Method and apparatus in communication node used for wireless communication

Abstract

The invention discloses a method and an apparatus in a communication node used for wireless communication. A terminal receives a first RRC message, wherein the first RRC message configures at least one reference signal resource and a first event; wherein the first event depends on the measurement for the at least one reference signal resource, and wherein the first event depends on a first threshold value, which varies over time. According to the scheme provided by the invention, a trigger event of a dynamic threshold value can be realized, and frequent high-level signaling interaction processes can be reduced; the design of the dynamic event threshold facilitates the selection of appropriate UE behaviors according to different channel environments to balance robustness and network optimization efficiency.

Description

Technical Field

[0001] This application relates to a transmission method and apparatus in a wireless communication system, and particularly to a method and apparatus for thresholds. Background Art

[0002] With the continuous development of wireless communication, the requirements for mobility, transmission delay, and system capacity are getting higher and higher. The 3GPP RAN (Radio Access Network) #94e meeting decided to study L1 (Layer 1) / L2 (Layer 2) Triggered Mobility (LTM) in the "Further NR mobility enhancements" research project (Work Item, WI).

[0003] To enhance the measurement reporting process of LTM, 3GPP will introduce event-triggered L1 measurement reports based on events in Release 19, including aspects such as configuration methods, event definitions, and filtering methods.

[0004] With the continuous development of wireless communication, the requirements are gradually diversified. Therefore, in future evolutions, 3GPP will further enhance some key technologies. For example, applying AI (Artificial Intelligence) or ML (Machine Learning) to mobility management, reporting measurement and prediction information in advance to help the network optimize mobility. Summary of the Invention

[0005] The inventors found that the existing thresholds for events that rely on measurements of reference signal resources configured through RRC signaling are static or semi-static, and the existing threshold configuration methods are not applicable to some specific scenarios. For example, in a situation where the channel state changes continuously and dynamically, if the threshold cannot be adjusted in time, it will lead to premature or late handovers or even handover failures. Therefore, it is necessary to enhance the determination of thresholds for events that rely on measurements of reference signal resources.

[0006] In view of the above problems, this application provides a solution. Taking the threshold of an event that relies on measurements of reference signal resources as an example in the above problem description, this application is also applicable to the threshold of an event that does not rely on measurements of reference signal resources, and obtains a similar threshold of an event that relies on measurements of reference signal resources.

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

[0008] As an example, the interpretation of the terminology in this application refers to the definitions in the 3GPP specification protocol series TS38.

[0009] As an example, the interpretation of the terminology in this application refers to the definitions in the 3GPP specification protocol series TS37.

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

[0011] This application discloses a method used in a terminal, which is characterized by including:

[0012] Receiving a first RRC message, where the first RRC message configures at least one reference signal resource and a first event;

[0013] Wherein, the first event depends on the measurement for the at least one reference signal resource, and the first event depends on a first threshold, and the first threshold changes over time.

[0014] As an example, the problems to be solved by this application include: how to enhance the existing event-triggering mechanism.

[0015] As an example, the problems to be solved by this application include: how to enhance the existing event-triggered measurement reporting process.

[0016] As an example, the problems to be solved by this application include: how to enhance the existing event-triggered prediction reporting process.

[0017] As an example, the problems to be solved by this application include: how to design an event-triggering mechanism based on a dynamic threshold.

[0018] As an example, the characteristics of the above method include: the first threshold changes over time.

[0019] As an example, the advantages of the above method include: being conducive to realizing the triggering event of the dynamic threshold and being conducive to reducing the frequent high-layer signaling interaction process.

[0020] As an example, the advantages of the above method include: being conducive to making the protocol flexibly adapt to different measurement results and being conducive to balancing the network interaction delay and reliability.

[0021] As an embodiment, the advantages of the above method include: the design of the dynamic event threshold is conducive to selecting appropriate UE behaviors according to different channel environments to balance robustness and network optimization efficiency.

[0022] As an embodiment, the advantages of the above method include: the above method considers the design of the dynamic event threshold in multiple situations, which is conducive to improving the signaling interaction efficiency in different situations.

[0023] As an embodiment, the advantages of the above method include: the above method is conducive to the network optimizing subsequent mobility decisions based on measurement reports.

[0024] According to one aspect of the present application, it is characterized in that the first event depends on the measurement for the at least one reference signal resource satisfying the first threshold.

[0025] As an embodiment, the advantages of the above method include: it is conducive to reducing the modification of the existing protocol.

[0026] As an embodiment, the advantages of the above method include: it is conducive to reducing signaling interaction.

[0027] According to one aspect of the present application, it is characterized in that

[0028] The first event includes predicting a second event; the prediction of the second event depends on the measurement for the at least one reference signal resource.

[0029] As an embodiment, the advantages of the above method include: it is conducive to realizing dynamic adjustment of the threshold for event prediction.

[0030] As an embodiment, the advantages of the above method include: it is conducive to optimizing the sending timing of event prediction reports.

[0031] As an embodiment, the advantages of the above method include that it is conducive to reducing signaling interaction.

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

[0033] The fact that the first threshold changes with time means that the first threshold depends on the length of the first time window; the prediction of the second event includes: predicting that the second event occurs within the first time window.

[0034] As an embodiment, the advantages of the above method include: it is conducive to realizing a dynamic threshold for predicted events.

[0035] According to one aspect of the present application, it is characterized in that

[0036] Said prediction of the second event includes: predicting that the metric value of the occurrence of the second event is greater than or not less than the first threshold value.

[0037] As an embodiment, the advantages of the above method include: being conducive to the dynamic adjustment of the confidence level or probability threshold of event prediction.

[0038] As an embodiment, the advantages of the above method include: being conducive to the network to perform load balancing.

[0039] As an embodiment, the advantages of the above method include being conducive to reducing signaling interaction.

[0040] According to one aspect of the present application, it is characterized in that

[0041] Said first threshold value varying with time means that: the first threshold value depends on the first time length and the target threshold value; wherein, the first threshold value depending on the first time length and the target threshold value includes:

[0042] If the first time length is less than the target threshold value, the first threshold value adopts a first value;

[0043] If the first time length is greater than the target threshold value, the first threshold value adopts a second value;

[0044] Wherein, the first time length depends on the time when the predicted second event occurs; the first value and the second value are different.

[0045] As an embodiment, the advantages of the above method include: being conducive to realizing the dynamic configuration of the trigger threshold of the predicted event.

[0046] As an embodiment, the advantages of the above method include: being conducive to reducing the complexity of protocol implementation.

[0047] As an embodiment, the advantages of the above method include: being conducive to reducing the modification to the existing protocol.

[0048] As an embodiment, the advantages of the above method include being conducive to reducing signaling interaction.

[0049] According to one aspect of the present application, it is characterized in that

[0050] As a response to the triggering of the first event, a first operation is executed; wherein,

[0051] The first operation includes sending a first report;

[0052] Or,

[0053] The first operation includes self-updating the RRC connection.

[0054] As an embodiment, the advantages of the above method include: facilitating the triggering of measurement reports.

[0055] As an embodiment, the advantages of the above method include: facilitating the triggering of prediction reports.

[0056] As an embodiment, the advantages of the above method include: facilitating the triggering of conditional mobility.

[0057] As an embodiment, the advantages of the above method include: facilitating the reduction of signaling interaction.

[0058] The present application discloses a method used in a base station, which is characterized by including:

[0059] Sending a first RRC message, where the first RRC message configures at least one reference signal resource and a first event;

[0060] Wherein, the first event depends on the measurement of the at least one reference signal resource, and the first event depends on a first threshold, and the first threshold changes with time.

[0061] According to one aspect of the present application, it is characterized in that the first event depends on the measurement of the at least one reference signal resource satisfying the first threshold.

[0062] According to one aspect of the present application, it is characterized in that the first event includes predicting a second event; the prediction of the second event depends on the measurement of the at least one reference signal resource.

[0063] According to one aspect of the present application, it is characterized in that

[0064] The fact that the first threshold changes with time means that the first threshold depends on the length of a first time window; the prediction of the second event includes: predicting that the second event occurs within the first time window.

[0065] According to one aspect of the present application, it is characterized in that the prediction of the second event includes: predicting that the metric value of the occurrence of the second event is greater than or not less than the first threshold.

[0066] According to one aspect of the present application, it is characterized in that

[0067] The fact that the first threshold changes with time means that the first threshold depends on a first time length and a target threshold; wherein, the fact that the first threshold depends on the first time length and the target threshold includes:

[0068] If the first time length is less than the target threshold, the first threshold adopts a first value;

[0069] If the first time length is greater than the target threshold, the first threshold adopts a second value;

[0070] Wherein, the first time length depends on the time when the predicted second event occurs; the first value is different from the second value.

[0071] According to one aspect of the present application, it is characterized in that

[0072] As a response to the triggering of the first event, perform a first operation; wherein,

[0073] The first operation includes receiving a first report;

[0074] Or,

[0075] The first operation includes the receiver of the first RRC message updating the RRC connection by itself.

[0076] The present application discloses a terminal, which is characterized in that it includes:

[0077] The terminal includes: one or more processors and a memory;

[0078] The memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the terminal to execute the method for the terminal.

[0079] The present application discloses a base station, which is characterized in that it includes:

[0080] The base station includes: one or more processors and a memory;

[0081] The memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the base station to execute the method for the base station.

[0082] As an embodiment, compared with the traditional solution, the present application has the following advantages:

[0083] -. It is beneficial to realize the triggering event of the dynamic threshold and reduce the frequent high-layer signaling interaction process.

[0084] -. It is beneficial to make the protocol flexibly adapt to different measurement results and balance the network interaction delay and reliability.

[0085] -. The design of the dynamic event threshold is beneficial to select appropriate UE behaviors according to different channel environments to balance the robustness and network optimization efficiency.

[0086] -. The above method considers the design of dynamic event thresholds in multiple scenarios, which is beneficial to improving the signaling interaction efficiency in different scenarios.

[0087] -. The above method is beneficial to implementing the network to optimize subsequent mobility decisions based on measurement reports.

[0088] -. It is beneficial to reduce the modification of the existing protocol.

[0089] -. It is beneficial to reduce signaling interaction.

[0090] -. It is beneficial to implement dynamic adjustment of the threshold for event prediction.

[0091] -. It is beneficial to the dynamic adjustment of the confidence or probability threshold for event prediction.

[0092] -. It is beneficial for the network to perform load balancing.

[0093] -. It is beneficial to reduce the complexity of protocol implementation.

