embodiment, the terms include and the terms have and their any variants in this application are intended to cover non-exclusive inclusion, and are not limited to the steps, elements, devices or resources that have been listed, and optionally further include other inherent steps, elements, devices or resources.

[0309] As one embodiment, the first set of air interface resources includes a resource grid.

[0310] As one embodiment, the first set of air interface resources includes a set of time domain resources.

[0311] As one sub-embodiment of this embodiment, the set of time domain resources included in the first set of air interface resources includes one or more subframes.

[0312] As one sub-embodiment of this embodiment, the set of time domain resources included in the first set of air interface resources includes one or more slots.

[0313] As one sub-embodiment of this embodiment, the set of time domain resources included in the first set of air interface resources includes one or more time units.

[0314] As one subembodiment of the embodiment, the set of time domain resources included in the first set of air interface resources comprises transmission direction.

[0315] As one subembodiment of the embodiment, the set of time domain resources included in the first set of air interface resources comprises at least one of DL (Down Link), UL (Up Link), Flexible, SBFD (SubBand non-overlapping Full Duplex), or Full-Duplex.

[0316] Typically, one slot in the present application comprises 14 symbols.

[0317] As one embodiment, one time unit in the present application is one or more time domain sampling points.

[0318] As one embodiment, one time unit in the present application is one or more symbols.

[0319] As one embodiment, one time unit in the present application is one or more time domain symbols.

[0320] As one embodiment, one time unit in the present application is one or more multicarrier symbols.

[0321] As one embodiment, the time domain symbol in the present application comprises DFT-s-OFDM (Discrete Fourier Transform Spread Orthogonal Frequency Division Multiplexing).

[0322] As one embodiment, the multicarrier symbol in the present application comprises OFDM (Orthogonal Frequency Division Multiplexing) symbol.

[0323] As one embodiment, the multicarrier symbol in the present application comprises enhanced OFDM symbol.

[0324] As one embodiment, the multicarrier symbol in the present application is OFDM symbol comprising CP (Cyclic Prefix).

[0325] As an embodiment, the multi-carrier symbol in this application includes one or more of FBMC (Filter Bank Multi Carrier) symbol, UFMC (Universal Filtered Multi Carrier) symbol, F-OFDM (Filtered-OFDM) symbol, OCDM-OFDM (Orthogonal Chirp Division Multiplexing-OFDM) symbol.

[0326] As an embodiment, the first set of air interface resources includes a set of frequency domain resources.

[0327] As a sub-embodiment of this embodiment, the set of frequency domain resources included in the first set of air interface resources includes one or more carriers.

[0328] As a sub-embodiment of this embodiment, the set of frequency domain resources included in the first set of air interface resources includes one or more subbands.

[0329] As a sub-embodiment of this embodiment, the set of frequency domain resources included in the first set of air interface resources includes one or more BWPs (BandWidth Parts).

[0330] As an embodiment, the first set of air interface resources includes a set of spatial domain resources.

[0331] As a sub-embodiment of this embodiment, the set of spatial domain resources included in the first set of air interface resources includes one or more beams.

[0332] As a sub-embodiment of this embodiment, the set of spatial domain resources included in the first set of air interface resources includes one or more spatial relations.

[0333] As a sub-embodiment of this embodiment, the set of spatial domain resources included in the first set of air interface resources includes one or more directions.

[0334] As a sub-embodiment of this embodiment, the set of spatial domain resources included in the first set of air interface resources includes one or more TCIs.

[0335] As a sub-embodiment of this embodiment, the set of spatial domain resources included in the first set of air interface resources includes one or more TCI states.

[0336] As a sub-example of the embodiment, the set of spatial domain resources included in the first set of air interface resources comprises one or more TCI-StateId.

[0337] As a sub-example of the embodiment, the set of spatial domain resources included in the first set of air interface resources comprises one or more SSB-Index.

[0338] As a sub-example of the embodiment, the set of spatial domain resources included in the first set of air interface resources comprises one or more CSI-RS resource.

[0339] As a sub-example of the embodiment, the set of spatial domain resources included in the first set of air interface resources comprises one or more NZP (Non Zero Power) CSI-RS resource.

[0340] As an example, the first set of air interface resources comprises a set of power domain resources.

[0341] As a sub-example of the embodiment, the set of power domain resources included in the first set of air interface resources comprises a maximum output power configured by the first node.

[0342] As a sub-example of the embodiment, the set of power domain resources included in the first set of air interface resources comprises a maximum output power configured by the first node for one carrier.

[0343] As an example, the first set of air interface resources is used for downlink reception of the first node.

[0344] As an example, the first set of air interface resources is used for uplink transmission of the first node.

[0345] As an example, the first set of air interface resources is used for random access.

[0346] As an example, the first set of air interface resources is a set of air interface resources for a serving cell of the first node.

[0347] As an example, the first set of air interface resources is a set of air interface resources for a neighboring cell of a serving cell of the first node.

[0348] As an example, the first set of air interface resources is a set of air interface resources for a candidate cell of the first node.

[0349] As one embodiment, the serving cell in the present application comprises a PCell (Primary Cell).

[0350] As one embodiment, the serving cell in the present application comprises a SCell (Secondary Cell).

[0351] As one embodiment, the serving cell in the present application comprises a SpCell (Special Cell).

[0352] As one embodiment, the first set of information elements comprises an object for which a prediction for the first measurement report is made.

[0353] As one embodiment, the object for which a prediction for the first measurement report is made means which kind of measurement result the prediction for the first measurement report is.

[0354] As one sub-embodiment of this embodiment, the kind of measurement result means which one of a set of candidate measurement results, the set of candidate measurement results comprising one or more of RSRP (Reference Signal Received Power), RSRQ (Reference Signal Received Quality), RSSI (Received Signal Strength Indicator) or SNR (Signal To Noise Ratio).

[0355] As one embodiment, the object for which a prediction for the first measurement report is made means which object's measurement result the prediction for the first measurement report is.

[0356] As one sub-embodiment of this embodiment, the object means which one of a set of candidate objects, the set of candidate objects comprising one or more of a serving cell, a neighbor cell, a SpCell, an Inter-RAT (Radio Access Technology) neighbor cell (inter-RAT neighbor cell) and a PCell.

[0357] As one embodiment, the object for which a prediction for the first measurement report is made means which condition being met the prediction for the first measurement report is.

[0358] As a sub-example of this example, which condition refers to which condition in the entering condition set or the leaving condition set, the entering condition set includes at least one entering condition, and the leaving condition set includes at least one leaving condition.

[0359] As an example, the object to which the prediction for the first measurement report refers to includes a frequency resource to which the prediction for the first measurement report refers to.

[0360] As an example, the object to which the prediction for the first measurement report refers to includes a beam to which the prediction for the first measurement report refers to.

[0361] As an example, the first information element set includes a confidence interval (CI) for the prediction of the first measurement report.

[0362] As an example, the confidence interval for the prediction of the first measurement report includes a confidence level for the prediction of the first measurement report.

[0363] As an example, the confidence interval for the prediction of the first measurement report includes a range of the confidence level for the prediction of the first measurement report.

[0364] As an example, the confidence interval for the prediction of the first measurement report includes a confidence level of the first node triggering the first measurement report within a prediction time.

[0365] As an example, the confidence interval for the prediction of the first measurement report includes a confidence level of a prediction result included in the first measurement report.

[0366] As an example, the confidence interval for the prediction of the first measurement report includes a degree of certainty of an AI / ML model for the first measurement report.

[0367] As an example, the first information element set includes a time window for the first measurement report.

[0368] As an example, the time window for the first measurement report includes a time window for which the first measurement report takes effect.

[0369] As an embodiment, the reporting criterion of the first measurement report is single-event based, and the time window for the first measurement report comprises a time window during which the event triggering the first measurement report lasts.

[0370] As an embodiment, the time window for the first measurement report comprises a time window during which a possible occurrence of a time instant at which the first measurement report is triggered based on measurement is predicted.

[0371] As an embodiment, the time window for the first measurement report comprises a time window during which the first node expects to receive feedback from a base station after sending the first measurement report.

[0372] As an embodiment, the first set of information units comprises a prediction mode of the first measurement report.

[0373] As an embodiment, the prediction mode of the first measurement report comprises a model Id of an AI / ML model employed for the prediction of the first measurement report.

[0374] As an embodiment, the Id in the present application comprises one or more of identity, identifier, identification, and Index.

[0375] As an embodiment, the AI / ML model identified by the model Id in the present application can be logical, and the mapping relationship from the logical AI / ML model to the physical AI / ML model is usually implemented by the device manufacturer; and the model Id corresponding to the same AI / ML model in different stages of LCM can be different, i.e., the model Id in the present application can not be globally unique.

