A method and apparatus used in a terminal

Abstract

The application discloses a method and device used in a terminal, comprising: receiving first signaling, the first signaling indicating a first threshold; performing a first measurement, the first measurement being for a first RS resource set; performing a first Radio Link Failure (RLF) prediction, the RLF prediction depending on the first measurement; performing a second measurement, the RS resource set for the second measurement depending on a probability of an RLF occurring within a first time window predicted. The method can reduce the influence of the RLF on the terminal.

Description

Technical Field

[0001] This application relates to a transmission method in a wireless communication system, and to a method for intelligently selecting RS (Reference Signal) resources. Background Technology

[0002] The application scenarios of future wireless communication systems are becoming increasingly diversified, and different application scenarios place different performance requirements on the system. In order to meet the different performance requirements of various application scenarios, the 3GPP (3rd Generation Partner Project) RAN (Radio Access Network) #72 plenary meeting decided to conduct research on New Radio (NR) (or Fifth Generation, 5G). The 3GPP RAN #75 plenary meeting adopted the NR WI (Work Item), and began the standardization work of NR.

[0003] In communications, both LTE (Long Term Evolution) and 5G NR involve reliable and accurate information reception, optimized energy efficiency, determination of information validity, flexible resource allocation, scalable system architecture, efficient non-access stratum information processing, low service interruption and drop rate, and support for low power consumption. These are crucial for normal communication between base stations and user equipment, rational resource scheduling, and balanced system load. They are the cornerstone of high throughput, meeting the communication needs of various services, improving spectrum utilization, and enhancing service quality. They are indispensable for eMBB (enhanced Mobile Broadband), URLLC (Ultra Reliable Low Latency Communication), and eMTC (enhanced Machine Type Communication). Considering the potential of AI (Artificial Intelligence) / ML (Machine Learning) algorithm-based mechanisms to achieve proactive solutions, and the related progress in AI / ML based on RAN1 and RAN3, 3GPP adopted a Study Item (SI) entitled "Study on AI / ML for mobility in NR," which investigates and evaluates the potential benefits and advantages of AI / ML-assisted mobility for network-triggered L3-based handover, including Radio Link Failure (RLF) prediction or Handover Failure (HOF) prediction. Relaxed measurement is an important technology in wireless communication systems, involving the efficient allocation and use of radio resources, reducing the frequency of network status monitoring, and lowering measurement costs while ensuring basic communication performance. With the development of wireless communication technology, relaxed measurement is constantly introducing new technologies and methods to adapt to higher data transmission rates, better quality of service requirements, and more complex network environments.

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

[0005] Researchers have found that in scenarios where terminals perform relaxation measurements in wireless communication systems, determining the set of RS resources targeted by the second measurement based on the prediction of the probability of RLF occurring within the first time window is a problem that needs to be solved.

[0006] To address the aforementioned problems, this application provides a solution. While using an NR system as an example in the problem description, this application is also applicable to scenarios such as LTE (Long-Term Evolution), LTE-A (Long-Term Evolution Advanced), 5G+, or 6G systems, achieving similar technical effects to NR systems. Furthermore, although this application provides specific implementation methods for determining the timing of entering or leaving the relaxed measurement state, it can also be used to solve other communication problems, such as network optimization, artificial intelligence, and mobility management. The method proposed in this application is also very suitable for solving problems in network convergence scenarios. Furthermore, adopting a unified design scheme for different scenarios helps reduce hardware complexity and cost. Furthermore, although this application was initially intended for the Uu air interface, it can also be used for the PC5 interface, achieving similar technical effects to the Uu air interface. Furthermore, although this application was initially intended for terminal and base station scenarios, it is also applicable to V2X (Vehicle-to-Everything) scenarios, communication scenarios between terminals and relays, and communication scenarios between relays and base stations, achieving similar technical effects. Furthermore, although this application was initially intended for terminal and base station scenarios, it is also applicable to IAB (Integrated Access and Backhaul) communication scenarios, achieving similar technical effects. Furthermore, although this application was initially intended for terrestrial network (TN) scenarios, it is also applicable to non-terrestrial network (NTN) communication scenarios, achieving similar technical effects. In addition, adopting a unified solution for different scenarios helps reduce hardware complexity and cost.

[0007] As an example, the interpretation of the terminology in this application is based on the definitions in the 3GPP specification protocol TS36 series.

[0008] As an example, the interpretation of terms in this application is based on the definitions in the 3GPP specification protocol TS38 series.

[0009] As an example, the interpretation of terms in this application is based on the definitions in the 3GPP specification protocol TS37 series.

[0010] It should be noted that, unless otherwise specified, the embodiments and features in any node of this application can be applied to any other node. Furthermore, unless otherwise specified, the embodiments and features in any embodiment of this application can be arbitrarily combined with each other.

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

[0012] include:

[0013] The process involves: receiving a first signaling instruction indicating a first threshold; performing a first measurement; the first measurement targeting a first RS resource set; performing a first RLF prediction, the first RLF prediction including predicting the probability of an RLF occurring within a first time window; the execution of the first RLF prediction depending on the first measurement; performing a second measurement; the second measurement depending on the predicted probability of an RLF occurring within the first time window; wherein the meaning of the second measurement 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 second measurement is targeting the first RS resource set and the second RS resource set; when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the second measurement is targeting the first RS resource set; wherein the length of the first time window is finite.

[0014] As an example, the problem this application aims to solve includes how to determine the set of RS resources for which the measurement is targeted. In the above method, the set of RS resources for which the second measurement is targeted depends on the predicted probability of an RLF occurring within a first time window, thereby solving the aforementioned problem.

[0015] As an example, the advantages of the above method include: reduced measurement costs, improved efficiency, guaranteed performance, guaranteed service transmission, better reduction of terminal power consumption, and extended battery life.

[0016] As an example, the above method is simple to implement. The above method is suitable for communication systems that utilize AI.

[0017] As an example, the above method can more accurately detect possible RLFs when the predicted probability of RLF occurrence is high, by taking the second measurement for the first RS resource set and the second RS resource set, thereby ensuring communication performance.

[0018] As an example, when the predicted probability of RLF occurrence is very low, the second measurement is only performed on the first RS resource set, reducing measurement costs and power consumption.

[0019] As an example, when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the meaning of the second measurement for the first RS resource set is: when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the second measurement is for the former of the first RS resource set and the second RS resource set.

[0020] As an example, when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the second measurement for the first RS resource set means that when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the second measurement is not applied to the second RS resource set.

[0021] According to one aspect of this application, it is characterized in that,

[0022] The second measurement is to determine the RLF.

[0023] As an example, the advantages of the above method include: ensuring the first RLF prediction.

[0024] According to one aspect of this application, it is characterized in that,

[0025] A second RLF prediction is performed, which depends on the second measurement; the second RLF prediction includes predicting the probability of an RLF occurring within a second time window; wherein the first RS resource set and the second RS resource set are associated with different PCIs (Physical Cell Identity); the length of the second time window is finite; and the different PCIs all belong to the serving cell of the terminal.

[0026] According to one aspect of this application, it is characterized in that,

[0027] Whether the state of relaxation measurement depends on a second RLF prediction, including at least one of: entering the state of relaxation measurement when the predicted probability of an RLF within the first time window is not greater than a second threshold, and leaving the state of relaxation measurement when the predicted probability of an RLF within the first time window is greater than the second threshold.

[0028] According to one aspect of this application, it is characterized in that,

[0029] Controlling a first timer; the control of the first timer includes starting the first timer when entering a relaxed measurement state, and rewinding the first timer when leaving the relaxed measurement state; stopping the rewinding when the first timer rewinds to 0; wherein, the expiration of the first timer triggers entry into the relaxed measurement state.

[0030] According to one aspect of this application, it is characterized in that,

[0031] The length of the measurement period depends on the probability of an RLF occurring within the first time window.

[0032] According to one aspect of this application, it is characterized in that,

[0033] The predicted probability of an RLF occurring 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; wherein the predicted out-of-step indications depend on measurements of at least the former of the first RS resource set and the second RS resource set.

[0034] According to one aspect of this application, it is characterized in that,

[0035] Send the first notification; Receive the first notification;

[0036] The predicted RLF depends on the first notification.

[0037] According to one aspect of this application, it is characterized in that,

[0038] The predicted RLF depends on at least one of the number of predicted synchronization indicators or the number of predicted out-of-synchronization indicators within a first time window; the predicted synchronization indicator or the predicted out-of-synchronization indicator depends on the at least first reference signal.

[0039] According to one aspect of this application, it is characterized in that,

[0040] When the first timer is running, entering the relaxation measurement state will not restart the first timer; when the first timer is rolled back, entering the relaxation measurement state will restart the first timer.

[0041] As one embodiment, the advantages of the above method include: not restarting the first timer facilitates a faster entry into the relaxation measurement state, thereby saving more power. That is, the first timer is only started when entering the relaxation measurement state if the first timer is not in a running state or in a rollback state.

[0042] This application discloses a terminal, including:

[0043] The terminal includes: one or more processors and memory;

[0044] The memory is coupled to the one or more processors and 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 perform any one of the methods in the terminal.

[0045] Specifically, according to one aspect of this application, the terminal is an Internet of Things (IoT) terminal.

[0046] Specifically, according to one aspect of this application, the terminal is a user equipment.

[0047] Specifically, according to one aspect of this application, the terminal is an access network device.

[0048] Specifically, according to one aspect of this application, the terminal is a vehicle-mounted terminal.

[0049] Specifically, according to one aspect of this application, the terminal is an aircraft.

[0050] Specifically, according to one aspect of this application, the terminal is a mobile phone.

[0051] As an example, compared with conventional solutions, this application has the following advantages:

[0052] It strikes a very good balance between power saving and avoiding RLF / fast response RLF.

[0053] It better supports the transmission of services with high latency requirements, such as XR services.

[0054] It better supports the transmission of services with strong bursts of traffic, such as XR services.

[0055] This approach better supports communication services, ensures communication quality, enhances relaxation measurement, and enables the network to allocate resources more rationally. Unlike traditional methods that require entering a relaxation measurement state when low mobility or not at the cell edge, this application considers determining the RS resources and period relied upon for measurement based on AI-predicted probability of RLF and a timer. This approach enriches the application scenarios of relaxation measurement, saves more terminal power consumption, ensures communication quality, and greatly expands the scope for network optimization. It represents a new and highly promising optimization method with significant implications for improving network performance.

[0056] In scenarios where the communication environment is constantly changing, especially when it involves XR services, it can reduce latency and avoid or reduce the impact of RLF on communication.

[0057] It helps save power and reduce signaling overhead. Attached Figure Description

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

[0059] Figure 1 A flowchart illustrating the execution of a measurement according to an embodiment of this application is shown;

[0060] Figure 2 A schematic diagram of a network architecture according to an embodiment of this application is shown;

[0061] Figure 3 A schematic diagram of an embodiment of a wireless protocol architecture for the user plane and control plane according to an embodiment of this application is shown;

[0062] Figure 4 A schematic diagram of a first communication device and a second communication device according to an embodiment of this application is shown;

[0063] Figure 5 A flowchart of wireless signal transmission according to an embodiment of this application is shown;

[0064] Figure 6 A schematic diagram of a predicted RLF dependency measurement according to an embodiment of this application is shown;

[0065] Figure 7 A schematic diagram of controlling a first timer according to an embodiment of this application is shown;

[0066] Figure 8 A schematic diagram illustrating the probability change of RLF occurring within a time window according to an embodiment of this application is shown;

[0067] Figure 9 A schematic diagram of a processing apparatus for a terminal according to an embodiment of this application is shown;

[0068] Figure 10 A structural block diagram of a processing apparatus for a base station according to an embodiment of this application is shown.

[0069] Figure 11 A schematic diagram illustrating the transmission of a first notification according to an embodiment of this application is shown;

[0070] Figure 12 A schematic diagram of an intelligent model according to an embodiment of this application is shown;

[0071] Figure 13A schematic diagram illustrating the deployment of intelligent functions in a RAN (Radio Access Network) domain according to an embodiment of this application is shown.