[0094] -. It is beneficial to trigger conditional mobility. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Other features, objects, and advantages of the present application will become more apparent by reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0096] Figure 1 shows a flowchart according to an embodiment of the present application;

[0097] Figure 2 shows a schematic diagram of a network architecture according to an embodiment of the present application;

[0098] Figure 3 shows a schematic diagram of an embodiment of a radio protocol architecture of a user plane and a control plane according to an embodiment of the present application;

[0099] Figure 4 shows a schematic diagram of a first communication device and a second communication device according to an embodiment of the present application;

[0100] Figure 5 shows a flowchart of radio signal transmission according to an embodiment of the present application;

[0101] Figure 6 shows a schematic diagram of a first event according to an embodiment of the present application;

[0102] Figure 7 shows a schematic diagram of a second event according to an embodiment of the present application;

[0103] Figure 8 Shows a schematic diagram of the variation of the first threshold with time according to an embodiment of the present application;

[0104] Figure 9 Shows a schematic diagram of predicting the second event according to an embodiment of the present application;

[0105] Figure 10 Shows a schematic diagram of the first time length according to an embodiment of the present application;

[0106] Figure 11 Shows a structural block diagram of a processing device in a terminal according to an embodiment of the present application;

[0107] Figure 12 Shows a structural block diagram of a processing device in a base station according to an embodiment of the present application;

[0108] Figure 13 Shows a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of the present application;

[0109] Figure 14 Shows a schematic diagram of the deployment of RAN (Radio Access Network) domain AI / ML functions according to an embodiment of the present application;

[0110] Figure 15 Shows a schematic diagram of the deployment of UE's AI / ML functions according to an embodiment of the present application;

[0111] Figure 16 Shows a schematic diagram of a processing system based on artificial intelligence or machine learning according to another embodiment of the present application. Detailed implementation manners

[0112] The technical solutions of the present application will be further described in detail below in conjunction with the accompanying drawings. It should be noted that, without conflict, the embodiments and features in the embodiments of the present application can be combined with each other arbitrarily.

[0113] Example 1

[0114] Embodiment 1 exemplifies a flowchart according to an embodiment of the present application, as shown in the accompanying Figure 1 drawing. In the accompanying Figure 1 drawing, each box represents a step. It should be emphasized in particular that the order of the boxes in the drawing does not represent the chronological order of the steps represented.

[0115] In Embodiment 1, in step 101 of the present application, the terminal receives a first RRC message, and the first RRC message configures at least one reference signal resource and a first event;

[0116] Wherein, the first event depends on the measurement of the at least one reference signal resource, and the first event depends on a first threshold, and the first threshold changes with time.

[0117] As an embodiment, the first event is a layer 1 measurement event.

[0118] As an embodiment, the first RRC message includes a CSI-MeasConfig field, and the CSI-MeasConfig field configures the measurement of the at least one reference signal resource.

[0119] As an embodiment, the first RRC message includes a CSI-ResourceConfigId field; the CSI-ResourceConfigId field indicates the at least one reference signal resource.

[0120] As an embodiment, the first event is an LTM layer 1 measurement event.

[0121] As an embodiment, the first RRC message includes an LTM-Config field.

[0122] As an embodiment, the first RRC message includes an LTM-CSI-MeasConfig field, and the LTM-CSI-MeasConfig field configures the measurement of the at least one reference signal resource.

[0123] As an embodiment, the first RRC message includes an LTM-CSI-ResourceConfigId field; the LTM-CSI-ResourceConfigId field indicates the at least one reference signal resource.

[0124] As an embodiment, the first event is a layer 3 measurement event.

[0125] As an embodiment, the first RRC includes a MeasConfig field.

[0126] As an embodiment, the first RRC message includes an EventTriggerConfig field; the EventTriggerConfig field configures the first event.

[0127] As an embodiment, the configuration of the at least one reference signal resource is performed by the first RRC message.

[0128] As an embodiment, the measurement of the at least one reference signal resource is configured by the first RRC message.

[0129] As an embodiment, the measurement refers to SSB measurement.

[0130] As an embodiment, the measurement refers to CSI-RS measurement.

[0131] As an embodiment, the first event is a measurement event.

[0132] As an embodiment, the first event is a layer 3 measurement event.

[0133] As an embodiment, the first event is a layer 1 measurement event.

[0134] As an embodiment, the first event is for LTM.

[0135] As an embodiment, the first event is used to trigger conditional mobility.

[0136] As an embodiment, the first event is used to trigger measurement reporting.

[0137] As an embodiment, the first event is used to trigger layer 1 measurement reporting.

[0138] As an embodiment, the first event is used to trigger layer 3 measurement reporting.

[0139] As an embodiment, the first event is a prediction event.

[0140] As an embodiment, the first event is used to trigger prediction reporting.

[0141] As an embodiment, the at least one reference signal resource is configured for the first event.

[0142] As an embodiment, the at least one reference signal resource is determined by the terminal itself from multiple reference signal resources; the first RRC message configures the multiple reference signal resources, and any reference signal resource in the at least one reference signal resource belongs to the multiple reference signal resources.

[0143] As an embodiment, the multiple reference signal resources have the same reference signal type.

[0144] As an embodiment, the multiple reference signal resources have different reference signal types.

[0145] As an example, the multiple reference signal resources are all CSI-RS.

[0146] As an example, the multiple reference signal resources are all SSB.

[0147] As an example, that the at least one reference signal resource is determined by the terminal itself from the multiple reference signal resources means that the at least one reference signal resource is one or more of the reference signal resources with the best measurement results among the multiple reference signal resources.

[0148] As an example, "the best" means the highest RSRP.

[0149] As an example, "the best" means the highest RSRQ.

[0150] As an example, "the best" means the highest SINR.

[0151] As an example, that the at least one reference signal resource is determined by the terminal itself from the multiple reference signal resources means that the at least one reference signal resource depends on the output of the AI module.

[0152] As an example, the input of the AI module includes the measurement results of the multiple reference signal resources.

[0153] As an example, "the best one or more" means K; the K is a positive integer.

[0154] As an example, the positive integer K is configured by the first RRC message.

[0155] As an example, the positive integer K depends on the output of the AI module.

[0156] As an example, that the first threshold changes with time means that the value of the first threshold at the first time is different from the value of the first threshold at the second time, and the first time and the second time are different.

[0157] As an example, the first time and the second time refer to time instants.

[0158] As an example, that the first threshold changes with time means that the value of the first threshold in the first time interval is different from the value of the first threshold in the second time interval.

[0159] As an example, the first time interval and the second time interval do not overlap.

[0160] As an embodiment, the first time interval is configured by the first RRC message.

[0161] As an embodiment, the second time interval is configured by the first RRC message.

[0162] As an embodiment, the first time interval is determined by the terminal.

[0163] As an embodiment, the second time interval is determined by the terminal.

[0164] As an embodiment, the first time is a time instant within the first time interval.

[0165] As an embodiment, the first time belongs to the first time interval.

[0166] As an embodiment, the second time is a time instant within the second time interval.

[0167] As an embodiment, the second time belongs to the second time interval.

[0168] As an embodiment, within the first time interval or within the second time interval, the first threshold is constant and does not change with time.

[0169] As an embodiment, both the first time and the second time belong to the first time interval.

[0170] As a sub - embodiment of the above - mentioned embodiment, if and only if within the first time interval, the first thresholds corresponding to the first time and the second time can be different.

[0171] As a sub - embodiment of the above - mentioned embodiment, if and only if within the first time interval, the terminal determines the first thresholds corresponding to the first time and the second time.

[0172] As a sub - embodiment of the above - mentioned embodiment, the first RRC message configures the first thresholds corresponding to the first time and the second time within the first time interval.

[0173] As a sub - embodiment of the above - mentioned embodiment, within the first time interval, the first threshold is not re - configured.

[0174] As an embodiment, during the period when the first threshold changes with time, the first threshold is not re - configured.

[0175] As an embodiment, during the period when the first threshold changes with time, the first threshold is not indicated by any RRC signaling.

[0176] As an embodiment, the first RRC message configures the first threshold.

[0177] As an embodiment, the first threshold is predicted.

[0178] As an embodiment, the first threshold is determined by the terminal itself.

[0179] As an embodiment, the first threshold is a constant.

[0180] As an embodiment, the first threshold is a dimension.

[0181] As an embodiment, the first threshold is a positive integer.

[0182] As an embodiment, the first threshold is a non - negative integer.

[0183] As an embodiment, the first threshold is a positive number.

[0184] As an embodiment, the first threshold is a non - negative number.

[0185] As an embodiment, the first threshold is a confidence threshold.

[0186] As an embodiment, the first threshold is a probability threshold.

[0187] As an embodiment, the first threshold is an RSRP threshold.

[0188] As an embodiment, the first threshold is an SINR threshold.

[0189] As an embodiment, the first threshold is an RSRQ threshold.

[0190] As an embodiment, the first threshold is a quantity threshold.

[0191] As an embodiment, the first threshold indicates the maximum value of the first counter.

[0192] As an embodiment, the first event depends on the measurement of the at least one reference signal resource, and the first event depends on the first threshold. The change of the first threshold over time means that the triggering of the first event depends on the first counter; the first threshold indicates the maximum value of the first counter; the change of the first counter depends on the measurement of the at least one reference signal resource; the first threshold changes over time.

[0193] As an embodiment, in response to the value of the first counter being not less than the maximum value of the first counter, the first event is triggered.

[0194] As an example, the first event is RLF.

[0195] As an example, the first counter is the N310 counter.

[0196] As an example, the first counter is the N311 counter.

[0197] As an example, the first counter is the maximum number of preamble retransmissions.

[0198] As an example, the first counter is the maximum number of RLC retransmissions.

[0199] Example 2

[0200] Embodiment 2 exemplifies a schematic diagram of a network architecture according to an embodiment of the present application, as shown in the appendix Figure 2 shown. Appendix Figure 2Describes the network architecture 200 of a 5G NR (New Radio) / LTE (Long-Term Evolution) / LTE-A (Long-Term Evolution Advanced) system. The 5G NR / LTE / LTE-A network architecture 200 may be referred to as 5GS (5G System) / EPS (Evolved Packet System) 200 or some other suitable term. The 5GS / EPS 200 includes at least one of UE (User Equipment) 201, RAN (Radio Access Network) 202, 5GC (5G Core Network) / EPC (Evolved Packet Core) 210, HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and Internet service 230. The 5GS / EPS may be interconnected with other access networks, but these entities / interfaces are not shown for simplicity. As shown, the 5GS / EPS provides packet-switched services. However, those skilled in the art will readily understand that the various concepts presented throughout this application can be extended to networks providing circuit-switched services or other cellular networks. The RAN includes node 203 and other nodes 204. Node 203 provides user and control plane protocol termination towards UE 201. Node 203 may be connected to other nodes 204 via the Xn interface (e.g., backhaul) / X2 interface. Node 203 may also be referred to as a base station, base transceiver station, radio base station, radio transceiver, transceiver function, basic service set (BSS), extended service set (ESS), TRP (Transmit Receive Point), or some other suitable term. Node 203 provides an access point to the 5GC / EPC 210 for UE 201. Examples of UE 201 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 Internet of Things devices, machine type communication devices, land vehicles, automobiles, wearable devices, or any other similar functional device. Those skilled in the art may also refer to UE 201 as a mobile station, subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable term.Node 203 is connected to 5GC / EPC 210 via the S1 / NG interface. 5GC / EPC 210 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 Date Network Gateway) / UPF 213. MME / AMF / SMF 211 is a control node that processes the signaling between UE 201 and 5GC / EPC 210. Generally, MME / AMF / SMF 211 provides bearer and connection management. All user IP (Internet Protocal) packets are transmitted through S-GW / UPF 212, and S-GW / UPF 212 itself is connected to P-GW / UPF 213. P-GW provides UE IP address allocation and other functions. P-GW / UPF 213 is connected to the Internet service 230. The Internet service 230 includes operator-corresponding Internet protocol services, specifically including the Internet, intranet, IMS (IP Multimedia Subsystem), and packet-switched streaming services.

