Method and apparatus used in terminal

Abstract

Disclosed in the present application are a method and apparatus used in a terminal. The method comprises: a terminal receiving first signaling, wherein the first signaling indicates a first threshold value; executing first radio link failure (RLF) prediction, wherein the first RLF prediction comprises predicting the probability of an RLF occurring within a first time window; and executing first measurement, wherein whether the first measurement is based on a first reference signal (RS) resource set or a second RS resource set depends on the predicted probability of the RLF occurring within the first time window. A positioning mode used by the terminal depends on the RS resource set on which the first measurement is based. The method provided in the present application can reduce the impact of an RLF on the communication of a terminal, especially the impact on measurement.

Description

A method and apparatus used in a terminal TECHNICAL FIELD

[0001] The present application relates to a transmission method in a wireless communication system, and to a method of intelligently selecting a reference signal (RS) resource, and in particular to positioning technology. BACKGROUND

[0002] The application scenarios of future wireless communication systems are increasingly diversified, and different application scenarios have different performance requirements for the system. In order to meet the different performance requirements of various application scenarios, it is decided at the 72nd plenary meeting of 3GPP (3rd Generation Partner Project) RAN (Radio Access Network) to study New Radio (NR) (or Fifth Generation, 5G), and the NR WI (Work Item) is passed at the 75th plenary meeting of 3GPP RAN, and the standardization work of NR begins.

[0003] In communication, whether it is LTE (Long Term Evolution) or 5G NR, it involves accurate reception of reliable information, optimized energy efficiency, determination of information effectiveness, flexible resource allocation, scalable system structure, efficient non-access layer information processing, low service interruption and drop rate, support for low power consumption, which is of great significance to the normal communication of base stations and user equipment, reasonable scheduling of resources, and balancing of system load. It can be said that it is the cornerstone of high throughput, meeting the communication needs of various services, improving spectrum utilization, and improving service quality. Whether it is eMBB (enhanced Mobile BroadBand), URLLC (Ultra Reliable Low Latency Communication) or eMTC (enhanced Machine Type Communication), it is indispensable. Considering the potential of AI (Artificial Intelligence) / ML (Machine Learning) algorithm-based mechanisms to implement proactive solutions and the related progress of RAN1 and RAN3 in AI / ML, 3GPP has approved the SI (Study Item) "Study on AI / ML for mobility in NR" to study and evaluate the potential benefits and gains of AI / ML-assisted mobility for network-triggered L3-based handover, including Radio Link Failure (RLF) prediction or Handover Failure (HOF) prediction. Relaxing measurement is an important technology in wireless communication systems, which involves effective allocation and use of wireless resources, reducing the monitoring frequency of network status, and reducing the cost of measurement under the premise of ensuring basic communication performance. With the development of wireless communication technology, new technologies and methods are constantly being introduced into relaxing measurement to adapt to higher data transmission rates, better service quality requirements, and more complex network environments.

[0004] With the increasing complexity and complexity of the system, there is a higher demand for reducing the interruption rate, reducing the delay, enhancing the reliability, enhancing the stability of the system, and the flexibility of the service, and the power saving. At the same time, when designing the system, the compatibility between different systems and different versions also needs to be considered. SUMMARY

[0005] The researchers find that in a wireless communication system, how to determine the RS resource set to which the first measurement is directed according to the prediction of the probability of RLF occurring in the first time window is a problem to be solved.

[0006] To solve the above problems, the present application provides a solution. In the above problem description, the signaling interaction between the base station and the terminal is taken as an example, and the present application is also applicable to scenarios such as IAB or V2X, and similar technical effects of the signaling interaction between the base station and the terminal are achieved. In addition, the unified solution for different scenarios also helps to reduce hardware complexity and cost.

[0007] As an embodiment, the explanation of the terminology in the present application refers to the definition of the specification agreement TS36 series of 3GPP.

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

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

[0010] It should be noted that the embodiments in any node of the present application and the features in the embodiments can be applied to any other node without conflict. The embodiments of the present application and the features in the embodiments can be arbitrarily combined with each other without conflict.

[0011] The present application discloses a method in a terminal, characterized in that,

[0012] comprising:

[0013] receiving first signaling, the first signaling indicating a first threshold; performing a first RLF prediction, the first RLF prediction including predicting a probability of RLF occurring in a first time window; performing a first measurement; the first measurement being based on a first RS resource set or a second RS resource set depending on the predicted probability of RLF occurring in the first time window; wherein the meaning that the first measurement is based on the first RS resource set or the second RS resource set depending on the predicted probability of RLF occurring in the first time window includes: when the predicted probability of RLF occurring in the first time window is greater than the first threshold, the first measurement is based on the first RS resource set; when the predicted probability of RLF occurring in the first time window is not greater than the first threshold, the first measurement is directed to the second RS resource set; the length of the first time window is limited.

[0014] As an embodiment, the problem to be solved by the present application includes how to determine a first RS resource set for a first measurement in a scenario where RLF is likely to occur. In the above method, the first RS resource set for the first measurement depends on the predicted probability of RLF occurring within a first time window, thereby solving the above problem.

[0015] As an embodiment, the benefits of the above method include reducing measurement cost, improving efficiency, ensuring performance, ensuring transmission of services, better reducing terminal power consumption, and prolonging battery life.

[0016] As an embodiment, the above method is simple to implement. The above method is suitable for a communication system that applies AI.

[0017] As an embodiment, the above method can use the first RS resource set for the first measurement when the predicted probability of RLF occurrence is high, to more accurately detect possible RLF occurrence and ensure communication performance.

[0018] As an embodiment, the above method uses the second RS resource set for the first measurement when the predicted probability of RLF occurrence is low, to reduce measurement cost and power consumption.

[0019] As an embodiment, when the predicted probability of RLF occurrence within the first time window is greater than the first threshold, the first measurement based on the first RS resource set means that when the predicted probability of RLF occurrence within the first time window is greater than the first threshold, the first measurement is not based on the second RS resource set.

[0020] As an embodiment, when the predicted probability of RLF occurrence within the first time window is not greater than the first threshold, the first measurement based on the second RS resource set means that when the predicted probability of RLF occurrence within the first time window is not greater than the first threshold, the first measurement is not based on the first RS resource set.

[0021] According to an aspect of the present application, it is characterized in that,

[0022] The first RS resource set corresponds to a first positioning method, and the second RS resource set corresponds to a second positioning method.

[0023] As an embodiment, the benefits of the above method include intelligently determining the positioning method based on the predicted probability of RLF occurrence within the first time window.

[0024] According to an aspect of the present application, it is characterized in that,

[0025] At least one of the first RS resource set and the second RS resource set comprises PRS (Positioning Reference Signals).

[0026] According to an aspect of the present application, there is provided a method for performing relaxed measurement, comprising:

[0027] The first measurement is a relaxed measurement depending on the first RLF prediction, wherein the first measurement is a relaxed measurement when a probability of predicting RLF in a first time window is not greater than a first threshold; the first measurement is not a relaxed measurement when the probability of predicting RLF in the first time window is greater than the first threshold.

[0028] According to an aspect of the present application, there is provided a method for performing relaxed measurement, comprising:

[0029] controlling a first timer; the controlling the first timer comprises starting the first timer when entering a relaxed measurement state, and retreating the first timer when leaving the relaxed measurement state; the first timer stops retreating when the first timer retreats to 0; wherein expiration of the first timer triggers entering the relaxed measurement state.

[0030] According to an aspect of the present application, there is provided a method for performing relaxed measurement, comprising:

[0031] The first measurement is one of a first measurement set; and terminating at least part of the first measurement set is triggered when a probability of predicting RLF in a first time window is greater than a second threshold.

[0032] According to an aspect of the present application, there is provided a method for performing relaxed measurement, comprising:

[0033] A period of the first measurement depends on the probability of predicting RLF in a first time window.

[0034] According to an aspect of the present application, there is provided a method for performing relaxed measurement, comprising:

[0035] The probability of predicting RLF in a first time window comprises a probability that a number of at least one of consecutive out-of-sync indications and predicted out-of-sync indications in the first time window reaches a third threshold; wherein the predicted out-of-sync indication depends on measurement of a RS resource set other than the first RS resource set and the second RS resource set.

[0036] According to an aspect of the present application, there is provided a method for performing relaxed measurement, comprising:

[0037] sending a first notification; and receiving the first notification.

[0038] The predicting RLF depends on the first notification.

[0039] According to an aspect of the present application, it is characterized in that

[0040] sending first location information; wherein the first location information is based on measurement when a probability of RLF occurring within a first time window is not greater than a first threshold; the first location information is based on prediction when the probability of RLF occurring within the first time window is greater than the first threshold.

[0041] As an embodiment, the benefits of the above method include: using predicted location information when the probability of RLF occurring is greater, avoiding measurement errors leading to inaccurate location information, and improving the accuracy of the first location information sent by the terminal.

[0042] The present application discloses a terminal, comprising:

[0043] The terminal comprises one or more processors and a memory;

[0044] The memory is coupled to the one or more processors, and the memory is configured to store computer program code comprising computer instructions, and the one or more processors are configured to invoke the computer instructions to cause the terminal to perform at least:

[0045] a first receiver 902 configured to receive first signaling indicating a first threshold; perform a first RLF prediction, the first RLF prediction comprising predicting a probability of RLF occurring within a first time window; perform a first measurement; the first measurement being based on a first RS resource set or a second RS resource set depending on the predicted probability of RLF occurring within the first time window; wherein the first measurement being based on the first RS resource set or the second RS resource set depending on the predicted probability of RLF occurring within the first time window comprises: when the predicted probability of RLF occurring within the first time window is greater than the first threshold, the first measurement is based on the first RS resource set; when the predicted probability of RLF occurring within the first time window is not greater than the first threshold, the first measurement is for the second RS resource set; and the length of the first time window is finite.

[0046] Specifically, according to an aspect of the present application, the terminal is an Internet of Things terminal.

[0047] Specifically, according to an aspect of the present application, the terminal is a user equipment.

[0048] Specifically, according to an aspect of the present application, the terminal is an access network device.

[0049] Specifically, according to an aspect of the present application, the terminal is a vehicle-mounted terminal.

[0050] Specifically, according to an aspect of the present application, the terminal is an aircraft.

[0051] Specifically, according to an aspect of the present application, the terminal is a mobile phone.

[0052] As an embodiment, compared with the conventional scheme, the present application has the following advantages:

[0053] The measurement is more targeted, and the measurement result is more reliable. The influence of poor signal quality on measurement when RLF occurs is avoided.

[0054] A very good balance is achieved in terms of power saving and avoiding RLF / quickly responding to RLF.

[0055] Better support is provided for the transmission of services with high delay requirements and strong burstiness, such as XR services.

[0056] For scenarios where the communication environment is constantly changing, especially when XR services are involved, the delay can be reduced, and the influence of RLF on communication can be avoided or reduced.

[0057] It is beneficial to save power and reduce signaling overhead. BRIEF DESCRIPTION OF DRAWINGS

[0058] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments with reference to the attached drawings:

[0059] FIG. 1 shows a flowchart of performing measurement according to an embodiment of the present application;

[0060] FIG. 2 shows a schematic diagram of a network architecture according to an embodiment of the present application;

[0061] FIG. 3 shows a schematic diagram of an embodiment of a wireless protocol architecture of a user plane and a control plane according to an embodiment of the present application;

[0062] FIG. 4 shows a schematic diagram of a first communication device and a second communication device according to an embodiment of the present application;

[0063] FIG. 5 shows a flowchart of wireless signal transmission according to an embodiment of the present application;

[0064] FIG. 6 shows a schematic diagram of a first measurement relying on a first RLF prediction according to an embodiment of the present application;

[0065] FIG. 7 shows a schematic diagram of first location information relying on a first RLF prediction according to an embodiment of the present application;

[0066] FIG. 8 shows a schematic diagram of a change in probability of RLF occurring within a first time window according to an embodiment of the present application;

[0067] FIG. 9 shows a structural block diagram of a processing device in a terminal according to an embodiment of the present application;

[0068] FIG. 10 shows a structural block diagram of a processing device in a base station according to an embodiment of the present application.

[0069] FIG. 11 shows a schematic diagram of transmission of a first notification according to an embodiment of the present application;

[0070] FIG. 12 shows a schematic diagram of an intelligent model according to an embodiment of the present application;

[0071] FIG. 13 shows a schematic diagram of intelligent function deployment of a RAN (Radio Access Network) domain according to an embodiment of the present application;

[0072] FIG. 14 shows a schematic diagram of UE intelligent function deployment according to an embodiment of the present application;

[0073] FIG. 15 shows a flowchart based on artificial intelligence or machine learning according to an embodiment of the present application.

[0074] Embodiment

[0075] The technical solutions of the present application will be further described in detail below with reference to the accompanying drawings, and it should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other arbitrarily without conflict.

[0076] Embodiment 1

[0077] Embodiment 1 shows a flowchart of performing measurement according to an embodiment of the present application, as shown in FIG. 1. In FIG. 1, each block represents a step, and it should be particularly emphasized that the order of the blocks in the figure does not represent the time sequence between the represented steps.

[0078] In embodiment 1, the terminal in the present application receives a first signaling in step 101, performs a first RLF prediction in step 102, performs a first measurement in step 103, and selects a RS resource set in step 104.