[0072] Figure 14 A schematic diagram illustrating the deployment of UE smart functions according to one embodiment of this application is provided;

[0073] Figure 15 A flowchart illustrating an embodiment of the present application based on artificial intelligence or machine learning is provided. Implementation

[0074] The technical solution of this application will be further described in detail below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0075] Example 1

[0076] Example 1 illustrates a flowchart of performing a measurement according to an embodiment of this application, as shown in the attached diagram. Figure 1 As shown. (Attached) Figure 1 In the diagram, each box represents a step. It is particularly important to emphasize that the order of the boxes does not represent the chronological order of the steps they represent.

[0077] In Embodiment 1, the terminal in this application receives a first signaling in step 101, performs a first measurement in step 102, performs a first RLF prediction in step 103, and performs a second measurement in step 104.

[0078] Wherein, the first signaling indicates a first threshold; a first measurement is performed; the first measurement is performed on a first RS resource set; a first RLF prediction is performed, the first RLF prediction including predicting the probability of an RLF occurring within a first time window; the performance of the first RLF prediction depends on the first measurement; a second measurement is performed; the second measurement depends on the predicted probability of an RLF occurring within the first time window; wherein, the meaning of the second measurement 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 second measurement is performed on the first RS resource set and a second RS resource set; when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the second measurement is performed on the first RS resource set; wherein, the length of the first time window is finite.

[0079] As one example, the terminal is a UE (User Equipment).

[0080] As an example, this application is directed to AI (artificial intelligence).

[0081] As an example, the AI ​​includes machine learning.

[0082] As an example, the terminal refers to a communication device consisting of hardware such as baseband, radio frequency, and one or two SIM cards.

[0083] As an example, the terminal is in an RRC (Radio Resource Control) connected state.

[0084] As an example, any parameter in this application may be configured by the network or may be generated by the terminal according to an internal algorithm, such as randomization.

[0085] As an example, the values ​​of any parameters in this application, including but not limited to the probability of RLF occurring, the length of the first time window, the first threshold, the second threshold, and the third threshold, are limited unless otherwise stated.

[0086] As a sub-implementation of this embodiment, the upper limit of the value of any parameter in this application is 1024 times 65536.

[0087] As a sub-implementation of this embodiment, the upper limit of the value of any parameter in this application is 65536 or 65535.

[0088] As a sub-implementation of this embodiment, the upper limit of the value of any parameter in this application is 1024.

[0089] As a sub-implementation of this embodiment, the upper limit of the value of any parameter in this application is 640 or 320.

[0090] As an example, this application is directed to NR.

[0091] As an example, this application is directed to wireless communication networks after NR.

[0092] As an example, the serving cell refers to the cell where the UE camps. Performing a cell search includes the UE searching for a suitable cell within a selected PLMN (Public Land Mobile Network) or SNPN (Stand-alone Non-Public Network), selecting the suitable cell to provide available services, and monitoring the control channel of the suitable cell. This process is defined as camping on a cell; that is, a camped cell is the serving cell for the UE. Camping on a cell in RRC idle or RRC inactive state has the following advantages: it allows the UE to receive system messages from the PLMN or SNPN; after registration, if the UE wishes to establish an RRC connection or continue a suspended RRC connection, the UE can perform initial access on the control channel of the camped 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.

[0093] As an example, the first threshold is a real number.

[0094] As an example, the first threshold is greater than or equal to 0 and less than or equal to 1.

[0095] As an example, the first threshold is the default.

[0096] As an example, the first threshold is specific to the RLF prediction.

[0097] As an example, the first threshold is specifically used for comparing the predicted probabilities of the RLF.

[0098] As an example, the first signaling indicating the first threshold means that the first signaling explicitly indicates the first threshold.

[0099] As an example, the first signaling indicating the first threshold means that the first signaling implicitly indicates the first threshold.

[0100] As an example, the first signaling indicating the first threshold means: the first signaling configures the first threshold.

[0101] As an example, the first signaling indicating the first threshold means that the first signaling enables the first threshold.

[0102] As an example, the first signaling is cell common.

[0103] As an example, the first signaling is UE-specific.

[0104] As an example, the first signaling is RRC (Radio Resource Control) signaling.

[0105] As an example, the first signaling includes RRC signaling and MAC (Medium Access Control) CE (Control Element).

[0106] As one embodiment, the first signaling includes RRC signaling and DCI (Downlink Control Information).

[0107] As one embodiment, the first signaling includes an RRC signaling that configures the first threshold.

[0108] As one embodiment, the first signaling includes an RRC signaling that configures the second threshold.

[0109] As an example, the first signaling includes an RRC signaling and a MAC (Medium Access Control) CE (Control Element), wherein the RRC signaling configures multiple thresholds, and the MAC CE indicates the first threshold from among the multiple thresholds.

[0110] As one embodiment, the first signaling includes an RRC signaling and a MAC CE, wherein the RRC signaling configures a plurality of thresholds, and the MAC CE indicates the second threshold from the plurality of thresholds.

[0111] As one embodiment, the first signaling includes an RRC signaling and a DCI, wherein the RRC signaling configures a plurality of thresholds, and the DCI indicates the first threshold from the plurality of thresholds.

[0112] As one embodiment, the first signaling includes an RRC signaling and a DCI, wherein the RRC signaling configures a plurality of thresholds, and the DCI indicates the second threshold from the plurality of thresholds.

[0113] As one example, the RLF prediction is performed by the terminal's intelligent module.

[0114] As an example, the RLF prediction is performed by the terminal's smart model for RLF prediction.

[0115] As an example, the RLF prediction is performed by the terminal based on the UE.

[0116] As an example, the RLF prediction is performed by the terminal based on network configuration.

[0117] As an example, the RLF prediction is performed by the terminal based on the UE implementation and network configuration.

[0118] As an example, the RLF prediction is based on the most recent measurement results.

[0119] As an example, the RLF prediction is based on previous measurements.

[0120] As an example, the RLF prediction is based on the information stored in the terminal.

[0121] As an example, the RLF prediction is based on information provided by the network.

[0122] As an example, the RLF prediction includes inference.

[0123] As an example, the RLF prediction includes training.

[0124] As one example, the RLF prediction includes training and inference.

[0125] As an example, the RLF prediction includes predicting link quality.

[0126] As an example, the RLF prediction includes a prediction synchronization indication.

[0127] As one embodiment, the RLF prediction includes a prediction of a step loss indication.

[0128] As one example, the RLF prediction includes predicting whether the timer has expired.

[0129] As an example, the RLF prediction refers to predicting whether an RLF will occur.

[0130] As an example, the RLF prediction refers to predicting the probability of an RLF occurring.

[0131] As an example, the RLF prediction refers to predicting the time when an RLF will occur.

[0132] As an example, the RLF prediction refers to predicting the time interval during which an RLF will not occur.

[0133] As an example, the RLF prediction refers to predicting the probability of an RLF occurring over time.

[0134] As an example, predicting RLF means predicting that an RLF will occur within a first time window.

[0135] As an example, "not predicted RLF" means that it is predicted that RLF will not occur within the first time window.

[0136] As one embodiment, the first time window includes a predicted time interval.

[0137] As one embodiment, the first time window includes a time interval after the current moment.

[0138] As an example, the first time window is the expected running time of a timer.

[0139] As an example, the first time window is the running time of a timer.

[0140] As an example, the first time window is the remaining time of T310.

[0141] As one embodiment, the first RS resource set and the second RS resource set are orthogonal.

[0142] As an example, the first RS resource set includes SSB.

[0143] As a sub-implementation of this embodiment, the second RS resource set includes SSB.

[0144] As a sub-implementation of this embodiment, the second RS resource set includes CSI-RS.

[0145] As a sub-implementation of this embodiment, the second RS resource set does not include SSB.

[0146] As a sub-implementation of this embodiment, the advantage of including SSBs in the second RS resource set is that more accurate results can be obtained by monitoring the beams determined by different SSBs, avoiding triggering RLFs only for the measurement of the current beam.

[0147] As a sub-example of this embodiment, the advantage of the second RS resource set including only CSI-RS is that using different types of RS resources is beneficial to improving measurement accuracy.

[0148] As one embodiment, the first RS resource set includes CSI-RS.

[0149] As a sub-implementation of this embodiment, the second RS resource set includes CSI-RS.

[0150] As a sub-example of this embodiment, the second RS resource set does not include CSI-RS.

[0151] As a sub-implementation of this embodiment, the second RS resource set includes SSB.

[0152] As a sub-example of this embodiment, the advantage of including CSI-RS in the second RS resource set is that measurement accuracy can be improved by performing more intensive measurements, including improving the accuracy of RLF prediction.

[0153] As a sub-example of this embodiment, the advantage of the second RS resource set including only SSB is that using different types of RS resources is beneficial to improving measurement accuracy.

[0154] As an example, the first RS resource set and the second RS resource set are orthogonal in the time domain.

[0155] As an example, the second RS resource set is a PRS (positioning RS).

[0156] As a sub-implementation of this embodiment, the advantage of the second RS resource set being PRS is that it can be located when the predicted RLF occurs with a high probability within the first time window, which is beneficial for wireless network optimization.

[0157] As an example, the periods of the first RS resource set and the second RS resource set are different.

[0158] As an example, the first RS resource set and the second RS resource set are associated with different PCIs.

[0159] As an example, the advantage of associating the first RS resource set and the second RS resource set with different PCIs is that it facilitates early monitoring of other PCI signals when predicting a high probability of RLF, thereby improving communication reliability.

[0160] As one embodiment, the first measurement for the first RS resource set includes the first measurement being implemented based on the first RS resource set.

[0161] As an example, the first measurement for the first RS resource set includes the result of measurement analysis of the first RS resource set.

[0162] As an example, the statement that performing the first RLF prediction depends on the first measurement means that performing the first RLF measurement depends on the result of the first measurement.

[0163] As an example, the execution of the first RLF prediction depends on the number of out-of-step indications in the results of the first measurement.

[0164] As one example, the execution of the first RLF prediction depends on the number of out-of-step indications predicted based on the results of the first measurement.

[0165] As an example, the second measurement differs from the first measurement.

[0166] As an example, the second measurement depends on the predicted probability of an RLF occurring within a first time window.

[0167] As an example, the second measurement depends on the set of RS resources determined by the predicted probability of an RLF occurring within a first time window.

[0168] As an example, when the predicted probability of an RLF occurring within the first time window is greater than a first threshold, the second measurement is performed on the first RS resource set and the second RS resource set. The advantage of doing this is that when the probability of an RLF occurring is high, using more RS resource sets can make the measurement more accurate and can detect possible RLFs in a timely manner.

[0169] As an example, when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the second measurement is performed on the first RS resource set. The advantage of doing this is that when the probability of an RLF occurring is very low, fewer RS ​​resources are used for measurement, reducing terminal power consumption and improving the terminal's battery life.

[0170] Example 2

[0171] Example 2 illustrates a schematic diagram of a network architecture according to this application, as shown in the attached diagram. Figure 2 As shown.