[0201] As an embodiment, the UE 201 corresponds to the terminal in the present application.

[0202] As an embodiment, the UE 201 is a user equipment (UE).

[0203] As an embodiment, the UE 201 is a base station equipment (BaseStation, BS).

[0204] As an embodiment, the UE 201 is a relay device.

[0205] As an embodiment, the UE 201 is a gateway device.

[0206] As an embodiment, the node 203 corresponds to the base station in the present application.

[0207] As an embodiment, the node 203 is a base station equipment.

[0208] As an embodiment, the node 203 is a user equipment.

[0209] As an embodiment, the node 203 is a relay device.

[0210] As an embodiment, the node 203 is a gateway device.

[0211] Typically, the UE 201 is a user equipment, and the node 203 is a base station equipment.

[0212] Typically, the UE 201 is a user equipment, and the node 203 is a user equipment.

[0213] Typically, the UE 201 is a base station equipment, and the node 203 is a base station equipment.

[0214] As an embodiment, the user equipment supports the transmission of a Non-Terrestrial Network (NTN).

[0215] As an embodiment, the user equipment supports the transmission of a Terrestrial Network.

[0216] As an embodiment, the user equipment supports Dual Connection (DC) transmission.

[0217] As an embodiment, the user equipment includes an aircraft.

[0218] As an embodiment, the user equipment includes a vehicle-mounted terminal.

[0219] As an embodiment, the user equipment includes a ship.

[0220] As an embodiment, the user equipment includes an Internet of Things (IoT) terminal.

[0221] As an embodiment, the user equipment includes a terminal of the industrial Internet of Things.

[0222] As an embodiment, the user equipment includes a device that supports low-latency and high-reliability transmission.

[0223] As an embodiment, the user equipment includes a test device.

[0224] As an embodiment, the user equipment includes a signaling tester.

[0225] As an embodiment, the user equipment includes an IAB (Integrated Access and Backhaul)-MT (Mobile Termination).

[0226] As an embodiment, the base station device supports transmission in a non-terrestrial network.

[0227] As an embodiment, the base station device supports transmission in a terrestrial network.

[0228] As an embodiment, the base station device includes a Base Transceiver Station (BTS).

[0229] As an embodiment, the base station device includes a NodeB (NB).

[0230] As an embodiment, the base station device includes a gNB.

[0231] As an embodiment, the base station device includes an eNB.

[0232] As an embodiment, the base station device includes an ng-eNB.

[0233] As an embodiment, the base station device includes an en-gNB.

[0234] As an embodiment, the base station device includes a CU (Centralized Unit).

[0235] As an embodiment, the base station device includes a DU (Distributed Unit).

[0236] As an embodiment, the base station device includes a TRP (Transmitter Receiver Point).

[0237] As an embodiment, the base station device includes a macro cellular base station.

[0238] As an embodiment, the base station device includes a micro cell base station.

[0239] As an embodiment, the base station device includes a pico cell base station.

[0240] As an embodiment, the base station device includes a femtocell.

[0241] As an embodiment, the base station device includes a flying platform device.

[0242] As an embodiment, the base station device includes a satellite device.

[0243] As an embodiment, the base station device includes a test device.

[0244] As an embodiment, the base station device includes a signaling tester.

[0245] As an embodiment, the base station device includes a gateway device.

[0246] As an embodiment, the base station device includes an IAB-node.

[0247] As an embodiment, the base station device includes an IAB-donor.

[0248] As an embodiment, the base station device includes an IAB-donor-CU.

[0249] As an embodiment, the base station device includes an IAB-donor-DU.

[0250] As an embodiment, the base station device includes an IAB-DU.

[0251] As an embodiment, the base station device includes an IAB-MT.

[0252] As an embodiment, the relay device includes a relay.

[0253] As an embodiment, the relay device includes an L3 relay.

[0254] As an embodiment, the relay device includes an L2 relay.

[0255] As an embodiment, the relay device includes a router.

[0256] As an embodiment, the relay device includes a switch.

[0257] As an embodiment, the relay device includes a gateway device.

[0258] As an embodiment, the relay device includes a user equipment.

[0259] As an embodiment, the relay device includes a base station device.

[0260] Example 3

[0261] Embodiment 3 shows a schematic diagram of an embodiment of a radio protocol architecture for a user plane and a control plane according to the present application, as shown in the appendix Figure 3 as shown. Figure 3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300, Figure 3 showing the radio protocol architecture for the control plane 300 in three layers: Layer 1, Layer 2, and Layer 3. Layer 1 (L1 layer) is the lowest layer and implements various PHY (Physical Layer) signal processing functions. The L1 layer will be referred to as PHY301 herein. Layer 2 (L2 layer) 305 is above PHY301 and includes a MAC (Medium Access Control) sublayer 302, an RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security by encrypting data packets and provides handover support. The RLC sublayer 303 provides segmentation and reassembly of upper layer data packets, retransmission of lost data packets, and reordering of data packets to compensate for disordered reception due to HARQ (Hybrid Automatic Repeat Request). The MAC sublayer 302 provides multiplexing between logical and transport channels. The MAC sublayer 302 is also responsible for allocating various radio resources (e.g., resource blocks) in a cell. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in Layer 3 (L3 layer) of the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and configuring the lower layers using RRC signaling. The radio protocol architecture of the user plane 350 includes Layer 1 (L1 layer) and Layer 2 (L2 layer). In the user plane 350, the radio protocol architecture is generally the same as the corresponding layers and sublayers in the control plane 300 for the physical layer 351, the PDCP sublayer 354 in the L2 layer 355, the RLC sublayer 353 in the L2 layer 355, and the MAC sublayer 352 in the L2 layer 355. However, the PDCP sublayer 354 also provides header compression for upper layer data packets to reduce radio transmission overhead. The L2 layer 355 in the user plane 350 also includes an SDAP (Service Data Adaptation Protocol) sublayer 356, and the SDAP sublayer 356 is responsible for mapping between QoS flows and data radio bearers (DRBs) to support service diversity.

[0262] As an example, the wireless protocol architecture in Figure 3 is applicable to the terminal in this application.

[0263] As an example, the wireless protocol architecture in Figure 3 is applicable to the base station in this application.

[0264] As an example, the first RRC message in this application is generated by the RRC306.

[0265] As an example, the first report in this application is generated by the RRC306.

[0266] As an example, the first report in this application is generated by the MAC302 or MAC352.

[0267] As an example, the first report in this application is generated by the PHY301 or PHY351.

[0268] Example 4

[0269] Example 4 shows a schematic diagram of a first communication device and a second communication device according to this application, as shown in Figure 4 as shown. Figure 4 is a block diagram of a first communication device 450 and a second communication device 410 that communicate with each other in an access network.

[0270] The first communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmit processor 468, a receive processor 456, a multi-antenna transmit processor 457, a multi-antenna receive processor 458, a transmitter / receiver 454, and an antenna 452.

[0271] The second communication device 410 includes a controller / processor 475, a memory 476, a receive processor 470, a transmit processor 416, a multi-antenna receive processor 472, a multi-antenna transmit processor 471, a transmitter / receiver 418, and an antenna 420.

[0272] 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 the functionality of the L2 layer. In the transmission from the second communication device 410 to the first communication device 450, the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocation for the first communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for retransmission of lost packets and signaling to the first communication device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for the L1 layer (i.e., the physical layer). The transmit processor 416 implements encoding and interleaving to facilitate forward error correction (FEC) at the second communication device 410, and mapping of signal constellations based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-phase shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The multi-antenna transmit processor 471 performs digital spatial precoding on the encoded and modulated symbols, including codebook-based precoding and non-codebook-based precoding, and beamforming processing, to generate one or more spatial streams. The transmit processor 416 then maps each spatial stream to subcarriers, multiplexes with reference signals (e.g., pilots) in the time domain and / or frequency domain, and then uses the inverse fast Fourier transform (IFFT) to generate a physical channel carrying a time-domain multi-carrier symbol stream. Subsequently, the multi-antenna transmit processor 471 performs transmit analog precoding / beamforming operations on the time-domain multi-carrier symbol stream. Each transmitter 418 converts the baseband multi-carrier symbol stream provided by the multi-antenna transmit processor 471 into a radio frequency stream and then provides it to different antennas 420.

[0273] 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 signals via its respective antenna 452. Each receiver 454 recovers the information modulated onto the radio frequency carrier and converts the radio frequency stream into a baseband multi-carrier symbol stream that is provided to the receive processor 456. The receive processor 456 and the multi-antenna receive processor 458 perform various signal processing functions of the L1 layer. The multi-antenna receive processor 458 performs receive analog precoding / beamforming operations on the baseband multi-carrier symbol stream from the receivers 454. The receive processor 456 uses the fast Fourier transform (FFT) to convert the baseband multi-carrier 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 receive processor 456, where the reference signal will be used for channel estimation, and the data signal recovers any spatial streams destined for the first communication device 450 after multi-antenna detection in the multi-antenna receive processor 458. The symbols on each spatial stream are demodulated and recovered in the receive processor 456, and soft decisions are generated. Subsequently, the receive processor 456 decodes and deinterleaves the soft decisions to recover the upper layer data and control signals transmitted by the second communication device 410 on the physical channel. Subsequently, the upper layer data and control signals are provided to the controller / processor 459. The controller / processor 459 performs the functions of the L2 layer. The controller / processor 459 may be associated with a memory 460 that stores program code and data. The memory 460 may be referred to as a computer-readable medium. In the transmission from the second communication device 410 to the first communication device 450, the controller / processor 459 provides demultiplexing between transport and logical channels, packet reassembly, decryption, header decompression, control signal processing to recover upper layer data packets from the core network. Subsequently, the upper layer data packets are provided to all protocol layers above the L2 layer. Various control signals may also be provided to the L3 for L3 processing.

[0274] In the transmission from the first communication device 450 to the second communication device 410, at the first communication device 450, the 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 transmit function at the second communication device 410 described in the transmission from the second communication device 410 to the first communication device 450, the controller / processor 459 implements header compression, encryption, packet segmentation and reordering, and multiplexing between logical and transport channels based on radio resource allocation, and implements L2 layer functions for the user plane and the control plane. The controller / processor 459 is also responsible for retransmitting lost packets and signaling to the second communication device 410. The transmit processor 468 performs modulation mapping and channel coding processing, and the multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based precoding and non-codebook-based precoding, and beamforming processing. Subsequently, the transmit processor 468 modulates the generated spatial streams into multi-carrier / single-carrier symbol streams, and after passing through the analog precoding / beamforming operation in the multi-antenna transmit processor 457, provides them to different antennas 452 via the transmitter 454. Each transmitter 454 first converts the baseband symbol stream provided by the multi-antenna transmit processor 457 into a radio frequency symbol stream and then provides it to the antenna 452.