[0376] As an embodiment, the prediction mode of the first measurement report comprises an Id of a functionality employed for the prediction of the first measurement report.

[0377] As an embodiment, the prediction mode of the first measurement report comprises an Id of an entity corresponding to the prediction of the first measurement report.

[0378] As an embodiment, the entity described in the present application includes an AI entity, and embodiments of the present application do not limit the specific implementation of the AI entity. The AI entity can be located at a network side, interact with a network device, or be located inside a network device; or the AI entity can be located at a user side, interact with a user equipment (UE), or be located inside the UE.

[0379] As an embodiment, one possible implementation of the AI entity described in the present application is that the AI entity is deployed in a server or a cloud device of an over the top (OTT) system. Optionally, the cloud device is located at one or more of a user equipment side, a network device side, or a core network side.

[0380] As an embodiment, the prediction manner of the first measurement report includes whether the first measurement report is specialized measurement or joint prediction.

[0381] As a sub-embodiment of this embodiment, the meaning that the first measurement report is specialized measurement includes that only one condition in a first condition set is based on prediction, and the first condition set includes one or more of determining that the first measurement report is sent, triggering that a condition corresponding to the first measurement report is satisfied, and triggering that a measurement result required for the first measurement report.

[0382] As a sub-embodiment of this embodiment, the meaning that the first measurement report is joint prediction includes that multiple conditions in a first condition set are based on prediction, and the first condition set includes multiple of the following three: determining that the first measurement report is sent, triggering that a condition corresponding to the first measurement report is satisfied, and triggering that a measurement result required for the first measurement report.

[0383] As a sub-embodiment of this embodiment, the meaning that the first measurement report is joint prediction includes that an input parameter for generating a prediction result triggering the first measurement report is also based on prediction.

[0384] Embodiment 2

[0385] Embodiment 2 illustrates a schematic diagram of a network architecture according to an embodiment of the present application, as shown in FIG. 2.

[0386] FIG. 2 illustrates a network architecture 200. The network architecture 200 is a network architecture for LTE (Long-Term Evolution), LTE-A (Long-Term Evolution Advanced), 5G systems, 5G-Advanced, and future 6G systems. The network architecture for LTE, LTE-A, 5G systems, 5G-Advanced, and future 6G systems is referred to as EPS (Evolved Packet System). The 5G NR or LTE network architecture can be referred to as 5GS (5G System) / EPS or some other suitable terminology; the 6G network architecture can be referred to as 6GS (6G System) / EPS or some other suitable terminology. The network architecture 200 can include one or more UEs 201, a RAN (Next Generation Radio Access Network) 202, a core network 210, a HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and Internet services 230. The network architecture 200 can be interconnected with other access networks, but these entities / interfaces are not shown for simplicity. As shown in FIG. 2, the network architecture 200 provides packet-switched services, however, those skilled in the art will readily appreciate that the various concepts presented throughout this application are amenable to use with networked systems providing circuit-switched services. The RAN 202 includes Node Bs 203 and other nodes 204. The Node Bs 203 provide user and control plane protocol terminations toward the UEs 201. The Node Bs 203 can be connected to the other nodes 204 via an Xn interface (e.g., backhaul). The Node Bs 203 can also be referred to as base stations, base transceiver stations, radio base stations, radio transceivers, transceiver functions, basic service sets (BSSs), extended service sets (ESSs), TRPs (Transmitter Receiver Points), or some other suitable terminology. The Node Bs 203 provide access points to the core network 210 for the UEs 201; the core network 210 is a 5GC (5G Core Network) / EPC (Evolved Packet Core), or alternatively, the core network 210 is a 6GC.Examples of a UE 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 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 flying vehicle, a narrowband physical web device, a machine type communication device, a land transport vehicle, a car, a wearable device, or any other similar functional device. Those skilled in the art will also The node 203 is connected by an SI / NG interface to the core network 210. The core network 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 Data Network Gateway) / UPF 213. The MME / AMF / SMF 211 is the control node that processes the signaling between the UE 201 and the 5G-CN / EPC 210. The MME / AMF / SMF 211 generally provides bearer and connection management. All user Internet Protocol (IP) packets are transferred through the S-GW / UPF 212, which is itself 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 the Internet services 230. The Internet services 230 include operator- correspondent Internet Protocol services, which can specifically include the Internet, intranet, IMS (IP Multimedia Subsystem), and packet-switched services.

[0387] As one embodiment, the first node described in this application includes the UE 201.

[0388] As one embodiment, the second node described in this application comprises the node 203.

[0389] As one embodiment, the node 203 is a Macro Cell base station.

[0390] As one embodiment, the node 203 is a Micro Cell base station.

[0391] As one embodiment, the node 203 is a Pico Cell base station.

[0392] As one embodiment, the node 203 is a Femto Cell base station.

[0393] As one embodiment, the node 203 is a base station device supporting large latency difference.

[0394] As one embodiment, the node 203 is a flying platform device.

[0395] As one embodiment, the node 203 is a satellite device.

[0396] As one embodiment, the node 203 is a test device (e.g. a transceiver simulating part of the functionality of a base station, a signaling tester).

[0397] As one embodiment, the UE 201 is a mobile phone.

[0398] As one embodiment, the UE 201 is a vehicle, including a car.

[0399] As one embodiment, the wireless link from the UE 201 to the node 203 is an uplink, which is used to perform uplink transmission.

[0400] As one embodiment, the wireless link from the node 203 to the UE 201 is a downlink, which is used to perform downlink transmission.

[0401] As one embodiment, the wireless link between the node 203 and the UE 201 comprises a cellular network link.

[0402] As one embodiment, the node 203 and the UE 201 are connected through a Uu air interface.

[0403] As one embodiment, the sender of the first information block comprises the node 203.

[0404] As one embodiment, the receiver of the first information block comprises the UE 201.

[0405] As one embodiment, the sender of the first measurement report comprises the UE 201.

[0406] As one embodiment, the receiver of the first measurement report comprises the node 203.

[0407] As one embodiment, the UE 201 supports NetGPT (Network GPT) based.

[0408] As one embodiment, the UE 201 supports AI / ML based generation of reporting.

[0409] As one embodiment, the UE 201 supports trained model or part of parameters in the model based on training data.

[0410] As one embodiment, the UE 201 supports AI / ML based measurement reporting.

[0411] As one embodiment, the UE 201 supports NN (Neural Networks) based measurement reporting.

[0412] As one embodiment, the UE 201 supports ANN (Artificial Neural Networks) based measurement reporting.

[0413] As one embodiment, the UE 201 supports CNN (Convolutional Neural Networks) based measurement reporting.

[0414] As one embodiment, the UE 201 supports LLM (Large Language Model) based measurement reporting.

[0415] As one embodiment, the UE 201 supports Transformer based measurement reporting.

[0416] As one embodiment, the UE 201 supports LSTM (Long Short-Term Memory) based measurement reporting.

[0417] As one embodiment, the UE 201 supports MLP (MultiLayer Perceptron) based measurement reporting.

[0418] As one embodiment, the UE 201 supports GAN (Generative Adversarial Nets) based measurement reporting.

[0419] As one embodiment, the UE 201 supports lightweight neural network based measurement reporting.

[0420] As one sub-embodiment of this embodiment, the lightweight neural network includes one or more of MobileNet, ShuffleNet, and SqueezeNet.

[0421] As one embodiment, the UE 201 supports a 5G system.

[0422] As one embodiment, the node 203 supports a 5G system.

[0423] As one embodiment, the UE 201 supports at least a 6G system.

[0424] As one embodiment, the node 203 supports at least a 6G system.

[0425] Embodiment 3

[0426] Embodiment 3 illustrates a diagram of an embodiment of a wireless protocol architecture of a user plane and a control plane according to one embodiment of the present application, as shown in FIG. 3.