[0172] Appendix Figure 2This diagram illustrates the network architecture 200 of 5G NR, LTE (Long-Term Evolution), and LTE-A (Long-Term Evolution Advanced) systems. The 5G NR or LTE network architecture 200 may be referred to as 5GS (5G System) / EPS (Evolved Packet System) 200 or some other suitable term. 5GS / EPS 200 may include one or more UE (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. 5GS / EPS can interconnect with other access networks, but these entities / interfaces are not shown for simplicity. As shown in the figure, 5GS / EPS provides packet-switched services; however, those skilled in the art will readily understand that the various concepts presented throughout this application can be extended to networks providing circuit-switched services or other cellular networks. NG-RAN includes NR Node B (gNB) 203 and other gNBs 204. gNB 203 provides user and control plane protocol termination to UE 201. gNB 203 can connect to other gNBs 204 via an Xn interface (e.g., backhaul). gNB 203 may also be referred to as a base station, base transceiver station, radio base station, radio transceiver, transceiver function, Basic Services Set (BSS), Extended Services Set (ESS), TRP (Transmitter Receiver Node), or some other suitable term. gNB 203 provides UE 201 with an access point to 5GC / EPC 210. Examples of UE201 include cellular phones, smartphones, Session Initiation Protocol (SIP) phones, laptop computers, personal digital assistants (PDAs), satellite radios, non-terrestrial base station communications, satellite mobile communications, global positioning systems, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, drones, aircraft, narrowband IoT devices, machine-type communication devices, land vehicles, automobiles, wearable devices, or any other similar functional devices. Those skilled in the art may also refer to UE201 as a mobile station, subscriber station, mobile unit, subscriber unit, radio unit, remote unit, mobile device, radio communication device, remote device, mobile subscriber station, access terminal, mobile terminal, radio terminal, remote terminal, handheld device, user agent, mobile client, client, or any other suitable term.gNB203 connects to 5GC / EPC210 via the S1 / NG interface. 5GC / EPC210 includes MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MME / AMF / SMF 214, S-GW (Service Gateway) / UPF (User Plane Function) 212, and P-GW (Packet Data Network Gateway) / UPF 213. MME / AMF / SMF 211 is the control node handling signaling between UE201 and 5GC / EPC210. ​​Generally, MME / AMF / SMF 211 provides bearer and connection management. All user IP (Internet Protocol) packets are transmitted through S-GW / UPF 212, which is itself connected to P-GW / UPF 213. The P-GW provides UE IP address allocation and other functions. The P-GW / UPF213 connects to Internet service 230. Internet service 230 includes carrier-compliant Internet protocol services, specifically including the Internet, intranet, IMS (IP Multimedia Subsystem), and packet-switched streaming services.

[0173] As an example, the terminal in this application is UE201.

[0174] As an example, the base station of the network node in this application is gNB203.

[0175] As an example, the radio link from UE201 to NR node B is an uplink.

[0176] As an example, the radio link from NR node B to UE201 is a downlink.

[0177] As an example, the UE201 supports relay transmission.

[0178] As an example, the UE201 includes a mobile phone.

[0179] As an example, the UE201 is a vehicle including a car.

[0180] As an example, the gNB203 is a macrocell base station.

[0181] As an example, the gNB203 is a microcell base station.

[0182] As an example, the gNB203 is a pico cell base station.

[0183] As one example, the gNB203 is a flight platform device.

[0184] As an example, the gNB203 is a satellite device.

[0185] Example 3

[0186] Example 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture for a user plane and a control plane according to this application, as shown in the attached diagram. Figure 3 As shown. Figure 3 This is a schematic diagram illustrating an embodiment of a radio protocol architecture for the user plane 350 and the control plane 300. Figure 3The radio protocol architecture for the control plane 300 between a terminal (UE, gNB) and a network node (gNB, UE), or between two UEs, is illustrated using three layers: Layer 1, Layer 2, and Layer 3. Layer 1 (L1 layer) is the lowest layer and implements various PHY (Physical Layer) signal processing functions. L1 layer will be referred to as PHY301 in this document. Layer 2 (L2 layer) 305 sits above PHY301 and is responsible for the link between the terminal and the network node, and between two UEs, via PHY301. L2 layer 305 includes the MAC (Medium Access Control) sublayer 302, the RLC (Radio Link Control) sublayer 303, and the PDCP (Packet Data Convergence Protocol) sublayer 304, which terminate at the network node. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security through encrypted data packets and supports cross-regional mobility between network nodes for the terminal. RLC sublayer 303 provides upper-layer packet segmentation and reassembly, retransmission of lost packets, and packet reordering to compensate for out-of-order reception due to HARQ. MAC sublayer 302 provides multiplexing between the logical and transport channels. MAC sublayer 302 is also responsible for allocating various radio resources (e.g., resource blocks) within a cell among terminals. MAC sublayer 302 is also responsible for HARQ operations. RRC (Radio Resource Control) sublayer 306 in Layer 3 (L3) of the control plane 300 is responsible for acquiring radio resources (i.e., radio bearers) and configuring the lower layers using RRC signaling between network nodes and terminals. PC5-S (PC5 Signaling Protocol) sublayer 307 is responsible for processing the signaling protocol of the PC5 interface. The radio protocol architecture of user plane 350 includes layer 1 (L1 layer) and layer 2 (L2 layer). The radio protocol architecture for terminals and network nodes in user plane 350 is largely the same as the corresponding layers and sublayers in control plane 300 for physical layer 351, PDCP sublayer 354 in L2 layer 355, RLC sublayer 353 in L2 layer 355 and MAC sublayer 352 in L2 layer 355. However, 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 the SDAP (Service Data Adaptation Protocol) sublayer 356. The SDAP sublayer 356 is responsible for mapping between QoS streams and Data Radio Bearers (DRBs) to support service diversity. SRBs can be seen as services or interfaces provided by the PDCP layer to higher layers, such as the RRC layer. In the NR system, SRBs include SRB1, SRB2, and SRB3, which are used to transmit different types of control signaling. SRBs are bearers between the UE and the access network, used to transmit control signaling, including RRC signaling, between the UE and the access network. SRB1 is particularly important for the UE; after each UE establishes an RRC connection, there is an SRB1 used to transmit RRC signaling. Most signaling is transmitted through SRB1. If SRB1 is interrupted or unavailable, the UE must re-establish RRC; one SRB1 is established for each RRC connection. SRB2 is generally only used to transmit NAS signaling or security-related signaling; one SRB2 is established for each RRC connection. The UE may not be configured with SRB3. Except for emergency services, the UE must establish an RRC connection with the network for subsequent communication. Although not illustrated, the terminal may have several upper layers above L2 layer 355. These also include a network layer (e.g., IP layer) terminating at the P-GW on the network side and an application layer terminating at the other end of the connection (e.g., remote UE, server, etc.).

[0187] As an example, Appendix Figure 3 The wireless protocol architecture described herein is applicable to the terminal described in this application.

[0188] As an example, Appendix Figure 3 The wireless protocol architecture described herein is applicable to the network nodes described in this application.

[0189] As an example, the first signaling in this application is generated in PHY301, MAC302, or RRC306.

[0190] Example 4

[0191] Example 4 illustrates a schematic diagram of a first communication device and a second communication device according to an embodiment of this application, as shown in the attached diagram. Figure 4 As shown. Figure 4 This is a block diagram of a first communication device 450 and a second communication device 410 communicating with each other in the access network.

[0192] The first communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmitting processor 468, a receiving processor 456, and optionally may also include a multi-antenna transmitting processor 457, a multi-antenna receiving processor 458, a transmitter / receiver 454, and an antenna 452.

[0193] The second communication device 410 includes a controller / processor 475, a memory 476, a receiver processor 470, a transmitter processor 416, and optionally may also include a multi-antenna receiver processor 472, a multi-antenna transmitter processor 471, a transmitter / receiver 418, and an antenna 420.

[0194] In the transmission from the second communication device 410 to the first communication device 450, at the second communication device 410, upper-layer data packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements L2 (Layer-2) layer functionality. In the transmission from the second communication device 410 to the first communication device 450, the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocation to the first communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for retransmitting lost packets and signaling to the first communication device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for the L1 layer (i.e., the physical layer). Transmit processor 416 performs encoding and interleaving to facilitate forward error correction (FEC) at the second communication device 410, and mapping of signal clusters based on various modulation schemes (e.g., Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), M-Phase Shift Keying (M-PSK), M-QAM). Multi-antenna transmit processor 471 performs digital spatial precoding on the encoded and modulated symbols, including codebook-based and non-codebook-based precoding, and beamforming processing, generating one or more spatial streams. Transmit processor 416 then maps each spatial stream to subcarriers, multiplexes it with a reference signal (e.g., a pilot) in the time and / or frequency domains, and subsequently uses inverse fast Fourier transform (IFFT) to generate a physical channel carrying the time-domain multicarrier symbol stream. Multi-antenna transmit processor 471 then performs transmit analog precoding / beamforming operations on the time-domain multicarrier symbol stream. Each transmitter 418 converts the baseband multicarrier symbol stream provided by the multi-antenna transmitter processor 471 into an radio frequency stream, which is then provided to different antennas 420.

[0195] In the transmission from the second communication device 410 to the first communication device 450, at the first communication device 450, each receiver 454 receives a signal through its corresponding antenna 452. Each receiver 454 recovers the information modulated onto the radio frequency carrier and converts the radio frequency stream into a baseband multicarrier symbol stream, which is then provided to the receiver processor 456. The receiver processor 456 and the multi-antenna receiver processor 458 implement various signal processing functions of the L1 layer. The multi-antenna receiver processor 458 performs receive analog precoding / beamforming operations on the baseband multicarrier symbol stream from the receiver 454. The receiver processor 456 uses a Fast Fourier Transform (FFT) to convert the baseband multicarrier symbol stream after the receive analog precoding / beamforming operations from the time domain to the frequency domain. In the frequency domain, the physical layer data signal and the reference signal are demultiplexed by the receiver processor 456, where the reference signal is used for channel estimation, and the data signal is recovered in the multi-antenna receiver processor 458 after multi-antenna detection to recover any spatial stream destined for the first communication device 450. Symbols on each spatial stream are demodulated and recovered in the receive processor 456, generating soft decisions. The receive processor 456 then decodes and deinterleaves the soft decisions to recover the upper-layer data and control signals transmitted by the second communication device 410 over the physical channel. The upper-layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements the functions of Layer 2. The controller / processor 459 may be associated with a memory 460 storing program code and data. The memory 460 may be referred to as computer-readable media. In the transmission from the second communication device 410 to the second communication device 450, the controller / processor 459 provides multiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transport and logical channels to recover upper-layer data packets from the core network. The upper-layer data packets are then provided to all protocol layers above Layer 2. Various control signals may also be provided to Layer 3 for Layer 3 processing.

[0196] In the transmission from the first communication device 450 to the second communication device 410, at the first communication device 450, a data source 467 is used to provide upper-layer data packets to the controller / processor 459. The data source 467 represents all protocol layers above the L2 layer. Similar to the transmission functions at the second communication device 410 described in the transmission from the second communication device 410 to the first communication device 450, the controller / processor 459 implements header compression, encryption, packet segmentation and reordering, and multiplexing between logical and transport channels based on radio resource allocation, implementing L2 layer functions for the user plane and control plane. The controller / processor 459 is also responsible for retransmitting lost packets and signaling to the second communication device 410. Transmit processor 468 performs modulation mapping and channel coding processing, while multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based and non-codebook-based precoding, and beamforming processing. Subsequently, transmit processor 468 modulates the generated spatial stream into a multi-carrier / single-carrier symbol stream. After analog precoding / beamforming operations in multi-antenna transmit processor 457, the stream is provided to different antennas 452 via transmitter 454. Each transmitter 454 first converts the baseband symbol stream provided by multi-antenna transmit processor 457 into a radio frequency symbol stream before providing it to antenna 452.

[0197] In the transmission from the first communication device 450 to the second communication device 410, the function at the second communication device 410 is similar to the receiving function at the first communication device 450 described in the transmission from the second communication device 410 to the first communication device 450. Each receiver 418 receives radio frequency signals through its corresponding antenna 420, converts the received radio frequency signals into baseband signals, and provides the baseband signals to the multi-antenna receiving processor 472 and the receiving processor 470. The receiving processor 470 and the multi-antenna receiving processor 472 jointly implement the L1 layer functions. The controller / processor 475 implements the L2 layer functions. The controller / processor 475 may be associated with a memory 476 that stores program code and data. The memory 476 may be referred to as computer-readable media. In the transmission from the first communication device 450 to the second communication device 410, the controller / processor 475 provides multiplexing between the transmission and logical channels, packet reassembly, decryption, header decompression, and control signal processing to recover upper-layer data packets from the UE 450. Upper-layer packets from the controller / processor 475 can be provided to the core network.