[0275] 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 receive 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 receive processor 472 and the receive processor 470. The receive processor 470 and the multi-antenna receive 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 a computer-readable medium. In the transmission from the first communication device 450 to the second communication device 410, the controller / processor 475 provides demultiplexing between transport and logical channels, packet reassembly, decryption, header decompression, and control signal processing to recover upper layer data packets from the UE 450. The upper layer data packets from the controller / processor 475 may be provided to the core network.

[0276] As an example, 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 is at least configured to: receive a first RRC message, the first RRC message configuring at least one reference signal resource and a first event; the first event depends on the measurement for the at least one reference signal resource, and the first event depends on a first threshold, the first threshold varying over time.

[0277] As an example, the first communication device 450 includes: a memory storing a computer-readable instruction program, the computer-readable instruction program causing actions when executed by at least one processor, the actions including: receive a first RRC message, the first RRC message configuring at least one reference signal resource and a first event; the first event depends on the measurement for the at least one reference signal resource, and the first event depends on a first threshold, the first threshold varying over time.

[0278] As an example, 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 is at least configured to: send a first RRC message, the first RRC message configuring at least one reference signal resource and a first event; the first event depends on the measurement for the at least one reference signal resource, and the first event depends on a first threshold, the first threshold varying over time.

[0279] As an example, the second communication device 410 includes: a memory storing a computer-readable instruction program, the computer-readable instruction program causing actions when executed by at least one processor, the actions including: send a first RRC message, the first RRC message configuring at least one reference signal resource and a first event; the first event depends on the measurement for the at least one reference signal resource, and the first event depends on a first threshold, the first threshold varying over time.

[0280] As an example, at least one of the antenna 452, the receiver 454, the receiving processor 456, and the controller / processor 459 is used to receive the first RRC message.

[0281] As an example, at least one of the antenna 420, the transmitter 418, the transmitting processor 416, and the controller / processor 475 is used to send the first RRC message.

[0282] As an example, at least one of the antenna 452, the transmitter 454, the transmitting processor 468, and the controller / processor 459 is used to send the first report.

[0283] As an example, at least one of the antenna 420, the receiver 418, the receiving processor 470, and the controller / processor 475 is used to receive the first report.

[0284] As an example, the first communication device 450 corresponds to the terminal in this application.

[0285] As an example, the second communication device 410 corresponds to the base station in this application.

[0286] As an example, the first communication device 450 is a user equipment.

[0287] As an example, the first communication device 450 is a base station equipment.

[0288] As an example, the first communication device 450 is a relay device.

[0289] As an example, the second communication device 410 is a user equipment.

[0290] As an example, the second communication device 410 is a base station equipment.

[0291] As an example, the second communication device 410 is a relay device.

[0292] Example 5

[0293] Example 5 exemplifies a wireless signal transmission flowchart according to an embodiment of the present application, as shown in the appendix Figure 5 It should be specifically noted that the order in this example does not limit the signal transmission order and the implementation order in the present application.

[0294] For Terminal U01:

[0295] In step S5101, a first RRC message is received, and the first RRC message configures at least one reference signal resource and a first event;

[0296] In step S5102, as a response to the triggering of the first event, a first operation is performed;

[0297] In step S5103, send the first report;

[0298] In step S5104, update the RRC connection by itself;

[0299] For Base Station N02:

[0300] In step S5201, send the first RRC message;

[0301] In step S5202, receive the first report;

[0302] In Embodiment 5, the first event depends on the measurement of the at least one reference signal resource, and the first event depends on a first threshold, and the first threshold varies with time.

[0303] As an embodiment, there is a wireless connection between the terminal U01 and the base station N02.

[0304] As an embodiment, there is a wired connection between the terminal U01 and the base station N02.

[0305] As an embodiment, there is a connection through the Uu interface between the terminal U01 and the base station N02.

[0306] As an embodiment, there is a connection through the IAB interface between the terminal U01 and the base station N02.

[0307] As an embodiment, there is a connection through the PC5 interface between the terminal U01 and the base station N02.

[0308] As an embodiment, the dashed box F5.1 is optional.

[0309] As an embodiment, the dashed box F5.1 exists.

[0310] As an embodiment, the dashed box F5.1 does not exist.

[0311] As an embodiment, the dashed box F5.2 is optional.

[0312] As an embodiment, the dashed box F5.2 exists.

[0313] As an embodiment, the dashed box F5.2 does not exist.

[0314] As an embodiment, the dashed box F5.3 is optional.

[0315] As an embodiment, the dashed box F5.3 exists.

[0316] As an embodiment, the dashed box F5.3 does not exist.

[0317] As an embodiment, when the dashed box F5.1 does not exist, then the dashed box F5.2 or the dashed box F5.3 also does not exist.

[0318] As an embodiment, when the dashed box F5.1 exists, then the dashed box F5.2 or the dashed box F5.3 may exist.

[0319] As an embodiment, the dashed box F5.2 and the dashed box F5.3 do not exist simultaneously.

[0320] As an embodiment, when the dashed box F5.2 exists, the dashed box F5.3 does not exist.

[0321] As an embodiment, when the dashed box F5.3 exists, the dashed box F5.2 does not exist.

[0322] As an embodiment, the dashed box F5.2 and the dashed box F5.3 exist simultaneously.

[0323] As an embodiment, whether the dashed box F5.2 and the dashed box F5.3 exist depends on the first RRC message.

[0324] As an embodiment, the first RRC message configures whether the dashed box F5.1 exists.

[0325] As an embodiment, the first RRC message configures whether the dashed box F5.2 exists.

[0326] As an embodiment, the first RRC message configures whether the dashed box F5.3 exists.

[0327] As an embodiment, the first operation is an operation of the RRC layer.

[0328] As an embodiment, the first report at least indicates the first event.

[0329] As an embodiment, the first report is an event prediction report.

[0330] As an embodiment, the first report is a measurement report.

[0331] As an embodiment, the first report is a layer 3 measurement report.

[0332] As an embodiment, the first report is a layer 1 measurement report.

[0333] As an example, the first report is an RRC message.

[0334] As an example, the first report is a MAC PDU.

[0335] As an example, the first report is a MAC CE.

[0336] As an example, the first report is a UCI.

[0337] As an example, the first report indicates the first event.

[0338] As an example, the first report indicates the second event.

[0339] As an example, the first report indicates the measurement result for the at least one reference signal.

[0340] As an example, the first report indicates the prediction result for the at least one reference signal.

[0341] As an example, the first report indicates the prediction result for the second event.

[0342] As an example, the self-updating RRC connection includes: performing RRC connection re-establishment.

[0343] As an example, the self-updating RRC connection includes: entering the RRC_IDLE state.

[0344] As an example, the self-updating RRC connection includes: performing cell selection.

[0345] As an example, the self-updating RRC connection includes: applying the configuration information of a candidate cell.

[0346] As an example, the self-updating RRC connection refers to: performing conditional mobility handover.

[0347] As an example, the conditional mobility handover refers to: conditional handover (CHO).

[0348] As an example, the conditional mobility handover refers to: conditional LTM.

[0349] As an example, the candidate cell is a candidate cell for conditional mobility handover.

[0350] As an example, in response to the triggering of the first event, a first operation is performed; wherein, the first operation includes applying the configuration information associated with the one candidate cell.

[0351] As an example, the first event is associated with the one candidate cell.

[0352] As an example, the first event is a conditional handover event.

[0353] As an example, whether the first operation includes sending the first report or includes the self-update of the RRC connection depends on the first RRC message.

[0354] As an example, whether the first operation includes sending the first report or includes the self-update of the RRC connection is implicitly indicated by the first RRC message.

[0355] As an example, when the first RRC message configures the reporting configuration of the first report, the first operation includes sending the first report.

[0356] As an example, when the first RRC message configures the configuration of the self-update of the RRC connection, the first operation includes the self-update of the RRC connection.

[0357] As an example, whether the first operation includes sending the first report or includes the self-update of the RRC connection is explicitly indicated by the first RRC message.

[0358] As an example, the first RRC message indicates whether to send the first report when the first event is triggered.

[0359] As an example, whether the first operation includes sending the first report or includes the self-update of the RRC connection depends on the terminal's own decision.

[0360] As an example, the first operation includes both sending the first report and the self-update of the RRC connection.

[0361] As an example, the first operation is: sending the first report; in response to the successful sending of the first report, self-update the RRC connection.

[0362] Example 6

[0363] Embodiment 6 exemplifies a schematic diagram of a first event according to an embodiment of the present application, as shown in the appendix Figure 6 as follows.

[0364] In Embodiment 6, the first event depends on the measurement for the at least one reference signal resource satisfying the first threshold.

[0365] As an embodiment, the first event depends on the measurement for the at least one reference signal resource being greater than or not less than the first threshold; wherein, the at least one reference signal resource is configured on an adjacent cell of a serving cell of the terminal.

[0366] As an embodiment, the first event depends on the measurement for the at least one reference signal resource being less than or not greater than the first threshold; wherein, the at least one reference signal resource is configured on a serving cell of the terminal.

[0367] As an embodiment, the one serving cell is a PCell.

[0368] As an embodiment, the one serving cell is a PSCell.

[0369] As an embodiment, the first event depends on the measurement for at least one reference signal satisfying a second threshold.

[0370] As an embodiment, the first event depends on the measurement for the at least one reference signal resource being greater than or not less than the second threshold; wherein, the at least one reference signal resource is configured on a candidate cell of the terminal.

[0371] As an embodiment, the one candidate cell is an LTM candidate cell.

[0372] As an embodiment, the one candidate cell is a conditional LTM candidate cell.

[0373] As an embodiment, the one candidate cell is a conditional handover candidate cell.

[0374] As an embodiment, as a response to the measurement for the at least one reference signal resource of the one serving cell being greater than or not less than the first threshold, it is considered that the first condition is satisfied.

[0375] As an embodiment, as a response to the measurement for the at least one reference signal resource of the one candidate cell being greater than or not less than the second threshold, it is considered that the first condition is satisfied.

[0376] As an example, in response to the measurement of at least one reference signal resource for the first serving cell being greater than or not less than the first threshold and the measurement of at least one reference signal resource for the one candidate cell being greater than or not less than the second threshold, it is considered that the first condition is satisfied.

[0377] As an example, in response to the measurement of at least one reference signal resource for the first serving cell being greater than or not less than the first threshold or the measurement of at least one reference signal resource for the one candidate cell being greater than or not less than the second threshold, it is considered that the first condition is satisfied.