[0079] The first signaling is received, and the first signaling indicates a first threshold value; a first RLF prediction is performed, the first RLF prediction including predicting a probability of an RLF occurring within a first time window; a first measurement is performed; the first measurement is based on a first set of RS resources or a second set of RS resources depending on the predicted probability of the RLF occurring within the first time window; a meaning that the first measurement is based on the first set of RS resources or the second set of RS resources depending on the predicted probability of the RLF occurring within the first time window includes: when the predicted probability of the RLF occurring within the first time window is greater than the first threshold value, the first measurement is based on the first set of RS resources; when the predicted probability of the RLF occurring within the first time window is not greater than the first threshold value, the first measurement is for the second set of RS resources; and a length of the first time window is finite.

[0080] As an embodiment, the terminal is a UE (User Equipment).

[0081] As an embodiment, the present application is directed to AI (artificial intelligence).

[0082] As an embodiment, the AI includes machine learning.

[0083] As an embodiment, the terminal refers to a communication device composed of hardware such as a baseband, a radio frequency, and one or two SIM cards.

[0084] As an embodiment, the terminal is in an RRC (Radio Resource Control) connected state.

[0085] As an embodiment, any parameter in the present application is either configured by a network or can be generated by the terminal according to an internal algorithm, for example, randomly.

[0086] As an embodiment, the value of any parameter in the present application, including but not limited to the probability of an RLF occurring, the length of a first time window, a first threshold value, a second threshold value, and a third threshold value, is finite unless otherwise stated.

[0087] As a sub-embodiment of this embodiment, the upper limit of the value of any parameter in the present application is 1024 times 65536.

[0088] As a sub-embodiment of this embodiment, the upper limit of the value of any parameter in the present application is 65536 or 65535.

[0089] As a sub-embodiment of this embodiment, the upper limit of the value of the parameter in the present application is 1024.

[0090] As one sub-example of this example, the upper limit of the value of any parameter in this application is 640 or 320.

[0091] As one example, this application is directed to NR.

[0092] As one example, this application is directed to wireless communication networks beyond NR.

[0093] As one example, the serving cell refers to a cell in which a UE is camped on. Performing cell search includes that the UE searches for a suitable cell of a selected PLMN (Public Land Mobile Network) or SNPN (Stand-alone Non-Public Network), selects the suitable cell to provide available service, and monitors a control channel of the suitable cell, which is defined as camping on a cell; that is, a camped-on cell is a serving cell of the UE with respect to the UE. The benefits of camping on a cell in RRC idle state or RRC inactive state include that the UE can receive system information from the PLMN or SNPN; if the UE wishes to establish an RRC connection or continue a suspended RRC connection after registration, the UE can do so by performing initial access on the control channel of the camped-on cell; the network can page the UE; and the UE can receive ETWS (Earthquake and Tsunami Warning System) and CMAS (Commercial Mobile Alert System) notifications.

[0094] As one example, the first threshold is a real number.

[0095] As one example, the first threshold is greater than or equal to 0 and less than or equal to 1.

[0096] As one example, the first threshold is default.

[0097] As one example, the first threshold is dedicated for the RLF prediction.

[0098] As one example, the first threshold is dedicated for comparing the RLF prediction probability.

[0099] As one example, the first signaling indicates the first threshold by indicating the first threshold explicitly.

[0100] As an embodiment, the first signaling indicates the first threshold by: the first signaling implicitly indicating the first threshold.

[0101] As an embodiment, the first signaling indicates the first threshold by: the first signaling configuring the first threshold.

[0102] As an embodiment, the first signaling indicates the first threshold by: the first signaling enabling the first threshold.

[0103] As an embodiment, the first signaling is cell common.

[0104] As an embodiment, the first signaling is UE specific.

[0105] As an embodiment, the first signaling is RRC (Radio Resource Control) signaling.

[0106] As an embodiment, the first signaling comprises RRC signaling and MAC (Medium Access Control) CE (Control Element).

[0107] As an embodiment, the first signaling comprises RRC signaling and DCI (Downlink Control Information).

[0108] As an embodiment, the first signaling comprises one RRC signaling, the one RRC signaling configuring the first threshold.

[0109] As an embodiment, the first signaling comprises one RRC signaling and one MAC CE, the one RRC signaling configuring a plurality of thresholds, the one MAC CE indicating the first threshold from the plurality of thresholds.

[0110] As an embodiment, the first signaling comprises one RRC signaling and one DCI, the one RRC signaling configuring a plurality of thresholds, the one DCI indicating the first threshold from the plurality of thresholds.

[0111] As an embodiment, the RLF prediction is performed by an intelligent module of the terminal.

[0112] As an embodiment, the RLF prediction is performed by an intelligent model of the terminal for the RLF prediction.

[0113] As an embodiment, the RLF prediction is performed by the terminal based on a UE implementation.

[0114] As one embodiment, the RLF prediction is performed by the terminal based on network configuration.

[0115] As one embodiment, the RLF prediction is performed by the terminal based on UE implementation and network configuration.

[0116] As one embodiment, the RLF prediction is based on recent measurements.

[0117] As one embodiment, the RLF prediction is based on previous measurements.

[0118] As one embodiment, the RLF prediction is based on information stored by the terminal.

[0119] As one embodiment, the RLF prediction is based on information provided by the network.

[0120] As one embodiment, the RLF prediction includes inference.

[0121] As one embodiment, the RLF prediction includes training.

[0122] As one embodiment, the RLF prediction includes both training and inference.

[0123] As one embodiment, the RLF prediction includes predicting link quality.

[0124] As one embodiment, the RLF prediction includes predicting synchronization indication.

[0125] As one embodiment, the RLF prediction includes predicting out-of-sync indication.

[0126] As one embodiment, the RLF prediction includes predicting whether a timer has expired.

[0127] As one embodiment, the RLF prediction refers to predicting whether RLF will occur.

[0128] As one embodiment, the RLF prediction refers to predicting the probability of RLF occurring.

[0129] As one embodiment, the RLF prediction refers to predicting the time of RLF occurrence.

[0130] As one embodiment, the RLF prediction refers to predicting the time interval during which RLF will not occur.

[0131] As one embodiment, the RLF prediction refers to predicting the probability of RLF occurrence as a function of time.

[0132] As one embodiment, the predicting the RLF refers to predicting that the RLF will occur within a first time window.

[0133] As one embodiment, the not predicting the RLF refers to predicting that the RLF will not occur within a first time window.

[0134] As one embodiment, the first time window comprises a predicted time interval.

[0135] As one embodiment, the first time window comprises a time interval after a current time.

[0136] As one embodiment, the first time window comprises an expected running time of a timer.

[0137] As one embodiment, the first time window comprises a running time of a timer.

[0138] As one embodiment, the first time window comprises a remaining time of T310.

[0139] As one embodiment, the first RS resource set and the second RS resource set are orthogonal.

[0140] As one embodiment, the first RS resource set comprises SSB.

[0141] As one subembodiment of this embodiment, the second RS resource set comprises SSB.

[0142] As one subembodiment of this embodiment, the second RS resource set comprises CSI-RS.

[0143] As one subembodiment of this embodiment, the second RS resource set does not comprise SSB.

[0144] As one subembodiment of this embodiment, the second RS resource set comprises SSB has the advantage that more accurate results can be obtained by monitoring beams determined from different SSBs, avoiding triggering RLF based on measurements of only the current beam.

[0145] As one subembodiment of this embodiment, the second RS resource set comprises only CSI-RS has the advantage that using different types of RS resources facilitates improving measurement accuracy.

[0146] As one embodiment, the first RS resource set comprises CSI-RS.

[0147] As one subembodiment of this embodiment, the second RS resource set comprises CSI-RS.

[0148] As one subembodiment of this embodiment, the second RS resource set does not include CSI-RS.

[0149] As one subembodiment of this embodiment, the second RS resource set includes SSB.

[0150] As one subembodiment of this embodiment, the second RS resource set includes CSI-RS has the advantage that measurement accuracy, including accuracy for RLF prediction, can be improved by performing more intensive measurement.

[0151] As one subembodiment of this embodiment, the second RS resource set only includes SSB has the advantage that using different types of RS resources facilitates improving measurement accuracy.

[0152] As one embodiment, the first RS resource set is orthogonal to the second RS resource set.

[0153] As one embodiment, the first RS resource set and the second RS resource set are orthogonal in time domain.

[0154] As one embodiment, the second RS resource set is PRS.

[0155] As one subembodiment of this embodiment, the second RS resource set is PRS has the advantage that positioning can be performed when it is predicted that RLF is more likely to occur in a first time window, facilitating wireless network optimization.

[0156] As one embodiment, the first RS resource set and the second RS resource set have different periodicity.

[0157] As one embodiment, the first RS resource set and the second RS resource set are associated with different PCIs (Physical Cell Identities).

[0158] As one embodiment, the first RS resource set and the second RS resource set being associated with different PCIs has the advantage that it facilitates early monitoring of signals of other PCIs when it is predicted that RLF is more likely to occur, improving communication reliability.

[0159] As one embodiment, the first RS resource set corresponds to a first positioning manner, and the second RS resource set corresponds to a second positioning manner.

[0160] As one embodiment, the first positioning manner is different from the second positioning manner.

[0161] As one embodiment, the first positioning manner is the same as the second positioning manner.

[0162] As one embodiment, the positioning method includes OTDOA positioning (Observed Time Difference Of Arrival Positioning).

[0163] As one embodiment, the positioning method includes A-GNSS positioning (Assisted Global Navigation Satellite System Positioning).

[0164] As one embodiment, the positioning method includes ECID positioning (Enhanced Cell ID Positioning)

[0165] As one embodiment, the positioning method includes TBS positioning (Terrestrial Beacon System Positioning)

[0166] As one embodiment, the positioning method includes sensor-based positioning (Sensor based Positioning)

[0167] As one embodiment, the positioning method includes WLAN-based positioning (WLAN-based Positioning)

[0168] As one embodiment, the positioning method includes Bluetooth-based positioning (Bluetooth-based Positioning).

[0169] As one embodiment, the positioning method includes uplink positioning (NR UL Positioning).

[0170] As one embodiment, the positioning method includes downlink TDOA positioning (NR DL Time Difference Of Arrival Positioning).

[0171] As one embodiment, the positioning method includes downlink AoD positioning (NR DL Angle-of-Departure Positioning).

[0172] As one embodiment, the positioning method includes Multi-RTT positioning (Multiple-Round Trip Time Positioning).

[0173] As one embodiment, the positioning method utilizes measurement results for positioning.

[0174] As one embodiment, the first positioning method utilizes measurement results based on the first set of RS resources for positioning.

[0175] As one embodiment, the second positioning method utilizes measurement results based on the second set of RS resources for positioning.

[0176] As one embodiment, the measurement results are first measurement results.

[0177] As one embodiment, the first measurement for the first set of RS resources includes that the first measurement is based on the first set of RS resources.

[0178] As one embodiment, the first measurement for the first set of RS resources includes that the first measurement is based on the first set of RS resources.

[0179] As one embodiment, the first measurement for the second set of RS resources includes that the first measurement is based on the second set of RS resources.

[0180] As one embodiment, the first measurement for the second set of RS resources includes that the first measurement is based on the second set of RS resources.

[0181] As one embodiment, the performing the first RLF prediction relying on the first measurement means that the performing the first RLF measurement relies on the results of the first measurement.

[0182] As one embodiment, the performing the first RLF prediction relying on the first measurement means that the performing the first RLF measurement relies on the results of the first measurement.

[0183] As one embodiment, the performing the first RLF prediction relying on the first measurement means that the performing the first RLF measurement relies on the results of the first measurement.

[0184] As one embodiment, the performing the first RLF prediction relying on the first measurement means that the performing the first RLF measurement relies on the results of the first measurement.

[0185] As one embodiment, the length of the first time window is explicitly indicated by the first signaling.

[0186] As one embodiment, the length of the first time window is explicitly indicated by the first signaling.

[0187] As one embodiment, the length of the first time window is explicitly indicated by the first signaling.

[0188] As one embodiment, the terminal sends the first signal.

[0189] As an embodiment, the first signal is a signal of a physical layer.

[0190] As an embodiment, the signal of the physical layer comprises a reference signal.

[0191] As an embodiment, the signal of the physical layer comprises a wake-up signal.

[0192] As an embodiment, the reference signal comprises a sounding reference signal (SRS).

[0193] As an embodiment, the resource occupied by the first signal depends on whether the first measurement is based on the first RS resource set or the second RS resource set.

[0194] As an embodiment, the first signal is used by the network for positioning.

[0195] As an embodiment, the meaning that the resource occupied by the first signal depends on whether the first measurement is based on the first RS resource set or the second RS resource set comprises: when the first measurement is based on the first RS resource set, the uplink frequency band of the first signal in the frequency domain corresponds to the downlink frequency band of the first RS resource set; when the first measurement is based on the second RS resource set, the uplink frequency band of the first signal in the frequency domain corresponds to the downlink frequency band of the second RS resource set.

[0196] As an embodiment, the meaning that the resource occupied by the first signal depends on whether the first measurement is based on the first RS resource set or the second RS resource set comprises: when the first measurement is based on the first RS resource set, the period of the first signal in the time domain is equal to the period of the RS resource in the first RS resource set; when the first measurement is based on the second RS resource set, the period of the first signal in the time domain is equal to the period of the RS resource in the second RS resource set.

[0197] As an embodiment, the transmission time of the first signal is referenced to the reception time of the RS resource set based on which the first measurement is made.

[0198] As a sub-embodiment of this embodiment, the RS resource set based on which the first measurement is made is the first RS resource set or the second RS resource set.

[0199] As an embodiment, the above method has the advantage of facilitating the coordination of positioning between the terminal and the network, and the positioning is more accurate.