[0198] As one embodiment, the first communication device 450 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor, and the first communication device 450 at least: receives a first signaling, the first signaling indicating a first threshold; performs a first measurement; the first measurement is for a first RS resource set; performs a first RLF prediction, the first RLF prediction including predicting the probability of an RLF occurring within a first time window; the performance of the first RLF prediction depends on the first measurement; performs a second measurement; the second measurement depends on the predicted probability of an RLF occurring within the first time window; wherein the meaning of the second measurement 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 second measurement is for the first RS resource set and a second RS resource set; when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the second measurement is for the first RS resource set; wherein the length of the first time window is finite.

[0199] As one embodiment, the first communication device 450 includes: a memory storing a computer-readable instruction program that, when executed by at least one processor, produces actions including: receiving a first signaling indicating a first threshold; performing a first measurement; the first measurement being directed to a first RS resource set; performing a first RLF prediction, the first RLF prediction including predicting the probability of an RLF occurring within a first time window; the execution of the first RLF prediction depending on the first measurement; performing a second measurement; the second measurement depending on the predicted probability of an RLF occurring within the first time window; wherein the meaning of the second measurement 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 second measurement is directed to the first RS resource set and a second RS resource set; when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the second measurement is directed to the first RS resource set; wherein the length of the first time window is finite.

[0200] As one embodiment, the second communication device 410 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor. The second communication device 410 at least: transmits a first signaling, the first signaling indicating a first threshold; performs a first measurement; the first measurement is for a first RS resource set; performs a first RLF prediction, the first RLF prediction including predicting the probability of an RLF occurring within a first time window; the performance of the first RLF prediction depends on the first measurement; performs a second measurement; the second measurement depends on the predicted probability of an RLF occurring within the first time window; wherein the meaning of the second measurement 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 second measurement is for the first RS resource set and a second RS resource set; when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the second measurement is for the first RS resource set; wherein the length of the first time window is finite.

[0201] As one embodiment, the second communication device 410 includes: a memory storing a computer-readable instruction program that, when executed by at least one processor, produces actions including: sending a first signaling indicating a first threshold; performing a first measurement; the first measurement being directed to a first RS resource set; performing a first RLF prediction, the first RLF prediction including predicting the probability of an RLF occurring within a first time window; the performance of the first RLF prediction depending on the first measurement; performing a second measurement; the second measurement depending on the predicted probability of an RLF occurring within the first time window; wherein the meaning of the second measurement 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 second measurement is directed to the first RS resource set and a second RS resource set; when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the second measurement is directed to the first RS resource set; wherein the length of the first time window is finite.

[0202] As an example, the first communication device 450 corresponds to the terminal in this application.

[0203] As an example, the second communication device 410 corresponds to the network node in this application.

[0204] As an example, the first communication device 450 is a UE.

[0205] As an example, the first communication device 450 is a vehicle-mounted terminal.

[0206] As an example, the first communication device 450 is a mobile phone.

[0207] As one embodiment, the second communication device 450 is a relay.

[0208] As one embodiment, the second communication device 410 is a satellite.

[0209] As one embodiment, the second communication device 410 is an aircraft.

[0210] As one embodiment, the second communication device 410 is a base station.

[0211] As one embodiment, receiver 454 (including antenna 452), receiver processor 456 and controller / processor 459 are used in this application to receive the first signaling.

[0212] As one embodiment, a transmitter 418 (including an antenna 420), a transmitter processor 416, and a controller / processor 475 are used in this application to transmit the first signaling.

[0213] Example 5

[0214] Example 5 illustrates a wireless signal transmission flowchart according to an embodiment of this application, as shown in the attached diagram. Figure 5 As shown. (Attached) Figure 5 In this example, U01 corresponds to the terminal of this application. It should be noted that the order in this example does not limit the signal transmission order and the implementation order in this application.

[0215] for Terminal U01In step S5101, a first signaling is received; in step S5102, a first measurement is performed; in step S5103, a first RLF prediction is performed; in step S5104, a first notification is sent; in step S5105, a first notification is received; in step S5106, it is determined whether the predicted RLF probability is greater than a first threshold. If the RLF probability is greater than the first threshold, step S5107 is executed; otherwise, step S5108 is executed; in step S5107, a second measurement is determined for the first RS resource set and the second resource set; in step S5108, the second measurement is determined for... For the first RS resource set; perform a second measurement in step S5109; perform a second RLF prediction in step S5110; determine whether the predicted RLF probability is greater than a second threshold in step S5111; if the predicted RLF probability is greater than the second threshold, proceed to step S5112; otherwise, proceed to step S5113; start a first timer in step S5112; roll back the first timer in step S5113; determine whether the first timer has expired in step S5114; if the first timer has not expired, proceed to step S5111; otherwise, end the current process.

[0216] for Base station N02 In step S5201, the first signaling is sent.

[0217] In Embodiment 5, a first signaling is received, the first signaling indicating a first threshold; a first measurement is performed; the first measurement is performed on a first RS resource set; a first RLF prediction is performed, the first RLF prediction including predicting the probability of an RLF occurring within a first time window; the performance of the first RLF prediction depends on the first measurement; a second measurement is performed; the second measurement depends on the predicted probability of an RLF occurring within the first time window; wherein, the meaning of the second measurement 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 second measurement is performed on the first RS resource set and the second RS resource set; when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the second measurement is performed on the first RS resource set; wherein, the length of the first time window is finite.

[0218] In Example 5, the second measurement is for determining an RLF (Restricted Range Flow). A second RLF prediction is performed, which depends on the second measurement. The second RLF prediction includes predicting the probability of an RLF occurring within a second time window. The first RS (Resource Set) and the second RS (Resource Set) are associated with different PCIs. The length of the second time window is finite. The different PCIs all belong to the serving cell of the terminal. Whether the device is in a relaxed measurement state depends on the second RLF prediction and includes at least one of: entering a relaxed measurement state when the predicted probability of an RLF occurring within the first time window is not greater than a second threshold, and leaving the relaxed measurement state when the predicted probability of an RLF occurring within the first time window is greater than the second threshold.

[0219] As an example, the base station N02 is the base station to which the serving cell of the terminal U01 belongs.

[0220] As an example, the base station N02 is the base station to which the PCell of the terminal U01 belongs.

[0221] As an example, the base station N02 is the base station to which the PSCell of the terminal U01 belongs.

[0222] As an example, the base station N02 is MN (Master Node).

[0223] As an example, the base station N02 is an SN (Secondary Node).

[0224] As one embodiment, the terminal U01 and the base station N02 are wirelessly connected.

[0225] As one embodiment, the terminal U01 and the base station N02 are connected by a wire.

[0226] As one embodiment, the terminal U01 and the base station N02 are connected via a Uu port.

[0227] As one embodiment, the terminal U01 and the base station N02 are connected via an IAB port.

[0228] As one embodiment, the terminal U01 and the base station N02 are connected via a PC5 interface.

[0229] As an example, the first signaling is downlink signaling.

[0230] As an example, the first signaling is RRC signaling.

[0231] As one embodiment, the first signaling includes RRC signaling.

[0232] As an example, the first signaling is directed to a specific UE.

[0233] As an example, the first signaling is sent via unicast.

[0234] As an example, the first signaling is sent on SRB1.

[0235] As an example, the first signaling is RRCReconfiguration.

[0236] As an example, the first signaling is MAC layer control signaling.

[0237] As an example, the first signaling is MAC CE (Control element).

[0238] As an example, the first signaling is physical layer control signaling.

[0239] As an example, the first signaling is DCI (downlink control information).

[0240] As an example, the first signaling indicates the first threshold and the second threshold.

[0241] As an example, the first signaling indicating the first threshold means that the first signaling explicitly indicates the first threshold.

[0242] As an example, the first signaling indicating the first threshold means that the first signaling implicitly indicates the first threshold.

[0243] As an example, the first signaling indicating the first threshold means: the first signaling configures the first threshold.

[0244] As an example, the first signaling indicating the first threshold means that the first signaling enables the first threshold.

[0245] As an example, the first signaling indicating the second threshold means that the first signaling explicitly indicates the second threshold.

[0246] As an example, the first signaling indicating the second threshold means that the first signaling implicitly indicates the second threshold.

[0247] As an example, the first signaling indicating the second threshold means that the first signaling configures the second threshold.

[0248] As an example, the first signaling indicating the second threshold means that the first signaling enables the second threshold.

[0249] As an example, both the first threshold and the second threshold are real numbers.

[0250] As an example, the first threshold and the second threshold are greater than or equal to 0 and less than or equal to 1.

[0251] As an example, the first threshold is equal to the second threshold.

[0252] As an example, the first threshold is less than or equal to the second threshold.

[0253] As an example, the first threshold is greater than or equal to the second threshold.

[0254] As one embodiment, the first RS resource set and the second RS resource set are orthogonal.

[0255] As an example, the first RS resource set includes SSB.

[0256] As a sub-implementation of this embodiment, the second RS resource set includes SSB.

[0257] As a sub-implementation of this embodiment, the second RS resource set includes CSI-RS.

[0258] As a sub-implementation of this embodiment, the second RS resource set does not include SSB.

[0259] As a sub-implementation of this embodiment, the advantage of including SSBs in the second RS resource set is that more accurate results can be obtained by monitoring the beams determined by different SSBs, avoiding triggering RLFs only for the measurement of the current beam.

[0260] As a sub-example of this embodiment, the advantage of the second RS resource set including only CSI-RS is that using different types of RS resources is beneficial to improving measurement accuracy.

[0261] As one embodiment, the first RS resource set includes CSI-RS.

[0262] As a sub-implementation of this embodiment, the second RS resource set includes CSI-RS.

[0263] As a sub-example of this embodiment, the second RS resource set does not include CSI-RS.

[0264] As a sub-implementation of this embodiment, the second RS resource set includes SSB.

[0265] As a sub-example of this embodiment, the advantage of including CSI-RS in the second RS resource set is that measurement accuracy can be improved by performing more intensive measurements, including improving the accuracy of RLF prediction.

[0266] As a sub-example of this embodiment, the advantage of the second RS resource set including only SSB is that using different types of RS resources is beneficial to improving measurement accuracy.

[0267] As an example, the first RS resource set and the second RS resource set are orthogonal in the time domain.

[0268] As an example, the second RS resource set is a PRS (positioning RS).

[0269] As a sub-implementation of this embodiment, the advantage of the second RS resource set being PRS is that it can be located when the predicted RLF occurs with a high probability within the first time window, which is beneficial for wireless network optimization.

[0270] As an example, the periods of the first RS resource set and the second RS resource set are different.

[0271] As an example, the first RS resource set and the second RS resource set are associated with different PCIs.

[0272] As an example, the advantage of associating the first RS resource set and the second RS resource set with different PCIs is that it facilitates early monitoring of other PCI signals when predicting a high probability of RLF, thereby improving communication reliability.

[0273] As an example, the first measurement is for performing a first RLF prediction.

[0274] As an example, the first RLF prediction is based on the result given by the first measurement.

[0275] As an example, the results given by the first measurement include measurement results for the first RS resource set.

[0276] As an example, the measurement results for the first RS resource set include: at least one measurement value for the first RS resource set.

[0277] As an example, the measurement results for the first RS resource set include at least one measurement value for each reference signal of the first RS resource set.

[0278] As an example, the second RLF prediction is based on the results given by the second measurement.

[0279] As an example, the results given by the second measurement include measurement results for the first RS resource set.

[0280] As an example, the results given by the second measurement include measurement results for the first RS resource set and the second RS resource set.

[0281] As an example, the measurement results for the first RS resource set and the second RS resource set include: at least one measurement value for at least one reference signal for the first RS resource set and the second RS resource set.

[0282] As an example, the measurement results for the first RS resource set and the second RS resource set include: at least one measurement value for each reference signal in the first RS resource set and the second RS resource set.

[0283] As an example, the at least one measurement value is only one measurement value.

[0284] As an example, the at least one measurement value is a plurality of measurement values.

[0285] As an example, the multiple measurements were obtained at different times.

[0286] As an example, the multiple measurements are obtained at the same time.

[0287] As an example, the measured value is RSRP (Reference Signal Receiving Power).

[0288] As an example, the measured value is RSRQ (Reference Signal Receiving Power).

[0289] As an example, the measured value is SINR (Signal to Interference plus NoiseRatio).