[0378] As an example, in response to the measurement of at least one reference signal for the first serving cell being greater than or not less than the first threshold and remaining greater than or not less than the first threshold for more than a first trigger time, it is considered that the first condition is satisfied.

[0379] As an example, in response to the measurement of at least one reference signal for the first candidate cell being greater than or not less than the first threshold and remaining greater than or not less than the first threshold for more than a second trigger time, it is considered that the first condition is satisfied.

[0380] As an example, in response to the measurement of at least one reference signal for the first serving cell being greater than or not less than the first threshold and remaining greater than or not less than the first threshold for more than a first trigger time or the measurement of at least one reference signal for the first candidate cell being greater than or not less than the first threshold and remaining greater than or not less than the first threshold for more than a second trigger time, it is considered that the first condition is satisfied.

[0381] As an example, in response to the measurement of at least one reference signal for the first serving cell being greater than or not less than the first threshold and remaining greater than or not less than the first threshold for more than a first trigger time and the measurement of at least one reference signal for the first candidate cell being greater than or not less than the first threshold and remaining greater than or not less than the first threshold for more than a second trigger time, it is considered that the first condition is satisfied.

[0382] As an example, the first trigger time is TimeToTrigger.

[0383] As an example, the second trigger time is TimeToTrigger.

[0384] As an example, the first trigger time is pre-configured.

[0385] As an example, the second trigger time is pre-configured.

[0386] As an example, the second trigger time is the first trigger time.

[0387] As an embodiment, that the measurement of the at least one reference signal resource is greater than or not less than the first threshold means that the measurement of the at least one reference signal resource is greater than or not less than the sum of the first threshold and the first hysteresis value.

[0388] As an embodiment, that the measurement of the at least one reference signal resource is less than or not greater than the second threshold means that the measurement of the at least one reference signal resource is less than or not greater than the sum of the second threshold and the second hysteresis value.

[0389] As an embodiment, the first hysteresis value is the first hysteresis value.

[0390] As an embodiment, the first hysteresis value is not the first hysteresis value.

[0391] As an embodiment, the first hysteresis value is preconfigured.

[0392] As an embodiment, the first hysteresis value is Hysteresis.

[0393] As an embodiment, the second hysteresis value is preconfigured.

[0394] As an embodiment, the second hysteresis value is Hysteresis.

[0395] As an embodiment, the first hysteresis value depends on the output of the AI module.

[0396] As an embodiment, the second hysteresis value depends on the output of the AI module.

[0397] As an embodiment, the first trigger time depends on the output of the AI module.

[0398] As an embodiment, the second trigger time depends on the output of the AI module.

[0399] As an embodiment, that the first event depends on the measurement of the at least one reference signal resource satisfying the first threshold means that the first event depends on the difference between the measurement of the at least one reference signal resource of the first candidate cell and the measurement of the at least one reference signal resource of the first serving cell being greater than or not less than the first threshold.

[0400] As an example, that the first event depends on the difference between the measurement of at least one reference signal resource for the first candidate cell and the measurement of at least one reference signal resource for the first serving cell being greater than or not less than the first threshold means that the first event depends on the difference between the measurement of at least one reference signal resource for the first candidate cell and the measurement of at least one reference signal resource for the first serving cell being greater than or not less than the sum of the first threshold and the first hysteresis value.

[0401] As an example, as a response to the first event depending on the difference between the measurement of at least one reference signal resource for the first candidate cell and the measurement of at least one reference signal resource for the first serving cell being greater than or not less than the first threshold and remaining above the first trigger time, it is considered that the first condition is satisfied.

[0402] Example 7

[0403] Example 7 exemplifies a schematic diagram of a second event according to an embodiment of the present application, as shown in the appendix Figure 7 as shown.

[0404] In Example 7, the first event includes predicting a second event; the prediction of the second event depends on the measurement of the at least one reference signal resource.

[0405] As an example, the second event is a measurement event.

[0406] As an example, the second event is a layer 3 measurement event.

[0407] As an example, the measurement of the at least one reference signal refers to a layer 3 measurement.

[0408] As an example, the second event is a layer 1 measurement event.

[0409] As an example, the measurement of the at least one reference signal refers to a layer 1 measurement.

[0410] As an example, the second event is RLF.

[0411] As an example, whether the first event is satisfied depends on whether the second event is predicted.

[0412] As an example, as a response to predicting the second event, it is considered that the first event is satisfied.

[0413] As an example, the prediction of the second event depends on the output of an AI module.

[0414] As an embodiment, the second event is that the handover execution condition is satisfied.

[0415] As an embodiment, the second event is that the measurement reporting condition is satisfied.

[0416] As an embodiment, in response to the satisfaction of the second event, measurement reporting is performed.

[0417] As an embodiment, in response to the satisfaction of the second event, condition switching is performed.

[0418] As an embodiment, the prediction for the second event is a direct event prediction.

[0419] As an embodiment, the direct event prediction means that the output of the AI module is the prediction for the second event.

[0420] As an embodiment, the direct event prediction means that the output of the AI module includes whether the second event will occur.

[0421] As an embodiment, the direct event prediction means that the output of the AI module includes the occurrence probability of the second event.

[0422] As an embodiment, the prediction for the second event is an indirect event prediction.

[0423] As an embodiment, the indirect event prediction means that the output of the AI module is the prediction for the measurement.

[0424] As an embodiment, the prediction for the second event depends on the prediction for the measurement.

[0425] As an embodiment, the prediction for the measurement refers to the prediction for the measurement of the at least one reference signal resource; the at least one reference signal resource is configured on the first serving cell.

[0426] As an embodiment, the prediction for the measurement refers to the prediction for the measurement of the at least one reference signal resource; the at least one reference signal resource is configured on the first candidate cell.

[0427] As an embodiment, the prediction for the measurement refers to the prediction for the measurement of a serving cell; the at least one reference signal resource is configured on the one serving cell; the one serving cell is the serving cell of the terminal.

[0428] As an embodiment, the prediction of the measurement refers to the prediction of the measurement of a candidate cell; the at least one reference signal resource is configured on the one candidate cell; the one candidate cell is a candidate cell of the terminal.

[0429] Example 8

[0430] Embodiment 8 exemplifies a schematic diagram of the variation of the first threshold over time according to an embodiment of the present application, as shown in the appendix Figure 8 as shown.

[0431] In Embodiment 8, the variation of the first threshold over time means that the first threshold depends on the length of the first time window; the prediction of the second event includes: predicting that the second event occurs within the first time window.

[0432] As an embodiment, the first time window is configured by the network.

[0433] As an embodiment, the first time window is configured by the first RRC message.

[0434] As an embodiment, the length of the first time window is determined by the terminal.

[0435] As an embodiment, the network configures the first coefficient value.

[0436] As an embodiment, the first RRC message configures the first coefficient value.

[0437] As an embodiment, the terminal determines the first coefficient value by itself.

[0438] As an embodiment, the first coefficient value depends on the output of the AI module.

[0439] As an embodiment, the first threshold depends on the first coefficient value and the first time window.

[0440] As an embodiment, the first threshold is equal to the product of the length of the first time window and the first coefficient value.

[0441] As an embodiment, the first threshold is equal to a first fixed value plus a first dynamic value.

[0442] As an embodiment, the first dynamic value is equal to the product of the length of the first time window and the first coefficient value.

[0443] As an embodiment, the first fixed value does not vary with time.

[0444] As an embodiment, the first RRC message configures the first fixed value.

[0445] As an example, the first coefficient value is a probability density.

[0446] As an example, the unit of the first coefficient value is 1 / s.

[0447] As an example, the first coefficient value is a rate of change of RSRP over time.

[0448] As an example, the unit of the first coefficient value is dB / s.

[0449] As an example, the start time of the first time window is the current time.

[0450] As an example, the end time of the first time window is pre-configured.

[0451] As an example, the end time of the first time window is configured by the first RRC message.

[0452] As an example, the end time of the first time window depends on the output of the AI module.

[0453] As an example, the end time of the first time window is the time when the second event is most likely to occur.

[0454] As an example, the end time of the first time window is the time when it is predicted that the second event will occur.

[0455] As an example, the first threshold changing over time means that: the first threshold depends on the first function; the first function depends on the first time window; the first time window changes over time.

[0456] As an example, within the first time window, the value of the first threshold is the function value of the first function.

[0457] As an example, the first function is pre-configured.

[0458] As an example, the first function is configured by the first RRC message.

[0459] As an example, the input of the first function includes the length of the first time window.

[0460] As an example, the first function is an AI module.

[0461] As an example, the parameters of the first function include the first coefficient value.

[0462] As an example, the independent variable of the first function is the length of the first time window.

[0463] As an example, the first function depends on the current time.

[0464] As an example, the independent variable of the first function depends on the current time.

[0465] As an example, the independent variable of the first function is the current time value.

[0466] As an example, the first function depends on the first reference point.

[0467] As an example, the first reference point is the start time of the first time window.

[0468] As an example, the independent variable of the first function is the time difference from the first reference point to the current time.

[0469] As an example, the independent variable of the first function is the time difference from the start of the first time window to the current time.

[0470] As an example, the unit of the time difference is milliseconds.

[0471] As an example, the unit of the time difference is the number of frames.

[0472] As an example, the unit of the time difference is the number of sub - frames.

[0473] As an example, the unit of the time difference is the number of slots.

[0474] As an example, the unit of the time difference is the number of symbols.

[0475] As an example, the first function is a monotonic function.

[0476] As an example, the first function is a linear function.

[0477] As an example, the first function is a direct proportional function, and the coefficient of the direct proportional function is the first coefficient value.

[0478] As an example, the first threshold is a direct proportional function of the length of the first time window.

[0479] As an example, the first threshold is a linear function of the length of the first time window.

[0480] As an example, the first threshold is equal to the product of the length of the first time window and the first coefficient value.

[0481] As an example, the first function depends on a first function value and a second function value.

[0482] As an example, the first function value indicates the function value of the first function at the start time of the first time window.

[0483] As an example, the second function value indicates the function value of the first function at the end time of the first time window.

[0484] As an example, the second function value indicates the function value of the first function at a fixed time after the start time of the first time window.

[0485] As an example, the first function linearly and uniformly varies with time between the first function value and the second function value.

[0486] As an example, predicting that the second event occurs within the first time window means that the probability of the second event occurring within the first time window is not less than or greater than the first threshold.

[0487] As an example, predicting that the second event occurs within the first time window means making a prediction within the first time window for the measured values of the at least one reference signal resource; wherein the predicted measured values trigger the second event.

[0488] As an example, in response to predicting the second event, the first report is sent.

[0489] Example 9

[0490] Example 9 exemplifies a schematic diagram of predicting the second event according to an embodiment of the present application, as shown in the appendix Figure 9 as shown.

[0491] In Example 9, predicting the second event includes: predicting that the metric value of the occurrence of the second event is greater than or not less than the first threshold. As an example, predicting the second event includes: predicting that the metric value of the occurrence of the second event within the first time window is greater than or not less than the first threshold.

