Method and device applied to node of wireless communication and artificial intelligence

Abstract

The invention discloses a method and a device in a node used for wireless communication and artificial intelligence. The node performs at least M1 times of measurement for the target cell in the target time window, wherein M1 is a positive integer; the duration of the target time window in the time domain depends on whether the first node generates a prediction result of the measurement for the target cell before the target time window; the prediction result depends on an AI / ML model. According to the method and the device, the relation is established between the prediction result and the length of the target time window, so that the measurement frequency is reduced under the scene that the node supports the AI / ML capability, the energy cost is saved, and the power loss is reduced.

Description

Technical Field

[0001] This application relates to signal transmission methods and apparatuses in a wireless communication system, and particularly to measurement methods and apparatuses. Background Art

[0002] The application scenarios of future wireless communication systems are becoming increasingly diversified. To meet the different performance requirements of different scenarios, 3GPP (3rd Generation Partnership Project) is actively researching how to combine AI / ML (Artificial Intelligence / Machine Learning) technologies with mobile communications. Among them, the main application scenarios involve multiple aspects such as network automation, optimized resource allocation, and improved service continuity for mobile users.

[0003] The mobility of a User Equipment (UE) is an important feature of a wireless network. In the Connected Mode of the UE, currently, for mobility measurement, there are mainly Intra-Frequency NR (NR within a frequency) measurement, Inter-Frequency NR (NR between frequencies) measurement, Inter-RAT (between RATs) measurement for E-UTRA (Evolved UMTS Terrestrial Radio Access Network), and Inter-RAT measurement for UTRA (UMTS Terrestrial Radio Access Network). In addition, in the Idle Mode and Inactive Mode of the UE, the UE also needs to perform measurements for cell selection and cell reselection to maintain the connection to the base station. Summary of the Invention

[0004] AI / ML can analyze and predict the movement trajectory of a terminal, the change of traffic volume, and the change of the resident cell. Mobility management and measurement control based on AI / ML not only help to improve the user experience but also reasonably adjust the frequency of user measurements, thereby playing a role in energy saving to increase the battery usage time and life. How to implement the above benefits brought by AI / ML in existing standards is one of the problems that need to be considered currently.

[0005] In view of the above problems, the present application discloses a solution. It should be noted that in the above problem description, the NR system is taken as an example, and the present application is also applicable to scenarios such as future 6G systems and achieves technical effects similar to those of the NR system. Further, although the original intention of the present application is for AI / ML scenarios, the present application can also be applied to other non-AI / ML scenarios. Further, adopting a unified design solution for different scenarios (such as other non-AI / ML scenarios, including but not limited to vehicle-to-everything (V2X), capacity enhancement systems, short-range communication systems, non-terrestrial networks (NTN), Internet of Things (IoT), ultra-reliable low-latency communication (URLLC) networks, etc.) helps to reduce hardware complexity and cost. Without conflict, the embodiments and features in any node of the present application can be applied to any other node. Without conflict, the embodiments and features in the embodiments of the present application can be combined with each other arbitrarily.

[0006] In particular, the explanations of the terms, nouns, functions, and variables in the present application (if not otherwise specified) can refer to the definitions in the TS38 series and TS37 series of the technical standards (Technical Specification, TS) of the 3rd Generation Partnership Project (3GPP). If necessary, reference can be made to TS38.211, TS38.212, TS38.213, TS38.214, TS38.215, TS38.300, TS38.304, TS38.305, TS38.321, TS38.331, TS37.355, TS38.423 in the 3GPP technical standards to assist in understanding the present application.

[0007] As an example, the explanations of the terms in the present application refer to the definitions in the TS38 series of the 3GPP specification protocol.

[0008] As an example, the explanations of the terms in the present application refer to the definitions in the TS37 series of the 3GPP specification protocol.

[0009] As an example, the explanations of the terms in the present application refer to the definitions in the Rel-17 version of the 3GPP specification protocol.

[0010] As an embodiment, the interpretation of the terms in this application refers to the definitions in the 3GPP specification protocol Rel-18 version.

[0011] This application discloses a method for a first node in wireless communication and artificial intelligence, which includes:

[0012] Performing at least M1 measurements on the target cell within the target time window, where M1 is a positive integer;

[0013] Wherein, the duration of the target time window in the time domain depends on whether the first node generates a prediction result of the measurement for the target cell before the target time window; the prediction result depends on the AI / ML model.

[0014] As an embodiment, the problems to be solved by this application include: the problem of reducing the measurement frequency of the first node.

[0015] As an embodiment, the problems to be solved by this application include: when the network, especially the first node, has prediction capabilities, how to reasonably reduce the measurement frequency of the first node through the prediction capabilities, reduce power consumption without affecting performance, and thus improve the battery usage time and service life.

[0016] As an embodiment, the characteristics of the above method include: when it is predicted that the first node does not need to maintain the traditional measurement frequency in a certain future time period, the first node reduces the measurement frequency to achieve the effect of reducing power consumption.

[0017] As an embodiment, the first node is a terminal.

[0018] As an embodiment, the first node is a UE.

[0019] As an embodiment, the first node has the ability of AI / ML.

[0020] As an embodiment, the first node has prediction capabilities.

[0021] According to one aspect of this application, the above method is characterized in that when the first node generates a prediction result of the measurement for the target cell before the target time window, the duration of the target time window is equal to a first time value; when the first node does not generate a prediction result of the measurement for the target cell before the target time window, the duration of the target time window is equal to a second time value; the first time value and the second time value are different.