[0290] As an example, the second measurement is to determine whether an RLF has occurred, thus verifying the second RLF prediction.

[0291] As an example, the second measurement determines that the RLF has occurred by determining that the number of consecutive out-of-step indications within a first time window reaches a third threshold.

[0292] As an example, the second measurement determines that the RLF has not occurred by determining that the number of consecutive synchronization indications within the first time window reaches a fourth threshold.

[0293] As an example, optionally, the terminal U01 sends first UE capability information (the appendix). Figure 5 (not shown in the image); wherein, the first UE capability information indicates that the terminal U01 supports RLF prediction.

[0294] As an example, the first UE capability information includes an RRC message; the RRC message indicates that the terminal U01 supports RLF prediction.

[0295] As an example, the first UE capability information includes an RRC message and a MAC CE; the RRC message indicates multiple UE capabilities; the MAC CE indicates from the multiple UE capabilities that the terminal U01 supports RLF prediction.

[0296] As an example, the RRC message is a UECapabilityInformation message.

[0297] As an example, the RRC message is a UEAssistanceInformation message.

[0298] As one embodiment, the first UE capability information indicates the parameters of at least one smart model supported by the terminal U01; optionally, the parameters may be the identifier of the smart model, the type of the smart model, the function of the smart model, etc.

[0299] As a sub-example of the above embodiment, one of the intelligent models supported by the terminal U01 is an intelligent model used for RLF prediction.

[0300] As an example, the first UE capability information indicates that the terminal U01 supports RLF prediction.

[0301] As an example, the first UE capability information indicates the RLF prediction from the RLF prediction and the HOF prediction.

[0302] As an example, the first UE capability information indicates both the RLF prediction and the HOF prediction from the RLF prediction and the HOF prediction.

[0303] As one example, the RLF prediction is performed by the terminal's intelligent module.

[0304] As an example, the relaxed measurement state is a measurement performed at a longer interval compared to the non-relaxed measurement state.

[0305] As an example, the relaxed measurement state means that measurements are not performed on some cells.

[0306] As an example, being in a relaxed measurement state means entering a relaxed measurement state.

[0307] As an example, "not in a relaxed measurement state" means leaving the relaxed measurement state.

[0308] As one embodiment, controlling the first timer means maintaining the first timer.

[0309] As one embodiment, controlling the first timer means changing the first timer.

[0310] As an example, starting the first timer means starting the first timer from 0.

[0311] As an example, starting the second timer means starting the second timer from 0.

[0312] As an example, stopping the first timer means ceasing to count down the first timer.

[0313] As an example, stopping the second timer means ceasing to count down the second timer.

[0314] As one example, the rollback first timer is the first timer decremented from its current value.

[0315] As an example, the speed of the first rollback timer is the same as the speed of the first timing timer.

[0316] As an example, the first timer expires when the value of the first timer is greater than a first time threshold.

[0317] As an example, the unit of the first time threshold is milliseconds (ms).

[0318] As an example, the first time threshold is configured by the network.

[0319] As an example, the first time threshold is a key configuration.

[0320] As an example, the first time threshold is predetermined.

[0321] As an example, the first time threshold is temporarily agreed upon.

[0322] As an example, the size of the first time threshold is related to the duration of the first time window.

[0323] As one example, the magnitude of the first time threshold depends on the duration of the first time window.

[0324] As an example, the first time threshold is less than the duration of the first time window.

[0325] As an example, the first time threshold is proportional to the duration of the first time window.

[0326] As an example, the expiration trigger of the first timer to enter the relaxed measurement state means that the relaxed measurement state is entered immediately when the first timer expires.

[0327] As an example, the expiration trigger of the first timer to enter the relaxed measurement state means entering the relaxed measurement state after a period of time following the expiration of the first timer.

[0328] As a sub-example of the above embodiment, the period of time is for further observation of the network environment to ensure that entering the relaxed measurement state will not affect the communication quality.

[0329] As an example, the method introduces a first notification within the terminal, which is simple to implement.

[0330] As an example, the method is conducive to standardization.

[0331] As an example, the method facilitates the separation of RLF detection and RLF prediction.

[0332] As one embodiment, the smart module sends the first notification; the legitimate module receives the first notification.

[0333] As an example, the first notification is sent when an RLF is predicted.

[0334] As an example, the first notification is sent when the probability of the predicted RLF is non-zero.

[0335] As an example, when it is predicted that an RLF will occur within the first time window, the first notification is sent.

[0336] As an example, the first notification is sent when the number of consecutive predictions that have lost synchronization within the first time window reaches a third threshold.

[0337] As an example, the first notification is an indication.

[0338] As an example, the first notification is a notification.

[0339] As an example, the first notification is a cross-layer indication.

[0340] As an example, the first notification is a cross-entity instruction.

[0341] As one example, the first notification is within the terminal U01.

[0342] As an example, the first notification is sent and received within the terminal U01.

[0343] As an example, the first notification instructs the predicted RLF to be generated.

[0344] As an example, the first notification indicates the predicted probability of an RLF occurring.

[0345] As an example, the first notification indicates that an RLF will occur within the first time window.

[0346] As an example, the first notification indicates the probability of an RLF occurring within the first time window.

[0347] As an example, the first notification indicates that the number of consecutive predicted out-of-step indications within the first time window reaches a third threshold.

[0348] As an example, the first notification indicates the probability that the number of consecutive predicted out-of-step indications within the first time window reaches a third threshold.

[0349] As an example, predicting an RLF includes: predicting that an RLF will occur within a first time window.

[0350] As an example, predicting RLF means predicting that an RLF will occur within a first time window.

[0351] As an example, the prediction that RLF will occur within the first time window means that the number of consecutive predictions that fail to synchronize within the first time window reaches a third threshold.

[0352] As an example, the failure to predict an RLF includes: predicting that an RLF will not occur within a first time window.

[0353] As an example, "not predicted RLF" means that it is predicted that RLF will not occur within the first time window.

[0354] As an example, the prediction that RLF will occur within the first time window means that the number of consecutive prediction synchronization indications within the first time window reaches a fourth threshold.

[0355] As one embodiment, the first time window includes a predicted time interval.

[0356] As one embodiment, the first time window includes a time interval after the current moment.

[0357] As an example, the first time window is the expected running time of a timer.

[0358] As an example, the first time window is the running time of a timer.

[0359] As an example, the first time window is the remaining time of T310.

[0360] As one embodiment, the second RLF prediction includes predicting the probability of an RLF occurring within a second time window.

[0361] As an example, the length of the second time window is determined by the duration during which the terminal is in a relaxed measurement state.

[0362] As an example, the length of the second time window is no greater than the duration during which the terminal is in the relaxed measurement state.

[0363] As an example, the length of the second time window is determined by the duration of time the terminal leaves the relaxation measurement state.

[0364] As an example, the length of the second time window is no greater than the duration during which the terminal leaves the relaxed measurement state.

[0365] As an example, the length of the second time window is determined by the probability predicted by the first RLF.

[0366] As an example, the length of the second time window is determined by the probability predicted based on the first RLF.

[0367] As an example, the probability predicted by the first RLF remains unchanged within the second time window.

[0368] As an example, the probabilities predicted by the first RLF are different for different second time windows.

[0369] As an example, the interval between the first time window and the second time window is equal to the least common multiple of the period of the first RS resource set and the period of the second RS resource set.

[0370] As an example, the advantage of the above method is that it can make full use of the first RS resources and the second RS resources for measurement, and obtain more comprehensive measurement results.

[0371] As one embodiment, the first RS resource set and the second RS resource set are associated with or correspond to the same reference signal resource index.

[0372] As an example, the advantage of the above method is that it can flexibly control which RS resources corresponding to an RS resource index belong to the first RS resource set and which belong to the second RS resource set.

[0373] As one embodiment, the first RS resource set and the second RS resource set are associated with or correspond to different reference signal resource indices.

[0374] As an example, the advantage of the above method is that it can increase the independence of the first RS resource set and the second RS resource set, thereby improving the accuracy of the measurement results.

[0375] Example 6

[0376] Example 6 illustrates a schematic diagram of a predicted RLF dependency measurement according to an embodiment of this application, as shown in the attached diagram. Figure 6 As shown. In the appendix Figure 6 In the diagram, the dashed arrow indicates that the second measurement is not necessarily for the second set of measurements.

[0377] In Embodiment 6, a first signaling is received, indicating a first threshold; a first measurement is performed; the first measurement is performed on a first RS resource set; a first RLF prediction is performed, the first RLF prediction including predicting the probability of an RLF occurring within a first time window; the performance of the first RLF prediction depends on the first measurement; a second measurement is performed; the second measurement depends on the predicted probability of an RLF occurring within the first time window; wherein, the meaning of the second measurement 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 second measurement is performed on the first RS resource set and the second RS resource set; when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the second measurement is performed on the first RS resource set; wherein, the length of the first time window is finite. A second RLF prediction is performed, the second RLF prediction depending on the second measurement; the second RLF prediction includes predicting the probability of an RLF occurring within a second time window; wherein, the first RS resource set and the second RS resource set are associated with different PCIs; the length of the second time window is finite; the different PCIs all belong to the serving cell of the terminal.

[0378] As an example, the prediction of the probability of RLF occurring within the first time window includes: the probability that the predicted number of out-of-step indications reaches the third threshold when the number of consecutive out-of-step indications within the first time window reaches the fifth threshold.

[0379] As an example, the fifth threshold is configured by the network.

[0380] As an example, the first signaling indicates a fifth threshold.

[0381] As an example, the fifth threshold is a constant N310.

[0382] As an example, the third threshold is configured by the network.

[0383] As an example, the third threshold is configured by the terminal.

[0384] As an example, the third threshold is determined by the fifth threshold.

[0385] As an example, the third threshold is equal to the fifth threshold.

[0386] As an example, the third threshold is less than the fifth threshold.

[0387] As an example, the advantage of having a third threshold that is less than the fifth threshold is that it enables earlier prediction of RLF, allowing the terminal to prepare in advance.

[0388] As an example, the third threshold is greater than the fifth threshold.

[0389] As an example, the advantage of having a third threshold greater than the fifth threshold is that it makes the predicted RLF results more reliable.

[0390] As one embodiment, the first RS resource set and the second RS resource set are obtained by receiving at least a first reference signal.

[0391] As one embodiment, receiving at least a first reference signal includes receiving the first reference signal at each available reception opportunity for each of the at least first reference signals.

[0392] As one embodiment, receiving at least a first reference signal includes receiving the first reference signal at at least one available reception opportunity for each of the at least first reference signals.

[0393] As an example, the available reception timing of a reference signal is the time-frequency resource configured for the reference signal.

[0394] As an example, an available reception opportunity for a reference signal is a time-frequency resource configured for the reference signal and not overlapping with a specified time-frequency resource.

[0395] As a sub-implementation of the above embodiments, the specified time-frequency resources include active measurement gaps.

[0396] As a sub-implementation of the above embodiments, the transmission on the specified time-frequency resource has a higher priority than the reference signal.

[0397] As an example, the sender of the at least first reference signal is the cell to which the RLF prediction is targeted.

[0398] As an example, the at least first reference signal is a physical layer signal.

[0399] As one embodiment, the at least first reference signal is a reference signal.

[0400] As an example, any one of the at least first reference signals is used for RLF prediction.

[0401] As an example, any one of the at least first reference signals is used for RLM (RadioLinkMonitoring).

[0402] As an example, any one of the at least first reference signals is used for RLM and RLF prediction.

[0403] As an example, the at least first reference signal is a downlink (DL) signal.

[0404] As an example, the at least first reference signal is a sidelink (SL).

[0405] As an example, the at least first reference signal is periodic.

[0406] As an example, the at least first reference signal is semi-continuous.

[0407] As one embodiment, the at least first reference signal includes a synchronization signal.

[0408] As an example, each of the at least first reference signals is an SSB (Synchronization Signal Block, or SS / PBCH).

[0409] As an example, the at least first reference signal is a synchronization signal.

[0410] As an example, each of the at least first reference signals is a CSI-RS (Channel State Information Reference Signal).