[0200] As an embodiment, the first signal implicitly indicates whether the first measurement is based on the first RS resource set or the second RS resource set.

[0201] As one embodiment, the terminal sends a first measurement report, the first measurement report comprising results of the first measurements.

[0202] As one embodiment, the first measurement report is sent to a core network.

[0203] As one embodiment, the first measurement report comprises a prediction of measurement results on a second set of RS resources, the prediction of measurement results on the second set of RS resources being based on measurements for the first set of RS resources.

[0204] As one sub-embodiment of this embodiment, the terminal predicts a probability of RLF occurring within a first time window to be greater than the first threshold.

[0205] As one embodiment, the first measurements are based on either the first set of RS resources or the second set of RS resources.

[0206] Embodiment 2

[0207] Embodiment 2 illustrates a schematic diagram of one network architecture according to the present application, as shown in FIG. 2.

[0208] FIG. 2 illustrates a diagram of a network architecture 200 for a 5G NR, LTE (Long-Term Evolution), and LTE-A (Long-Term Evolution Advanced) system. The 5G NR or LTE network architecture 200 can be referred to as a 5GS (5G System) / EPS (Evolved Packet System) 200 or some other suitable terminology. The 5GS / EPS 200 can include one or more UEs (User Equipment) 201, NG-RAN (Next Generation Radio Access Network) 202, 5GC (5G Core Network) / EPC (Evolved Packet Core) 210, HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and Internet services 230. The 5GS / EPS can interconnect with other access networks, but these entities / interfaces are not shown for simplicity. As shown, the 5GS / EPS provides packet-switched services, however, those skilled in the art will readily appreciate, that the various concepts presented throughout this application are amenable to use with networked systems including a mixture of packet-switched and circuit-switched services or other cellular networks. The NG-RAN includes an NR Node-B (gNB) 203 and other gNBs 204. The gNB 203 provides user and control plane protocol terminations toward the UE 201. The gNB 203 can be connected to the other gNBs 204 via an Xn interface (e.g., backhaul). The gNB 203 can also be referred to as a base station, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS), an extended service set (ESS), a TRP (Transmit Receive Point), or some other suitable terminology. The gNB 203 provides access to the 5GC / EPC 210 for the UE 201. Examples of UEs 201 include a cellular phone, a smart phone, a session initiation protocol (SIP) phone, a laptop, a personal digital assistant (PDA), a satellite radio, a non-tower based communication, satellite mobile communication, 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 internet of things device, a machine type communication device, a land vehicle, a car, a wearable device, or any other similar functional device. Those skilled in the art will also readily appreciate that the UE 201 can be referred to as a mobile station, a subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a mobile device, a wireless device, a wirelessThe gNB 203 is connected by means of the S1 / NG interface to the 5GC / EPC 210. The 5GC / EPC 210 comprises a MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, further 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 handles signaling between the UE 201 and the 5GC / EPC 210. Generally, the MME / AMF / SMF 211 provides bearer and connection management. All user IP (Internet Protocal) packets are transferred through the S-GW / UPF 212, which itself is connected to the P-GW / UPF 213. The P-GW provides UE IP address allocation as well as other functions. The P-GW / UPF 213 is connected to Internet services 230. The Internet services 230 comprise operator corresponding Internet protocol services, in particular the Internet, intranet, IMS (IP Multimedia Subsystem), and packet switched streaming services.

[0209] As one embodiment, the terminal in the present application is the UE 201.

[0210] As one embodiment, the network node's base station in the present application is the gNB 203.

[0211] As one embodiment, the wireless link from the UE 201 to the NR NodeB is an uplink.

[0212] As one embodiment, the wireless link from the NR NodeB to the UE 201 is a downlink.

[0213] As one embodiment, the UE 201 supports relay transmission.

[0214] As one embodiment, the UE 201 is a mobile phone.

[0215] As one embodiment, the UE 201 is a vehicle, including a car.

[0216] As one embodiment, the gNB 203 is a Macro Cellular base station.

[0217] As one embodiment, the gNB 203 is a Micro Cell base station.

[0218] As one embodiment, the gNB 203 is a Pico Cell base station.

[0219] As one embodiment, the gNB 203 is a flying platform device.

[0220] As one embodiment, the gNB 203 is a satellite device.

[0221] Embodiment 3

[0222] Figure 3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for the user plane 350 and the control plane 300, Figure 3 showing three layers of the radio protocol architecture for the control plane 300 between a terminal (UE, gNB) and a network node (gNB, UE), or between two UEs: Layer 1, Layer 2, and Layer 3. Layer 1 (LI layer) is the lowest layer and implements various PHY (Physical layer) signal processing functions. The LI layer will be referred to as the PHY 301 herein. Layer 2 (L2 layer) 305 is above the PHY 301 and is responsible for the link between a terminal and a network node, as well as between two UEs, using the PHY 301. The L2 layer 305 includes a MAC (Medium Access Control) sublayer 302, a RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, which are terminated at the network node. 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 terminal between network nodes. 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. 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 terminals. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in Layer 3 (L3 layer) in the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and configuring the lower layers using RRC signaling between the network node and the terminal. The PC5-S (PC5 Signaling Protocol) sublayer 307 is responsible for handling the signaling protocol for the PC5 interface. The radio protocol architecture for the user plane 350 includes Layer 1 (LI layer) and Layer 2 (L2 layer), which are generally the same as the corresponding layers and sublayers in the control plane 300 for the physical layer 351, the PDCP sublayer 354 in the L2 layer 355, the RLC sublayer 353 in the L2 layer 355, and the MAC sublayer 352 in the L2 layer 355 for a terminal and a network node, but the PDCP sublayer 354 also provides header compression for upper layer packets to reduce radio transmission overhead.The L2 layer 355 in the user plane 350 also includes a SDAP (Service Data Adaptation Protocol) sublayer 356, which is responsible for the mapping between a QoS flow and a data radio bearer (DRB) to support the diversity of services. SRB can be seen as a service or interface provided by the PDCP layer to a higher layer, such as the RRC layer. In the NR system, SRB includes SRB1, SRB2, and SRB3, which are used to transmit different types of control signaling. SRB is a bearer between the UE and the access network, used to transmit control signaling including RRC signaling between the UE and the access network. SRB1 is of particular significance to the UE, and each UE establishes an RRC connection after which there is an SRB1 for transmitting RRC signaling, and most signaling is transmitted through SRB1. If SRB1 is interrupted or cannot be used, the UE must perform RRC reestablishment; each RRC connection establishes an SRB1. SRB2 is generally used only to transmit NAS signaling or signaling related to security; each RRC connection establishes an SRB2. The UE can not configure SRB3. Except for emergency services, the UE must establish an RRC connection with the network to enable subsequent communication. Although not shown, the terminal can have several upper layers above the L2 layer 355. In addition, a network layer (for example, an IP layer) that terminates at a P-GW on the network side and an application layer that terminates at the other end (for example, a remote UE, a server, etc.) of the connection are also included.

[0223] As one embodiment, the wireless protocol architecture in FIG. 3 is applicable to the terminal in the present application.

[0224] As one embodiment, the wireless protocol architecture in FIG. 3 is applicable to the network node in the present application.

[0225] As one embodiment, the first signaling in the present application is generated at the PHY 301 or the MAC 302 or the RRC 306.

[0226] As one embodiment, the first location information in the present application is generated at the PHY 351 or the MAC 352.

[0227] Embodiment 4

[0228] Embodiment 4 shows 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 450 and a second communication device 410 communicating with each other in an access network.

[0229] The first communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmit processor 468, a receive processor 456, and optionally, a multi-antenna transmit processor 457, a multi-antenna receive processor 458, a transmitter / receiver 454, and an antenna 452.

[0230] The second communication device 410 includes a controller / processor 475, a memory 476, a receive processor 470, a transmit processor 416, and optionally, a multi-antenna receive processor 472, a multi-antenna transmit processor 471, a transmitter / receiver 418, and an antenna 420.

[0231] In the transmission from the second communication device 410 to the first communication device 450, upper layer packets from a core network are provided to the controller / processor 475 at the second communication device 410. The controller / processor 475 implements functionality of the L2 layer. In the transmission from the second communication device 410 to the first communication device 450, the controller / processor 475 provides header compression, ciphering, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocation for the first communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for retransmission of lost packets, and signaling to the first communication device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for the LI layer (i.e., physical layer). The transmit processor 416 implements coding and interleaving to facilitate forward error correction (FEC) at the second communication device 410, and mapping of coded and interleaved data onto signal constellations based on various modulation schemes (e.g., binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), M-phase-shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The multi-antenna transmit processor 471 performs digital spatial precoding of the coded and modulated symbols, including codebook-based precoding and non-codebook-based precoding, and beamforming processing, generating one or more spatial streams. The transmit processor 416 then maps to each spatial stream to a subcarrier, multiplexes the stream with reference signals (e.g., pilot) in the time and / or frequency domain, and then performs an inverse fast Fourier transform (IFFT) to generate a time-domain multicarrier symbol stream for the physical channel. The multi-antenna transmit processor 471 then performs transmit analog precoding / 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 streams, and then provides the radio frequency streams to the different antennas 420.

[0232] In transmissions from the second communication device 410 to the first communication device 450, at the first communication device 450, each receiver 454 receives a signal through its respective antenna 452. Each receiver 454 recovers information modulated onto an RF carrier and provides the recovered information at baseband as a stream of symbols to a receive processor 456. The receive processor 456 and a multiple access receiver processor 458 implement various signal processing functions of the Ll layer. The multiple access receiver processor 458 performs receive analog precoding / beamforming operations on the baseband multiple access symbol streams from the receivers 454. The receive processor 456 converts the baseband multiple access symbol streams from the time-domain to the frequency domain using a Fast Fourier Transform (FFT). In the frequency domain, the physical layer data signals and the reference signals are demultiplexed from the received symbol streams by the receive processor 456, with the reference signals to be used for channel estimation and the data signals to be recovered after multiple access detection in the multiple access receiver processor 458 for any spatial streams destined for the first communication device 450. The symbols on each spatial stream are demodulated and recovered by the receive processor 456 and used to generate soft decisions. The receive processor 456 then decodes and de-interleaves the soft decisions to recover the upper layer data and control signals transmitted by the second communication device 410 on the physical channel. The upper layer data and control signals are then provided to a controller / processor 459. The controller / processor 459 implements the functions of the L2 layer. The controller / processor 459 can be associated with a memory 460 that stores program codes and data. The memory 460 can be referred to as a computer-readable medium. In transmissions from the second communication device 410 to the second communication device 450, the controller / processor 459 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover upper layer data packets from the core network. The upper layer data packets are then provided to all protocol layers above the L2 layer. Various control signals can also be provided to the L3 for L3 processing.

[0233] In the transmission from the first communication device 450 to the second communication device 410, at the first communication device 450, a data source 467 is used to provide upper layer data packets to a controller / processor 459. The data source 467 represents all protocol layers above the L2 layer. Similar to the transmit function at the second communication device 410 described in the transmission from the second communication device 410 to the first communication device 450, the controller / processor 459 implements header compression, ciphering, packet segmentation and reordering, and multiplexing between logical and transport channels based on radio resource allocations, implements L2 layer functionality for the user plane and control plane. The controller / processor 459 is also responsible for error detection, retransmission of lost packets, and signaling to the second communication device 410. A transmit processor 468 performs modulation mapping, channel coding processing, and a multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based precoding and non-codebook-based precoding, and beamforming processing, and then the transmit processor 468 modulates the resulting spatial streams into multi-carrier / single-carrier symbol streams, which are then provided to different antennas 452 via transmitters 454 after analog precoding / beamforming operations in the multi-antenna transmit processor 457. Each transmitter 454 first converts the baseband symbol stream provided by the multi-antenna transmit processor 457 into a radio frequency signal, and then provides the radio frequency signal to the antenna 452.

[0234] In the transmission from the first communication device 450 to the second communication device 410, the functions at the second communication device 410 are similar to the receive functions at the first communication device 450 described in the transmission from the second communication device 410 to the first communication device 450. Each receiver 418 receives a radio frequency signal through its respective antenna 420, converts the received radio frequency signal into a baseband signal, and provides the baseband signal to a multi-antenna receive processor 472 and a receive processor 470. The receive processor 470 and the multi-antenna receive processor 472 collectively implement the functionality of the L1 layer. A controller / processor 475 implements the functionality of the L2 layer. The controller / processor 475 can be associated with a memory 476 that stores program codes and data. The memory 476 can be referred to as a computer readable medium. In the transmission from the first communication device 450 to the second communication device 410, the controller / processor 475 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover upper layer data packets from the UE 450. Upper layer data packets from the controller / processor 475 can be provided to a core network.

[0235] As one embodiment, the first communication device 450 comprises: at least one processor and at least one memory including a computer program code; the at least one memory and the computer program code are configured to, with the at least one processor, cause the first communication device 450 to perform at least the following: receive first signaling, the first signaling indicating a first threshold; perform a first RLF prediction, the first RLF prediction comprising predicting a probability of an RLF occurring within a first time window; perform a first measurement; the first measurement relying on the predicted probability of the RLF occurring within the first time window based on either a first set of RS resources or a second set of RS resources; wherein the meaning of the first measurement relying on the predicted probability of the RLF occurring within the first time window based on either the first set of RS resources or the second set of RS resources comprises: when the predicted probability of the RLF occurring within the first time window is greater than the first threshold, the first measurement is based on the first set of RS resources; when the predicted probability of the RLF occurring within the first time window is not greater than the first threshold, the first measurement is for the second set of RS resources; the length of the first time window is finite.