[0022] As an embodiment, the characteristics of the above method include: determining whether the first node uses the first time value or the second time value for measurement based on the prediction result, thereby adjusting the duration of the target time window in real time with the prediction result, and being more adaptable to the possible future states of the first node.

[0023] According to one aspect of the present application, the above method is characterized in that it includes:

[0024] Sending a first information block;

[0025] Wherein, the first information block includes the prediction result of the measurement of the target cell generated by the first node.

[0026] As an embodiment, the characteristics of the above method include: telling the second node in the present application the prediction result of the first node through the first information block, and further informing the second node that the first node will perform measurement according to the first time value.

[0027] As an embodiment, the characteristics of the above method include: telling the second node in the present application that the first node will enter a state with a lower measurement frequency through the first information block.

[0028] According to one aspect of the present application, the above method is characterized in that the first time value is equal to the product of a first integer and a first reference time value, the first integer depends on the prediction result or the first reference time value depends on the prediction result, and the first reference time depends on the length of the discontinuous reception period of the first node.

[0029] As an embodiment, the characteristics of the above method include: adjusting the duration of the target time window in the time domain through the first integer, thereby achieving the technical effect of reducing the measurement frequency with less modification to the existing standard.

[0030] According to one aspect of the present application, the above method is characterized in that the target integer depends on the indication of the first information block, and the first time value depends on the target integer and the length of the discontinuous reception period of the first node.

[0031] As an embodiment, the characteristics of the above method include: the first node indicates the target integer, and further informs the second node of the length of the first time value, so as to achieve the effect that the first node can flexibly adjust the first time value.

[0032] According to one aspect of the present application, the above method is characterized in that the target cell includes at least one of the serving cell of the first node, an Intra-Frequency cell, an Inter-Frequency cell, and an Inter-RAT cell.

[0033] According to one aspect of the present application, the above method is characterized in that the first information block includes a confidence interval of the prediction result.

[0034] As an embodiment, the characteristics of the above method include: by indicating the confidence interval to inform the second node of the accuracy of the prediction result.

[0035] As an embodiment, the characteristics of the above method include: by indicating the confidence interval to monitor the performance of the AI / ML model that generates the prediction result.

[0036] According to one aspect of the present application, the above method is characterized in that it includes:

[0037] Receiving a first signaling;

[0038] wherein the first signaling is a feedback for the first information block.

[0039] As an embodiment, the characteristics of the above method include: the second node informs the first node through the first signaling that it knows the first node adjusts the measurement frequency.

[0040] According to one aspect of the present application, the above method is characterized in that the first information block includes an ID of the AI / ML model that generates the prediction result.

[0041] As an embodiment, the characteristics of the above method include: by informing the ID of the AI / ML model to realize the network's performance monitoring of the AI / ML model.

[0042] The present application discloses a method in a second node for wireless communication and artificial intelligence, which includes:

[0043] A reference signal configured for measurement of a target cell;

[0044] wherein the receiver of the reference signal includes a terminal, the first node performs at least M1 measurements on the target cell in a target time window, M1 is a positive integer; the duration of the target time window in the time domain depends on whether the first node generates a prediction result for the measurement of the target cell before the target time window; the prediction result depends on an AI / ML model.

[0045] As an embodiment, the second node is a base station.

[0046] As an embodiment, the second node is a gNB.

[0047] As an embodiment, the second node is an eNB.

[0048] As an embodiment, the second node includes a base station.

[0049] As an embodiment, the second node includes a network device.

[0050] As an embodiment, the second node includes a core network.

[0051] As an embodiment, the second node includes a base station and a core network.

[0052] As an embodiment, the second node includes a base station and a network device.

[0053] According to one aspect of the present application, the above method is characterized in that when the first node generates a prediction result of the measurement for the target cell before the target time window, the duration of the target time window is equal to a first time value; when the first node does not generate a prediction result of the measurement for the target cell before the target time window, the duration of the target time window is equal to a second time value; the first time value and the second time value are different.

[0054] According to one aspect of the present application, the above method is characterized by including:

[0055] Receiving a first information block;

[0056] Wherein, the first information block includes the prediction result of the measurement for the target cell generated by the first node.

[0057] According to one aspect of the present application, the above method is characterized in that the first time value is equal to the product of a first integer and a first reference time value, the first integer depends on the prediction result or the first reference time value depends on the prediction result, and the first reference time depends on the length of the discontinuous reception period of the first node.

[0058] According to one aspect of the present application, the above method is characterized in that a target integer depends on the indication of the first information block, and the first time value depends on the target integer and the length of the discontinuous reception period of the first node.

[0059] According to one aspect of the present application, the above method is characterized in that the target cell includes at least one of the serving cell of the first node, an Intra-Frequency cell, an Inter-Frequency cell, and an Inter-RAT cell.

[0060] According to one aspect of the present application, the above method is characterized in that the first information block includes the confidence interval of the prediction result.

[0061] According to one aspect of the present application, the above method is characterized by including:

[0062] Sending a first signaling;

[0063] wherein the first signaling is a feedback for the first information block.

[0064] According to one aspect of the present application, the above method is characterized in that the first information block includes the ID of the AI / ML model that generates the prediction result.

[0065] The present application discloses a device for a first node in wireless communication and artificial intelligence, which includes:

[0066] A first receiver, performing at least M1 measurements on a target cell in a target time window, where M1 is a positive integer;

[0067] wherein the duration of the target time window in the time domain depends on whether the first node generates a prediction result for the measurement of the target cell before the target time window; the prediction result depends on an AI / ML model.