[0411] As an example, the at least first reference signal is indicated by RRC signaling.

[0412] As an example, the at least first reference signal is determined by the terminal U01.

[0413] As one embodiment, the at least first reference signal is the first reference signal.

[0414] As one embodiment, the at least first reference signal is a plurality of reference signals; the plurality of reference signals includes at least the first reference signal.

[0415] As an example, the number of reference signals is no more than M1; where M1 is a positive integer.

[0416] As an example, M1 depends on L_max in TS 38.213.

[0417] As an example, M1 is N_RLM in TS 38.213.

[0418] As one example, M1 is 2, 4, or 8.

[0419] As an example, M1 is not greater than 8.

[0420] As an example, M1 is no greater than 16.

[0421] As an example, the number of reference signal resources included in the at least first reference signal resource depends on the predicted probability of an RLF occurring within the first time window.

[0422] As an example, if the probability of an RLF occurring within the first time window is predicted to be very small, the number of reference signal resources included in the at least first reference signal resource is a subset of the at least first reference signal resources.

[0423] As a sub-example of the above embodiments, the probability of RLF occurring is very small, meaning it is less than a first threshold.

[0424] As a sub-example of the above embodiments, the probability of RLF occurring is very small, meaning it is less than a predetermined value.

[0425] As a sub-implementation of the above embodiments, the portion at least the first reference signal refers to at least the first reference signal that is sparser in time and frequency.

[0426] As a sub-implementation of the above embodiments, the portion of at least the first reference signal refers to a smaller number of at least the first reference signals.

[0427] As an example, the fourth threshold depends on the third threshold.

[0428] As an example, the fourth threshold is linearly related to the third threshold.

[0429] As an example, the predicted out-of-synchronization indication depends on the predicted link quality for the at least first reference signal.

[0430] As an example, the predicted link quality is RSRP (Reference Signal Receiving Power).

[0431] As an example, the predicted link quality is RSRQ (Reference Signal Receiving Quality).

[0432] As an example, the predicted link quality is BLER (BlockErrorRatio).

[0433] As an example, the predicted link quality is SINR (Signal to Interference plus Noise Ratio).

[0434] As an example, the predicted link quality is obtained by L1 filtering or L3 filtering.

[0435] As an example, the predicted link quality is unfiltered.

[0436] As an example, the predicted link quality is a new quantity.

[0437] As an example, if the predicted link quality for each of the at least first reference signals is worse than a first predicted out-of-sync threshold within a predicted out-of-sync evaluation period, an out-of-sync indication is predicted.

[0438] As an example, the first prediction out-of-sync threshold is Qout configured by rlmInSyncOutOfSyncThreshold.

[0439] As an example, the first prediction out-of-sync threshold is not the Qout configured by rlmInSyncOutOfSyncThreshold.

[0440] As an example, the predicted synchronization indication depends on the predicted link quality for the at least first reference signal.

[0441] As an example, if the predicted link quality for at least one of the at least first reference signals is better than a first predicted synchronization threshold within a predicted synchronization evaluation period, a synchronization indication is predicted.

[0442] As an example, the first predicted synchronization threshold is Qin, which is configured by rlmInSyncOutOfSyncThreshold.

[0443] As an example, the first predicted synchronization threshold is not the Qin configured by rlmInSyncOutOfSyncThreshold.

[0444] As an example, the timing of the prediction and evaluation is only for clarity and may not exist in actual applications.

[0445] As an example, at each prediction evaluation time, there is a prediction synchronization indicator or a prediction desynchronization indicator.

[0446] As an example, at a prediction evaluation time, there may be neither a prediction synchronization indication nor a prediction out-of-sync indication.

[0447] As an example, the length of the prediction synchronization evaluation period is equal to the length of the prediction out-of-sync evaluation period.

[0448] As an example, the length of the prediction synchronization evaluation period and the length of the prediction out-of-sync evaluation period are not equal.

[0449] As an example, the length of the prediction synchronization evaluation period is the length of the synchronization evaluation period.

[0450] As an example, the length of the prediction synchronization evaluation period is not the length of the synchronization evaluation period.

[0451] As an example, the length of the prediction indication period is equal to the length of the indication period.

[0452] As an example, the length of the synchronous evaluation period is referenced to TEvaluate_in_SSB in TS 38.133.

[0453] As an example, the length of the out-of-step evaluation period is referenced to TEvaluate_out_SSB in TS 38.133.

[0454] As an example, the length of the synchronization evaluation period is equal to the length of the out-of-step evaluation period.

[0455] As an example, the length of the synchronization evaluation period and the length of the out-of-step evaluation period are not equal.

[0456] As an example, the above method can maintain the continuity of the predicted synchronization indication and / or the predicted out-of-synchronization indication on the basis of the synchronization indication and / or out-of-synchronization indication.

[0457] As one example, the length of the prediction indication period is not equal to the length of the indication period.

[0458] As an example, the above method is independent of synchronization indicators and / or out-of-sync indicators, making predictions more flexible.

[0459] As an example, this embodiment does not limit the position of the evaluation timing within the prediction indication period.

[0460] As an example, the timing of the prediction evaluation is only to clearly illustrate that the evaluation is performed once every prediction indication period to determine whether there is a prediction out-of-sync indication or a prediction synchronization indication. In practical applications, no specific restrictions are imposed.

[0461] As an example, the predicted RLF depends on the number of predicted synchronization indications within a first time window.

[0462] As an example, the time periods of the different first time windows do not overlap.

[0463] As an example, the different first time windows predict different probabilities of RLF occurring.

[0464] As an example, the duration of the different first time windows is the same.

[0465] As an example, the time periods of the different second time windows do not overlap.

[0466] As an example, the different second time windows predict different probabilities of RLF occurring.

[0467] As an example, the duration of the different second time windows is the same.

[0468] Example 7

[0469] Example 7 illustrates a schematic diagram of controlling a first timer according to an embodiment of this application, as shown in the attached diagram. Figure 7 As shown. In the appendix Figure 7 In the diagram, the square containing "entering" represents the time period during which the relaxation measurement state is entered, and the square containing "leaving" represents the time period during which the relaxation measurement state is left.

[0470] In Embodiment 7, a first timer is controlled; the control of the first timer includes starting the first timer when entering a relaxed measurement state, and rewinding the first timer when leaving the relaxed measurement state; the rewinding stops when the first timer rewinds to 0; wherein, the expiration of the first timer triggers entry into the relaxed measurement state.

[0471] In Example 7, when the first timer is running, entering the relaxation measurement state will not restart the first timer; when the first timer is rolling back, entering the relaxation measurement state will restart the first timer.

[0472] As an example, the advantage of the above method is that it improves the sensitivity of controlled relaxation measurement.

[0473] As an example, when the first timer is started and running, it continues to count when it enters the relaxed measurement state.

[0474] As an example, when the first timer rolls back, entering the relaxation measurement state will stop the rollback of the first timer and restart the first timer.

[0475] As an example, when the first timer stops or expires, it is restarted when entering the relaxation measurement state.

[0476] As an example, the first timer is started when the relaxation measurement state is first entered.

[0477] As an example, the first entry into the relaxed measurement state is the first entry into the relaxed measurement state after entering the connected state.

[0478] As an example, the first entry into the relaxation measurement state refers to the first entry into the relaxation measurement state after the first timer expires.

[0479] As an example, the first entry into the relaxation measurement state refers to the first entry into the relaxation measurement state after the first timer has rolled back.

[0480] As an example, the start of the first timer is from 0.

[0481] The first timer expires when its value exceeds the first time threshold.

[0482] As an example, the first time threshold is configured by the network.

[0483] As an example, the first time threshold is the default.

[0484] As an example, the duration of each time period for entering or leaving the relaxation measurement is different.

[0485] As an example, the duration of each time period for entering or leaving relaxation measurement depends on the probability of predicted RLF.

[0486] As an example, the lower the predicted probability of RLF occurring, the longer the time period for entering relaxation measurement.

[0487] As an example, the higher the predicted probability of RLF occurring, the shorter the duration of the relaxation measurement period.

[0488] As an example, the lower the predicted probability of RLF, the shorter the time period away from the relaxation measurement.

[0489] As an example, the greater the predicted probability of RLF occurring, the longer the time period away from the relaxation measurement.

[0490] As an example, the first time window refers to the duration of the time period during which relaxation measurement is performed.

[0491] As an example, the first time window refers to the duration of the period away from the relaxation measurement.

[0492] As an example, the probability of predicting the occurrence of RLF within the first time window remains unchanged.

[0493] Example 8

[0494] Example 8 illustrates a schematic diagram of the probability change of RLF occurring within a time window according to an embodiment of this application, as shown in the attached diagram. Figure 8 As shown. In the appendix Figure 7 In the diagram, the horizontal length of the box represents the duration of the first time window, and the probability within the box represents the probability of an RLF occurring within the first time window.

[0495] In Example 8, 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 at least the former of a first RS resource set and a second RS resource set.

[0496] In Example 8, the length of the measurement period depends on the probability of the predicted RLF occurring within the first time window.

[0497] As one embodiment, the time window includes a first time window and a second time window.

[0498] As an example, the probability of the first RLF prediction occurring within the first time window refers to 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.

[0499] As an example, the probability of the second RLF prediction occurring within the second time window refers to the probability that the number of at least one of consecutive out-of-step indications and predicted out-of-step indications within the second time window reaches a third threshold.

[0500] As an example, the probability of an RLF occurring within the first time window is related to the duration of the first time window.

[0501] As an example, the probability of an RLF occurring within the first time window is increasing.

[0502] As an example, the longer the duration of the first time window, the greater the probability of an RLF occurring within the first time window.

[0503] As an example, for two completely non-overlapping first time windows, the probability of an RLF occurring within the first time window is independent of the duration of the two first time windows.

[0504] As an example, for two completely overlapping first time windows, the longer the duration of the first time window, the greater the probability of RLF occurring within the first time window.

[0505] As an example, complete overlap means that the time period in which one first time window is located is completely covered by another first time window.

[0506] As an example, when the duration of the first time window is sufficiently large, the probability of RLF occurring within the first time window is 100%.

[0507] As an example, the duration of the first time window is sufficiently large, meaning that the duration of the first time window can include the moment when the RLF occurs.

[0508] As an example, when the duration of the first time window is sufficiently small, the probability of an RLF occurring within the first time window is 0%.

[0509] As an example, the duration of the first time window is sufficiently small, meaning that the duration of the first time window cannot include the moment when an RLF occurs.

[0510] As an example, the probability of RLF occurring within the second time window is related to the duration of the second time window.

[0511] As an example, the probability of RLF occurring within the second time window is increasing.

[0512] As an example, the longer the duration of the second time window, the greater the probability of an RLF occurring within the second time window.

[0513] As an example, for two completely non-overlapping second time windows, the probability of an RLF occurring within the second time window is independent of the duration of the two second time windows.

[0514] As an example, for two completely overlapping second time windows, the longer the duration of the first time window, the greater the probability of RLF occurring within the second time window.

[0515] As an example, complete overlap means that the time period in which one second time window is located is completely covered by another second time window.

[0516] As an example, when the duration of the second time window is sufficiently large, the probability of an RLF occurring within the second time window is 100%.

[0517] As an example, the duration of the second time window is sufficiently large means that the duration of the second time window can include the moment when the RLF occurs.

[0518] As an example, when the duration of the second time window is sufficiently small, the probability of an RLF occurring within the second time window is 0%.

[0519] As an example, the duration of the second time window is sufficiently small, meaning that the duration of the second time window cannot include the moment when the RLF occurs.

[0520] As an example, the third threshold is indicated by the network.

[0521] As an example, the third threshold is indicated by the first signaling.

[0522] As an example, the third threshold is pre-configured.

[0523] As an example, the third threshold is the default.

[0524] As an example, the prediction of the probability of an RLF occurring within the first time window includes the probability that the number of consecutive out-of-step indications within the first time window reaches a third threshold.

[0525] As an example, the probability of predicting that RLF will occur within the first time window includes the probability that the number of predicted out-of-step indications within the first time window reaches a third threshold.