[0427] Figure 3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300, Figure 3 shows the radio protocol architecture for the control plane 300 between a first communication node device (UE or RSU (Road Side Unit) in V2X (Vehicle to Everything), a vehicle mounted device or a vehicle mounted communication module) and a second node device (gNB, UE or RSU in V2X, a vehicle mounted device or a vehicle mounted communication module), or between two UEs, using three layers: Layer 1 (L1), Layer 2 (L2) and Layer 3 (L3). L1 is the lowest layer and implements various PHY (PHYsical layer) signal processing functions. L1 will be referred to as the PHY 301 herein. Layer 2 305 is above the PHY 301 and is responsible for the link between the first node device and the second node device, or between two UEs. Layer 2 305 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 device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security, by encrypting packets, and handover support for the first communication node device between second communication node devices. The RLC sublayer 303 provides segmentation and reassembly of upper layer packets, retransmission of lost packets, and reordering of packets to compensate for out-of-order reception due to HARQ (Hybrid Automatic Repeat reQuest). 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 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 communication node device and the first communication node device.The radio protocol architecture of the user plane 350 includes Layer 1 (L1) and Layer 2 (L2) for the first and second communication node devices in the user plane 350 is substantially the same as the corresponding layers and sub-layers in the control plane 300 for the physical layer 351, the PDCP sub-layer 354 in L2 355, the RLC sub-layer 353 in L2 355, and the MAC sub-layer 352 in L2 355, but the PDCP sub-layer 354 also provides header compression for upper layer data packets to reduce radio transmission overhead. Also included in L2 355 in the user plane 350 is the SDAP (Service Data Adaptation Protocol) sub-layer 356, which is responsible for mapping between QoS (Quality of Service) flows and data radio bearers (DRBs) to support diverse traffic types. Although not illustrated, the first communication node device can have several upper layers above L2 355, including a network layer (e.g., IP (Internet Protocol) layer) that terminates at the P-GW on the network side and an application layer that terminates at the other end of the connection (e.g., a remote UE, a server, etc.).

[0428] As one embodiment, the radio protocol architecture in FIG. 3 is applicable to the first node in the present application.

[0429] As one embodiment, the radio protocol architecture in FIG. 3 is applicable to the second node in the present application.

[0430] As one embodiment, the first information block is generated at the RRC 306.

[0431] As one embodiment, the first measurement report is generated at the RRC 306.

[0432] As one embodiment, the higher layer in the present application refers to layers above the physical layer.

[0433] As one embodiment, the higher layer in the present application includes the MAC layer.

[0434] As one embodiment, the higher layer in the present application includes the RRC layer.

[0435] Embodiment 4

[0436] Embodiment 4 illustrates a schematic diagram of a first communication device and a second communication device according to one embodiment of the present application, as shown in FIG. 4. FIG. 4 is a block diagram of a first communication device 410 and a second communication device 450 that communicate with each other in an access network.

[0437] The first communication device 410 includes a controller / processor 475, a memory 476, a receive processor 470, a transmit processor 416, a multiple antenna receive processor 472, a multiple antenna transmit processor 471, a transmitter / receiver 418, and antennas 420.

[0438] The second communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmit processor 468, a receive processor 456, a multiple antenna transmit processor 457, a multiple antenna receive processor 458, a transmitter / receiver 454, and antennas 452.

[0439] In transmissions from the first communication device 410 to the second communication device 450, at the first communication device 410, upper layer packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements functionality of L2. In DL, the controller / processor 475 provides header compression, ciphering, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocations for second communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the second communication device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for Ll (i.e., physical layer). The transmit processor 416 implements coding and interleaving to facilitate forward error correction (FEC) at the second communication device 450 and mapping onto signal constellations based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-ary phase shift keying (M-PSK), M-ary quadrature amplitude modulation (M-QAM)). The multi-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 parallel streams. The transmit processor 416 then maps to each of the parallel streams to subcarriers, multiplexes the modulated symbols in time domain and / or frequency domain with reference signals (e.g., pilot) and then performs an inverse fast Fourier transform (IFFT) to generate time domain multicarrier symbol streams. The multi-antenna transmit processor 471 then performs transmit analog pre-coding / beamforming operations on the time domain multicarrier symbol streams. Each transmitter 418 converts the baseband multicarrier symbol streams provided by the multi-antenna transmit processor 471 into radio frequency signals that are transmitted via the corresponding antennas 420.

[0440] In transmission from the first communication device 410 to the second communication device 450, at the second 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 converts the RF stream into a baseband, multicarrier symbol stream to receive processor 456. The receive processor 456 and the multi-antenna receive processor 458 implement various signal processing functions of the LI. The multi-antenna receive processor 458 performs receive analog precoding / beamforming operation on the baseband, multicarrier symbol stream from the receivers 454. The receive processor 456 converts the baseband, multicarrier symbol stream from the receive analog precoding / beamforming operation 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 by the receive processor 456, where the reference signals will be used for channel estimation, and the data signals are recovered after multi-antenna detection in the multi-antenna receive processor 458 for any parallel streams destined to the second communication device 450. The symbols on each parallel stream are demodulated and recovered in the receive processor 456 and 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 first communication device 410 on the physical channels. The upper layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements the functions of the L2. 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 the DL, 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. Various control signals can also be provided to the L3 for L3 processing. The controller / processor 459 is also responsible for error detection using an ACK and / or negative ACK (NACK) protocol to support HARQ operations.

[0441] In the transmission from the second communication device 450 to the first communication device 410, at the second communication device 450, a data source 467 is used to provide upper layer packets to a controller / processor 459. The data source 467 represents all protocol layers above L2. Similar to the transmit function described at the first communication device 410 in the DL, the controller / processor 459 implements header compression, ciphering, packet segmentation and reordering, and multiplexing between logical and transport channels based on radio resource allocations for the first communication device 410, implements L2 layer functionality for the user plane and control plane. The controller / processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communication device 410. A transmit processor 468, in conjunction with a multi-antenna transmit processor 457, performs modulation mapping, channel coding processing, digital multi-antenna spatial pre-coding including codebook-based and non-codebook-based precoding, and beamforming processing, and then the transmit processor 468 generates parallel streams of symbols that are modulated onto different carriers, and the modulated symbol streams are then provided to different antennas 452 via transmitters 454 after analog pre-coding / beamforming operations in the multi-antenna transmit processor 457. Each transmitter 454 converts a baseband symbol stream into a radio frequency signal that is transmitted via the corresponding antenna 452.

[0442] In the transmission from the second communication device 450 to the first communication device 410, the functionality at the first communication device 410 is similar to the functionality described in connection with the reception at the second communication device 450 in the transmission from the first communication device 410 to the second 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 together implement L1 functionality. A controller / processor 475 implements L2 functionality. 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. The controller / processor 475 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover upper layer packets from the second communication device 450. Upper layer packets from the controller / processor 475 can be provided to a core network. The controller / processor 475 is also responsible for error detection using an ACK and / or NACK protocol to support HARQ operations.

[0443] As an embodiment, the second 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 second communication device 450 to perform at least: receive the first information block as described in the present application, the first information block configuring a first set of RSs; perform measurements in the first set of RSs, the measurements or predictions for the first set of RSs being used to trigger the first measurement report as described in the present application; transmit the first measurement report; whether or not an information element in a first set of information elements is included in the first measurement report depends on a triggering manner of the first measurement report, the triggering manner of the first measurement report being one of: predicted and not predicted; the first set of information elements comprising at least one of:

[0444] - a first set of air interface resources for the second communication device 450;

[0445] - an object for which a prediction for the first measurement report is made;

[0446] - a confidence interval for the prediction for the first measurement report;

[0447] - a time window for the first measurement report;

[0448] - a prediction manner of the first measurement report.

[0449] As an embodiment, the second communication device 450 comprises: a memory storing a computer readable program of instructions, the computer readable program of instructions, when executed by at least one processor, causing performance of actions comprising: receiving the first information block as described in the present application; performing measurements in the first set of RSs as described in the present application; transmitting the first measurement report as described in the present application.

[0450] As an embodiment, the first communication device 410 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 410 to perform at least the following: sending the first information block as described in the present application, the first information block configuring a first set of RSs; receiving the first measurement report as described in the present application; the sender of the first measurement report is the second communication device 450, the second communication device 450 performing measurements in the first set of RSs, the measurements or predictions for the first set of RSs being used to trigger the first measurement report; whether or not an information element in a first set of information elements is included in the first measurement report depends on the triggering manner of the first measurement report, the triggering manner of the first measurement report being one of: predicted and not predicted; the first set of information elements comprises at least one of the following:

[0451] - a first set of air interface resources for the second communication device 450;

[0452] - an object for which the prediction for the first measurement report is made;

[0453] - a confidence interval for the prediction for the first measurement report;

[0454] - a time window for the first measurement report;

[0455] - a prediction manner of the first measurement report.

[0456] As an embodiment, the first communication device 410 comprises: a memory storing a computer readable program of instructions which, when executed by at least one processor, causes the actions comprising: sending the first information block as described in the present application; receiving the first measurement report as described in the present application.

[0457] As an embodiment, the first node as described in the present application comprises the second communication device 450.

[0458] As an embodiment, the second node as described in the present application comprises the first communication device 410.

[0459] As an embodiment, at least one of {the antenna 420, the transmitter 418, the transmit processor 416, the multi-antenna transmit processor 471, the controller / processor 475, the memory 476} is configured to transmit the first information block; 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, the data source 467} is configured to receive the first information block.