[0068] The present application discloses a device for a second node in wireless communication and artificial intelligence, which includes:

[0069] A second transmitter, configured for a reference signal for the measurement of a target cell;

[0070] wherein the receiver of the reference signal includes a terminal, the first node performs at least M1 measurements on the target cell in a target time window, where M1 is a positive integer; the duration of the target time window in the time domain depends on whether the first node generates a prediction result for the measurement of the target cell before the target time window; the prediction result depends on an AI / ML model.

[0071] As an embodiment, compared with the traditional solution, the present application has the following advantageous but not limited advantages:

[0072] The first node adjusts the measurement frequency in real time according to the prediction result of AI / ML to reduce power consumption and improve battery usage time and lifespan;

[0073] The first node determines the length of the target time window used for measurement on its own according to the prediction result of AI / ML, thereby more effectively reducing power consumption.

[0074] The above operations are carried out through the first information block and the first signaling to ensure that the first node and the second node have the same understanding of the length of the target time window, and to monitor in real time the performance of the solution of adjusting the target time window according to the prediction result, so as to ensure the overall system performance. Brief Description of the Drawings

[0075] 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:

[0076] Figure 1 Shows a flowchart of the transmission of the first node according to an embodiment of the present application;

[0077] Figure 2 Shows a schematic diagram of a network architecture according to an embodiment of the present application;

[0078] Figure 3 Shows a schematic diagram of an embodiment of the radio protocol architecture of the user plane and the control plane according to an embodiment of the present application;

[0079] Figure 4 Shows a schematic diagram of a first communication device and a second communication device according to an embodiment of the present application;

[0080] Figure 5 Shows a first flowchart of the transmission between the first node and the second node according to an embodiment of the present application;

[0081] Figure 6 Shows a second flowchart of the transmission between the first node and the second node according to an embodiment of the present application;

[0082] Figure 7 Shows a third flowchart of the transmission between the first node and the second node according to an embodiment of the present application;

[0083] Figure 8 Shows a schematic diagram of the target time window according to an embodiment of the present application;

[0084] Figure 9 Shows a schematic diagram of the timing according to an embodiment of the present application;

[0085] Figure 10 Shows a schematic diagram of the timing according to another embodiment of the present application;

[0086] Figure 11 Shows a schematic diagram of the RAN domain AI / ML function deployment according to an embodiment of the present application;

[0087] Figure 12 Shows a schematic diagram of the AI / ML function deployment of a UE according to an embodiment of the present application;

[0088] 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;

[0089] Figure 14 Shows a schematic diagram of artificial intelligence or machine learning according to an embodiment of the present application;

[0090] Figure 15 Shows a structural block diagram of a processing device in a first node according to an embodiment of the present application;

[0091] Figure 16 Shows a structural block diagram of a processing device in a second node according to an embodiment of the present application. Detailed implementation

[0092] 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. Based on considerations such as performance, flexibility, complexity, overhead, and compatibility, those skilled in the art have the motivation to flexibly combine the embodiments in different drawings on the premise of non-conflict, including but not limited to the embodiments in the attached Figure 1 drawings and the embodiments in the attached Figure 5 -attached Figure 16 drawings, the embodiments in the attached Figure 5 drawings and the embodiments in the attached Figure 6 -attached Figure 16 drawings, and so on.

[0093] Example 1

[0094] Embodiment 1 exemplifies a flowchart of the transmission of a first node according to an embodiment of the present application, as shown in the attached Figure 1 drawings. In the attached Figure 1 drawings, each box represents a step.

[0095] The first node performs at least M1 measurements on the target cell in the target time window in step 101, where M1 is a positive integer;

[0096] In Embodiment 1, the duration of the target time window in the time domain depends on whether the first node generates a prediction result of the measurement for the target cell before the target time window; the prediction result depends on the AI / ML model.

[0097] Typically, step 101 includes: receiving a reference signal in the target time window, where the reference signal is used to perform at least M1 measurements for the target cell.

[0098] As an embodiment, step 101 includes: calculating at least the RSRP (Reference Signal Received Power) of the reference signal according to the received reference signal, where RSRP is the reference signal received power, RSRQ is the reference signal received quality, RSSI is the received signal strength indicator, and SNR is the signal to noise ratio.

[0099] As an embodiment, how the first node performs at least M1 measurements for the target cell in the target time window is implementation-related.

[0100] As an embodiment, the reference signal includes an SSB.

[0101] As an embodiment, the reference signal includes a CSI-RS (Channel State Information-Reference Signal).

[0102] As an embodiment, the SSB in this application refers to: Synchronization Signal Block, that is, the synchronization signal block.

[0103] As an embodiment, the SSB in this application refers to: SS (Synchronization Signal) / PBCH (Physical Broadcast CHannel) block, that is, the synchronization signal / physical broadcast channel block.

[0104] Typically, the reception occasions of PBCH, PSS (Primary Synchronization Signal), and SSS (Secondary Synchronization Signal) are in consecutive symbols and form an SS / PBCH block.

[0105] As an embodiment, the target time window is continuous in the time domain.

[0106] As an embodiment, the length of the target time window in the time domain is equal to a positive integer greater than 1 of the DRX cycles of the first node.

[0107] As an embodiment, the length of the target time window in the time domain is equal to a positive integer greater than 1 of the eDRX cycles of the first node.

[0108] As an embodiment, the unit of the target time window is seconds.

[0109] As an embodiment, M1 is equal to 1.

[0110] As an embodiment, M1 is equal to 2.

[0111] As an embodiment, the value of M1 depends on the type of the measurement for the target cell.