[0236] As one embodiment, the first communication device 450 comprises: a memory storing a computer readable program of instructions which, when executed by at least one processor, causes actions comprising: receiving first signaling, the first signaling indicating a first threshold; performing a first RLF prediction, the first RLF prediction comprising predicting a probability of an RLF occurring within a first time window; performing a first measurement; the first measurement relying on the predicted probability of the RLF occurring within the first time window based on either a first set of RS resources or a second set of RS resources; wherein the meaning of the first measurement relying on the predicted probability of the RLF occurring within the first time window based on either the first set of RS resources or the second set of RS resources comprises: when the predicted probability of the RLF occurring within the first time window is greater than the first threshold, the first measurement is based on the first set of RS resources; when the predicted probability of the RLF occurring within the first time window is not greater than the first threshold, the first measurement is for the second set of RS resources; the length of the first time window is finite.

[0237] As one embodiment, the second communication device 410 comprises: at least one processor and at least one memory including computer program code; the at least one memory and the computer program code configured to, with the at least one processor, cause the performance of the following: sending first signaling, the first signaling indicating a first threshold; performing a first RLF prediction, the first RLF prediction comprising predicting a probability of RLF occurring within a first time window; performing a first measurement; the first measurement depending on the predicted probability of RLF occurring within the first time window based on a first set of RS resources or based on a second set of RS resources; wherein the meaning of the first measurement depending on the predicted probability of RLF occurring within the first time window based on the first set of RS resources or the second set of RS resources comprises: when the predicted probability of RLF occurring within the first time window is greater than the first threshold, the first measurement is based on the first set of RS resources; when the predicted probability of RLF occurring within the first time window is not greater than the first threshold, the first measurement is for the second set of RS resources; the length of the first time window is finite.

[0238] As one embodiment, the second communication device 410 comprises: a memory storing a computer readable program of instructions which, when executed by at least one processor, causes the performance of the following: sending first signaling, the first signaling indicating a first threshold; performing a first RLF prediction, the first RLF prediction comprising predicting a probability of RLF occurring within a first time window; performing a first measurement; the first measurement depending on the predicted probability of RLF occurring within the first time window based on a first set of RS resources or based on a second set of RS resources; wherein the meaning of the first measurement depending

[0239] As one embodiment, the first communication device 450 corresponds to a terminal in the present application.

[0240] As one embodiment, the second communication device 410 corresponds to a network node in the present application.

[0241] As one embodiment, the first communication device 450 is a UE.

[0242] As one embodiment, the first communication device 450 is a vehicle-mounted terminal.

[0243] As an example, the first communication device 450 is a mobile phone.

[0244] As an example, the second communication device 450 is a relay.

[0245] As an example, the second communication device 410 is a satellite.

[0246] As an example, the second communication device 410 is an aircraft.

[0247] As an example, the second communication device 410 is a base station.

[0248] As an example, the receiver 454 (including antenna 452), receive processor 456, and controller / processor 459 are used in the present application to receive the first signaling.

[0249] As an example, the transmitter 418 (including antenna 420), transmit processor 416, and controller / processor 475 are used in the present application to transmit the first signaling.

[0250] Embodiment 5

[0251] Embodiment 5 illustrates a flow chart of wireless signal transmission according to an embodiment of the present application, as shown in FIG. 5. In FIG. 5, U01 corresponds to a terminal of the present application, and it is particularly stated that the sequence in this example does not limit the sequence of signal transmission and implementation in the present application.

[0252] For the terminal U01, the first signaling is received in step S5101; the first RLF prediction is performed in step S5102; the first notification is transmitted in step S5103; the first notification is received in step S5104; it is judged whether the predicted RLF probability is greater than the first threshold value in step S5105, if the RLF probability is greater than the first threshold value, step S5106 is performed, otherwise, step S5107 is performed; it is determined that the first measurement is for the first RS resource set in step S5106; it is determined that the first measurement is for the second RS resource set in step S5107; the first measurement is not relaxed measurement in step S5108; the first measurement is relaxed measurement in step S5109; the first measurement is performed in step S5110; the first location information is transmitted in step S5111.

[0253] For the base station N02, the first signaling is transmitted in step S5201; the first location information is received in step S5202.

[0254] In embodiment 5, the first signaling is received, the first signaling indicating a first threshold; a first RLF prediction is performed, the first RLF prediction comprising predicting a probability of RLF occurring within a first time window; a first measurement is performed; the first measurement is based on a first set of RS resources or a second set of RS resources depending on the predicted probability of RLF occurring within the first time window; wherein the meaning that the first measurement is based on the first set of RS resources or the second set of RS resources depending on the predicted probability of RLF occurring within the first time window comprises: when the predicted probability of RLF occurring within the first time window is greater than the first threshold, the first measurement is based on the first set of RS resources; when the predicted probability of RLF occurring within the first time window is not greater than the first threshold, the first measurement is for the second set of RS resources; the length of the first time window is finite.

[0255] In embodiment 5, whether the first measurement is a relaxed measurement depends on the first RLF prediction, wherein the first measurement is a relaxed measurement when the predicted probability of RLF occurring within the first time window is not greater than the first threshold; the first measurement is not a relaxed measurement when the predicted probability of RLF occurring within the first time window is greater than the first threshold.

[0256] In embodiment 5, first location information is transmitted; wherein the first location information is based on a measurement when the predicted probability of RLF occurring within the first time window is not greater than the first threshold; the first location information is based on a prediction when the predicted probability of RLF occurring within the first time window is greater than the first threshold.

[0257] As one embodiment, the base station N02 is a base station to which a serving cell of the terminal U01 belongs.

[0258] As one embodiment, the base station N02 is a base station to which a PCell of the terminal U01 belongs.

[0259] As one embodiment, the base station N02 is a base station to which a PSCell of the terminal U01 belongs.

[0260] As one embodiment, the base station N02 is a Master Node (MN).

[0261] As one embodiment, the base station N02 is a Secondary Node (SN).

[0262] As one embodiment, the terminal U01 and the base station N02 are connected by wireless connection.

[0263] As one embodiment, the terminal U01 and the base station N02 are connected by wired connection.

[0264] As an embodiment, the terminal U01 and the base station N02 are connected through a Uu interface.

[0265] As an embodiment, the terminal U01 and the base station N02 are connected through an IAB interface.

[0266] As an embodiment, the terminal U01 and the base station N02 are connected through a PC5 interface.

[0267] As an embodiment, the first signaling is downlink signaling.

[0268] As an embodiment, the first signaling is RRC signaling.

[0269] As an embodiment, the first signaling comprises RRC signaling.

[0270] As an embodiment, the first signaling is specific to a certain UE.

[0271] As an embodiment, the first signaling is sent through unicast.

[0272] As an embodiment, the first signaling is sent on SRB1.

[0273] As an embodiment, the first signaling is RRCReconfiguration.

[0274] As an embodiment, the first signaling is MAC layer control signaling.

[0275] As an embodiment, the first signaling is MAC CE (Control element).

[0276] As an embodiment, the first signaling is physical layer control signaling.

[0277] As an embodiment, the first signaling is DCI (downlink control information).

[0278] As an embodiment, the first signaling indicates the first threshold.

[0279] As an embodiment, the first signaling indicates the first threshold means that the first signaling explicitly indicates the first threshold.

[0280] As an embodiment, the first signaling indicates the first threshold means that the first signaling implicitly indicates the first threshold.

[0281] As an embodiment, the first signaling indicates that the first threshold refers to that the first signaling configures the first threshold.

[0282] As an embodiment, the first signaling indicates that the first threshold refers to that the first signaling enables the first threshold.

[0283] As an embodiment, the first threshold is a real number.

[0284] As an embodiment, the first threshold is greater than or equal to 0 and less than or equal to 1.

[0285] As an embodiment, the first RS resource set and the second RS resource set are orthogonal.

[0286] As an embodiment, the first RS resource set includes SSB.

[0287] As a sub-embodiment of this embodiment, the second RS resource set includes SSB.

[0288] As a sub-embodiment of this embodiment, the second RS resource set includes CSI-RS.

[0289] As a sub-embodiment of this embodiment, the second RS resource set does not include SSB.

[0290] As a sub-embodiment of this embodiment, the second RS resource set includes SSB, which has the advantage that more accurate results can be obtained by monitoring beams determined by different SSBs, and RLF triggered by measurements on the current beam only can be avoided.

[0291] As a sub-embodiment of this embodiment, the second RS resource set includes only CSI-RS, which has the advantage that using different types of RS resources helps to improve measurement accuracy.

[0292] As an embodiment, the first RS resource set includes CSI-RS.

[0293] As a sub-embodiment of this embodiment, the second RS resource set includes CSI-RS.

[0294] As a sub-embodiment of this embodiment, the second RS resource set does not include CSI-RS.

[0295] As a sub-embodiment of this embodiment, the second RS resource set includes SSB.

[0296] As one subembodiment of this embodiment, the benefit of the second RS resource set including CSI-RS is that measurement accuracy, including accuracy for RLF prediction, can be improved by performing more intensive measurement.

[0297] As one subembodiment of this embodiment, the benefit of the second RS resource set including only SSB is that using different types of RS resources facilitates improving measurement accuracy.

[0298] As one embodiment, the first RS resource set and the second RS resource set are orthogonal in time domain.

[0299] As one embodiment, at least one of the first RS resource set and the second RS resource set includes PRS.

[0300] As one embodiment, the first RS resource set includes PRS.

[0301] As one embodiment, the second RS resource set includes PRS.

[0302] As one embodiment, both the first RS resource set and the second RS resource set include PRS.

[0303] As one embodiment, the benefit of at least one of the first RS resource set and the second RS resource set including PRS is that positioning can be performed when it is predicted that RLF is more likely to occur within a first time window, which facilitates wireless network optimization.

[0304] As one embodiment, the first RS resource set includes NPRS (Narrowband Positioning Reference Signals).

[0305] As one embodiment, the first RS resource set and the second RS resource set have different periodicity.

[0306] As one embodiment, the first RS resource set and the second RS resource set are associated with different PCIs.

[0307] As one embodiment, the benefit of the first RS resource set and the second RS resource set being associated with different PCIs is that it facilitates monitoring signals of other PCIs early when it is predicted that RLF is more likely to occur, which improves communication reliability.

[0308] As one embodiment, the meaning of whether the first measurement is relaxed measurement depends on the first RLF prediction includes that the first measurement is relaxed measurement when a probability of RLF occurring within a first time window predicted is not greater than a first threshold.

[0309] As one embodiment, the meaning of whether the first measurement is a relaxed measurement depends on the first RLF prediction comprises: the first measurement is not a relaxed measurement when the predicted probability of RLF occurring in the first time window is greater than the first threshold.

[0310] As one embodiment, the relaxed measurement has a longer measurement period than the measurement that is not relaxed.

[0311] As one embodiment, the meaning of the length of the period of the first measurement depends on the predicted probability of RLF occurring in the first time window comprises: the period of the first measurement is longer when the predicted probability of RLF occurring in the first time window is not greater than the first threshold.

[0312] As one embodiment, the meaning of the length of the period of the first measurement depends on the predicted probability of RLF occurring in the first time window comprises: the period of the first measurement is shorter when the predicted probability of RLF occurring in the first time window is greater than the first threshold.

[0313] As one embodiment, the meaning of the length of the period of the first measurement depends on the predicted probability of RLF occurring in the first time window comprises: the period of the first measurement is shorter as the predicted probability of RLF occurring in the first time window is greater.

[0314] As one embodiment, the result given by the first measurement comprises a measurement result for a first set of RS resources or a measurement result for a second set of RS resources.

[0315] As one embodiment, the measurement result for the first set of RS resources comprises at least one measurement value for the first set of RS resources.

[0316] As one embodiment, the measurement result for the first set of RS resources comprises at least one measurement value for each reference signal of the first set of RS resources.

[0317] As one embodiment, the measurement result for the second set of RS resources comprises at least one measurement value for at least one reference signal of the second set of RS resources.

[0318] As one embodiment, the measurement result for the second set of RS resources comprises at least one measurement value for each reference signal of the second set of RS resources.

[0319] As one embodiment, the at least one measurement value is only one measurement value.

[0320] As one embodiment, the at least one measurement value is a plurality of measurement values.

[0321] As one embodiment, the multiple measurement values are obtained at different times.

[0322] As one embodiment, the multiple measurement values are obtained at the same time.

[0323] As one embodiment, the measurement value is RSRP (Reference Signal Receiving Power).

[0324] As one embodiment, the measurement value is RSRQ (Reference Signal Receiving Power).

[0325] As one embodiment, the measurement value is SINR (Signal to Interference plus Noise Ratio).

[0326] As one embodiment, optionally, the terminal U01 sends first UE capability information (not shown in the figure 5); wherein, the first UE capability information indicates that the terminal U01 supports RLF prediction.

[0327] As one embodiment, the first UE capability information includes one RRC message; the one RRC message indicates that the terminal U01 supports RLF prediction.

[0328] As one embodiment, the first UE capability information includes one RRC message and one MAC CE; the one RRC message indicates multiple UE capabilities; the one MAC CE indicates from the multiple UE capabilities that the terminal U01 supports RLF prediction.

[0329] As one embodiment, the one RRC message is a UECapabilityInformation message.

[0330] As one embodiment, the one RRC message is a UEAssistanceInformation message.