[0460] As an embodiment, 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, the data source 467} is configured to measure in the first RS set.

[0461] As an embodiment, 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, the data source 467} is configured to transmit the first measurement report; at least one of {the antenna 420, the receiver 418, the receive processor 470, the multi-antenna receive processor 472, the controller / processor 475, the memory 476} is configured to receive the first measurement report.

[0462] Embodiment 5

[0463] Embodiment 5 illustrates a flow chart of transmission between a first node and a second node according to an embodiment of the present application, as shown in FIG. 5. In FIG. 5, the first node U1 communicates with the second node N2 through a wireless link. It is particularly explained that the sequence in this embodiment does not limit the sequence of signal transmission and implementation in the present application.

[0464] For the first node U1, the first information block is received in step S510; the measurement in the first RS set is performed in step S511; the first measurement report is transmitted in step S512.

[0465] For the second node N2, the first information unit is transmitted in step S520; in step S521.

[0466] In embodiment 5, the first information block configures a first RS set, the measurement or prediction of the first RS set by the first node U1 is used by the first node U1 to trigger the first measurement report; whether an information unit in the first information unit set is included in the first measurement report depends on the triggering manner of the first measurement report, the triggering manner of the first measurement report is one of predicted and not predicted; the first information unit set includes at least one of the following:

[0467] - a first set of air interface resources for the first node U1;

[0468] - an object to which the prediction for the first measurement report is directed;

[0469] - a confidence interval of the prediction for the first measurement report;

[0470] - a time window for the first measurement report;

[0471] - a prediction manner of the first measurement report.

[0472] As an embodiment, the first node U1 is the first node in the present application.

[0473] As an embodiment, the second node N2 is the second node in the present application.

[0474] As an embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between a base station device and a user equipment.

[0475] As an embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between a relay node device and a user equipment.

[0476] As an embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between a user equipment and a user equipment.

[0477] As an embodiment, the second node N2 and the first node U1 communicate through a Uu interface.

[0478] As an embodiment, the second node N2 is a serving cell maintaining base station of the first node U1.

[0479] As an embodiment, the step S511 of measuring in the first RS set includes receiving an RS in the first RS set.

[0480] As a sub-embodiment of this embodiment, the sender of the RS in the first RS set includes the second node N2.

[0481] As one sub-embodiment of the embodiment, the transmitter of the RS in the first RS set comprises the AI entity as described in the present application.

[0482] As one sub-embodiment of the embodiment, the transmitter of the RS in the first RS set comprises a maintaining base station of a neighboring cell.

[0483] As one sub-embodiment of the embodiment, the first RS set comprises a first RS subset and a second RS subset, the first node U1 receives and measures the first RS subset, and predicts the measurement result of the first RS subset and the second RS subset according to the first RS subset.

[0484] As one embodiment, the step S511 of measuring in the first RS set comprises measuring the RS in the first RS set, and predicting other RS according to the measurement result.

[0485] As one embodiment, the transmission channel occupied by the first information block comprises a DL-SCH (DownLink-Shared CHannel).

[0486] As one embodiment, the physical layer channel occupied by the first information block comprises a PDSCH (Physical Downlink Shared CHannel).

[0487] As one embodiment, the first measurement report is carried by an SRB1 (Signal Radio Bearer 1).

[0488] As one embodiment, the first measurement report is carried by an SRB3 (Signal Radio Bearer 3).

[0489] As one embodiment, the logical channel occupied by the first measurement report comprises a DCCH (Dedicated Control Channel).

[0490] As one embodiment, the transmission channel occupied by the first measurement report comprises an UL-SCH (UpLink-Shared CHannel).

[0491] As one embodiment, the step S511 is after the step S510.

[0492] As one embodiment, the step S512 is after the step S511; and the step S521 is after the step S520.

[0493] Embodiment 6

[0494] Embodiment 6 illustrates a diagram of a first set of air interface resources according to an embodiment of the present application, as shown in FIG. 6. In FIG. 6, the first set of air interface resources comprises at least one of spatial resources, power resources, or transmission directions.

[0495] In Embodiment 6, the first set of air interface resources is for a serving cell of the first node, or the first set of air interface resources is for a candidate cell of the first node.

[0496] As one embodiment, the first set of air interface resources comprising spatial resources means that the first set of air interface resources comprises spatial Tx parameters.

[0497] As one embodiment, the first set of air interface resources comprising spatial resources means that the first set of air interface resources comprises spatial Rx parameters.

[0498] As one embodiment, the first set of air interface resources comprising spatial resources means that the first set of air interface resources comprises Spatial Filters.

[0499] As one embodiment, the power resources comprise a maximum transmit power value of the first node.

[0500] As one embodiment, the power resources comprise a range of transmit power values of the first node.

[0501] As one embodiment, the transmission directions comprise downlink.

[0502] As one embodiment, the transmission directions comprise uplink.

[0503] As one embodiment, the transmission directions comprise flexible.

[0504] As one embodiment, the transmission directions comprise SBFD.

[0505] As one embodiment, the transmission directions comprise full duplex.

[0506] As one embodiment, the first set of air interface resources is for a serving cell of the first node.

[0507] As one embodiment, the first set of air interface resources is for a candidate cell of the first node.

[0508] As an embodiment, the first set of air interface resources is configured to a serving cell of the first node.

[0509] As an embodiment, the first set of air interface resources belongs to a candidate cell of the first node.

[0510] As an embodiment, the first set of air interface resources belongs to a serving cell of the first node.

[0511] As an embodiment, the candidate cell of the first node comprises a neighbor cell of the first node.

[0512] As an embodiment, the candidate cell of the first node comprises a neighbor cell of a serving cell of the first node.

[0513] As an embodiment, the candidate cell of the first node comprises a target cell of a HO of the first node.

[0514] As an embodiment, the first node is based on a prediction indication for a set of air interface resources of a serving cell of the first node or for a set of air interface resources of a candidate cell of the first node, and the first set of information units included in the first set of information units can assist a base station to configure a set of air interface resources occupied by the first node after a handover, which is conducive to the base station to coordinate resource occupation among different user equipment, while improving the resource utilization and the quality of the transmitted signal of the first node.

[0515] Embodiment 7

[0516] Embodiment 7 illustrates a schematic diagram of a first timer according to an embodiment of the present application, as shown in FIG. 7. In FIG. 7, the cross-hatched rectangle represents the time domain resources occupied by the first measurement report in time, and when the first node in the present application does not receive a response for the first measurement report in the time window for the first measurement report, the first timer of the first node expires.

[0517] As an embodiment, when the first node does not receive a response for the first measurement report in the time window for the first measurement report, the first timer of the first node expires.

[0518] As an embodiment, the expressions "when" and "when" and "if" in the present application represent that the node or device in the present application will make corresponding processing under certain objective conditions, which is not limited in time, and does not require the node or device to have a judgment action when it is implemented, nor does it mean that there are other limitations.

[0519] As one embodiment, the first node starts the first timer when the first node sends the first measurement report.

[0520] As one embodiment, the first node starts the first timer after a first time offset value after the first node sends the first measurement report, the first time offset value being fixed or configurable.

[0521] As one sub-embodiment of this embodiment, the first time offset value is non-negative.

[0522] As one embodiment, the response to the first measurement report includes a LTM (L1 / L2-Triggered Mobility) Command.

[0523] As one embodiment, the response to the first measurement report includes a PDCCH (Physical Downlink Control Chanel) order message.

[0524] As one embodiment, the response to the first measurement report includes a LTM candidate configuration.

[0525] As one embodiment, the response to the first measurement report includes a ltm-Config IE.

[0526] As one embodiment, the response to the first measurement report includes a LTM-Candidate IE.

[0527] As one embodiment, the response to the first measurement report includes a LTM Cell switch command MAC CE.

[0528] As one embodiment, the response to the first measurement report includes a HO Command.

[0529] As one embodiment, the response to the first measurement report includes a RRCReconfiguration message.

[0530] As one embodiment, the response to the first measurement report includes a MobilityFromNRCommand message.

[0531] As one embodiment, the first timer of the first node expires, and the first node enters HOF (HandOver Failure).

[0532] As one embodiment, the first timer of the first node expires, and the first node enters RLF (Radio Link Failure).

[0533] As one embodiment, the first timer of the first node expires, and the first node initiates random access.

[0534] As one embodiment, the first timer of the first node expires, and the first node initiates cell synchronization.

[0535] As one embodiment, the first timer of the first node expires, and the first node retransmits the first measurement report.

[0536] As one embodiment, the first timer of the first node comprises T304.

[0537] As one embodiment, the first timer of the first node comprises T310.

[0538] As one embodiment, the first timer of the first node comprises T312.