[0112] As a sub - embodiment of this embodiment, the type of the measurement includes measurements for RSRP or RSRQ.

[0113] As a sub - embodiment of this embodiment, the type of the measurement includes filtered measurements.

[0114] As a sub - embodiment of this embodiment, the type of the measurement includes measurements for a new detectable cell.

[0115] As an embodiment, the target cell includes a serving cell.

[0116] As an embodiment, the target cell includes a neighboring cell.

[0117] As an embodiment, the target cell includes a cell providing 5G services.

[0118] As an embodiment, the target cell includes a cell providing 6G services.

[0119] As an embodiment, the predicted result of the measurement for the target cell is used to determine the duration of the target time window in the time domain.

[0120] As a sub - embodiment of this embodiment, the predicted result of the measurement for the target cell is used to determine a target coefficient, where the target coefficient is a positive integer, and the product of the target coefficient and the target reference time is equal to the duration of the target time window in the time domain.

[0121] As an ancillary embodiment of this sub - embodiment, the target reference time is the DRX (Discontinuous Reception) cycle length of the first node.

[0122] As an ancillary embodiment of this sub - embodiment, the target reference time is the eDRX (extended Discontinuous Reception) cycle length of the first node.

[0123] As an ancillary embodiment of this sub - embodiment, the target reference time is the DTX (Discontinuous Transmission) cycle length of the second node.

[0124] As an ancillary embodiment of this sub - embodiment, the target reference time depends on the AI / ML model that generates the predicted result.

[0125] As an ancillary embodiment of this sub - embodiment, the target reference time depends on the ID of the AI / ML model that generates the predicted result.

[0126] As an embodiment, the predicted result includes: predicting a decrease in the mobility of the first node.

[0127] As an embodiment, the predicted result includes: predicting a decrease in the data volume of the first node.

[0128] As an embodiment, the predicted result includes: predicting that the first node enters a sleep state.

[0129] As an embodiment, the predicted result includes: predicting that the first node does not need to perform the measurement for the target cell in a given future time window.

[0130] As an embodiment, the predicted result includes: predicting that the first node reduces the frequency of the measurement for the target cell in a given future time window.

[0131] As an embodiment, the meaning that the predicted result depends on the AI / ML model includes: the predicted result is generated by the AI / ML model.

[0132] As an example, the prediction result depending on the AI / ML model means that the prediction result is generated by the AI / ML model.

[0133] As an example, the prediction result depending on the AI / ML model means that the prediction result is inferred by the AI / ML model.

[0134] As an example, the prediction result depending on the AI / ML model means that the prediction result is calculated by the AI / ML model.

[0135] As an example, the prediction result depending on the AI / ML model means that the prediction result is obtained by the AI / ML model in combination with measurement results.

[0136] As an example, the prediction result depending on the AI / ML model means that the prediction result depends on the ID for generating the AI / ML model.

[0137] As an example, the prediction result depending on the AI / ML model means that the prediction result depends on the ID for generating the AI / ML model.

[0138] As an example, the prediction result depending on the AI / ML model means that different IDs of the AI / ML model for generating the prediction result correspond to different generated prediction results.

[0139] As an example, the prediction result depending on the AI / ML model means that different IDs of the AI / ML model for generating the prediction result correspond to different confidence intervals of the generated prediction results.

[0140] As a sub - example of this example, the different confidence intervals are used to determine the durations of different target time windows.

[0141] Example 2

[0142] Example 2 illustrates a schematic diagram of a network architecture according to an embodiment of the present application, as shown in the appendix Figure 2 as follows.

[0143] Appendix Figure 2Describes the network architecture 200. The network architecture 200 is the network architecture of LTE (Long-Term Evolution), LTE-A (Long-Term Evolution Advanced), 5G system, 5G-Advanced and future 6G system. The network architectures of LTE, LTE-A, 5G system, 5G-Advanced and future 6G system are referred to as EPS (Evolved Packet System). The 5G NR or LTE network architecture may be referred to as 5GS (5G System) / EPS or some other suitable term; the 6G network architecture may be referred to as 6GS (6G System) / EPS or some other suitable term. The network architecture 200 may include one or more UEs 201, RAN (Next Generation Radio Access Network) 202, core network 210, HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and Internet service 230. The network architecture 200 may be interconnected with other access networks, but these entities / interfaces are not shown for simplicity. As attached Figure 2As shown, the network architecture 200 provides packet switching 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 switching services. The RAN 202 includes Node B 203 and other nodes 204. Node 203 provides user and control plane protocol termination towards the UE 201. Node 203 can be connected to other nodes 204 via the Xn interface (e.g., backhaul). Node 203 can 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), Transmitter Receiver Point (TRP), or some other suitable term. Node 203 provides an access point to the core network 210 for the UE 201; the core network 210 is a 5GC (5G Core Network) / EPC (Evolved Packet Core), or alternatively, the core network 210 is a 6GC. Examples of the UE 201 include cellular phones, smartphones, Session Initiation Protocol (SIP) phones, laptop computers, Personal Digital Assistants (PDAs), satellite radios, Global Positioning Systems, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, drones, aircraft, narrowband physical network devices, machine type communication devices, land vehicles, automobiles, wearable devices, or any other similar functional devices. Those skilled in the art may also refer to the 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 the core network 210 via the S1 / NG interface.The core network 210 includes an MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MME / AMF / SMFs 214, an S-GW (Service Gateway) / UPF (User Plane Function) 212, and a P-GW (Packet Data Network Gateway) / UPF 213. The MME / AMF / SMF 211 is a control node that processes signaling between the UE 201 and the 5G-CN / EPC 210. Generally, the MME / AMF / SMF 211 provides bearer and connection management. All user IP (Internet Protocol) packets are transmitted through the S-GW / UPF 212, which is itself connected to the P-GW / UPF 213. The P-GW provides UE IP address allocation and other functions. The P-GW / UPF 213 is connected to the Internet service 230. The Internet service 230 includes operator-corresponding Internet protocol services, which may specifically include the Internet, intranet, IMS (IP Multimedia Subsystem), and packet switching services.