[0526] As an example, the prediction of the probability of an RLF occurring within the second time window includes the probability that the number of consecutive out-of-step indications within the second time window reaches a third threshold.

[0527] As an example, the probability of predicting that RLF will occur within the second time window includes the probability that the number of predicted out-of-step indications within the second time window reaches a third threshold.

[0528] As an example, the predicted RLF depends on the number of predicted synchronization indications within a first time window.

[0529] As an example, the predicted synchronization indication depends on the predicted link quality for the at least first reference signal.

[0530] As an example, if the predicted link quality for at least one of the at least first reference signals is better than a first predicted synchronization threshold within a predicted synchronization evaluation period, a synchronization indication is predicted.

[0531] As an example, the first predicted synchronization threshold is Q configured by rlmInSyncOutOfSyncThreshold. in .

[0532] As an example, the first predicted synchronization threshold is not the Q value configured by rlmInSyncOutOfSyncThreshold. in .

[0533] As an example, the predicted RLF depends on the number of out-of-step indications predicted within a first time window.

[0534] As an example, if the predicted link quality for each of the at least first reference signals is worse than a first predicted out-of-sync threshold within a predicted out-of-sync evaluation period, an out-of-sync indication is predicted.

[0535] As an example, the first prediction out-of-sync threshold is configured by Q_rmInSyncOutOfSyncThreshold. out .

[0536] As an example, the first prediction out-of-sync threshold is not the Q value configured by rlmInSyncOutOfSyncThreshold. out .

[0537] As one embodiment, the predicted out-of-step indication depends on measurements in at least the first RS resource set and the second RS resource set, including: the out-of-step indication predicted by the first RLF depends on measurements in the first RS resource set.

[0538] As one embodiment, the predicted out-of-step indication depends on measurements of at least the former of the first RS resource set and the second RS resource set, including: the predicted out-of-step indication by the second RLF depends on measurements of at least the former of the first RS resource set and the second RS resource set.

[0539] As one embodiment, the step loss indication of the second RLF prediction depends on the measurement of at least the former of the first RS resource set and the second RS resource set, including: when the probability of RLF occurring within the first time window predicted by the first RLF is greater than a first threshold, the step loss indication of the second RLF prediction depends on the measurement of the first RS resource set and the second RS resource set.

[0540] As an example, the advantage of the above method is that more reference signals are used for measurement when the RLF probability is high, and the second RLF prediction is more accurate and reliable.

[0541] As one embodiment, the step loss indication of the second RLF prediction depends on the measurement of at least the first RS resource set and the second RS resource set, including: when the probability of RLF occurring within the first time window predicted by the first RLF is not greater than a first threshold, the step loss indication of the second RLF prediction depends on the measurement of the first RS resource set.

[0542] As an example, the advantages of the above method are that it can save measurement resources, reduce signaling overhead, and reduce attack rate loss.

[0543] As an example, the length of the measurement period depends on the probability of RLF occurring within the first time window, meaning that the length of the measurement period is related to the duration during which the probability of RLF occurring within the first time window remains unchanged.

[0544] As a sub-implementation of the above embodiments, the longer the duration during which the probability of RLF occurring within the first time window remains unchanged according to the first RLF prediction, the longer the measurement period.

[0545] As a sub-implementation of the above embodiments, the shorter the duration during which the probability of RLF occurring within the first time window remains unchanged according to the first RLF prediction, the shorter the measurement period.

[0546] As a sub-implementation of the above embodiments, the measurement period is equal to the duration during which the probability of RLF occurring within the first time window remains unchanged as predicted by the first RLF.

[0547] 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.

[0548] 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.

[0549] As a sub-example of the above embodiments, the fixed constant is agreed upon or defaulted to in advance.

[0550] Example 9

[0551] Example 9 illustrates a structural block diagram of a processing device for a terminal according to an embodiment of this application; as shown in the appendix. Figure 9 As shown. In the appendix Figure 9 In the terminal, the processing device 900 includes a first receiver 901 and a first processor 902.

[0552] In embodiment 9, the terminal includes: one or more processors and a memory;

[0553] 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:

[0554] A first receiver 901 receives a first signaling, the first signaling indicating a first threshold; performs a first measurement; the first measurement is performed on a first RS resource set; performs a first RLF prediction, the first RLF prediction including predicting the probability of an RLF occurring within a first time window; the performance of the first RLF prediction depends on the first measurement; performs a second measurement; the second measurement depends on the predicted probability of an RLF occurring within the first time window; wherein, the meaning of the second measurement 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 second measurement is performed on the first RS resource set and a second RS resource set; when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the second measurement is performed on the first RS resource set; wherein, the length of the first time window is finite.

[0555] As an example, the second measurement is to determine the RLF.

[0556] As one embodiment, the first processor 902 performs a second RLF prediction, which depends on the second measurement; the second RLF prediction includes predicting the probability of an RLF occurring within a second time window; wherein the first RS resource set and the second RS resource set are associated with different PCIs; the length of the second time window is finite; and the different PCIs all belong to the serving cell of the terminal.

[0557] As one embodiment, whether the state of relaxation measurement depends on a second RLF prediction, including at least one of: entering the relaxation measurement state when the predicted probability of an RLF within a first time window is not greater than a second threshold, and leaving the relaxation measurement state when the predicted probability of an RLF within a first time window is greater than the second threshold.

[0558] As one embodiment, a first timer is controlled; the control of the first timer includes starting the first timer when entering a relaxed measurement state, and rewinding the first timer when leaving the relaxed measurement state; the rewinding stops when the first timer rewinds to 0; wherein, the expiration of the first timer triggers entry into the relaxed measurement state.

[0559] As an example, the length of the measurement period depends on the probability of an RLF occurring within the first time window.

[0560] 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 at least the former of a first RS resource set and a second RS resource set.

[0561] As one embodiment, a first notification is sent; the first notification is received; wherein the prediction of RLF depends on the first notification.

[0562] As an example, when the first timer is running, entering the relaxation measurement state will not restart the first timer; when the first timer is rolled back, entering the relaxation measurement state will restart the first timer.

[0563] As one example, the terminal is a user equipment (UE).

[0564] As an example, the terminal is one that supports large latency differences.

[0565] As an example, the terminal is an NTN-enabled terminal.

[0566] As one example, the terminal is an aircraft or a ship.

[0567] As one example, the terminal is a mobile phone or a vehicle-mounted terminal.

[0568] As an example, the terminal is an Internet of Things (IoT) terminal or an Industrial Internet of Things (IIoT) terminal.

[0569] As an example, the terminal is a device that supports low-latency, high-reliability transmission.

[0570] 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.

[0571] As one embodiment, the first processor 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.

[0572] Example 10

[0573] 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 the attached diagram. Figure 11 As shown. In the appendix Figure 10 In the base station, the processing device 1000 includes a second transmitter 1001 and a second receiver 1002.

[0574] 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,

[0575] The second transmitter 1001 transmits a first signaling, the first signaling indicating a first threshold; performs a first measurement; the first measurement is for a first RS resource set; performs a first RLF prediction, the first RLF prediction including predicting the probability of an RLF occurring within a first time window; the execution of the first RLF prediction depends on the first measurement; performs a second measurement; the second measurement depends on the predicted probability of an RLF occurring within the first time window; wherein, the meaning of the second measurement 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 second measurement is for the first RS resource set and the second RS resource set; when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the second measurement is for the first RS resource set; wherein, the length of the first time window is finite.

[0576] As an example, the second measurement is to determine the RLF.

[0577] As an example, a second RLF prediction is performed, which depends on the second measurement; the second RLF prediction includes predicting the probability of an RLF occurring within a second time window; wherein the first RS resource set and the second RS resource set are associated with different PCIs; the length of the second time window is finite; and the different PCIs all belong to the serving cell of the terminal.

[0578] As one embodiment, whether the state of relaxation measurement depends on a second RLF prediction, including at least one of: entering the relaxation measurement state when the predicted probability of an RLF within a first time window is not greater than a second threshold, and leaving the relaxation measurement state when the predicted probability of an RLF within a first time window is greater than the second threshold.

[0579] As one embodiment, a first timer is controlled; the control of the first timer includes starting the first timer when entering a relaxed measurement state, and rewinding the first timer when leaving the relaxed measurement state; the rewinding stops when the first timer rewinds to 0; wherein, the expiration of the first timer triggers entry into the relaxed measurement state.

[0580] As an example, the length of the measurement period depends on the probability of an RLF occurring within the first time window.

[0581] 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 at least the former of a first RS resource set and a second RS resource set.

[0582] As one embodiment, a first notification is sent; the first notification is received; wherein the prediction of RLF depends on the first notification.

[0583] As an example, when the first timer is running, entering the relaxation measurement state will not restart the first timer; when the first timer is rolled back, entering the relaxation measurement state will restart the first timer.

[0584] As one example, the base station is a satellite.

[0585] As an example, the base station is a terrestrial base station.

[0586] As one example, the base station is a relay.

[0587] As one example, the base station is an access point.

[0588] 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.

[0589] 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.

[0590] Example 11

[0591] Example 11 illustrates a schematic diagram of the transmission of a first notification according to an embodiment of this application, as shown in the attached diagram. Figure 11 As shown. Terminal 1100 includes a smart module 1101 and a legacy module 1102.

[0592] 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.

[0593] As one embodiment, performing RLF prediction includes sending the first notification.

[0594] 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.

[0595] As one embodiment, performing RLF prediction includes sending the first notification and receiving the first notification.

[0596] As an example, the first notification includes relevant information about the predicted RLF event.

[0597] As a sub-implementation of the above embodiments, the above method avoids triggering unreasonable UE behavior by updating prediction information.

[0598] As a sub-implementation of the above embodiments, the above method is beneficial for UE decision-making.

[0599] 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.

[0600] 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.

[0601] As a sub-implementation of the above embodiments, the instruction indicates the first notification.

[0602] As a sub-implementation of the above embodiments, the instruction includes the first notification.

[0603] As an example, the legal module is logical.

[0604] As an example, the legitimate module is physical.

[0605] As an example, the legal module, in response to the receipt of the first notification, records the prediction result for the RLF event.

[0606] As an example, the legitimate module is a protocol entity.

[0607] As an example, the legitimate module is an RRC protocol entity.

[0608] As an example, the legitimate module is located in the RRC sublayer.

[0609] As one example, the legitimate module is located at a higher level than the RRC sublayer.

[0610] As one example, the legitimate module is located at a lower level than the RRC sublayer.

[0611] As an example, the legitimate module supports 3GPP Release 17.

[0612] As an example, the legitimate module supports 3GPP Release 18.

[0613] As an example, the legitimate module does not have either training or inference functions.

[0614] As an example, the legal module is not a smart module.

[0615] As one example, the smart module is a piece of hardware.

[0616] As one example, the intelligent module is software.

[0617] As an example, the smart module is a program.

[0618] As an example, the intelligent module is a function.

[0619] As one example, the smart module is a protocol entity.

[0620] As one example, the intelligent module is an AI entity.

[0621] As an example, the intelligent module is an ML entity.

[0622] As an example, the intelligent module is an AI / ML entity.

[0623] As one example, the intelligent module is logical.

[0624] As one example, the smart module is physical.

[0625] As an example, the intelligent module performs RLF prediction.

[0626] As one embodiment, the intelligent module processes the at least one intelligent model.

[0627] 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.

[0628] As one example, the interface between the legal module and the smart module is defined by the 3GPP protocol.

[0629] As one example, the interface between the legal module and the intelligent module is implemented based on the UE.

[0630] As one example, the interface between the legal module and the intelligent module is logical.

[0631] As one example, the interface between the legal module and the smart module is physical.

[0632] Example 12

[0633] Example 12 illustrates a schematic diagram of an intelligent model according to an embodiment of this application, as shown in the attached diagram. Figure 15 As shown. (Attached) Figure 15 It includes Module 1, Module 2, Module 3, Module 4, and Module 5.

[0634] In Example 12, in the appendix Figure 12 In the intelligent model shown, 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.

[0635] 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.

[0636] The above method avoids air interface signaling interaction and shortens transmission latency.