[0539] Embodiment 8

[0540] Embodiment 8 illustrates a diagram of one case of serving cell and neighbor cell according to one embodiment of the present application, as shown in FIG. 8. In FIG. 8, one ellipse represents the coverage of one cell, and the serving cell and the neighbor cell of the first node are adjacent and the coverage of one cell does not completely include the coverage of another cell.

[0541] As one embodiment, the object for which the prediction for the first measurement report is directed to comprises a serving cell, a neighbor cell, or a serving cell and a neighbor cell.

[0542] As one embodiment, the object for which the prediction for the first measurement report is directed to comprises a serving cell.

[0543] As one embodiment, the object for which the prediction for the first measurement report is directed to comprises a neighbor cell.

[0544] As one embodiment, the object for which the prediction for the first measurement report is directed to comprises a serving cell and a neighbor cell.

[0545] As one embodiment, the serving cell of the first node is associated to one MeasObjectNR IE, the neighbor cell of the first node is associated to another MeasObjectNR IE, the neighbor cell for which the prediction for the first measurement report is directed to comprises a serving cell associated to the other MeasObjectNR IE.

[0546] As one embodiment, the serving cell and the neighbor cell of the first node are adjacent and the coverage of one cell does not completely comprise the coverage of the other cell.

[0547] As one embodiment, the coverage of the serving cell of the first node comprises the coverage of the neighbor cell.

[0548] As one embodiment, the coverage of the neighbor cell comprises the coverage of the serving cell of the first node.

[0549] As one embodiment, the value range of the confidence interval for the prediction for the first measurement report depends on at least one of:

[0550] - the number of objects for which the prediction for the first measurement report is directed to;

[0551] - the prediction mode of the first measurement report.

[0552] As one embodiment, the confidence interval is a percentage.

[0553] As one embodiment, the confidence interval is a number between 0 and 100.

[0554] As one embodiment, the confidence interval is a range of values between 0 and 100.

[0555] As one embodiment, the value range of the confidence interval for the prediction for the first measurement report depends on the number of objects for which the prediction for the first measurement report is directed to and the prediction mode of the first measurement report.

[0556] As one embodiment, the value range of the confidence interval for the prediction for the first measurement report depends on the number of objects for which the prediction for the first measurement report is directed to.

[0557] As one embodiment, the number of the objects for which the prediction for the first measurement report is equal to 1, the value range of the confidence interval is a first candidate range; the number of the objects for which the prediction for the first measurement report is greater than 1, the value range of the confidence interval is a second candidate range; the first candidate range and the second candidate range are different.

[0558] As one sub-embodiment of the embodiment, the first candidate range includes a plurality of value ranges.

[0559] As one sub-embodiment of the embodiment, the first candidate range includes a plurality of numerical values.

[0560] As one sub-embodiment of the embodiment, the second candidate range includes a plurality of value ranges.

[0561] As one sub-embodiment of the embodiment, the second candidate range includes a plurality of numerical values.

[0562] As one sub-embodiment of the embodiment, the number of the objects for which the prediction for the first measurement report is equal to 1 means that one result used for triggering and generating the first measurement report is based on prediction.

[0563] As one sub-embodiment of the embodiment, the number of the objects for which the prediction for the first measurement report is equal to 1 means that one condition used for triggering and generating the first measurement report is based on prediction.

[0564] As one sub-embodiment of the embodiment, the number of the objects for which the prediction for the first measurement report is equal to 1 means that a measurement result of one object used for triggering and generating the first measurement report is based on prediction.

[0565] As one sub-embodiment of the embodiment, the number of the objects for which the prediction for the first measurement report is greater than 1 means that a plurality of results used for triggering and generating the first measurement report are based on prediction.

[0566] As one sub-embodiment of the embodiment, the number of the objects for which the prediction for the first measurement report is greater than 1 means that a plurality of conditions used for triggering and generating the first measurement report are based on prediction.

[0567] As one sub-embodiment of the embodiment, the number of the objects for which the prediction for the first measurement report is equal to 1 means that a plurality of measurement results of objects used for triggering and generating the first measurement report are based on prediction.

[0568] As one embodiment, the value range of the confidence interval for the prediction of the first measurement report depends on the prediction manner of the first measurement report.

[0569] As one embodiment, the value range of the confidence interval for the prediction of the first measurement report depends on the type of AI / ML model employed for the prediction of the first measurement report.

[0570] As one embodiment, the AI / ML model employed for the prediction of the first measurement report is a CNN model, a RNN model or a traditional light-weight neural network model, the value range of the confidence interval is a third candidate range; the AI / ML model employed for the prediction of the first measurement report is a LLM or a Transformer model, the value range of the confidence interval is a fourth candidate range; the AI / ML model employed for the prediction of the first measurement report is a GAN model, the value range of the confidence interval is a fifth candidate range; the third candidate range and the fourth candidate and the fourth candidate range are different.

[0571] As one sub-embodiment of this embodiment, the third candidate range includes multiple value ranges.

[0572] As one sub-embodiment of this embodiment, the third candidate range includes multiple numerical values.

[0573] As one sub-embodiment of this embodiment, the fourth candidate range includes multiple value ranges.

[0574] As one sub-embodiment of this embodiment, the fourth candidate range includes multiple numerical values.

[0575] As one sub-embodiment of this embodiment, the fifth candidate range includes multiple value ranges.

[0576] As one sub-embodiment of this embodiment, the fifth candidate range includes multiple numerical values.

[0577] Embodiment 9

[0578] Embodiment 9 illustrates a schematic diagram of the first parameter according to one embodiment of the present application, as shown in FIG. 9. In FIG. 9, the configuration for the first measurement report includes the first parameter.

[0579] In embodiment 9, the value of the first parameter depends on the trigger manner of the first measurement report, or whether the first parameter is used to trigger the first measurement report depends on the trigger manner of the first measurement report.

[0580] As one embodiment, the first information block comprises the configuration for the first measurement report.

[0581] As one embodiment, the first information block carries the configuration for the first measurement report.

[0582] As one embodiment, the first node receives a second information block, which carries the configuration for the first measurement report.

[0583] As one sub-embodiment of this embodiment, the second information block and the first information block belong to different RRC messages or different RRC IEs.

[0584] As one sub-embodiment of this embodiment, the second information block and the first information block belong to the same RRC message or different domains of the same RRC IE.

[0585] As one embodiment, the configuration for the first measurement report is generated based on an AI / ML model.

[0586] As one embodiment, the configuration for the first measurement report is generated by the first node based on an AI / ML model.

[0587] As one sub-embodiment of this embodiment, the configuration for the first measurement report is included in the first set of information elements.

[0588] As one embodiment, the configuration for the first measurement report comprises a first parameter, a value of which depends on the triggering manner of the first measurement report.

[0589] As one embodiment, the triggering manner of the first measurement report is prediction, and the first parameter is equal to a first value; the triggering manner of the first measurement report is not prediction, and the first parameter is equal to a second value; the first value and the second value are different.

[0590] As one embodiment, the first parameter is in units of dB (decibel).

[0591] As one embodiment, the first parameter comprises a threshold value for determining that the first measurement report is triggered.

[0592] As one embodiment, the first parameter comprises a power offset value for determining that the first measurement report is triggered.

[0593] As one sub-embodiment of this embodiment, the power offset value is for a serving cell.

[0594] As one subembodiment of the embodiment, the power offset value is for a neighbor cell.

[0595] As one subembodiment of the embodiment, the power offset value is for a SpCell.

[0596] As one subembodiment of the embodiment, the power offset value depends on a frequency band in which a cell to which the power offset value is applied is located.

[0597] As one subembodiment of the embodiment, the power offset value depends on an event to which the first measurement report corresponds.

[0598] As one embodiment, the first parameter comprises a time duration value for deciding whether the first measurement report is triggered.

[0599] As one embodiment, the first parameter comprises timeToTrigger.

[0600] As one embodiment, the first parameter comprises Hys.

[0601] As one embodiment, the first parameter is one of Ofn, Ocn, Ocs, and Off.

[0602] As one embodiment, the specific definitions of Hys, Ofn, Ocn, Ocs, and Off in the present application refer to 3GPP TS 38.331.

[0603] As one embodiment, the first measurement report is triggered in a predicted manner, and the first parameter is not used to trigger the first measurement report; or the first measurement report is not triggered in a predicted manner, and the first parameter is used to trigger the first measurement report.

[0604] As one embodiment, the first parameter is a legacy (network traditional) parameter.

[0605] As one embodiment, the first parameter is a parameter in a Release 19 and before Release version.

[0606] As one embodiment, the configuration for the first measurement report comprises a first parameter, and whether the first parameter is used to trigger the first measurement report depends on the triggering manner of the first measurement report.