[0331] As one embodiment, the first UE capability information indicates parameters of at least one intelligent model supported by the terminal U01; optionally, the parameters can be an identification of an intelligent model, can also be a type of an intelligent model, can also be a function of an intelligent model, etc.

[0332] As one sub-embodiment of the above-mentioned embodiment, one intelligent model in the at least one intelligent model supported by the terminal U01 is an intelligent model for the RLF prediction.

[0333] As an embodiment, the first UE capability information indicates that the terminal U01 supports RLF prediction.

[0334] As an embodiment, the first UE capability information indicates RLF prediction from RLF prediction and HOF prediction.

[0335] As an embodiment, the first UE capability information indicates both RLF prediction and HOF prediction from RLF prediction and HOF prediction.

[0336] As an embodiment, the RLF prediction is performed by a smart module of the terminal.

[0337] As an embodiment, the relaxed measurement state is that measurement is performed once in a longer time interval than in a non-relaxed measurement state.

[0338] As an embodiment, the relaxed measurement state is that measurement is not performed on part of cells.

[0339] As an embodiment, the relaxed measurement state is that the terminal enters the relaxed measurement state.

[0340] As an embodiment, the non-relaxed measurement state is that the terminal exits the relaxed measurement state.

[0341] As an embodiment, the method introduces a first notification inside the terminal, and is simple.

[0342] As an embodiment, the method is conducive to standardization.

[0343] As an embodiment, the method is conducive to separation of RLF detection and RLF prediction.

[0344] As an embodiment, the smart module sends the first notification; and the legal module receives the first notification.

[0345] As an embodiment, the first notification is sent when RLF is predicted.

[0346] As an embodiment, the first notification is sent when the probability of predicting RLF is not 0.

[0347] As an embodiment, the first notification is sent when RLF is predicted within the first time window.

[0348] As an embodiment, the first notification is sent when the number of out-of-sync indications of consecutive predictions within the first time window reaches a third threshold.

[0349] As one embodiment, the first notification is sent when a number of consecutive out-of-sync indications within the first time window reaches a third threshold.

[0350] As one embodiment, the predicted out-of-sync indications are dependent on measurements of RS resource sets other than the first RS resource set and the second RS resource set.

[0351] As one embodiment, the predicted out-of-sync indications are independent of the first RS resource set and the second RS resource set.

[0352] As one embodiment, the RS resource sets other than the first RS resource set and the second RS resource set include CRS (Cell-specific Reference Signals).

[0353] As one embodiment, the first notification is an indication.

[0354] As one embodiment, the first notification is a notification.

[0355] As one embodiment, the first notification is a cross-layer indication.

[0356] As one embodiment, the first notification is a cross-entity indication.

[0357] As one embodiment, the first notification is within the terminal U01.

[0358] As one embodiment, the first notification is sent and received within the terminal U01.

[0359] As one embodiment, the first notification indicates the predicted RLF.

[0360] As one embodiment, the first notification indicates a probability of the predicted RLF.

[0361] As one embodiment, the first notification indicates that an RLF will occur within the first time window.

[0362] As one embodiment, the first notification indicates a probability that an RLF will occur within the first time window.

[0363] As one embodiment, the first notification indicates that a number of consecutive predicted out-of-sync indications within the first time window reaches a third threshold.

[0364] As one embodiment, the first notification indicates a probability that a number of consecutive predicted out-of-sync indications within the first time window reaches a third threshold.

[0365] As one embodiment, the predicting the RLF comprises predicting that the RLF will occur within a first time window.

[0366] As one embodiment, the predicting the RLF comprises predicting that the RLF will occur within a first time window.

[0367] As one embodiment, the predicting the RLF comprises predicting that the RLF will occur within a first time window.

[0368] As one embodiment, the predicting the RLF comprises predicting that the RLF will occur within a first time window.

[0369] As one embodiment, the predicting the RLF comprises predicting that the RLF will occur within a first time window.

[0370] As one embodiment, the predicting the RLF comprises predicting that the RLF will occur within a first time window.

[0371] As one embodiment, the first time window comprises a predicted time interval.

[0372] As one embodiment, the first time window comprises a time interval after a current time.

[0373] As one embodiment, the first time window comprises an expected running time of a timer.

[0374] As one embodiment, the first time window comprises a running time of a timer.

[0375] As one embodiment, the first time window comprises a remaining time of T310.

[0376] As one embodiment, the first RS resource set and the second RS resource set are associated with or correspond to a same reference signal resource index.

[0377] As one embodiment, the above method has the advantage that it can flexibly control which RS resources corresponding to one RS resource index belong to the first RS resource set and which belong to the second RS resource set.

[0378] As one embodiment, the first RS resource set and the second RS resource set are associated with or correspond to different reference signal resource indexes.

[0379] As one embodiment, the above method has the advantage that it can increase the independence of the first RS resource set and the second RS resource set, and improve the accuracy of the measurement result.

[0380] As one embodiment, the terminal sends the first location information.

[0381] As one sub-embodiment of this embodiment, the first location information is based on measurement when the predicted probability of RLF occurring within the first time window is not greater than a first threshold; the first location information is based on prediction when the predicted probability of RLF occurring within the first time window is greater than the first threshold.

[0382] As one sub-embodiment of this embodiment, the first location information is indirect predicted location information when the predicted probability of RLF occurring within the first time window is not greater than a first threshold; the first location information is direct predicted location information when the predicted probability of RLF occurring within the first time window is greater than the first threshold.

[0383] As one sub-embodiment of this embodiment, the first location information is direct predicted location information when the predicted probability of RLF occurring within the first time window is not greater than a first threshold; the first location information is indirect predicted location information when the predicted probability of RLF occurring within the first time window is greater than the first threshold.

[0384] As one embodiment, the first location information includes IE locationInformationType.

[0385] As one embodiment, the first location information includes IE locationEstimateAndMeasurementReporting.

[0386] Embodiment 6

[0387] Embodiment 6 illustrates a schematic diagram of first measurement depending on first RLF prediction according to one embodiment of the present application, as shown in FIG. 6. In the FIG. 6, it is particularly stated that the sequence in this example does not limit the sequence of signal transmission and implementation in the present application.

[0388] In Embodiment 6, in step S601, the first RLF prediction is performed; in step S602, it is determined whether the predicted RLF probability is greater than the first threshold value, if the RLF probability is greater than the first threshold value, step S603 is performed, otherwise, step S604 is performed; in step S603, the first measurement is determined for the first RS resource set; in step S604, the first measurement is determined for the second RS resource set; in step S605, the first measurement is not relaxed measurement; in step S606, the first measurement is relaxed measurement; in step S607, it is determined whether the predicted RLF probability is greater than the second threshold value, if the predicted RLF probability is greater than the second threshold value, step S608 is performed, otherwise, step S608 is not performed; in step S608, part of the measurements in the first measurement set is terminated; in step S609, the first measurement is performed.

[0389] In Embodiment 6, the first measurement is one of the measurements in the first measurement set; the predicted probability of RLF occurring in the first time window being greater than the second threshold value triggers termination of at least part of the measurements in the first measurement set.

[0390] As an embodiment, the first measurement set comprises at least the first measurement.

[0391] As an embodiment, the measurements comprised in the first measurement set correspond to different positioning manners.

[0392] As an embodiment, the measurements comprised in the first measurement set correspond to different RS resource sets.

[0393] As an embodiment, the measurements comprised in the first measurement set occur at different times.

[0394] As an embodiment, the termination of part of the measurements in the first measurement set means termination of at least one measurement.

[0395] As an embodiment, the termination of part of the measurements in the first measurement set means termination of part of the reference signals in the RS resource set corresponding to the first measurement.

[0396] As an embodiment, the above method has the benefit that termination of part of the measurements in the first measurement set can save measurement power and prolong terminal endurance time.

[0397] As an embodiment, part of the measurements in the first measurement set is terminated only when the predicted probability of RLF occurring in the first time window is greater than the second threshold value.

[0398] As an embodiment, the termination of part of the measurements in the first measurement set can terminate part of the reference signals in the RS resource set corresponding to the first measurement.

[0399] As one embodiment, the terminating part of the first measurement set of measurements cannot terminate all reference signals in the first measurement corresponding RS resource set.

[0400] As one embodiment, the terminating part of the first measurement set of measurements cannot terminate the first measurement.

[0401] As one embodiment, the number of reference signals terminated in the terminating part of the at least part of the first measurement set of measurements depends on the probability of predicting RLF occurrence within a first time window.

[0402] As one sub-embodiment of the above embodiment, the greater the probability of predicting RLF occurrence within the first time window, the fewer the number of reference signals terminated in the terminating part of the at least part of the first measurement set of measurements.

[0403] As one embodiment, the first signaling indicates the second threshold.

[0404] As one embodiment, the second threshold is a constant.

[0405] As one embodiment, the second threshold is network configured.

[0406] As one embodiment, the second threshold is terminal configured.

[0407] As one embodiment, the second threshold is equal to the first threshold.

[0408] As one embodiment, the second threshold being equal to the first threshold has the advantage of less comparison of RLF prediction probability, reducing complexity.

[0409] As one embodiment, the second threshold is less than the first threshold.

[0410] As one embodiment, the second threshold being less than the first threshold has the advantage of more power saving for the first measurement, prolonging terminal endurance.

[0411] As one embodiment, the second threshold is greater than the first threshold.

[0412] As one embodiment, the second threshold being greater than the first threshold has the advantage of the first measurement being able to obtain more comprehensive measurement results.

[0413] As one embodiment, the first RS resource set and the second RS resource set are obtained by receiving at least the first reference signal.

[0414] As one embodiment, the receiving at least the first reference signal includes: receiving the first reference signal at each available reception occasion of each reference signal in the at least first reference signal.

[0415] As one embodiment, the receiving the at least first reference signal comprises: receiving each reference signal of the at least first reference signal at at least one available reception occasion of the each reference signal.

[0416] As one embodiment, the available reception occasion of one reference signal is time-frequency resource configured to the one reference signal.

[0417] As one embodiment, the available reception occasion of one reference signal is time-frequency resource configured to the one reference signal and non-overlapping with specified time-frequency resource.

[0418] As one sub-embodiment of the above embodiment, the specified time-frequency resource comprises active measurement gap.

[0419] As one sub-embodiment of the above embodiment, the transmission on the specified time-frequency resource has higher priority than the one reference signal.

[0420] As one embodiment, the transmitter of the at least first reference signal is the cell for which the RLF prediction is performed.

[0421] As one embodiment, the at least first reference signal is physical layer signal.

[0422] As one embodiment, the at least first reference signal is Reference Signal.

[0423] As one embodiment, any reference signal of the at least first reference signal is used for RLF prediction.

[0424] As one embodiment, any reference signal of the at least first reference signal is used for Radio Link Monitoring (RLM).

[0425] As one embodiment, any reference signal of the at least first reference signal is used for both RLM and RLF prediction.

[0426] As one embodiment, the at least first reference signal is Downlink (DL).

[0427] As one embodiment, the at least first reference signal is Sidelink (SL).

[0428] As one embodiment, the at least first reference signal is periodic.

[0429] As one embodiment, the at least first reference signal is semi-persistent.

[0430] As one embodiment, the at least first reference signal comprises a Synchronization Signal.

[0431] As one embodiment, each of the at least first reference signal is a Synchronization Signal Block (SS / PBCH).

[0432] As one embodiment, the at least first reference signal is a Synchronization Signal.

[0433] As one embodiment, each of the at least first reference signal is a Channel State Information Reference Signal (CSI-RS).

[0434] As one embodiment, the at least first reference signal is indicated by RRC signaling.

[0435] As one embodiment, the at least first reference signal is determined by the terminal U01.

[0436] As one embodiment, the at least first reference signal is the first reference signal.

[0437] As one embodiment, the at least first reference signal is a plurality of reference signals; the plurality of reference signals comprises at least the first reference signal.

[0438] As one embodiment, the at least first reference signal is no more than M1 reference signals; the M1 is a positive integer.

[0439] As one embodiment, the M1 depends on L_max in TS 38.213.

[0440] As one embodiment, the M1 is N_RLM in TS 38.213.

[0441] As one embodiment, the M1 is 2 or 4 or 8.

[0442] As one embodiment, the M1 is no more than 8.

[0443] As one embodiment, the M1 is no more than 16.

[0444] As one embodiment, the predicted out-of-sync indication depends on a predicted link quality for the at least first reference signal.

[0445] As one embodiment, the predicted link quality is RSRP (Reference Signal Receiving Power).

[0446] As one embodiment, the predicted link quality is RSRQ (Reference Signal Receiving Quality).

[0447] As one embodiment, the predicted link quality is BLER (Block Error Ratio).

[0448] As one embodiment, the predicted link quality is SINR (Signal to Interference plus Noise Ratio).

[0449] As one embodiment, the predicted link quality is filtered by L1 or L3.

[0450] As one embodiment, the predicted link quality is unfiltered.

[0451] As one embodiment, the predicted link quality is a new quantity.

[0452] As one embodiment, a predicted out-of-sync indication is predicted if the predicted link quality for each of the at least first reference signals is worse than a first predicted out-of-sync threshold for a predicted out-of-sync evaluation period.

[0453] As one embodiment, the first predicted out-of-sync threshold is Qout configured by rlmInSyncOutOfSyncThreshold.

[0454] As one embodiment, the first predicted out-of-sync threshold is not Qout configured by rlmInSyncOutOfSyncThreshold.

[0455] As one embodiment, the predicted in-sync indication depends on the predicted link quality for the at least first reference signals.

[0456] As one embodiment, a predicted in-sync indication is predicted if the predicted link quality for at least one of the at least first reference signals is better than a first predicted in-sync threshold for a predicted in-sync evaluation period.