[0637] 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.

[0638] The above methods reduce the hardware complexity of the terminal.

[0639] 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.

[0640] The above method balances the hardware complexity of the terminal with the transmission latency.

[0641] As an example, the first module is used for data collection.

[0642] As an example, the first module is responsible for data collection.

[0643] As an example, the first module has a data collection function.

[0644] As one example, the second module has a training function.

[0645] As an example, the training function is used for model training.

[0646] As an example, the training function is responsible for model training.

[0647] As an example, the training function includes a model training function.

[0648] As an example, the training function performs model training.

[0649] As an example, the second module performs validation.

[0650] As an example, the second module performs testing.

[0651] As an example, the second module generates model performance metrics.

[0652] As one example, the second module is responsible for data preparation.

[0653] As one embodiment, the data preparation includes at least one of data pre-processing, cleaning, formatting, or transformation.

[0654] As an example, the third module has reasoning capabilities.

[0655] As an example, the inference function is used for inference.

[0656] As an example, the reasoning function is responsible for reasoning.

[0657] As one example, the fourth module is used for model storage.

[0658] As an example, the fourth module has a model storage function.

[0659] As an example, the fourth module is responsible for storing the trained model.

[0660] As an example, the fourth module is responsible for storing trained models that can be used to perform inference processing.

[0661] As one example, the fifth module is used for management.

[0662] As an example, the fifth module is responsible for management.

[0663] As one example, the fifth module has management functions.

[0664] As an example, the fifth module manages the intelligent model.

[0665] As an example, the first dataset is training data.

[0666] As an example, the first dataset is the input to the second module.

[0667] As an example, the second dataset is inference data.

[0668] As an example, the second dataset is the input to the third module.

[0669] As an example, the third dataset is monitoring data.

[0670] As an example, the third dataset is the input to the fifth module.

[0671] As an example, the first type of parameter group includes monitoring output.

[0672] As one example, the second type of parameter group includes management instructions.

[0673] As an example, the second type of parameter group is used for fine-tuning operations of the inference function.

[0674] As an example, the second type of parameter group includes the model's identifier.

[0675] As an example, the second type of parameter group is used to select the model.

[0676] As an example, the second type of parameter group is used to switch models.

[0677] As an example, the second type of parameter group is used to activate / deactivate the model.

[0678] As an example, the second type of parameter group is used to fall back to the smart model.

[0679] As an example, the third type of parameter group includes Model Transfer Request.

[0680] As an example, the third type of parameter group includes Model Delivery Request.

[0681] As an example, the fourth parameter group includes the trained model.

[0682] As an example, the fourth parameter group includes the updated model.

[0683] As an example, the fourth type of parameter group indicates the identifier of the model.

[0684] As an example, the fifth group of parameters includes model transfer.

[0685] As an example, the fifth parameter group includes Model Delivery.

[0686] As an example, the fifth group of parameters indicates the identifier of the model.

[0687] As an example, the first type of output does not exist.

[0688] As an example, the first type of output exists.

[0689] As an example, the second module sends the first type of output to the fifth module.

[0690] As an example, the first type of output includes monitoring output.

[0691] As an example, the second type of output does not exist.

[0692] As an example, the second type of output exists.

[0693] As an example, the third module sends the second type of output to the fifth module.

[0694] As an example, the second type of output includes inference output.

[0695] As an example, the second type of output is used by the fifth module to monitor the performance of the AI / ML model.

[0696] As an example, the second type of output indicates the result of performing RLF event prediction.

[0697] As an example, the second type of output indicates the predicted RLF event.

[0698] As an example, the second type of output includes relevant information about the predicted RLF event.

[0699] As an example, the first dataset in the intelligent model is configured by the network.

[0700] As an example, the first dataset in the intelligent model is determined by the terminal.

[0701] 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.

[0702] 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.

[0703] As an example, the first dataset in the intelligent model includes measurements on at least one RS resource filtered by an L1 filter.

[0704] As an example, the second dataset in the intelligent model is configured by the network.

[0705] As an example, the second dataset in the intelligent model is determined by the terminal.

[0706] 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.

[0707] 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.

[0708] As an example, the second dataset in the intelligent model includes measurements on at least one RS resource filtered by an L1 filter.

[0709] As an example, the third dataset in the intelligent model is configured by the network.

[0710] As an example, the third dataset in the intelligent model is determined by the terminal.

[0711] 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.

[0712] 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.

[0713] As an example, the third dataset in the intelligent model includes measurements on at least one RS resource filtered by an L1 filter.

[0714] As an example, RLF event prediction is performed using the intelligent model.

[0715] As an example, Example 11 is merely illustrative of how this application can be used in intelligent models. This example does not limit the application to non-intelligent operations, nor does it limit the application to other types of intelligent models to achieve and supplement [the desired results]. Figure 11 The intelligent model shown has a fairly good effect.

[0716] Example 13

[0717] Example 13 illustrates a schematic diagram of intelligent function deployment in a RAN domain according to an embodiment of this application; as shown in the appendix. Figure 13 As shown. In Example 13, the gNB can be replaced with, for example, an eNB, or a network device such as a 6G base station.

[0718] 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.

[0719] 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).

[0720] Similarly, inference functions can be deployed in cross-domain management systems or domain-specific management systems; for example, the inference function is an MDAF, or the inference function is an AnLF (Analytics logical function) located in an NWDAF.

[0721] Similarly, testing functionality can also be deployed in cross-domain management systems or domain-specific management systems.

[0722] 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.

[0723] Appendix Figure 13 In this context, the management of inference functions for multiple base stations is handled by the RAN domain management function 1303, which interacts with the RAN domain MnS (Management Service) consumer / cross-domain management 1301 (as shown in the attached diagram). Figure 13 (As shown by the dashed arrow 1308 in the image).

[0724] 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.

[0725] 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.

[0726] As an example, one of the gNBs (or base stations) in Example 13 is the base station described in this application.

[0727] As an example, Appendix Figure 13 One of the inference functions performs RLF event prediction.

[0728] Example 14

[0729] Example 14 illustrates a schematic diagram of UE smart function deployment according to an embodiment of this application; as shown in the appendix. Figure 14 As shown. (Attached) Figure 14 The training function 1405 for the RAN domain is optional.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] Optionally, the UE intelligent function 1404 also includes a CN domain training function. Figure 14 (Not included in the text).

[0734] Optionally, the UE intelligent function 1404 also includes an intelligent deployment function. Figure 14 It is not included in the list, which is used to load intelligent models and data.

[0735] 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.

[0736] As an example, the intelligent model and related metadata are loaded by the terminal from a network device or a remote server.

[0737] 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).

[0738] 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).

[0739] As an example, the intelligent model is based on a neural network.

[0740] As an example, the intelligent model is based on CNN (Conventional Neural Networks).

[0741] As an example, the smart model is based on the Transformer architecture.

[0742] As one embodiment, the terminal in this application includes an appendix. Figure 14 The reasoning function 1406 mentioned above.

[0743] As one embodiment, the first processor in this application includes an appendix. Figure 14 The reasoning function 1406 mentioned above.

[0744] As an example, Appendix Figure 2 UE201 in the middle includes appendix Figure 14 The reasoning function 1406 mentioned above.

[0745] As an example, Appendix Figure 4 The first communication device 450 in the middle includes an attachment Figure 14 The reasoning function 1406 mentioned above.

[0746] As an example, Appendix Figure 9 The first processor 902 in the process includes an appendix Figure 14 The reasoning function 1406 mentioned above.

[0747] As an example, Appendix Figure 11 The intelligent module 1101 in the middle includes an attached Figure 14 The reasoning function 1406 mentioned above.

[0748] As an example, Appendix Figure 12 The third module includes appendices. Figure 14 The reasoning function 1406 mentioned above.

[0749] As an example, Appendix Figure 14 The inference function 1406 in the above performs RLF event prediction.

[0750] As an example, Appendix Figure 14 The inference function 1406 in the inference function indicates relevant information about the predicted RLF event.

[0751] Example 15

[0752] Example 15 illustrates a flowchart based on artificial intelligence or machine learning according to an embodiment of this application; as attached. Figure 15 As shown. (Attached) Figure 15 This includes a third, fourth, fifth, sixth, and seventh operation. In Example 15, the third and fourth operations belong to the first stage, the fifth operation belongs to the second stage, the sixth operation belongs to the third stage, and the seventh operation belongs to the fourth stage. (See Appendix...) Figure 15 In the diagram, the lines with arrows indicate the sequence of processes.

[0753] 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.

[0754] As one embodiment, 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.

[0755] As an example, the first stage includes AI / ML model training.

[0756] As an example, the first stage includes AI / ML model training and AI / ML testing.

[0757] As an example, the AI / ML model training includes initial training and re-training of one or a group of AI / ML entities.

[0758] As an example, the training of the AI / ML model depends on training data.

[0759] As an example, the AI / ML model training includes AI / ML entity validation.

[0760] As an example, the AI / ML entity verification is used to evaluate the performance of the AI / ML entity.

[0761] As an example, the AI / ML entity verification relies on verification data.

[0762] As an example, if the AI / ML entity verification results do not meet expectations, the AI / ML model will be retrained.

[0763] As an example, the AI / ML testing includes testing the validated AI / ML entities to estimate the performance of the trained AI / ML model.

[0764] 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.

[0765] As an example, the AI / ML test relies on test data.

[0766] As one embodiment, the second stage includes AI / ML simulation, which performs AI / ML entity reasoning in a simulation environment.

[0767] As an example, the AI / ML simulation estimates the performance of AI / ML entity reasoning in a simulation environment before using AI / ML entities.

[0768] As one embodiment, the second stage is optional.

[0769] 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.

[0770] As an example, the third stage is optional.

[0771] As an example, the third stage is no longer needed when the training and inference functions are co-located.

[0772] As an example, the fourth stage includes AI / ML inference.

[0773] 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.

[0774] 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 the first measurement; The first measurement is performed on the first RS resource set; Perform a first RLF prediction, which includes predicting the probability of an RLF occurring within a first time window; the performance of the first RLF prediction depends on the first measurement. Perform a second measurement; the second measurement depends on the predicted probability of RLF occurring within the first time window; The second measurement depends on the predicted probability of an RLF occurring within the first time window. This includes the following meanings: when the predicted probability of an RLF occurring within the first time window is greater than a first threshold, the second measurement is applied to the first RS resource set and the second RS resource set; when the predicted probability of an RLF occurring within the first time window is not greater than the first threshold, the second measurement is applied to the first 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 second measurement is to determine the RLF.

3. The method in the terminal according to claim 1 or 2, characterized in that, include: Perform a second RLF prediction, which depends on the second measurement; the second RLF prediction includes predicting the probability of an RLF occurring within a second time window; The first RS resource set and the second RS resource set are associated with different PCIs; the length of the second time window is finite; and the different PCIs all belong to the serving cell of the terminal.

4. The method in the terminal according to claims 1 to 3, characterized in that, include: Whether the state of relaxation measurement depends on a second RLF prediction, including at least one of: entering the state of relaxation measurement when the predicted probability of an RLF within the first time window is not greater than a second threshold, and leaving the state of relaxation measurement when the predicted probability of an RLF within the first time window is greater than the second threshold.

5. The method in the terminal according to claims 1 to 4, characterized in that, include: Controlling a first timer; the control of the first timer includes starting the first timer when entering a relaxation measurement state, and rewinding the first timer when leaving the relaxation measurement state; the rewinding stops when the first timer rewinds to 0; In this case, the expiration of the first timer triggers the entry into a relaxed measurement state.

6. The method in the terminal according to claims 1 to 5, characterized in that, include: The length of the measurement period 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 at least the former of the first RS resource set and the second RS resource set.

8. The method in the terminal according to 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 claims 1 to 8, characterized in that, When the first timer is running, entering the relaxation measurement state will not restart the first timer; when the first timer is rolled back, entering the relaxation measurement state will restart the first timer.

10. A terminal, wherein, include: The terminal includes: one or more processors and memory; The memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the terminal to perform the method as described in any one of claims 1-9.

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