[0492] As an example, predicting the second event includes: predicting that the metric value of the occurrence of the second event at the first moment is greater than or not less than the first threshold.

[0493] As an example, the metric value refers to probability.

[0494] As an example, the metric value refers to confidence.

[0495] As an example, the metric of the occurrence of the second event refers to the probability value of the occurrence of the second event.

[0496] As an example, the metric of the occurrence of the second event refers to the confidence level of the occurrence of the second event.

[0497] As an example, the occurrence of the second event means that the second event occurs within a certain time window.

[0498] As an example, predicting that the metric of the occurrence of the second event is greater than or not less than the first threshold means that the probability of the occurrence of the second event is greater than or not less than the first threshold.

[0499] As an example, predicting that the metric of the occurrence of the second event is greater than or not less than the first threshold means that the confidence level of the occurrence of the second event is greater than or not less than the first threshold.

[0500] As an example, the certain time window is configured by the first RRC message.

[0501] As an example, the occurrence of the second event means that the second event occurs at a certain event point.

[0502] As an example, the certain event point is configured by the first RRC message.

[0503] As an example, the metric refers to RSRP.

[0504] As an example, the metric refers to the predicted value of the measurement quantity.

[0505] As an example, the metric of the occurrence of the second event refers to the predicted value of the measurement quantity associated with the second event.

[0506] As an example, the predicted value refers to the predicted value within a certain time window.

[0507] As an example, the predicted value refers to the predicted value at a certain time point.

[0508] As an example, the certain time window is configured by the first RRC message.

[0509] As an example, the certain time point is configured by the first RRC message.

[0510] As an example, the measurement quantity associated with the second event depends on at least one reference signal resource of the first candidate cell.

[0511] As an example, the predicted metric value indicating the occurrence of the second event being greater than or not less than the first threshold means that the predicted value of the measured quantity associated with the second event is greater than or not less than the first threshold.

[0512] As an example, the predicted metric value indicating the occurrence of the second event being greater than or not less than the first threshold means that the predicted value of the measured quantity associated with the second event is greater than or not less than the first threshold.

[0513] As an example, the predicted metric value indicating the occurrence of the second event being greater than or not less than the first threshold means that the predicted metric value of the occurrence of the second event is greater than or not less than the sum of the first threshold and the first offset value.

[0514] As an example, the predicted metric value indicating the occurrence of the second event being greater than or not less than the first threshold means that the predicted metric value of the occurrence of the second event remains greater than or not less than the first threshold for more than the first hysteresis time.

[0515] As an example, the predicted metric value indicating the occurrence of the second event being greater than or not less than the first threshold means that the predicted metric value of the occurrence of the second event remains greater than or not less than the sum of the first threshold and the first offset value for more than the first hysteresis time.

[0516] As an example, in response to the predicted metric value of the occurrence of the second event remaining less than or not greater than the first threshold, it is considered that the second event does not occur.

[0517] As an example, in response to the predicted metric value of the occurrence of the second event remaining less than or not greater than the first threshold, it is considered that the second event will not occur.

[0518] As an example, in response to the predicted metric value of the occurrence of the second event remaining less than or not greater than the first threshold, it is considered that the second event will not occur. If there is an unsent first report, cancel the unsent first report.

[0519] As an example, in response to the predicted metric value of the occurrence of the second event remaining less than or not greater than the first threshold, it is considered that the second event will not occur. If there is an uncompleted first operation, cancel the uncompleted first operation.

[0520] As an example, the cancellation means termination.

[0521] Example 10

[0522] Example 10 exemplifies a schematic diagram of a first time length according to an embodiment of the present application, as shown in the appendix Figure 10 as shown

[0523] In Example 10, the fact that the first threshold changes with time means that the first threshold depends on the first time length and the target threshold; wherein, the fact that the first threshold depends on the first time length and the target threshold includes:

[0524] If the first time length is less than the target threshold, the first threshold adopts a first value;

[0525] If the first time length is greater than the target threshold, the first threshold adopts a second value;

[0526] wherein, the first time length depends on the predicted time of occurrence of the second event; the first value and the second value are different.

[0527] As an embodiment, the first RRC message configures the first value and the second value of the first threshold.

[0528] As an embodiment, the first threshold is a probability threshold.

[0529] As an embodiment, the first threshold is a confidence threshold.

[0530] As an embodiment, the first time length is the time length between the current moment and the predicted time of occurrence of the second event.

[0531] As an embodiment, the first time length is the time length between the current time when the second event is predicted to occur and the predicted time of occurrence of the second event.

[0532] As an embodiment, the target threshold is a time metric value.

[0533] As an embodiment, the target threshold is pre-configured.

[0534] As an embodiment, the target threshold is configured by the first RRC message.

[0535] As an embodiment, the target threshold is determined by the terminal itself.

[0536] As an embodiment, the target threshold depends on the output of the AI module.

[0537] As an embodiment, the target threshold is fixed.

[0538] As an example, the target threshold is relative.

[0539] As an example, the unit of the target threshold is milliseconds.

[0540] As an example, the unit of the target threshold is the number of frames.

[0541] As an example, the unit of the target threshold is the number of sub - frames.

[0542] As an example, the unit of the target threshold is the number of slots.

[0543] As an example, the unit of the target threshold is the number of symbols.

[0544] As an example, the first value is higher than the second value.

[0545] As an example, the first value is pre - configured.

[0546] As an example, the first value is configured by the first RRC message.

[0547] As an example, the first value is fixed.

[0548] As an example, the second value is pre - configured.

[0549] As an example, the second value is configured by the first RRC message.

[0550] As an example, the second value is fixed.

[0551] As an example, the second value depends on the first value and the first offset value.

[0552] As an example, the second value is equal to the sum of the first value and the first offset value.

[0553] As an example, the first offset value is pre - configured.

[0554] As an example, the first offset value is configured by the first RRC message.

[0555] As an example, the first value depends on the second value and the second offset value.

[0556] As an example, the first value is equal to the sum of the second value and the second offset value.

[0557] As an example, the second offset value is pre - configured.

[0558] As an embodiment, the second bias value is configured by the first RRC message.

[0559] As an embodiment, the second value is equal to 1.

[0560] Example 11

[0561] Embodiment 11 illustrates a structural block diagram of a processing device in a terminal according to an embodiment of the present application; as shown in the appendix Figure 11 shown. In the appendix Figure 11 the terminal 1100 includes a first transmitter 1101 and a first processor 1102.

[0562] The first processor 1102 receives a first RRC message, and the first RRC message configures at least one reference signal resource and a first event;

[0563] In Embodiment 11, wherein the first event depends on the measurement for the at least one reference signal resource, and the first event depends on a first threshold, and the first threshold changes with time.

[0564] As an embodiment, the first event depends on the measurement for the at least one reference signal resource satisfying the first threshold.

[0565] As an embodiment, the first event includes predicting a second event; the predicting of the second event depends on the measurement for the at least one reference signal resource.

[0566] As an embodiment, the first threshold changing with time means that the first threshold depends on the length of a first time window; the predicting of the second event includes predicting that the second event occurs within the first time window.

[0567] As an embodiment, the predicting of the second event includes predicting that a metric value at which the second event occurs is greater than or not less than the first threshold.

[0568] As an embodiment, the first threshold changing with time means that the first threshold depends on a first time length and a target threshold; wherein the first threshold depending on the first time length and the target threshold includes:

[0569] If the first time length is less than the target threshold, the first threshold adopts a first value;

[0570] If the first time length is greater than the target threshold, the first threshold adopts a second value;

[0571] Among them, the time length of the first time length dependence predicts the time when the second event occurs; the first value is different from the second value.

[0572] As an embodiment, in response to the first event being triggered, the terminal 1100 performs a first operation; among them,

[0573] The first operation includes sending a first report;

[0574] Or,

[0575] The first operation includes self-updating the RRC connection.

[0576] As an embodiment, the terminal includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the terminal to execute the method for the terminal in this application.

[0577] As an embodiment, the first receiver includes at least one of the antenna 452 or the receiver 454 or the multi-antenna receiving processor 458 or the receiving processor 456 or the controller / processor 459 or the memory 460 or the data source 467 in the appendix of this application. Figure 4

[0578] As an embodiment, the first receiver includes at least the antenna 452 and the receiver 454 in the appendix of this application. Figure 4

[0579] As an embodiment, the first transmitter 1101 includes at least one of the antenna 452 or the transmitter 454 or the multi-antenna transmitting processor 457 or the transmitting processor 468 or the controller / processor 459 or the memory 460 or the data source 467 in the appendix of this application. Figure 4

[0580] As an embodiment, the first transmitter 1101 includes at least the antenna 452 and the transmitter 454 in the appendix of this application. Figure 4

[0581] Example 12

[0582] Embodiment 12 exemplifies a structural block diagram of a processing device for a base station according to an embodiment of this application; as shown in the appendix. Figure 12 Shown. In the appendix Figure 12 The base station 1200 includes a second transmitter 1201 and a second receiver 1202.

[0583] The second transmitter 1201 sends a first RRC message, and the first RRC message configures at least one reference signal resource and a first event;

[0584] In Embodiment 12, the first event depends on the measurement for the at least one reference signal resource, and the first event depends on a first threshold, and the first threshold varies with time.

[0585] As an embodiment, the first event depends on the measurement for the at least one reference signal resource satisfying the first threshold.

[0586] As an embodiment, the first event includes predicting a second event; the prediction of the second event depends on the measurement for the at least one reference signal resource.

[0587] As an embodiment, the fact that the first threshold varies with time means that the first threshold depends on the length of a first time window; the prediction of the second event includes predicting that the second event occurs within the first time window.

[0588] As an embodiment, the prediction of the second event includes predicting that a metric value at which the second event occurs is greater than or not less than the first threshold.

[0589] As an embodiment, the fact that the first threshold varies with time means that the first threshold depends on a first time length and a target threshold; wherein, the fact that the first threshold depends on the first time length and the target threshold includes:

[0590] If the first time length is less than the target threshold, the first threshold adopts a first value;

[0591] If the first time length is greater than the target threshold, the first threshold adopts a second value;

[0592] wherein, the first time length depends on the time at which the predicted second event occurs; the first value and the second value are different.

[0593] As an embodiment, in response to the first event being triggered, the base station 1200 performs a first operation; wherein,

[0594] The first operation includes receiving a first report;

[0595] Or,

[0596] The first operation includes the receiver of the first RRC message updating the RRC connection by itself.

[0597] As an embodiment, the base station includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the base station to execute the method described in the present application that is used for the base station.

[0598] As an embodiment, the second transmitter 1201 includes the attached Figure 4 At least one of the antenna 420 or the transmitter 418 or the multi-antenna transmit processor 471 or the transmit processor 416 or the controller / processor 475 or the memory 476.

[0599] As an embodiment, the second transmitter 1201 includes the attached Figure 4 At least antenna 420 and transmitter 418.