[0457] As one embodiment, the first predicted in-sync threshold is Qin configured by rlmInSyncOutOfSyncThreshold.

[0458] As one embodiment, the first in-sync threshold is not Qin configured by rlmInSyncOutOfSyncThreshold.

[0459] As one embodiment, the prediction evaluation occasion can not exist in actual application for the sake of clarity.

[0460] As one embodiment, at each prediction evaluation occasion, there is either a predicted in-sync indication or a predicted out-of-sync indication.

[0461] As one embodiment, at a prediction evaluation occasion, there can be neither a predicted in-sync indication nor a predicted out-of-sync indication.

[0462] As one embodiment, the length of the prediction in-sync evaluation period is equal to the length of the prediction out-of-sync evaluation period.

[0463] As one embodiment, the length of the prediction in-sync evaluation period is not equal to the length of the prediction out-of-sync evaluation period.

[0464] As one embodiment, the length of the prediction in-sync evaluation period is the length of the in-sync evaluation period.

[0465] As one embodiment, the length of the prediction in-sync evaluation period is not the length of the in-sync evaluation period.

[0466] As one embodiment, the length of the prediction indication period is equal to the length of the indication period.

[0467] As one embodiment, the length of the in-sync evaluation period refers to TEvaluate_in_SSB of TS 38.133.

[0468] As one embodiment, the length of the out-of-sync evaluation period refers to TEvaluate_out_SSB of TS 38.133.

[0469] As one embodiment, the length of the in-sync evaluation period is equal to the length of the out-of-sync evaluation period.

[0470] As one embodiment, the length of the in-sync evaluation period is not equal to the length of the out-of-sync evaluation period.

[0471] As one embodiment, the above method can maintain the continuity of the predicted in-sync indication and / or the predicted out-of-sync indication based on the in-sync indication and / or the out-of-sync indication.

[0472] As one embodiment, the length of the prediction indication period is not equal to the length of the indication period.

[0473] As an embodiment, the above method is independent of the synchronization indication and / or the out-of-sync indication, making the prediction more flexible.

[0474] As an embodiment, the embodiment does not limit the position of the evaluation occasion in the prediction indication period.

[0475] As an embodiment, the prediction evaluation occasion is only for the sake of clarity, and in actual application, it is not specifically limited to evaluation once every other prediction indication period to determine whether there is a predicted out-of-sync indication or a predicted synchronization indication.

[0476] As an embodiment, the predicted RLF depends on the number of synchronization indications in the first time window.

[0477] As an embodiment, the different first time windows are in time periods that do not overlap with each other.

[0478] As an embodiment, the different first time windows have different probabilities of predicting RLF.

[0479] As an embodiment, the different first time windows have the same length.

[0480] Embodiment 7

[0481] Embodiment 7 illustrates a schematic diagram of first location information depending on first RLF prediction according to an embodiment of the present application, as shown in FIG. 7. In the FIG. 7, it is particularly pointed out that the sequence in this example does not limit the sequence of signal transmission and implementation in the present application.

[0482] In embodiment 7, in step S701, first RLF prediction is performed; in step S702, first measurement is performed; in step S703, it is determined whether the predicted RLF probability is greater than a first threshold value, if the RLF probability is greater than the first threshold value, step S704 is performed, otherwise, step S705 is performed; in step S704, it is determined that the first location information is based on prediction; in step S705, it is determined that the first location information is based on measurement; in step S706, the first location information is sent.

[0483] In embodiment 7, the first location information is sent; wherein when the predicted probability of RLF occurring in a first time window is not greater than a first threshold value, the first location information is based on measurement; when the predicted probability of RLF occurring in the first time window is greater than the first threshold value, the first location information is based on prediction.

[0484] As an embodiment, the first location information includes CommonIEsProvideCapabilities.

[0485] As an embodiment, the first location information comprises remoteUE-Indication.

[0486] As an embodiment, the first location information comprises locationEstimateAndMeasurementReporting.

[0487] As an embodiment, the first location information comprises CommonIEsRequestLocationInformation.

[0488] As an embodiment, the first location information comprises CommonIEsProvideLocationInformation.

[0489] As an embodiment, the first location information can be obtained by measurement or prediction.

[0490] As an embodiment, the first location information is obtained by first measurement.

[0491] As an embodiment, optionally, the terminal sends first UE capability information (not shown in the figure 7); wherein the first UE capability information indicates that the terminal supports first location information prediction.

[0492] As an embodiment, the first UE capability information comprises one RRC message and one MAC CE; the one RRC message indicates a plurality of UE capabilities; the one MAC CE indicates from the plurality of UE capabilities that the terminal supports first location information prediction.

[0493] As an embodiment, the one RRC message is a UECapabilityInformation message.

[0494] As an embodiment, the one RRC message is a UEAssistanceInformation message.

[0495] As an embodiment, the first UE capability information indicates parameters of at least one intelligent model supported by the terminal; optionally, the parameters can be an identification of an intelligent model, can also be a type of an intelligent model, can also be a function of an intelligent model, etc.

[0496] As a sub-embodiment of the above embodiment, one intelligent model in the at least one intelligent model supported by the terminal is an intelligent model for the first location information prediction.

[0497] As an embodiment, the first UE capability information indicates that the terminal supports first location information prediction.

[0498] As one embodiment, the first location information is predicted by a smart module of the terminal.

[0499] As one embodiment, the first location information can be obtained by prediction if the first UE capability information indicates that the terminal supports first location information prediction.

[0500] As one embodiment, the smart model for predicting the first location information is different from the model for predicting RLF.

[0501] As one embodiment, the first location information is based on prediction when the predicted probability of RLF occurring within the first time window is greater than the first threshold, and the benefit of doing so is that the first location information based on prediction is more reliable than the first location information based on measurement at this time, and the transmitted first location information is more accurate.

[0502] Embodiment 8

[0503] Embodiment 8 illustrates a diagram of the change of the probability of RLF occurring within the first time window according to one embodiment of the present application, as shown in FIG. 8. In the FIG. 8, the horizontal length of the box represents the duration of the first time window, and the probability within the box represents the probability of RLF occurring within the first time window.

[0504] In embodiment 8, the prediction of the probability of RLF occurring within the first time window includes: the probability that the number of at least one of the consecutive out-of-sync indications and the predicted out-of-sync indications within the first time window reaches a third threshold; wherein the predicted out-of-sync indications depend on the measurement of the RS resource set other than the first RS resource set and the second RS resource set.

[0505] In embodiment 8, the length of the measurement period depends on the prediction of the probability of RLF occurring within the first time window.

[0506] As one embodiment, the probability of RLF occurring within the first time window is related to the length of the first time window.

[0507] As one embodiment, the probability of RLF occurring within the first time window is increasing.

[0508] As one embodiment, the longer the length of the first time window, the greater the probability of RLF occurring within the first time window.

[0509] As one embodiment, for two completely non-overlapping first time windows, the probability of RLF occurring within the first time window is not related to the length of the two first time windows.

[0510] As an embodiment, for two completely overlapped first time windows, the longer the duration of the first time window is, the greater the probability of RLF occurring in the first time window is.

[0511] As an embodiment, the completely overlapped means that one first time window is completely covered by another first time window.

[0512] As an embodiment, when the duration of the first time window is large enough, the probability of RLF occurring in the first time window is 100%.

[0513] As an embodiment, the duration of the first time window being large enough means that the duration of the first time window can contain the time of RLF occurring.

[0514] As an embodiment, when the duration of the first time window is small enough, the probability of RLF occurring in the first time window is 0%.

[0515] As an embodiment, the duration of the first time window being small enough means that the duration of the first time window cannot contain the time of RLF occurring.

[0516] As an embodiment, the third threshold is indicated by the network.

[0517] As an embodiment, the third threshold is indicated by the first signaling.

[0518] As an embodiment, the third threshold is pre-configured.

[0519] As an embodiment, the third threshold is default.

[0520] As an embodiment, the predicting the probability of RLF occurring in the first time window comprises: predicting the probability of the number of consecutive out-of-sync indications in the first time window reaching a third threshold.

[0521] As an embodiment, the predicting the probability of RLF occurring in the first time window comprises: predicting the probability of the number of predicted out-of-sync indications in the first time window reaching a third threshold.

[0522] As an embodiment, the predicting the RLF depends on the number of predicted in-sync indications in the first time window.

[0523] As an embodiment, the predicting the in-sync indication depends on the predicted link quality for the at least first reference signal.

[0524] As an embodiment, if the predicted link quality for at least one of the at least first reference signal is better than a first predicted in-sync threshold in a predicted in-sync evaluation period, an in-sync indication is predicted.

[0525] As one embodiment, the first predicted in-sync threshold is Q configured by rlmInSyncOutOfSyncThreshold in .

[0526] As one embodiment, the first predicted in-sync threshold is not Q configured by rlmInSyncOutOfSyncThreshold in .

[0527] As one embodiment, the predicted out-of-RF depends on the number of predicted out-of-sync indications within a first time window.

[0528] As one embodiment, a predicted out-of-sync indication is predicted if a predicted link quality for each of the at least first reference signal is worse than a first predicted out-of-sync threshold within a predicted out-of-sync evaluation period.

[0529] As one embodiment, the first predicted out-of-sync threshold is Q configured by rlmInSyncOutOfSyncThreshold out .

[0530] As one embodiment, the first predicted out-of-sync threshold is not Q configured by rlmInSyncOutOfSyncThreshold out .

[0531] As one embodiment, the method can save measurement resource, reduce signaling overhead and reduce attack rate loss.

[0532] As one embodiment, the length of the first measurement period depends on the probability of the first RLF prediction within a first time window.

[0533] As one sub-embodiment of the above embodiment, the longer the time length of the probability of the first RLF prediction within a first time window is constant, the longer the measurement period is.

[0534] As one sub-embodiment of the above embodiment, the shorter the time length of the probability of the first RLF prediction within a first time window is constant, the shorter the measurement period is.

[0535] As one sub-embodiment of the above embodiment, the measurement period is equal to the time length of the probability of the first RLF prediction within a first time window is constant.

[0536] As a sub-example of the above embodiment, the measurement period is longer than the duration during which the probability of an RLF occurring within the first time window, as predicted by the first RLF, remains unchanged.

[0537] As a sub-example of the above embodiment, the measurement period is the duration during which the probability of RLF occurring within the first time window remains unchanged, plus a fixed constant, as predicted by the first RLF.

[0538] As a sub-example of the above embodiments, the fixed constant is agreed upon or defaulted to in advance.

[0539] Example 9

[0540] Example 9 illustrates a structural block diagram of a processing device in a terminal according to an embodiment of this application; as shown in Figure 9. In Figure 9, the processing device 900 in the terminal includes a first transmitter 901 and a first receiver 902.

[0541] In embodiment 9, the terminal includes: one or more processors and a memory;

[0542] The memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, which the one or more processors invoke to cause the terminal to execute at least:

[0543] A first receiver 902 receives a first signaling, the first signaling indicating a first threshold; performs a first RLF prediction, the first RLF prediction including predicting the probability of an RLF occurring within a first time window; performs a first measurement; the first measurement is based on a first RS resource set or a second RS resource set depending on the predicted probability of an RLF occurring within the first time window; wherein, the meaning of whether the first measurement is based on the first RS resource set or the second RS resource set depending on the predicted probability of an RLF occurring within the first time window includes: when the predicted probability of an RLF occurring within the first time window is greater than the first threshold, the first measurement is based on the first RS resource set; when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the first measurement is for the second RS resource set; the length of the first time window is finite.

[0544] As one embodiment, a first positioning method is based on the first RS resource set; and a second positioning method is based on the second RS resource set.

[0545] As an example, at least one of the first RS resource set and the second RS resource set includes PRS.

[0546] As an example, whether the first measurement is a relaxed measurement depends on the first RLF prediction, wherein the first measurement is a relaxed measurement when the predicted probability of RLF occurring within the first time window is not greater than a first threshold; and the first measurement is not a relaxed measurement when the predicted probability of RLF occurring within the first time window is greater than the first threshold.

[0547] As an example, the first measurement is a measurement in a first set of measurements; the predicted probability of an RLF occurring within a first time window being greater than a second threshold triggers the termination of at least some of the measurements in the first set of measurements.

[0548] As an example, the length of the period of the first measurement depends on the probability of the predicted RLF occurring within the first time window.

[0549] As an example, the prediction of the probability of an RLF occurring within a first time window includes: the probability that the number of at least one of consecutive out-of-step indications and predicted out-of-step indications within the first time window reaches a third threshold; wherein the predicted out-of-step indications depend on measurements of RS resource sets outside of the first RS resource set and the second RS resource set.

[0550] As one embodiment, a first notification is sent; the first notification is received; wherein the prediction of RLF depends on the first notification.

[0551] As one embodiment, a first transmitter 901 transmits first location information; wherein, the first location information is based on measurement when the predicted probability of an RLF occurring within a first time window is not greater than a first threshold; and the first location information is based on prediction when the predicted probability of an RLF occurring within a first time window is greater than the first threshold.

[0552] As one example, the terminal is a user equipment (UE).

[0553] As an example, the terminal is one that supports large latency differences.

[0554] As an example, the terminal is an NTN-enabled terminal.

[0555] As one example, the terminal is an aircraft or a ship.

[0556] As one example, the terminal is a mobile phone or a vehicle-mounted terminal.

[0557] As an example, the terminal is an Internet of Things (IoT) terminal or an Industrial Internet of Things (IIoT) terminal.