[0607] As one embodiment, the first measurement report is triggered in a predicted manner, and the first parameter is used to trigger the first measurement report; the first measurement report is not triggered in a predicted manner, and the first parameter is not used to trigger the first measurement report.

[0608] As one embodiment, the first parameter is AI / ML Model Id specific.

[0609] As one embodiment, the first parameter is configured to the AI / ML measurement.

[0610] As one embodiment, the first parameter is configured to the LCM.

[0611] Embodiment 10

[0612] Embodiment 10 illustrates a schematic diagram of RAN domain AI / ML function deployment according to one embodiment of the present application, as shown in FIG. 10. In FIG. 10, gNB can be replaced by eNB, or 6G base station, or other network device.

[0613] In embodiment 10, the management of ML inference functions of multiple base stations is accomplished by RAN domain management function 1002, i.e. data interaction with RAN domain MnS (Management Service) consumer / cross-domain management 1001 (as shown by the dashed arrow in FIG. 10). RAN domain ML training function 1003 is located in RAN domain management function 1002; while ML inference function is located in base station, i.e. AI / ML inference function 1004 is located in gNB 1005, AI / ML inference function 1006 is located in gNB 1007, and so on.

[0614] 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. ML training function, ML testing function, ML inference function can be independently deployed, or co-located deployed. The deployment of AI / ML related functions can be implemented through software, such as the download and / or running of executable files; or can be implemented through the combination of software and hardware, such as accelerating the operation speed or saving power consumption through hardware acceleration of specific computing units.

[0615] For ML training function, it can be deployed in a cross-domain management system, or a domain-specific management system, which is used to manage RAN domain or CN (Core Network) domain. For example, for MDA (Management Data Analytics) ML training function, it can be deployed in MDAF (Management Data Analytic Function); for network data analytics ML training, it can be deployed in NWDAF (NetWork Data Analytics Function), i.e. ML training function is MTLF (Model Training Logical Function).

[0616] For ML inference function, it can also be deployed in a cross-domain management system, or a domain-specific management system; for example, ML inference function is MDAF, or ML inference function is AnLF (Analytics Logical Function) in NWDAF.

[0617] Similarly, ML testing function can also be deployed in a cross-domain management system, or a domain-specific management system.

[0618] Optionally, the management of ML inference function can also be completed by the base station itself, i.e. each base station can independently interact with RAN domain MnS consumer / cross-domain management 1001.

[0619] It should be noted that embodiment 10 is only one non-limiting implementation; optionally, RAN domain ML training function can also be deployed in a base station; or optionally, part of the base stations deploy ML inference function and RAN domain ML training function, and part of the base stations only deploy ML inference function.

[0620] As an embodiment, one gNB (or base station) in embodiment 10 is the second node of the application.

[0621] Embodiment 11

[0622] Embodiment 11 illustrates a schematic diagram of AI / ML function deployment of UE according to an embodiment of the application, as shown in FIG. 11. In FIG. 11, RAN domain ML training function 1104 is optional.

[0623] The UE function 1103 is deployed in the first node of the present application, and includes an AI / ML inference function 1105; the AI / ML inference function 1105 uses an ML model (also referred to as an AI model) for inference; one ML model is usually trained before being used for AI / ML inference.

[0624] As one embodiment, the UE function 1103 includes a RAN-domain ML training function 1104, which runs training data through an 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.

[0625] The above embodiment can reduce the complexity of the base station, or save the air interface resources caused by reporting training data; however, the above embodiment puts forward higher requirements on the processing capability of the UE side.

[0626] Optionally, the UE function 1103 further includes a CN-domain ML training function (not included in FIG. 11).

[0627] Optionally, the UE function 1103 further includes an AI / ML deployment function (not included in FIG. 11), which is used to load ML models and data.

[0628] As one embodiment, the first node indicates whether the ML training function (RAN domain or CN domain) is supported through capability reporting, and the capability reporting is RRC signaling or NAS (Non-Access Stratum) signaling.

[0629] As one embodiment, the ML model and related metadata are loaded by the first node from a network device or a remote server.

[0630] Optionally, the UE function 1103 is an MnS producer, which provides data to the CN-domain MnF (Management Function) and / or the RAN-domain MnF and / or the cross-domain management system 1101 for management or analysis (as shown by the double-headed arrow 1102).

[0631] Optionally, the UE function 1103 is a MnS consumer that loads data from the CN domain MnF and / or the RAN domain MnF and / or the cross-domain management system 1101 for AI / ML related management, such as management data requests, ML model activation, and / or ML training, etc. (as shown by the double-headed arrow 1102).

[0632] As an embodiment, the first CSI in the present application is obtained through inference of the AI / ML inference function 1105.

[0633] As an embodiment, the ML model is based on a NN.

[0634] As an embodiment, the ML model is based on an ANN.

[0635] As an embodiment, the ML model is based on a CNN.

[0636] As an embodiment, the ML model is based on a LLM architecture.

[0637] As an embodiment, the ML model is based on a Transformer architecture.

[0638] As an embodiment, the ML model is based on an LSTM.

[0639] As an embodiment, the ML model is based on an MLP.

[0640] As an embodiment, the ML model is based on a GAN.

[0641] As an embodiment, the ML model is based on a light-weight neural network.

[0642] As a sub-embodiment of this embodiment, the light-weight neural network includes one or more of MobileNet, ShuffleNet, and SqueezeNet.

[0643] Embodiment 12

[0644] Embodiment 12 illustrates a schematic diagram of an artificial intelligence or machine learning based processing system according to an embodiment of the present application, as shown in FIG. 12. In FIG. 12, the artificial intelligence or machine learning based processing system includes a first processing machine, a second processing machine, a third processing machine, and a fourth processing machine.

[0645] In embodiment 12, the first processor sends a first data set to the second processor, and sends a second data set to the third processor; the second processor generates a target first-type parameter group according to the first data set, and sends the generated target first-type parameter group to the third processor; the third processor processes the second data set using the target first-type parameter group to obtain a first-type output, and optionally, the third processor sends the first-type output to the fourth processor. In FIG. 12, the first-type feedback and the second-type feedback are optional; the second processor includes an ML training function; and the third processor includes an ML inference function.

[0646] As an embodiment, the fourth processor includes an ML testing function.

[0647] As an embodiment, the fourth processor includes performance monitoring / evaluation of the ML model.

[0648] As an embodiment, the third processor sends the first-type feedback to the second processor; the first-type feedback is used to trigger recalculation or update of the target first-type parameter group, i.e., trigger ML initial training or ML retraining.

[0649] As an embodiment, the fourth processor sends the second-type feedback to the first processor; the second-type feedback is used to generate the first data set or the second data set, or the second-type feedback is used to trigger sending of the first data set or sending of the second data set.

[0650] As an embodiment, the first processor generates the first data set and the second data set according to measurement of a reference signal.

[0651] As an embodiment, the third processor belongs to the first node, and the fourth processor belongs to the second node.

[0652] As an embodiment, the first-type output includes the first information.

[0653] As an embodiment, the first data set includes training data.

[0654] As an embodiment, the second processor is used to train an ML model, and the trained model is described by the target first-type parameter group.

[0655] As an embodiment, the second processor belongs to the first node; and the above method avoids passing the first data set to the second node.

[0656] As an embodiment, the second processing machine belongs to the second node; the above method supports joint training, and optimizes system performance.

[0657] As an embodiment, the second processing machine belongs to the core network; the above method supports network-wide joint training, and further optimizes system performance.

[0658] As an embodiment, the second data set includes inference data.

[0659] As an embodiment, the third processing machine belongs to the first node.

[0660] As an embodiment, the third processing machine constructs a model according to the target first-type parameter group, and then inputs the second data set into the constructed model to obtain the first-type output.

[0661] As an embodiment, the second data set includes the first RS set.

[0662] As an embodiment, the second data set includes L1 measurement results obtained by measuring the first RS set.

[0663] As an embodiment, the second data set includes L3 measurement results obtained by measuring the first RS set.

[0664] As an embodiment, the second data set includes beam-level measurement results obtained by measuring the first RS set.

[0665] As an embodiment, the second data set includes cell-level measurement results obtained by measuring the first RS set.

[0666] As an embodiment, the second data set includes L3 prediction results generated by the first node based on the first RS set.

[0667] As an embodiment, the second data set includes beam-level prediction results generated by the first node based on the first RS set.

[0668] As an embodiment, the second data set includes cell-level prediction results generated by the first node based on the first RS set.

[0669] As an embodiment, the first-type output includes the first measurement report.

[0670] As an embodiment, the first-type output includes whether to trigger the first measurement report.