[0144] As an embodiment, the first node described in the present application includes the UE 201.

[0145] As an embodiment, the second node described in the present application includes the node 203.

[0146] As an embodiment, the second node described in the present application includes a unit in the core network 210.

[0147] As a sub-embodiment of this embodiment, the unit in the core network includes at least one of the MME / AMF / SMF 211, other MME / AMF / SMFs 214, S-GW / UPF 212, or P-GW / UPF 213 shown in the figure.

[0148] As an embodiment, the node 203 is a macro cell base station.

[0149] As an embodiment, the node 203 is a micro cell base station.

[0150] As an embodiment, the node 203 is a pico cell base station.

[0151] As an embodiment, the node 203 is a femtocell.

[0152] As an embodiment, the node 203 is a base station device supporting large delay differences.

[0153] As an embodiment, the node 203 is an airborne platform device.

[0154] As an embodiment, the node 203 is a satellite device.

[0155] As an embodiment, the node 203 is a test device (such as a transceiver simulating some functions of a base station and a signaling tester).

[0156] As an embodiment, the UE 201 includes a mobile phone.

[0157] As an embodiment, the UE 201 is a vehicle including an automobile.

[0158] As an embodiment, the radio link from the UE 201 to the node 203 is an uplink, and the uplink is used to perform uplink transmission.

[0159] As an embodiment, the radio link from the node 203 to the UE 201 is a downlink, and the downlink is used to perform downlink transmission.

[0160] As an embodiment, the radio link between the node 203 and the UE 201 includes a cellular network link.

[0161] As an embodiment, the node 203 and the UE 201 are connected through the Uu air interface.

[0162] As an embodiment, the receivers of the first information block in this application include the node 203.

[0163] As an embodiment, the receivers of the first information block in this application include the units in the core network 210.

[0164] As an embodiment, the senders of the first information block in this application include the UE 201.

[0165] As an embodiment, the senders of the first signaling in this application include the node 203.

[0166] As an example, the sender of the first signaling in the present application includes a unit in the core network 210.

[0167] As an example, the receiver of the first signaling in the present application includes the UE 201.

[0168] As an example, the node 203 supports the deployment of a network-side AI / ML model.

[0169] As an example, the UE 201 supports the deployment of a UE-side AI / ML model.

[0170] As an example, the node 203 supports AI / ML-based mobility management.

[0171] As an example, the UE 201 supports AI / ML-based mobility management.

[0172] As an example, the node 203 supports AI / ML prediction-based measurements.

[0173] As an example, the UE 201 supports AI / ML prediction-based measurements.

[0174] As an example, the UE 201 supports the 5G system.

[0175] As an example, the node 203 supports the 5G system.

[0176] As an example, the UE 201 supports at least the 6G system.

[0177] As an example, the node 203 supports at least the 6G system.

[0178] Example 3

[0179] Embodiment 3 exemplifies a schematic diagram of an embodiment of the radio protocol architecture of the user plane and the control plane according to an embodiment of the present application, as shown in the appendix Figure 3 as follows.

[0180] Figure 3 is a schematic diagram illustrating an embodiment of the radio protocol architecture for the user plane 350 and the control plane 300, Figure 3Show the radio protocol architecture of the control plane 300 for a first communication node device (UE or RSU (Road Side Unit), in-vehicle device or in-vehicle communication module in V2X (Vehicle to Everything)) and a second node device (gNB, UE or RSU in V2X, in-vehicle device or in-vehicle communication module), or between two UEs, with three layers: Layer 1 (L1), Layer 2 (L2) and Layer 3 (L3). L1 is the lowest layer and implements various PHY (PHYsical layer) signal processing functions. L1 will be referred to as PHY 301 in this document. L2 305 is above PHY 301 and is responsible for the link between the first node device and the second node device, or between two UEs, through PHY 301. L2 305 includes a MAC (Medium Access Control) sublayer 302, an RLC (Radio Link Control) sublayer 303 and a PDCP (Packet Data Convergence Protocol) sublayer 304, and these sublayers terminate at the second node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security by encrypting data packets, and provides handover support for the first communication node device between the second communication node devices. 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 between the first communication node devices. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in L3 in the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and configuring the lower layers using RRC signaling between the second communication node device and the first communication node device.The radio protocol architecture of the user plane 350 includes Layer 1 (L1) and Layer 2 (L2). The radio protocol architecture for the first communication node device and the second communication node device in the user plane 350 is substantially the same as the corresponding layers and sub-layers in the control plane 300 for the physical layer 351, the PDCP sub-layer 354 in L2 355, the RLC sub-layer 353 in L2 355, and the MAC sub-layer 352 in L2 355. However, the PDCP sub-layer 354 also provides header compression for upper layer data packets to reduce wireless transmission overhead. The L2 355 in the user plane 350 also includes an SDAP (Service Data Adaptation Protocol) sub-layer 356. The SDAP sub-layer 356 is responsible for the mapping between QoS (Quality of Service) flows and data radio bearers (DRBs) to support service diversity. Although not shown, the first communication node device may have several upper layers above L2 355, including a network layer (e.g., an IP (Internet Protocol) layer) that terminates at the P-GW on the network side and an application layer that terminates at the other end of the connection (e.g., a remote UE, a server, etc.).