[0600] As an embodiment, the second receiver 1202 includes the attached Figure 4 At least one of the antenna 420 or the receiver 418 or the multi-antenna reception processor 472 or the reception processor 470 or the controller / processor 475 or the memory 476.

[0601] As an embodiment, the second receiver 1202 includes the attached Figure 4 At least an antenna 420 and a receiver 418.

[0602] Example 13

[0603] Embodiment 13 illustrates a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of the present application, as shown in the attached Figure 13 Attached Figure 13 It includes a first module, a second module, a third module, a fourth module and a fifth module.

[0604] In Example 13, the first module sends a first data set to the second module, the first module sends a second data set to the third module, the first module sends a third data set 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.

[0605] As an embodiment, the first module, the second module, the third module, the fourth module and the fifth module all belong to the terminal.

[0606] The above method avoids the empty signaling interaction and shortens the transmission delay.

[0607] As an embodiment, any one of the first module, the second module, the third module, the fourth module, and the fifth module does not belong to the terminal.

[0608] The above method reduces the hardware complexity of the terminal.

[0609] As an embodiment, at least the first module of the first module, the second module, the third module, the fourth module, and the fifth module 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 does not belong to the terminal.

[0610] The above method balances the hardware complexity and transmission delay of the terminal.

[0611] As an embodiment, the first module is used for data collection.

[0612] As an embodiment, the first module is responsible for data collection.

[0613] As an embodiment, the first module has the function of data collection.

[0614] As an embodiment, the second module is used for model training.

[0615] As an embodiment, the second module is responsible for model training.

[0616] As an embodiment, the second module has the function of model training.

[0617] As an embodiment, the second module performs AI / ML model training.

[0618] As an embodiment, the second module performs validation.

[0619] As an embodiment, the second module performs testing.

[0620] As an embodiment, the second module generates model performance metrics.

[0621] As an embodiment, the second module is responsible for data preparation.

[0622] As an embodiment, the data preparation includes at least one of data pre - processing, cleaning, formatting, or transformation.

[0623] As an embodiment, the third module is used for Inference.

[0624] As an embodiment, the third module has an Inference function.

[0625] As an embodiment, the third module is responsible for Inference.

[0626] As an embodiment, the fourth module is used for Model Storage.

[0627] As an embodiment, the fourth module has a Model Storage function.

[0628] As an embodiment, the fourth module is responsible for storing the trained model.

[0629] As an embodiment, the fourth module is responsible for storing the trained model that can be used to perform inference processing.

[0630] As an embodiment, the fifth module is used for Management.

[0631] As an embodiment, the fifth module is responsible for Management.

[0632] As an embodiment, the fifth module has a Management function.

[0633] As an embodiment, the first data set is Training Data.

[0634] As an embodiment, the second data set is Inference Data.

[0635] As an embodiment, the third data set is Monitoring Data.

[0636] As an embodiment, the first type of parameter group includes Monitoring output.

[0637] As an embodiment, the second type of parameter group includes Management Instruction.

[0638] As an example, the second type of parameter group is used for the fine-tuning operation of the inference function.

[0639] As an example, the second type of parameter group includes the identification of the model.

[0640] As an example, the second type of parameter group is used to select a model.

[0641] As an example, the second type of parameter group is used to switch the model.

[0642] As an example, the second type of parameter group is used to activate / deactivate the model.

[0643] As an example, the second type of parameter group is used to fallback from AI-ML operations to non-AI-ML operations.

[0644] As an example, the third type of parameter group includes a Model Transfer Request.

[0645] As an example, the third type of parameter group includes a Model Delivery Request.

[0646] As an example, the fourth type of parameter group includes a Trained Model.

[0647] As an example, the fourth type of parameter group includes an Updated Model.

[0648] As an example, the fourth type of parameter group indicates the identification of the model.

[0649] As an example, the fifth type of parameter group includes a Model Transfer.

[0650] As an example, the fifth type of parameter group includes a Model Delivery.

[0651] As an example, the fifth type of parameter group indicates the identification of the model.

[0652] As an example, the first type of output includes a monitoring output.

[0653] As an example, the first type of output exists.

[0654] As an example, the first type of output does not exist.

[0655] As an example, the second type of output includes an Inference Output.

[0656] As an example, the second type of output is used by the fifth module to monitor the performance of the AI / ML model.

[0657] As an example, the second type of output is used by the fifth module to monitor the performance of the AI / ML function.

[0658] As an example, the second type of output exists.

[0659] As an example, the second type of output does not exist.

[0660] As an example, the artificial intelligence processing system generates or assists in generating at least one of the first threshold, the prediction for the second event, the metric predicting the occurrence of the second event, the length of the first time window, the target threshold, the first value, the second value, the first operation, the first report, or the self-updating RRC connection.

[0661] As an example, the fifth module generates or assists in generating at least one of the first threshold, the prediction for the second event, the metric predicting the occurrence of the second event, the length of the first time window, the target threshold, the first value, the second value, the first operation, the first report, or the self-updating RRC connection.

[0662] As an example, the third module generates or assists in generating at least one of the first threshold, the prediction for the second event, the metric predicting the occurrence of the second event, the length of the first time window, the target threshold, the first value, the second value, the first operation, the first report, or the self-updating RRC connection.

[0663] As an example, the second type of output includes at least one of the first threshold, the prediction for the second event, the metric predicting the occurrence of the second event, the length of the first time window, the target threshold, the first value, the second value, the first operation, the first report, or the self-updating RRC connection.

[0664] As an example, at least one of the first data set or the second data set includes the measurement of the at least one reference signal resource.

[0665] As an example, at least one of the first data set or the second data set includes the first RRC message.

[0666] As an example, at least one of the first data set or the second data set includes the first event.

[0667] As an example, at least one of the first data set or the second data set includes the second event.

[0668] As an example, at least one of the first data set, the second data set, or the third data set includes the measurement of the at least one reference signal resource.

[0669] As an example, at least one of the first data set, the second data set, or the third data set includes the first RRC message.

[0670] As an example, at least one of the first data set, the second data set, or the third data set includes the first event.

[0671] As an example, at least one of the first data set, the second data set, or the third data set includes the second event.

[0672] As an example, Example 13 is only to illustrate that the present application can be used in an artificial intelligence processing system. This example does not limit the application of the present application to a non-artificial intelligence processing system, and this example does not limit the application of the present application to other types of artificial intelligence processing systems to achieve the same effect as the artificial intelligence processing system shown in the appendix. Figure 13 shown.

[0673] Example 14

[0674] Example 14 exemplifies a schematic diagram of the deployment of RAN (Radio Access Network) domain AI / ML functions according to an embodiment of the present application; as shown in the appendix. Figure 14 shown. The gNB in Example 14 can be replaced with network devices such as an eNB or a 6G base station, for example.

[0675] AI / ML-related functions include ML training functions (also known as AI training, or AI / ML training), ML testing functions, ML inference (also known as AI inference, or AI / ML inference) functions, and so on. The ML training function, ML testing function, and ML inference function can be deployed independently or co-located. The deployment of AI / ML-related functions can be achieved through software, such as the download and / or execution of executable files; or through a combination of software and hardware, such as accelerating specific computing units through hardware to improve computing speed or save power consumption.

[0676] For the ML training function, it can be deployed in a cross-domain management system or a domain-specific management system; the domain-specific management system is used to manage the RAN domain or the CN (Core Network) domain. For example, the ML training function for MDA (Management Data Analytics) can be deployed in the MDAF (MDA function); the ML training for network data analysis can be deployed in the NWDAF (Network Data Analytics Function), that is, the ML training function is the MTLF (Model Training logical function).

[0677] For the ML inference function, it can also be deployed in a cross-domain management system or a domain-specific management system; for example, the ML inference function is the MDAF, or the ML inference function is the AnLF (Analytics logical function) located in the NWDAF.

[0678] Similarly, the ML testing function can also be deployed in a cross-domain management system or a domain-specific management system.

[0679] In Embodiment 14, the RAN domain ML training function 1402 is located in the RAN domain management function 1403; while the ML inference function is located in the base station, that is, the AI / ML inference function 1404 is located in the gNB 1405, the AI / ML inference function 1406 is located in the gNB 1407,....

[0680] Appendix Figure 14Among them, the management of the ML inference functions of multiple base stations is completed by the RAN domain management function 1403, that is, data interaction is performed with the RAN domain MnS (Management Service) consumer / cross-domain management 1401 (as shown by the dashed arrows in Figure 14 ).

[0681] Optionally, the management of the ML inference function can also be completed by the base station itself, that is, each base station can independently perform data interaction with the RAN domain MnS consumer / cross-domain management 1401.

[0682] It should be noted that Embodiment 14 is merely a non-limiting implementation; optionally, the ML training function of the RAN domain may also be deployed at the base station; or optionally, some base stations deploy the ML inference function and the ML training function of the RAN domain, while some base stations only deploy the ML inference function.

[0683] As an embodiment, one gNB (or base station) in Embodiment 2 is the base station described in this application.

[0684] As an embodiment, the first processor includes one AL / ML inference function in Figure 14 , that is, 1404 or 1406.

[0685] As an embodiment, the second processor includes one AL / ML inference function in Figure 14 , that is, 1404 or 1406.

[0686] As an embodiment, one AL / ML inference function in Figure 14 performs ML training based on the measurement results of the first serving cell or the second cell collected.

[0687] As an embodiment, one AL / ML inference function in Figure 14 performs ML training based on the measurement of the at least one reference signal resource collected.

[0688] As an embodiment, one AL / ML inference function in Figure 14 performs ML training based on the first RRC message collected.

[0689] Example 15

[0690] Embodiment 15 exemplifies a schematic diagram of the AI / ML function deployment of a UE according to an embodiment of this application; as shown in Figure 15 . The RAN domain ML training function 1505 in Figure 15 is optional.

[0691] The UE function 1504 is deployed in the terminal of this application. The UE function 1504 includes an AI / ML inference function 1506. The AI / ML inference function 1506 uses an ML model (also known as an AI model) for inference. An ML model usually needs to be trained before being used for AI / ML inference.

[0692] As an embodiment, the UE function 1504 includes a RAN domain ML training function 1505. The RAN domain ML training function 1505 runs training data through the ML model to obtain relevant losses, and adjusts the parameters of the ML model based on the calculated losses. The ML training includes at least one of ML initial training, ML re-training, and reinforcement learning.

[0693] The above embodiment can reduce the complexity of the base station, or save the radio interface resources caused by reporting training data. However, the above embodiment places relatively high requirements on the processing capabilities of the UE side.

[0694] Optionally, the UE function 1504 further includes a CN domain ML training function ( Figure 15 not included in).

[0695] Optionally, the UE function 1504 further includes an AI / ML deployment function - Figure 15 not included in, for loading the ML model and data.

[0696] As an embodiment, the terminal indicates whether it supports the ML training function (RAN domain or CN domain) through capability reporting. The capability reporting is RRC signaling or NAS (Non-Access Stratum) signaling.

[0697] As an embodiment, the ML model and related metadata are loaded by the terminal from a network device or a remote server.