[0558] As an example, the terminal is a device that supports low-latency, high-reliability transmission.

[0559] As one embodiment, the first transmitter 901 includes at least one of the following in embodiment 4: antenna 452, transmitter 454, transmission processor 468, multi-antenna transmission processor 457, controller / processor 459, memory 460, or data source 467.

[0560] As one embodiment, the first receiver 902 includes at least one of the following in embodiment 4: antenna 452, receiver 454, receiver processor 456, multi-antenna receiver processor 458, controller / processor 459, memory 460, or data source 467.

[0561] Example 10

[0562] Example 10 illustrates a structural block diagram of a processing apparatus in a base station according to an embodiment of this application, as shown in Figure 10. In Figure 10, the processing apparatus 1000 in the base station includes a second transmitter 1001 and a second receiver 1002.

[0563] In embodiment 10, the base station includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, wherein the one or more processors call the computer instructions to cause the base station to perform the method described in this application used in the base station; the one or more processors and the memory include the second transmitter 1001, wherein,

[0564] The second transmitter 1001 transmits a first signaling instruction indicating a first threshold; performs a first RLF prediction, which includes predicting the probability of an RLF occurring within a first time window; performs a first measurement; the first measurement is based on a first RS resource set or a second RS resource set depending on the predicted probability of an RLF occurring within the first time window; wherein, the meaning of whether the first measurement is based on the first RS resource set or the second RS resource set depending on the predicted probability of an RLF occurring within the first time window includes: when the predicted probability of an RLF occurring within the first time window is greater than the first threshold, the first measurement is based on the first RS resource set; when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the first measurement is for the second RS resource set; the length of the first time window is finite.

[0565] As one embodiment, a first positioning method is based on the first RS resource set; and a second positioning method is based on the second RS resource set.

[0566] As an example, at least one of the first RS resource set and the second RS resource set includes PRS.

[0567] As an example, whether the first measurement is a relaxed measurement depends on the first RLF prediction, wherein the first measurement is a relaxed measurement when the predicted probability of RLF occurring within the first time window is not greater than a first threshold; and the first measurement is not a relaxed measurement when the predicted probability of RLF occurring within the first time window is greater than the first threshold.

[0568] As an example, the first measurement is a measurement in a first set of measurements; the predicted probability of an RLF occurring within a first time window being greater than a second threshold triggers the termination of at least some of the measurements in the first set of measurements.

[0569] As an example, the length of the period of the first measurement depends on the probability of the predicted RLF occurring within the first time window.

[0570] As an example, the prediction of the probability of an RLF occurring within a first time window includes: the probability that the number of at least one of consecutive out-of-step indications and predicted out-of-step indications within the first time window reaches a third threshold; wherein the predicted out-of-step indications depend on measurements of RS resource sets outside of the first RS resource set and the second RS resource set.

[0571] As one embodiment, a first notification is sent; the first notification is received; wherein the prediction of RLF depends on the first notification.

[0572] As one embodiment, the second receiver 1002 receives first location information; wherein, when the predicted probability of an RLF occurring within a first time window is not greater than a first threshold, the first location information is based on measurement; when the predicted probability of an RLF occurring within a first time window is greater than the first threshold, the first location information is based on prediction.

[0573] As one example, the base station is a satellite.

[0574] As an example, the base station is a terrestrial base station.

[0575] As one example, the base station is a relay.

[0576] As one example, the base station is an access point.

[0577] As one embodiment, the second transmitter 1001 includes at least one of the following in embodiment 4: antenna 420, transmitter 418, transmission processor 416, multi-antenna transmission processor 471, controller / processor 475, and memory 476.

[0578] As one embodiment, the second receiver 1002 includes at least one of the following in embodiment 4: antenna 420, receiver 418, receiver processor 470, multi-antenna receiver processor 472, controller / processor 475, and memory 476.

[0579] Example 11

[0580] Example 11 illustrates a schematic diagram of the transmission of a first notification according to an embodiment of this application, as shown in Figure 11. Terminal 1100 includes a smart module 1101 and a legacy module 1102.

[0581] In embodiment 11, the smart module 1101 of the terminal 1100 sends the first notification; the legal module 1102 of the terminal 1100 receives the first notification; wherein, the first notification indicates the probability of predicting RLF.

[0582] As one embodiment, performing RLF prediction includes sending the first notification.

[0583] As an example, the intelligent module 1101 of the terminal 1100 sends a first notification in response to performing RLF prediction; the legal module 1102 of the terminal 1100 receives the first notification; wherein the first notification indicates the probability of predicting RLF.

[0584] As one embodiment, performing RLF prediction includes sending the first notification and receiving the first notification.

[0585] As an example, the first notification includes relevant information about the predicted RLF event.

[0586] As a sub-implementation of the above embodiments, the above method avoids triggering unreasonable UE behavior by updating prediction information.

[0587] As a sub-implementation of the above embodiments, the above method is beneficial for UE decision-making.

[0588] As one example, in response to the receipt of the first notification, an indication is sent to a lower layer; the indication is received at the RRC sublayer.

[0589] As one example, in response to the receipt of the first notification, an indication is sent to a higher layer; the indication is received at the RRC sublayer.

[0590] As a sub-implementation of the above embodiments, the instruction indicates the first notification.

[0591] As a sub-implementation of the above embodiments, the instruction includes the first notification.

[0592] As an example, the legal module is logical.

[0593] As an example, the legitimate module is physical.

[0594] As an example, the legal module, in response to the receipt of the first notification, records the prediction result for the RLF event.

[0595] As an example, the legitimate module is a protocol entity.

[0596] As an example, the legitimate module is an RRC protocol entity.

[0597] As an example, the legitimate module is located in the RRC sublayer.

[0598] As one example, the legitimate module is located at a higher level than the RRC sublayer.

[0599] As one example, the legitimate module is located at a lower level than the RRC sublayer.

[0600] As an example, the legitimate module supports 3GPP Release 17.

[0601] As an example, the legitimate module supports 3GPP Release 18.

[0602] As an example, the legitimate module does not have either training or inference functions.

[0603] As an example, the legal module is not a smart module.

[0604] As one example, the smart module is a piece of hardware.

[0605] As one example, the intelligent module is software.

[0606] As an example, the smart module is a program.

[0607] As an example, the intelligent module is a function.

[0608] As one example, the smart module is a protocol entity.

[0609] As one example, the intelligent module is an AI entity.

[0610] As an example, the intelligent module is an ML entity.

[0611] As an example, the intelligent module is an AI / ML entity.

[0612] As one example, the intelligent module is logical.

[0613] As one example, the smart module is physical.

[0614] As an example, the intelligent module performs RLF prediction.

[0615] As one embodiment, the intelligent module processes the at least one intelligent model.

[0616] As one embodiment, the intelligent module includes at least one of the second module or the third module in the intelligent model shown in embodiment 12.

[0617] As one example, the interface between the legal module and the smart module is defined by the 3GPP protocol.

[0618] As one example, the interface between the legal module and the intelligent module is implemented based on the UE.

[0619] As one example, the interface between the legal module and the intelligent module is logical.

[0620] As one example, the interface between the legal module and the smart module is physical.

[0621] Example 12

[0622] Example 12 illustrates a schematic diagram of an intelligent model according to an embodiment of this application, as shown in Figure 12. Figure 12 includes a first module, a second module, a third module, a fourth module, and a fifth module.

[0623] In Example 12, in the intelligent model shown in Figure 12, the first module sends a first dataset to the second module, the first module sends a second dataset to the third module, the first module sends a third dataset to the fifth module, the fifth module sends a first type of parameter group to the second module, the fifth module sends a second type of parameter group to the third module, the fifth module sends a third type of parameter group to the fourth module, the second module sends a fourth type of parameter group to the fourth module, and the fourth module sends a fifth type of parameter group to the third module.

[0624] As an example, the first module, the second module, the third module, the fourth module, and the fifth module in an intelligent model all belong to the terminal.

[0625] The above method avoids air interface signaling interaction and shortens transmission latency.

[0626] As an example, any one of the first module, second module, third module, fourth module, and fifth module in an intelligent model does not belong to the terminal.

[0627] The above methods reduce the hardware complexity of the terminal.

[0628] As an example, at least one of the first module, the second module, the third module, the fourth module, and the fifth module in an intelligent model belongs to the terminal; and at least one of the first module, the second module, the third module, the fourth module, and the fifth module belongs to a network node.

[0629] The above method balances the hardware complexity of the terminal with the transmission latency.

[0630] As an example, the first module is used for data collection.

[0631] As an example, the first module is responsible for data collection.

[0632] As an example, the first module has a data collection function.

[0633] As one example, the second module has a training function.

[0634] As an example, the training function is used for model training.

[0635] As an example, the training function is responsible for model training.

[0636] As an example, the training function includes a model training function.

[0637] As an example, the training function performs model training.

[0638] As an example, the second module performs validation.

[0639] As an example, the second module performs testing.

[0640] As an example, the second module generates model performance metrics.

[0641] As one example, the second module is responsible for data preparation.

[0642] As one embodiment, the data preparation includes at least one of data pre-processing, cleaning, formatting, or transformation.

[0643] As an example, the third module has reasoning capabilities.

[0644] As an example, the inference function is used for inference.

[0645] As an example, the reasoning function is responsible for reasoning.

[0646] As one example, the fourth module is used for model storage.

[0647] As an example, the fourth module has a model storage function.

[0648] As an example, the fourth module is responsible for storing the trained model.

[0649] As an example, the fourth module is responsible for storing trained models that can be used to perform inference processing.

[0650] As one example, the fifth module is used for management.

[0651] As an example, the fifth module is responsible for management.

[0652] As one example, the fifth module has management functions.

[0653] As an example, the fifth module manages the intelligent model.

[0654] As an example, the first dataset is training data.

[0655] As an example, the first dataset is the input to the second module.

[0656] As an example, the second dataset is inference data.

[0657] As an example, the second dataset is the input to the third module.

[0658] As an example, the third dataset is monitoring data.

[0659] As an example, the third dataset is the input to the fifth module.

[0660] As an example, the first type of parameter group includes monitoring output.

[0661] As one example, the second type of parameter group includes management instructions.

[0662] As an example, the second type of parameter group is used for fine-tuning operations of the inference function.

[0663] As an example, the second type of parameter group includes the model's identifier.

[0664] As an example, the second type of parameter group is used to select the model.

[0665] As an example, the second type of parameter group is used to switch models.

[0666] As an example, the second type of parameter group is used to activate / deactivate the model.

[0667] As an example, the second type of parameter group is used to fall back to the smart model.

[0668] As an example, the third type of parameter group includes Model Transfer Request.

[0669] As an example, the third type of parameter group includes a Model Delivery Request.

[0670] As an example, the fourth parameter group includes the trained model.

[0671] As an example, the fourth type of parameter group includes the updated model.

[0672] As an example, the fourth type of parameter group indicates the identifier of the model.

[0673] As an example, the fifth group of parameters includes model transfer.

[0674] As an example, the fifth parameter group includes Model Delivery.

[0675] As an example, the fifth group of parameters indicates the identifier of the model.

[0676] As an example, the first type of output does not exist.

[0677] As an example, the first type of output exists.

[0678] As an example, the second module sends the first type of output to the fifth module.

[0679] As an example, the first type of output includes monitoring output.

[0680] As an example, the second type of output does not exist.

[0681] As an example, the second type of output exists.

[0682] As an example, the third module sends the second type of output to the fifth module.

[0683] As an example, the second type of output includes inference output.

[0684] As an example, the second type of output is used by the fifth module to monitor the performance of the AI / ML model.

[0685] As an example, the second type of output indicates the result of performing RLF event prediction.

[0686] As an example, the second type of output indicates the predicted RLF event.

[0687] As an example, the second type of output includes relevant information about the predicted RLF event.

[0688] As an example, the first dataset in the intelligent model is configured by the network.

[0689] As an example, the first dataset in the intelligent model is determined by the terminal.

[0690] As an example, the first dataset in the intelligent model includes the terminal's stored data; the stored data may come from the network, the terminal's logs, or other RAN nodes.

[0691] As an example, the first dataset in the intelligent model includes measurement information of the terminal; the measurement information may be the mobile state of the terminal, such as the mobile speed, or the number of cells switched within a given time interval; the measurement information may also be measurement results for a reference signal, such as cell-level measurement results, or beam-level measurement results, or time-domain measurement results, or frequency-domain measurement results, or spatial-domain measurement results, or a combination thereof.

[0692] As an example, the first dataset in the intelligent model includes measurements on at least one RS resource filtered by an L1 filter.

[0693] As an example, the second dataset in the intelligent model is configured by the network.

[0694] As an example, the second dataset in the intelligent model is determined by the terminal.

[0695] As an example, the second dataset in the intelligent model includes the terminal's stored data; the stored data may come from the network, the terminal's logs, or other RAN nodes.

[0696] As an example, the second dataset in the intelligent model includes measurement information of the terminal; the measurement information may be the mobile state of the terminal, such as the mobile speed, or the number of cells switched within a given time interval; the measurement information may also be measurement results for a reference signal, such as cell-level measurement results, or beam-level measurement results, or time-domain measurement results, or frequency-domain measurement results, or spatial-domain measurement results, or a combination thereof.

[0697] As an example, the second dataset in the intelligent model includes measurements on at least one RS resource filtered by an L1 filter.

[0698] As an example, the third dataset in the intelligent model is configured by the network.

[0699] As an example, the third dataset in the intelligent model is determined by the terminal.