[0181] As an example, the Figure 3 radio protocol architecture in is applicable to the first node in this application.

[0182] As an example, the Figure 3 radio protocol architecture in is applicable to the second node in this application.

[0183] As an example, the first information block in this application is generated in the RRC 306.

[0184] As an example, the first information block in this application is generated above the RRC 306 layer.

[0185] As an example, the first information block in this application is generated in the core network.

[0186] As an example, the first information block in this application is generated in the NAS (Non-Access Stratum).

[0187] As an example, the first signaling in this application is generated in the RRC 306.

[0188] As an example, the first signaling in this application is generated in the MAC 302 or the MAC 352.

[0189] As an example, the first signaling in the present application is generated at the RRC 306.

[0190] As an example, the higher layer in the present application refers to the layer above the physical layer.

[0191] As an example, the higher layer in the present application includes the RRC layer.

[0192] As an example, the higher layer signaling in the present application includes RRC IEs.

[0193] As an example, the higher layer signaling in the present application includes RRC messages.

[0194] As an example, the higher layer in the present application includes the MAC layer.

[0195] As an example, the higher layer signaling in the present application includes MAC CEs.

[0196] As an example, the higher layer signaling in the present application includes NAS messages.

[0197] Example 4

[0198] Example 4 illustrates a schematic diagram of a first communication device and a second communication device according to an embodiment of the present application, as shown in the attached Figure 4 shown. The attached Figure 4 is a block diagram of a first communication device 410 and a second communication device 450 that communicate with each other in an access network.

[0199] The first communication device 410 includes a controller / processor 475, a memory 476, a receiving processor 470, a transmitting processor 416, a multi-antenna receiving processor 472, a multi-antenna transmitting processor 471, a transmitter / receiver 418, and an antenna 420.

[0200] The second communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmitting processor 468, a receiving processor 456, a multi-antenna transmitting processor 457, a multi-antenna receiving processor 458, a transmitter / receiver 454, and an antenna 452.

[0201] In the transmission from the first communication device 410 to the second communication device 450, at the first 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 L2. In the DL, the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocation to the second communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the second communication device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for L1 (i.e., the physical layer). The transmit processor 416 implements encoding and interleaving to facilitate forward error correction (FEC) at the second communication device 450, and mapping of signal constellations based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-ary phase shift keying (M-PSK), M-ary quadrature amplitude modulation (M-QAM)). The multi-antenna transmit processor 471 performs digital spatial precoding on the encoded and modulated symbols, including codebook-based precoding, non-codebook-based precoding, and beamforming processing, to generate one or more parallel streams. The transmit processor 416 then maps each parallel stream to subcarriers, multiplexes the modulated symbols 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.

[0202] In the transmission from the first communication device 410 to the second communication device 450, at the second 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 and provides it to the receive processor 456. The receive processor 456 and the multi-antenna receive processor 458 perform various signal processing functions of L1. 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 parallel streams destined for the second communication device 450 after multi-antenna detection in the multi-antenna receive processor 458. The symbols on each parallel 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 first 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 L2. 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 DL, the controller / processor 459 provides demultiplexing between the transmission and the logical channel, packet reassembly, decryption, header decompression, control signal processing to recover the upper layer data packets from the core network. Subsequently, the upper layer data packets are provided to all protocol layers above L2. Various control signals may also be provided to L3 for L3 processing. The controller / processor 459 is also responsible for error detection using the Acknowledgement (ACK) and / or Negative Acknowledgement (NACK) protocols to support HARQ operations.

[0203] In the transmission from the second communication device 450 to the first communication device 410, at the second communication device 450, a data source 467 is used to provide upper layer data packets to a controller / processor 459. The data source 467 represents all protocol layers above L2. Similar to the transmission function at the first communication device 410 described in DL, the controller / processor 459 implements header compression, encryption, packet segmentation and reordering, and multiplexing between logical and transport channels based on the radio resource allocation of the first communication device 410, and implements L2 functions for the user plane and the control plane. The controller / processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communication device 410. A transmit processor 468 performs modulation mapping and channel coding processing, and a multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based precoding and non-codebook-based precoding, and beamforming processing. Subsequently, the transmit processor 468 modulates the generated parallel streams into multi-carrier / single-carrier symbol streams, and after passing through analog precoding / beamforming operations in the multi-antenna transmit processor 457, provides them to different antennas 452 via a 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.

[0204] In the transmission from the second communication device 450 to the first communication device 410, the functions at the first communication device 410 are similar to the receiving functions at the second communication device 450 described in the transmission from the first communication device 410 to the second 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 a multi-antenna receive processor 472 and a receive processor 470. The receive processor 470 and the multi-antenna receive processor 472 jointly implement the functions of L1. A controller / processor 475 implements L2 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. 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 second communication device 450. The upper layer data packets from the controller / processor 475 may be provided to the core network. The controller / processor 475 is also responsible for error detection using the ACK and / or NACK protocols to support HARQ operations.

[0205] As an example, the second 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. The second communication device 450 performs at least M1 measurements on the target cell at least in a target time window, where M1 is a positive integer; the duration of the target time window in the time domain depends on whether the first node generates a prediction result for the measurement of the target cell before the target time window; the prediction result depends on an AI / ML model.