[0698] Optionally, the UE function 1504 is an MnS (Management Service) producer, providing data to the CN domain MnF (Management Function) 1501, and / or the RAN domain MnF 1502, and / or the cross-domain management system 1503 for management or analysis (as shown by the double arrow 1507).

[0699] Optionally, the UE function 1504 is an MnS consumer, loading data for AI / ML-related management, such as management data requests, ML model activation, and / or ML training, etc. (as shown by the double arrow 1507), from the CN domain MnF (Management Function) 1501, and / or the RAN domain MnF 1502, and / or the cross-domain management system 1503.

[0700] As an example, the first threshold in this application is obtained through the inference of the AI / ML inference function 1506.

[0701] As an example, the prediction for the second event in this application is obtained through the inference of the AI / ML inference function 1506.

[0702] As an example, the metric value predicting the occurrence of the second event in this application is obtained through the inference of the AI / ML inference function 1506.

[0703] As an example, the length of the first time window in this application is obtained through the inference of the AI / ML inference function 1506.

[0704] As an example, whether the target threshold is met in this application is obtained through the inference of the AI / ML inference function 1506.

[0705] As an example, the first value in this application is obtained through the inference of the AI / ML inference function 1506.

[0706] As an example, the second value in this application is obtained through the inference of the AI / ML inference function 1506.

[0707] As an example, the first operation in this application is obtained through the inference of the AI / ML inference function 1506.

[0708] As an example, the first report in this application is obtained through the inference of the AI / ML inference function 1506.

[0709] As an example, the self-update of the RRC connection in this application is obtained through the inference of the AI / ML inference function 1506.

[0710] As an example, at least one of the first threshold, the prediction for the second event, the metric predicting the occurrence of the second event, the length of the first time window, the target threshold, the first value, the second value, the first operation, the first report, and the self-updating RRC connection in the present application is obtained through the inference of the AI / ML inference function 1506.

[0711] As an example, the first processor includes an attached Figure 15 AL / ML inference function 1506 among them.

[0712] As an example, the ML model is based on a Neural Network.

[0713] As an example, the ML model is based on a CNN (Conventional Neural Networks, Convolutional Neural Network).

[0714] As an example, the ML model is based on a ResNet (Deep Residual Networks, Deep Residual Network).

[0715] As an example, the ML model is based on an RNN (Recurrent Neural Networks, Recurrent Neural Network).

[0716] As an example, the ML model is based on an LSTM (Long Short Term Memory, Long Short-Term Memory) network.

[0717] As an example, the ML model is based on a Transformer architecture.

[0718] Example 16

[0719] Example 16 illustrates a schematic diagram of an artificial intelligence or machine learning-based processing system according to another embodiment of the present application; as shown in the attached Figure 16 shown. Attached Figure 16 It includes a third processor, a fourth processor, a fifth processor, and a sixth processor.

[0720] In Embodiment 16, the third processor sends a first data set to the fourth processor and a second data set to the fifth processor; the fourth processor generates a target first type of parameter set based on the first data set, and the fourth processor sends the generated target first type of parameter set to the fifth processor; the fifth processor processes the second data set by using the target first type of parameter set to obtain a first type of output. Optionally, the fifth processor sends the first type of output to the sixth processor. In the appendix Figure 16 the first type of feedback and the second type of feedback are optional; the fourth processor includes an ML training function; the fifth processor includes an ML inference function.

[0721] As an embodiment, the sixth processor includes an ML testing function.

[0722] As an embodiment, the sixth processor includes performance monitoring / evaluation of the ML model.

[0723] As an embodiment, the fifth processor sends a first type of feedback to the fourth processor, and the first type of feedback is used to trigger recalculation or update of the target first type of parameter set, that is, to trigger ML initial training or ML retraining.

[0724] As an embodiment, the sixth processor sends a second type of feedback to the third processor, and the second type of feedback is used to generate the first data set or the second data set, or the second type of feedback is used to trigger the sending of the first data set or the sending of the second data set.

[0725] As an embodiment, the third processor generates the first data set and the second data set based on measurements.

[0726] As an embodiment, the fifth processor belongs to the terminal and the sixth processor belongs to the base station.

[0727] As an embodiment, the first type of output includes the first message.

[0728] As an embodiment, the first type of output includes the first threshold.

[0729] As an embodiment, the first type of output includes the first hysteresis value.

[0730] As an embodiment, the first type of output includes the first trigger time.

[0731] As an embodiment, the first type of output includes whether the first condition is satisfied.

[0732] As an embodiment, the first type of output includes whether to switch to the second cell or to send a first message on the first serving cell.

[0733] As an embodiment, the first type of output includes the second threshold.

[0734] As an embodiment, the first type of output includes the second hysteresis value.

[0735] As an embodiment, the first type of output includes the second trigger time.

[0736] As an embodiment, the first type of output includes whether the second condition is satisfied.

[0737] As an embodiment, the first type of output includes the third threshold.

[0738] As an embodiment, the first type of output includes the first time length.

[0739] As an embodiment, the first type of output includes a prediction of the measurement of at least one reference signal resource of the first serving cell.

[0740] As an embodiment, the first type of output includes a prediction of the measurement of at least one reference signal resource of the second cell.

[0741] As an embodiment, the first type of output includes the length of the second timer.

[0742] As an embodiment, the second data set includes measurements for reference signals.

[0743] As an embodiment, the first data set includes training data.

[0744] As an embodiment, the fourth processor is used to train an ML model, and the trained model is described by the target first type of parameter group.

[0745] As an embodiment, the fourth processor belongs to the terminal.

[0746] The above embodiment avoids transmitting the first data set to the base station.

[0747] As an embodiment, the fourth processor belongs to the base station.

[0748] The above embodiment supports joint training and optimizes system performance.

[0749] As an embodiment, the fourth processor belongs to the core network.

[0750] The above embodiments support full-network joint training, further optimizing the system performance.

[0751] As an embodiment, the second data set includes Inference Data.

[0752] As an embodiment, the fifth processor belongs to the terminal.

[0753] As an embodiment, the fifth processor constructs a model according to the target first type of parameter group, and then inputs the second data set into the constructed model to obtain the first type of output.

[0754] As an embodiment, the fifth processor generates a recovery data set according to the first type of output, and the error between the recovery data set and the second data set is used to generate the first type of feedback.

[0755] As an embodiment, the first type of feedback is used to reflect the performance of the trained model; when the performance of the trained model does not meet the requirements, the fourth processor will recalculate the target first type of parameter group.

[0756] As an embodiment, when the error is too large or there is no update for too long, the performance of the trained model is considered not to meet the requirements.

[0757] As an embodiment, the target first type of parameter group includes one or more of: convolution kernel size, number of convolution layers, convolution stride, pooling kernel size, pooling kernel stride, pooling function, activation function, or number of feature maps.

[0758] As an embodiment, the target first type of parameter group includes one or more of: convolution kernel, pooling kernel, pooling function, activation function, parameters of the pooling function, or parameters of the activation function.

[0759] Those of ordinary skill in the art can understand that all or part of the steps in the above method can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a hard disk, or an optical disc, etc. Optionally, all or part of the steps of the above embodiments can also be implemented using one or more integrated circuits. Correspondingly, each module unit in the above embodiments can be implemented in a hardware form or in the form of a software function module. This application is not limited to any specific form of the 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 control airplanes, aircraft, small airplanes, mobile phones, tablet computers, notebooks, in-vehicle communication devices, wireless sensors, network cards, Internet of Things terminals, RFID terminals, NB-IOT terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, network cards, in-vehicle communication devices, low-cost mobile phones, low-cost tablet computers, and other wireless communication devices. 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) NR node B, TRP (Transmitter Receiver Point), and other wireless communication devices.

[0760] The above are only the preferred embodiments of the present application and are not intended to limit the protection scope of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method used in a terminal, characterized in that: include: Receiving a first RRC message, wherein the first RRC message configures at least one reference signal resource and a first event; The first event depends on the measurement of the at least one reference signal resource, and the first event depends on a first threshold, which varies with time.

2. The method according to claim 1, characterized in that: The first event is dependent upon the measurement of the at least one reference signal resource satisfying the first threshold.

3. The method according to claim 1, characterized in that The first event includes predicting a second event; the predicting the second event depends on the measurement of the at least one reference signal resource.

4. The method according to claim 3, characterized in that: The first threshold value changes with time means that: the first threshold value depends on the length of the first time window; and the predicting of the second event includes: predicting that the second event occurs within the first time window.

5. The method according to claim 3, characterized in that: The predicting the second event includes: predicting that a metric value of the occurrence of the second event is greater than or not less than the first threshold.

6. The method according to claim 5, characterized in that The first threshold value changes with time means that the first threshold value depends on the first time length and the target threshold value; wherein the first threshold value depends on the first time length and the target threshold value includes: If the first time length is less than the target threshold, the first threshold adopts a first value; If the first time length is greater than the target threshold, the first threshold adopts a second value; The first time length depends on the predicted time of occurrence of the second event; and the first value is different from the second value.

7. The method according to any one of claims 1 to 6, characterized in that: In response to the first event being triggered, a first operation is performed; wherein, The first operation includes sending a first report; or, The first operation includes automatically updating the RRC connection.

8. A terminal, characterized in that: The terminal 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 codes, where the computer program codes include computer instructions. The one or more processors call the computer instructions to enable the terminal to execute the method according to any one of claims 1 to 7.

9. A method used in a base station, characterized in that: include: Sending a first RRC message, wherein the first RRC message configures at least one reference signal resource and a first event; The first event depends on the measurement of the at least one reference signal resource, and the first event depends on a first threshold, which varies with time.

10. The method according to claim 9, characterized in that The first event is dependent upon the measurement of the at least one reference signal resource satisfying the first threshold.

11. The method according to claim 9, characterized in that The first event includes predicting a second event; the predicting the second event depends on the measurement of the at least one reference signal resource.

12. The method according to claim 11, characterized in that The first threshold value changes with time means that: the first threshold value depends on the length of the first time window; and the predicting of the second event includes: predicting that the second event occurs within the first time window.

13. The method according to claim 11, characterized in that The predicting the second event includes: predicting that a metric value of the occurrence of the second event is greater than or not less than the first threshold.

14. The method according to claim 13, characterized in that The first threshold value changes with time means that the first threshold value depends on the first time length and the target threshold value; wherein the first threshold value depends on the first time length and the target threshold value includes: If the first time length is less than the target threshold, the first threshold adopts a first value; If the first time length is greater than the target threshold, the first threshold adopts a second value; The first time length depends on the predicted time of occurrence of the second event; and the first value is different from the second value.

15. The method according to any one of claims 9 to 14, characterized in that: In response to the first event being triggered, a first operation is performed; wherein, The first operation includes receiving a first report; or, The first operation includes the receiver of the first RRC message updating the RRC connection by itself.

16. A base station, characterized in that: 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 codes, where the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the base station to perform the method according to any one of claims 9 to 15.

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