[0671] As one embodiment, the first type of output comprises a time at which the first node predicts that the first measurement report is triggered based on the first RS set.

[0672] As one embodiment, the first type of output comprises an information unit in the first set of information units.

[0673] As one embodiment, the first type of output comprises the confidence interval as described in the present application.

[0674] As one embodiment, the first type of feedback comprises the confidence interval as described in the present application.

[0675] As one embodiment, the third processor generates a recovery data set according to the first type of output, and an error of the recovery data set and the second data set is used to generate the first type of feedback.

[0676] As one embodiment, the first type of feedback is used to reflect the performance of the trained model; when the performance of the trained model cannot meet the requirement, the second processor recalculates the target first type of parameter group.

[0677] As one embodiment, when the error is too large or the update is not performed for too long a time, the performance of the trained model is considered to be unable to meet the requirement.

[0678] As one embodiment, the target first type of parameter group comprises one or more of a convolution kernel, a pool core, a pooling function, an activation function, a parameter of the pooling function, or a parameter of the activation function.

[0679] As one embodiment, the target first type of parameter group comprises one or more of a convolution kernel size, a convolution layer number, a convolution step, a pool core size, a pool core step, a pooling function, an activation function, or a feature map number.

[0680] Embodiment 13

[0681] Embodiment 13 illustrates an artificial intelligence or machine learning based schematic diagram according to one embodiment of the present application, as shown in FIG. 13. In FIG. 13, the first operation and the second operation belong to the first stage, the third operation belongs to the second stage, the fourth operation belongs to the third stage, and the fifth operation belongs to the fourth stage; the line with an arrow indicates the order of the flow.

[0682] As an embodiment, the first operation comprises AI / ML training, the second operation comprises AI / ML testing, the third operation comprises AI / ML emulation, the fourth operation comprises AI / ML entity loading, and the fifth operation comprises AI / ML inference.

[0683] 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.

[0684] As an embodiment, the first phase comprises AI / ML model training.

[0685] As an embodiment, the first phase comprises AI / ML model training and AI / ML testing.

[0686] As an embodiment, the AI / ML model training comprises initial training and re-training of one or a set of AI / ML entities.

[0687] As an embodiment, the AI / ML model training relies on training data.

[0688] As an embodiment, the AI / ML model training comprises AI / ML entity validation.

[0689] As an embodiment, the AI / ML entity validation is used to evaluate the performance of the AI / ML entity.

[0690] As an embodiment, the AI / ML entity validation relies on validation data.

[0691] As an embodiment, if the result of AI / ML entity validation does not meet expectations, the AI / ML model will be re-trained.

[0692] As an embodiment, the AI / ML testing comprises testing the validated AI / ML entity to evaluate the performance of the trained AI / ML model.

[0693] As an embodiment, if the result of AI / ML testing meets expectations, the AI / ML entity proceeds to the next phase; otherwise, the AI / ML model will be re-trained.

[0694] As one embodiment, the AI / ML testing relies on test data.

[0695] As one embodiment, the second stage includes AI / ML simulation, which simulates the inference of the AI / ML entity in a simulation environment.

[0696] As one embodiment, the AI / ML simulation estimates the performance of the inference of the AI / ML entity in a simulation environment before the AI / ML entity is used.

[0697] As one embodiment, the second stage is optional.

[0698] As one embodiment, the third stage includes AI / ML entity loading, which is to obtain the trained AI / ML entity to obtain the desired AI / ML inference function.

[0699] As one embodiment, the third stage is optional.

[0700] As one embodiment, the third stage is no longer needed when the training function and the inference function are co-located.

[0701] As one embodiment, the fourth stage includes AI / ML inference.

[0702] Embodiment 14

[0703] Embodiment 14 illustrates a structural block diagram of a processing apparatus in a first node according to one embodiment of the present application, as shown in FIG. 14. In FIG. 14, the processing apparatus 1400 in the first node includes a first receiver 1401 and a first transmitter 1402.

[0704] In embodiment 14, the first receiver 1401 receives a first information block, the first information block configuring a first RS set; a measurement is made in the first RS set, and the measurement or prediction for the first RS set is used to trigger a first measurement report; the first transmitter 1402 transmits the first measurement report.

[0705] In embodiment 14, whether an information unit in a first information unit set is included in the first measurement report depends on the triggering manner of the first measurement report, the triggering manner of the first measurement report being one of predicted and not predicted; the first information unit set includes at least one of the following:

[0706] - a first set of air interface resources for the first node;

[0707] - an object for which the prediction for the first measurement report is directed;

[0708] - a confidence interval of the prediction for the first measurement report;

[0709] - a time window for the first measurement report;

[0710] - a prediction manner of the first measurement report.

[0711] As one embodiment, when the trigger manner of the first measurement report is prediction, the first measurement report comprises the first set of information elements; when the trigger manner of the first measurement report is not prediction, the first measurement report does not comprise the first set of information elements.

[0712] As one embodiment, the first set of air interface resources comprises at least one of spatial resources, power resources or transmission directions; the first set of air interface resources is for a serving cell of the first node, or the first set of air interface resources is for a candidate cell of the first node.

[0713] As one embodiment, the object for which the prediction for the first measurement report is directed comprises a serving cell, a neighbor cell or a serving cell and a neighbor cell.

[0714] As one embodiment, when the first node does not receive a response for the first measurement report in the time window for the first measurement report, a first timer of the first node expires.

[0715] As one embodiment, a value range of the confidence interval of the prediction for the first measurement report depends on at least one of:

[0716] - a number of the object for which the prediction for the first measurement report is directed;

[0717] - the prediction manner of the first measurement report.

[0718] As one embodiment, the configuration for the first measurement report comprises a first parameter; a value of the first parameter depends on the trigger manner of the first measurement report, or whether the first parameter is used to trigger the first measurement report depends on the trigger manner of the first measurement report.

[0719] As one embodiment, when the signals comprised in the first set of RSs are CSI-RSs or SSBs, the above method has good forward compatibility; however, in order to adapt to the performance requirements of future wireless networks such as 6G, the signals comprised in the first set of RSs can also be other kinds of RSs to better meet the performance requirements of measurement reporting.

[0720] As an embodiment, whether the first set of information elements is included in the first measurement report depends on a triggering manner of the first measurement report.

[0721] As an embodiment, the first node predicts an air interface resource set for a serving cell of the first node or an air interface resource set for a candidate cell of the first node based on the prediction indication, and the first air interface resource set included in the first set of information elements can assist a base station to configure air interface resources occupied by the first node after handover, which is beneficial to the base station to coordinate resource occupation among different user equipments, and meanwhile improves resource utilization and quality of transmission signals of the first node.

[0722] As an embodiment, the measurement on the first RS set comprises receiving an RS in the first RS set.

[0723] As a sub-embodiment of the embodiment, a sender of the RS in the first RS set comprises a maintaining base station of a serving cell.

[0724] As a sub-embodiment of the embodiment, the sender of the RS in the first RS set comprises the AI entity in the present application.

[0725] As a sub-embodiment of the embodiment, the sender of the RS in the first RS set comprises a maintaining base station of a neighboring cell.

[0726] As a sub-embodiment of the embodiment, the first RS set comprises a first RS subset and a second RS subset, the first node U1 receives and measures the first RS subset, and predicts measurement results of the first RS subset and the second RS subset according to the first RS subset.

[0727] As an embodiment, the first node is a user equipment.

[0728] As an embodiment, the first node is a relay node equipment.

[0729] As an embodiment, the first receiver 1401 comprises at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, the data source 467} in Embodiment 4.

[0730] As one embodiment, the first transmitter 1402 includes 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, the data source 467} in embodiment 4.

[0731] Embodiment 15

[0732] Embodiment 15 illustrates a structural block diagram of a processing apparatus in a second node according to an embodiment of the present application, as shown in FIG. 15. In FIG. 15, the processing apparatus 1500 in the second node includes a second transmitter 1501 and a second receiver 1502.

[0733] In embodiment 15, the second transmitter 1501 transmits a first information block, the first information block configuring a first RS set; the second receiver 1502 receives a first measurement report.

[0734] In embodiment 15, the sender of the first measurement report is a first node, the first node performing measurement in the first RS set, the measurement or prediction for the first RS set being used to trigger the first measurement report; whether the first measurement report includes a first information unit set in the first measurement report depends on a triggering manner of the first measurement report, the triggering manner of the first measurement report being one of predicted and not predicted; the first information unit set includes at least one of:

[0735] - a first set of air interface resources for the first node;

[0736] - an object for which a prediction for the first measurement report is directed;

[0737] - a confidence interval of the prediction for the first measurement report;

[0738] - a time window for the first measurement report;

[0739] - a prediction manner of the first measurement report.