[0206] As an example, the second communication device 450 includes: a memory storing a computer-readable instruction program, the computer-readable instruction program generating actions when executed by at least one processor, the actions including: performing at least M1 measurements on the target cell in a target time window, where M1 is a positive integer.

[0207] As an example, the first 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 first communication device 410 configures at least a reference signal for the measurement of the target cell; the receiver of the reference signal includes a terminal, the first node performs at least M1 measurements on the target cell in a target time window, where M1 is a positive integer; the duration of the target time window in the time domain depends on whether the first node generates a prediction result for the measurement of the target cell before the target time window; the prediction result depends on an AI / ML model.

[0208] As an example, the first communication device 410 includes: a memory storing a computer-readable instruction program, the computer-readable instruction program generating actions when executed by at least one processor, the actions including: configuring a reference signal for the measurement of the target cell.

[0209] As an example, the first node in this application includes the second communication device 450.

[0210] As an example, the second node in this application includes the first communication device 410.

[0211] As an example, at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, the memory 476} is configured to generate a reference signal for measurement of a target cell; at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, the data source 467} is configured to perform at least M1 measurements of the target cell within a target time window.

[0212] As an example, at least one of {the antenna 452, the transmitter 454, the transmitting processor 468, the multi-antenna transmitting processor 457, the controller / processor 459, the memory 460, the data source 467} is used to transmit the first information block described in this application; at least one of {the antenna 420, the receiver 418, the receiving processor 470, the multi-antenna receiving processor 472, the controller / processor 475, the memory 476} is used to receive the first information block described in this application.

[0213] As an example, at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, the memory 476} is used to transmit the first signaling in this application; at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, the data source 467} is used to receive the first signaling in this application.

[0214] Example 5

[0215] Embodiment 5 exemplifies a first flowchart of transmission between a first node and a second node according to an embodiment of this application, as shown in the appendix Figure 5 as shown. In the appendix Figure 5 , communication is performed between the first node U1 and the second node N2 via a wireless link. It should be specifically noted that the order in this embodiment does not limit the signal transmission order and the implementation order in this application; without conflict, the embodiments, sub-embodiments, and accessory embodiments in Embodiment 5 can be applied to Embodiment 6 and Embodiment 7; conversely, without conflict, any embodiment, sub-embodiment, and accessory embodiment in Embodiment 6 and Embodiment 7 can be applied to Embodiment 5.

[0216] For the first node U1, at least M1 measurements for the target cell are performed within the target time window in step S510.

[0217] For the second node N2, a reference signal configured for the measurement of the target cell is configured in step S520.

[0218] In Embodiment 5, the duration of the target time window in the time domain depends on whether the first node generates a prediction result for the measurement of the target cell before the target time window; the prediction result depends on the AI / ML model; and M1 is a positive integer.

[0219] As an embodiment, the first node U1 is the first node described in this application.

[0220] As an embodiment, the second node N2 is the second node described in this application.

[0221] As an embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between a base station device and a user equipment.

[0222] As an embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between a relay node device and a user equipment.

[0223] As an embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between user equipments.

[0224] As an embodiment, the second node N2 and the first node U1 communicate through the Uu interface.

[0225] As an embodiment, the second node N2 is the serving base station of the service cell of the first node U1.

[0226] As an embodiment, the second node N2 is the serving base station of an adjacent cell of the service cell of the first node U1.

[0227] As an embodiment, the second node N2 is a network device.

[0228] As an embodiment, the second node N2 is a core network.

[0229] As an embodiment, the reference signal configured for the measurement of the target cell includes: indicating at least one of the time domain resources or frequency domain resources occupied by the reference signal for the measurement of the target cell.

[0230] As an example, the reference signal configured for the measurement of the target cell includes: configuration information for transmitting the reference signal for the measurement of the target cell.

[0231] As an example, the reference signal configured for the measurement of the target cell includes: transmitting the reference signal for the measurement of the target cell.

[0232] Typically, when the first node generates a prediction result for the measurement of the target cell before the target time window, the duration of the target time window is equal to a first time value; when the first node does not generate a prediction result for the measurement of the target cell before the target time window, the duration of the target time window is equal to a second time value; the first time value and the second time value are different.

[0233] As an example, the first time value is greater than the second time value.

[0234] As an example, the first time value is a positive integer multiple greater than 1 of the second time value.

[0235] As a sub - example of this example, the first time value is equal to K1 times the second time value, where K1 is a positive integer greater than 1.

[0236] As an accessory example of this sub - example, the value of K1 depends on the prediction result of the measurement.

[0237] As an accessory example of this sub - example, the prediction result of the measurement enables K1.

[0238] As an accessory example of this sub - example, the value of K1 is predefined.

[0239] As an accessory example of this sub - example, the value of K1 is configured by RRC signaling.

[0240] Typically, the first time value is equal to the product of a first integer and a first reference time value, where the first integer depends on the prediction result or the first reference time value depends on the prediction result, and the first reference time depends on the length of the discontinuous reception period of the first node.

[0241] Typically, the first time value is equal to the product of the first integer and the first reference time value, where the first integer depends on the prediction result and the first reference time is the length of the discontinuous reception period of the first node.

[0242] As an example, the length of the discontinuous reception period includes the DRX cycle.

[0243] As an example, the length of the discontinuous reception period includes an eDRX period.

[0244] As an example, the first integer is equal to the product of a plurality of coefficients, and the prediction result is used to determine one of the plurality of coefficients, and any of the plurality of coefficients is a positive integer.

[0245] As an example, the prediction result is used to determine that the first integer is equal to the product of a plurality of legacy coefficients and a first coefficient, the plurality of legacy coefficients being coefficients in Rel-19 and previous Release versions, and the first coefficient being a coefficient other than the plurality of legacy coefficients.