[0700] As an example, the third dataset in the intelligent model includes the terminal's stored data; the stored data may come from the network, the terminal's logs, or other RAN nodes.

[0701] As an example, the third dataset in the intelligent model includes measurement information of the terminal; the measurement information may be the mobile state of the terminal, such as the mobile speed, or the number of cells switched within a given time interval; the measurement information may also be measurement results for a reference signal, such as cell-level measurement results, or beam-level measurement results, or time-domain measurement results, or frequency-domain measurement results, or spatial-domain measurement results, or a combination thereof.

[0702] As an example, the third dataset in the intelligent model includes measurements on at least one RS resource filtered by an L1 filter.

[0703] As an example, RLF event prediction is performed using the intelligent model.

[0704] As an example, Example 11 is only intended to illustrate that this application can be used in intelligent models. This example does not limit the application of this application to non-intelligent operations, nor does it limit the application of this application to other types of intelligent models to achieve effects comparable to the intelligent model shown in Figure 11.

[0705] Example 13

[0706] Example 13 illustrates a schematic diagram of intelligent function deployment in a RAN domain according to one embodiment of this application; as shown in Figure 13. The gNB in ​​Example 13 can be replaced with, for example, an eNB, or a network device such as a 6G base station.

[0707] Intelligent functions in the RAN domain include training (also known as ML training, AI training, or AI / ML training), testing (also known as ML testing, AI testing, or AI / ML testing), and inference (also known as ML inference, AI inference, or AI / ML inference), among others. Training, testing, and inference functions can be deployed independently or co-located. Deployment of intelligent functions can be achieved through software, such as downloading and / or running executable files; or through a combination of software and hardware, such as accelerating specific computing units through hardware to improve processing speed or save power.

[0708] Training functions can be deployed in a cross-domain management system or a domain-specific management system; the domain-specific management system is used to manage the RAN domain or the CN (Core Network) domain. For example, training functions for MDA (Management Data Analytics) can be deployed in MDAF (MDA Function); training functions for network data analytics can be deployed in NWDAF (Network Data Analytics Function), meaning the training function is MTLF (Model Training Logical Function).

[0709] Inference functions can also be deployed in cross-domain management systems or domain-specific management systems; for example, the inference function is MDAF, or the inference function is AnLF (Analytics logical function) located in NWDAF.

[0710] Similarly, testing functionality can also be deployed in cross-domain management systems or domain-specific management systems.

[0711] In Example 13, the training function 1302 of the RAN domain is located in the management function 1303 of the RAN domain; while the inference function is located in the base station, that is, the inference function 1304 is located in gNB1305 and the inference function 1306 is located in gNB1307.

[0712] In Figure 13, the management of the inference function of multiple base stations is completed by the RAN domain management function 1303, that is, data interaction with the RAN domain MnS (Mangement Service) consumer / cross-domain management 1301 (as shown by the dashed arrow 1308 in Figure 13).

[0713] Optionally, the management of the inference function can also be completed by the base station itself, that is, each base station can independently interact with the RAN domain MnS consumer / cross-domain management 1301.

[0714] It should be noted that Embodiment 13 is merely a non-limiting implementation method; optionally, the training function of the RAN domain may also be deployed at the base station; or optionally, some base stations may deploy both the inference function and the training function of the RAN domain, while some base stations may only deploy the inference function.

[0715] As an example, one of the gNBs (or base stations) in Example 13 is the base station described in this application.

[0716] As an example, one of the inference functions in Figure 13 performs RLF event prediction.

[0717] Example 14

[0718] Example 14 illustrates a schematic diagram of UE smart function deployment according to one embodiment of this application; as shown in Figure 14. The RAN domain training function 1405 in Figure 14 is optional.

[0719] The UE intelligent function 1404 is deployed in the terminal of this application. The UE intelligent function 1404 includes an inference function 1406. The inference function 1406 uses an intelligent model (also known as an AI model, or an ML model, or an AI / ML model) for inference. An intelligent model is usually trained before it is used for AI / ML inference.

[0720] As an example, the UE intelligent function 1404 includes a RAN domain training function 1405, which runs training data through an intelligent model to obtain a relevant loss and adjusts the parameters of the intelligent model based on the calculated loss; the training includes at least one of ML initial training, ML re-training, and reinforcement learning.

[0721] The above embodiments can reduce the complexity of the base station, or save air interface resources caused by reporting training data; however, the above embodiments place high demands on the processing capabilities of the UE side.

[0722] Optionally, the UE smart function 1404 also includes a CN domain training function (not shown in Figure 14).

[0723] Optionally, the UE smart function 1404 also includes a smart deployment function—not shown in Figure 14—for loading smart models and data.

[0724] As an example, the terminal indicates whether it supports training functions (RAN domain or CN domain) through capability reporting. The capability reporting is RRC signaling or NAS (Non-Access Stratum) signaling.

[0725] As an example, the intelligent model and related metadata are loaded by the terminal from a network device or a remote server.

[0726] Optionally, the UE intelligent function 1404 is an MnS (Management Service) producer that provides data to the CN domain MnF (Management Function) 1401, and / or the RAN domain MnF 1402, and / or the cross-domain management system 1403 for management or analysis (as shown by double arrow 1407).

[0727] Optionally, the UE intelligent function 1404 is an MnS consumer that loads data from the CN domain MnF1401, and / or the RAN domain MnF1402, and / or the cross-domain management system 1403 for AI / ML-related management, such as managing data requests, intelligent model activation, and / or intelligent model training (as shown by double arrow 1407).

[0728] As an example, the intelligent model is based on a neural network.

[0729] As an example, the intelligent model is based on CNN (Conventional Neural Networks).

[0730] As an example, the smart model is based on the Transformer architecture.

[0731] As an example, the terminal in this application includes the inference function 1406 shown in Figure 14.

[0732] As an example, the first processor in this application includes the inference function 1406 shown in Figure 14.

[0733] As an example, UE201 in Figure 2 includes the inference function 1406 described in Figure 14.

[0734] As an example, the first communication device 450 in Figure 4 includes the reasoning function 1406 described in Figure 14.

[0735] As an example, the first processor 902 in Figure 9 includes the inference function 1406 in Figure 14.

[0736] As an example, the intelligent module 1101 in Figure 11 includes the reasoning function 1406 described in Figure 14.

[0737] As an example, the third module in Figure 12 includes the reasoning function 1406 described in Figure 14.

[0738] As an example, the inference function 1406 in Figure 14 performs RLF event prediction.

[0739] As an example, the inference function 1406 in Figure 14 indicates relevant information about the predicted RLF event.

[0740] Example 15

[0741] Example 15 illustrates a flowchart based on artificial intelligence or machine learning according to an embodiment of this application, as shown in Figure 15. Figure 15 includes a third operation, a fourth operation, a fifth operation, a sixth operation, and a seventh operation. In Example 15, the third and fourth operations belong to a first stage, the fifth operation belongs to a second stage, the sixth operation belongs to a third stage, and the seventh operation belongs to a fourth stage. In Figure 15, lines with arrows indicate the sequence of the process.

[0742] As an example, the third operation includes AI / ML training, the fourth operation includes AI / ML testing, the fifth operation includes AI / ML emulation, the sixth operation includes AI / ML entity loading, and the seventh operation includes AI / ML inference.

[0743] As an example, the first stage includes a training phase, the second stage includes an emulation phase, the third stage includes a deployment phase, and the fourth stage includes an inference phase.

[0744] As an example, the first stage includes AI / ML model training.

[0745] As an example, the first stage includes AI / ML model training and AI / ML testing.

[0746] As an example, the AI / ML model training includes initial training and re-training of one or a group of AI / ML entities.

[0747] As an example, the training of the AI / ML model depends on training data.

[0748] As an example, the AI / ML model training includes AI / ML entity validation.

[0749] As an example, the AI / ML entity verification is used to evaluate the performance of the AI / ML entity.

[0750] As an example, the AI / ML entity verification relies on verification data.

[0751] As an example, if the AI / ML entity verification results do not meet expectations, the AI / ML model will be retrained.

[0752] As an example, the AI / ML testing includes testing the validated AI / ML entities to estimate the performance of the trained AI / ML model.

[0753] As an example, if the AI / ML test results meet expectations, the AI / ML entity proceeds to the next stage; otherwise, the AI / ML model will be retrained.

[0754] As an example, the AI / ML test relies on test data.

[0755] As one embodiment, the second stage includes AI / ML simulation, which performs AI / ML entity reasoning in a simulation environment.

[0756] As an example, the AI / ML simulation estimates the performance of AI / ML entity reasoning in a simulation environment before using AI / ML entities.

[0757] As one embodiment, the second stage is optional.

[0758] As an example, the third stage includes AI / ML entity loading, which is to obtain trained AI / ML entities to obtain the desired AI / ML inference capabilities.

[0759] As an example, the third stage is optional.

[0760] As an example, the third stage is no longer needed when the training and inference functions are co-located.

[0761] As an example, the fourth stage includes AI / ML inference.

[0762] Those skilled in the art will understand that all or part of the steps in the above methods can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium, such as a read-only memory, hard disk, or optical disk. Optionally, all or part of the steps in the above embodiments can also be implemented using one or more integrated circuits. Accordingly, each module unit in the above embodiments can be implemented in hardware or in the form of software functional modules. This application is not limited to any specific combination of software and hardware. The user equipment, terminal, and UE in this application include, but are not limited to, drones, communication modules on drones, remote-controlled aircraft, aircraft, small aircraft, mobile phones, tablets, laptops, vehicle-mounted communication equipment, wireless sensors, internet cards, IoT terminals, RFID terminals, NB-IoT terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, internet cards, vehicle-mounted communication equipment, low-cost mobile phones, low-cost tablets, satellite communication equipment, ship communication equipment, NTN user equipment, and other wireless communication equipment. The base station or system equipment in this application includes, but is not limited to, macrocell base stations, microcell base stations, home base stations, relay base stations, gNB (NR Node B), TRP (Transmitter Receiver Point), NTN base stations, satellite equipment, flight platform equipment, and other wireless communication equipment.

[0763] This invention may be practiced in other specified forms without departing from its core or essential characteristics. Therefore, the embodiments disclosed herein should in any way be considered descriptive rather than restrictive. The scope of the invention is defined by the appended claims rather than the foregoing description, and all modifications within their equivalent meaning and scope are considered to be included therein.

Claims

1. A method used in a terminal for wireless communication, wherein, include: Receive a first signaling message, the first signaling message indicating a first threshold; Perform a first RLF prediction, which includes predicting the probability of an RLF occurring within a first time window; Perform a first measurement; whether the first measurement is based on a first RS resource set or a second RS resource set depends on the predicted probability of an RLF occurring within a first time window; The meaning of whether the first measurement is based on the first RS resource set or the second RS resource set depending on the predicted probability of RLF occurring within the first time window includes: when the predicted probability of RLF occurring within the first time window is greater than the first threshold, the first measurement is based on the first RS resource set; when the predicted probability of RLF occurring within the first time window is not greater than the first threshold, the first measurement is for the second RS resource set; the length of the first time window is finite.

2. The method in the terminal according to claim 1, characterized in that, The first location method is based on the first RS resource set; the second location method is based on the second RS resource set.

3. The method in the terminal according to claim 1 or 2, characterized in that, At least one of the first RS resource set and the second RS resource set includes PRS.

4. The method in the terminal according to any one of claims 1 to 3, characterized in that, Whether the first measurement is a relaxed measurement depends on the first RLF prediction, wherein the first measurement is a relaxed measurement when the predicted probability of RLF occurring within the first time window is not greater than a first threshold; and the first measurement is not a relaxed measurement when the predicted probability of RLF occurring within the first time window is greater than the first threshold.

5. The method in the terminal according to any one of claims 1 to 4, characterized in that, include: The first measurement is one of the measurements in a first set of measurements; the predicted probability of an RLF occurring within a first time window being greater than a second threshold triggers the termination of at least some of the measurements in the first set of measurements.

6. The method in the terminal according to any one of claims 1 to 5, characterized in that, include: The length of the period of the first measurement depends on the probability of an RLF occurring within the first time window.

7. The first node according to any one of claims 1 to 6, characterized in that, The probability of predicting that RLF will occur within the first time window includes the probability that the number of at least one of consecutive out-of-step indications and predicted out-of-step indications within the first time window reaches a third threshold. The predicted out-of-step indication depends on measurements of RS resource sets outside the first and second RS resource sets.

8. The method in the terminal according to any one of claims 1 to 7, characterized in that, The method includes: Send the first notification; Receive the first notification; The predicted RLF depends on the first notification.

9. The method in the terminal according to any one of claims 1 to 8, characterized in that, include: Send first location information; wherein, when the predicted probability of an RLF occurring within a first time window is not greater than a first threshold, the first location information is based on measurement; when the predicted probability of an RLF occurring within a first time window is greater than the first threshold, the first location information is based on prediction.

10. A terminal, wherein, include: A first receiver receives a first signaling instruction, the first signaling instruction indicating a first threshold. Perform a first RLF prediction, which includes predicting the probability of an RLF occurring within a first time window; perform a first measurement; the first measurement is based on a first RS resource set or a second RS resource set depending on the predicted probability of an RLF occurring within the first time window; wherein, the meaning of whether the first measurement is based on the first RS resource set or the second RS resource set depending on the predicted probability of an RLF occurring within the first time window includes: when the predicted probability of an RLF occurring within the first time window is greater than a first threshold, the first measurement is based on the first RS resource set; when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the first measurement is for the second RS resource set; the length of the first time window is finite.

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