[0740] As one embodiment, when the triggering manner of the first measurement report is predicted, the first measurement report includes the first information unit set; when the triggering manner of the first measurement report is not predicted, the first measurement report does not include the first information unit set.

[0741] As one embodiment, the first set of air interface resources includes at least one of spatial resources, power resources or transmission directions; the first set of air interface resources is for a serving cell of the first node, or the first set of air interface resources is for a candidate cell of the first node.

[0742] As one embodiment, the object for which the prediction for the first measurement report is made comprises a serving cell, a neighbor cell, or a serving cell and a neighbor cell.

[0743] As one embodiment, a first timer of the first node expires when the first node does not receive a response for the first measurement report in the time window for the first measurement report.

[0744] As one embodiment, a range of values of the confidence interval of the prediction for the first measurement report depends on at least one of:

[0745] - a number of the object for which the prediction for the first measurement report is made;

[0746] - a manner of the prediction of the first measurement report.

[0747] As one embodiment, the configuration for the first measurement report comprises a first parameter; a value of the first parameter depends on the manner of the trigger of the first measurement report, or whether the first parameter is used to trigger the first measurement report depends on the manner of the trigger of the first measurement report.

[0748] As one embodiment, the signal included in the first RS set is CSI-RS or SSB, the above method has good forward compatibility; however, in order to adapt to the performance requirements of future wireless networks such as 6G, the signal included in the first RS set can also be other kinds of RSs to better meet the performance requirements of measurement reporting.

[0749] As one embodiment, whether the first information unit set is included in the first measurement report depends on the manner of the trigger of the first measurement report.

[0750] As one embodiment, the first node indicates an air interface resource set for a serving cell of the first node or an air interface resource set for a candidate cell of the first node based on the prediction, and the first air interface resource set included in the first information unit set can assist a second node to configure the air interface resource occupied after the first node switches, which is beneficial to the second node to coordinate the resource occupation between different user equipment, and at the same time improves the resource utilization and the quality of the transmission signal of the first node.

[0751] As one embodiment, the measurement of the first node in the first RS set comprises: the first node receives the RS in the first RS set.

[0752] As one subembodiment of this embodiment, the transmitter of the RS in the first RS set comprises the second node.

[0753] As one subembodiment of this embodiment, the transmitter of the RS in the first RS set comprises the AI entity.

[0754] As one subembodiment of this embodiment, the transmitter of the RS in the first RS set comprises a maintaining base station of a neighboring cell.

[0755] As one subembodiment of this embodiment, the first RS set comprises a first RS subset and a second RS subset, the first node U1 receives and measures the first RS subset, and predicts the measurement results of the first RS subset and the second RS subset according to the first RS subset.

[0756] As one embodiment, the second node is a base station device.

[0757] As one embodiment, the second node is a user equipment.

[0758] As one embodiment, the second node is a TRP.

[0759] As one embodiment, the second transmitter 1501 comprises at least one of {the antenna 420, the transmitter 418, the transmit processor 416, the multi-antenna transmit processor 471, the controller / processor 475, the memory 476} in Embodiment 4.

[0760] As one embodiment, the second receiver 1502 comprises at least one of {the antenna 420, the receiver 418, the receive processor 470, the multi-antenna receive processor 472, the controller / processor 475, the memory 476} in Embodiment 4.

[0761] Those skilled in the art can understand that all or part of the steps in the foregoing method can be instructed by programs to related hardware, and the programs can be stored in a computer readable storage medium, such as a read-only memory, a hard disk, an optical disk or the like. Alternatively, all or part of the steps of the foregoing embodiments can also be implemented using one or more integrated circuits. Correspondingly, each module unit in the foregoing 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 aircraft, aircraft, small aircraft, mobile phones, tablet computers, notebook computers, vehicle-mounted communication devices, vehicles, vehicles, RSUs, wireless sensors, network cards, Internet of Things terminals, RFID (Radio Frequency Identification) terminals, NB-IoT (Narrow Band Internet of Things) 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 and other wireless communication devices. The base station or system device in the present application includes but is not limited to macro cellular base stations, micro cellular base stations, small cellular base stations, home base stations, relay base stations, eNB (evolved Node B), gNB, TRP, GNSS (Global Navigation Satellite System), relay satellites, satellite base stations, air base stations, RSUs, unmanned aerial vehicles, test equipment such as wireless communication devices that simulate part of the functions of base stations or signaling testers, and the like.

[0762] Those skilled in the art will understand that the application can be implemented by other specified forms without departing from the core or essential characteristics thereof. Therefore, the presently disclosed embodiments should in no way be considered as descriptive rather than limiting. The scope of the application is determined by the appended claims rather than the preceding description, and all modifications within the equivalent meaning and range of the claims are considered to be included therein.

Claims

1. A first node for wireless communication and artificial intelligence, comprising: Comprising: a first receiver, receiving a first information block, the first information block configuring a first RS set; performing measurement in the first RS set, measurement or prediction for the first RS set being used to trigger a first measurement report; a first transmitter, transmitting the first measurement report; wherein whether or not information elements in a first information element set are included in the first measurement report depends on a triggering manner of the first measurement report, the triggering manner of the first measurement report being one of predicted and not predicted; the first information element set comprising at least one of: - a first set of air interface resources for the first node; - an object for which prediction for the first measurement report is directed; - a confidence interval of prediction for the first measurement report; - a time window for the first measurement report; - a prediction manner of the first measurement report.

2. The first node of claim 1, characterized by When the triggering manner of the first measurement report is predicted, the first measurement report includes the first information element set; when the triggering manner of the first measurement report is not predicted, the first measurement report does not include the first information element set.

3. The first node of claim 1 or 2, wherein, The first set of air interface resources comprises at least one of spatial resources, power resources or transmission directions; the first set of air interface resources is for a serving cell of the first node, or the first set of air interface resources is for a candidate cell of the first node.

4. The first node of any of claims 1 to 3, wherein, The object for which the prediction for the first measurement report is directed comprises a serving cell, a neighbor cell or a serving cell and a neighbor cell.

5. The first node of any of claims 1 to 4, wherein, When the first node does not receive a response for the first measurement report in the time window for the first measurement report, a first timer of the first node expires.

6. The first node of any of claims 1 to 5, wherein, A value range of the confidence interval of the prediction for the first measurement report depends on at least one of: - a number of the object for which the prediction for the first measurement report is directed; - the prediction manner of the first measurement report.

7. The first node of any of claims 1-6, wherein, Configuration for the first measurement report comprises a first parameter; a value of the first parameter depends on the triggering manner of the first measurement report, or whether or not the first parameter is used to trigger the first measurement report depends on the triggering manner of the first measurement report. 8.A second node for wireless communication and artificial intelligence, comprising: Comprising: a second transmitter, transmitting a first information block, the first information block configuring a first RS set; a second receiver, receiving a first measurement report; wherein a sender of the first measurement report is a first node, the first node performing measurement in the first RS set, measurement or prediction for the first RS set being used to trigger the first measurement report; whether or not information elements in a first information element set are included in the first measurement report depends on a triggering manner of the first measurement report, the triggering manner of the first measurement report being one of predicted and not predicted; the first information element set comprising at least one of: - a first set of air interface resources for the first node; - an object for which prediction for the first measurement report is directed; - a confidence interval of prediction for the first measurement report; - a time window for the first measurement report; - a prediction manner of the first measurement report. 9.A method for a first node in wireless communication and artificial intelligence, comprising: comprising: receiving a first information block, the first information block configuring a first RS set; performing measurement in the first RS set, measurement or prediction for the first RS set being used to trigger a first measurement report; sending the first measurement report; wherein whether or not an information unit in a first information unit set is included in the first measurement report depends on a triggering manner of the first measurement report, the triggering manner of the first measurement report being one of predicted and not predicted; the first information unit set comprising at least one of: - a first set of air interface resources for the first node; - an object for which prediction for the first measurement report is directed; - a confidence interval of prediction for the first measurement report; - a time window for the first measurement report; - a prediction manner of the first measurement report. 10.A method for a second node in wireless communication and artificial intelligence, comprising: comprising: sending a first information block, the first information block configuring a first RS set; receiving a first measurement report; wherein a sender of the first measurement report is a first node, the first node performing measurement in the first RS set, measurement or prediction for the first RS set being used to trigger the first measurement report; whether or not an information unit in a first information unit set is included in the first measurement report depends on a triggering manner of the first measurement report, the triggering manner of the first measurement report being one of predicted and not predicted; the first information unit set comprising at least one of: - a first set of air interface resources for the first node; - an object for which prediction for the first measurement report is directed; - a confidence interval of prediction for the first measurement report; - a time window for the first measurement report; - a prediction manner of the first measurement report.

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