[0246] As a sub-example of this example, the prediction result enables the first coefficient.

[0247] As a sub-example of this example, the first coefficient is predefined.

[0248] As a sub-example of this example, the first coefficient is configured by higher layer signaling.

[0249] Typically, the first time value is equal to the product of the first integer and the first reference time value, the first reference time value depending on the prediction result, and the first reference time depending on the length of the discontinuous reception period of the first node.

[0250] As an example, the first reference time value is equal to a positive integer multiple greater than 1 of the length of the discontinuous reception period of the first node.

[0251] As an example, the length of the discontinuous reception period includes a DRX period.

[0252] As an example, the length of the discontinuous reception period includes an eDRX period.

[0253] As an example, the first reference time value is predefined, or the first reference time value is configured by RRC signaling.

[0254] As an example, the first reference time value depends on the AI / ML model that generates the prediction result.

[0255] Typically, the target integer depends on the indication of the first information block, and the first time value depends on the target integer and the length of the discontinuous reception period of the first node.

[0256] As an embodiment, the target integer is equal to the first integer in the present application.

[0257] As an embodiment, the target integer is one of a plurality of coefficients, and the first integer is equal to the product of the plurality of coefficients.

[0258] Typically, the target cell includes at least one of the serving cell of the first node, an Intra-Frequency cell, an Inter-Frequency cell, and an Inter-RAT cell.

[0259] As an embodiment, the target cell includes the serving cell of the first node.

[0260] As an embodiment, the target cell includes a neighboring cell of the first node.

[0261] As an embodiment, the target cell includes the Intra-Frequency cell.

[0262] As an embodiment, the target cell includes the Inter-Frequency cell.

[0263] As an embodiment, the target cell includes the Inter-RAT cell.

[0264] As an embodiment, the target cell includes the Inter-RAT E-UTRAN cell.

[0265] As an embodiment, the target cell includes a 5G cell.

[0266] As an embodiment, the target cell includes a 6G cell.

[0267] As an embodiment, the duration of the target time window in the time domain is equal to N serv .

[0268] As an embodiment, the duration of the target time window in the time domain is equal to T detect,NR_Intra .

[0269] As an embodiment, the duration of the target time window in the time domain is equal to T measure,NR_Intra .

[0270] As an embodiment, the duration of the target time window in the time domain is equal to T evaluate,NR_Intra .

[0271] As an embodiment, the duration of the target time window in the time domain is equal to T detect,NR_Inter .

[0272] As an example, the duration of the target time window in the time domain is equal to T measure,NR_Inter .

[0273] As an example, the duration of the target time window in the time domain is equal to T evaluate,NR_Inter .

[0274] As an example, the duration of the target time window in the time domain is equal to T detect,NR_Inter_HST .

[0275] As an example, the duration of the target time window in the time domain is equal to T measure,NR_Inter_HST .

[0276] As an example, the duration of the target time window in the time domain is equal to T evaluate,NR_Inter_HST .

[0277] As an example, the duration of the target time window in the time domain is equal to T detect,EUTRAN .

[0278] As an example, the duration of the target time window in the time domain is equal to T measure,EUTRAN .

[0279] As an example, the duration of the target time window in the time domain is equal to T evaluate,EUTRAN .

[0280] As an example, the duration of the target time window in the time domain is equal to T detect,EUTRAN_HST .

[0281] As an example, the duration of the target time window in the time domain is equal to T measure,EUTRAN_HST .

[0282] As an example, the duration of the target time window in the time domain is equal to T evaluate,EUTRAN_HST .

[0283] Typically, the first information block includes the confidence interval of the prediction result.

[0284] As an example, the confidence interval is an integer between 0 and 100.

[0285] As an example, the first information block indicates the confidence interval for the prediction result.

[0286] As an example, the confidence interval of the prediction result includes: the credibility of the prediction result.

[0287] As an example, the confidence interval of the prediction result includes: the range of the credibility of the prediction result.

[0288] Example 6

[0289] Example 6 exemplifies a second flowchart for transmission between a first node and a second node according to an embodiment of the present application, as shown in the appendix Figure 6 as shown. In the appendix Figure 6 as shown, the first node U3 communicates with the second node N4 via a wireless link. It should be specifically noted that the order in this embodiment does not limit the signal transmission order and implementation order in the present application; without conflict, the embodiments, sub - embodiments, and affiliated embodiments in Example 6 can be applied to Example 5 and Example 7; conversely, without conflict, any embodiment, sub - embodiment, and affiliated embodiment in Example 5 and Example 7 can be applied to Example 6.

[0290] For the first node U3, a first information block is sent in step S630.

[0291] For the second node N4, the first information block is received in step S640.

[0292] In Example 6, the first information block includes the prediction result of the measurement for the target cell generated by the first node.

[0293] As an embodiment, the first information block is sent through physical layer signaling.

[0294] As an embodiment, the first information block is transmitted through UCI (Uplink Control Information).

[0295] As an embodiment, the physical layer channel occupied by the first information block includes PUSCH (Physical Uplink Shared Channel).

[0296] As an embodiment, the physical layer channel occupied by the first information block includes PUCCH (Physical Uplink Control Channel).

[0297] As an embodiment, the first information block is sent through MAC CE (Control Element).

[0298] As an embodiment, the first information block is sent through RRC signaling.

[0299] Typically, the first information block includes the ID of the AI / ML model that generates the prediction result.

[0300] As an example, the first information block indicates the ID of the AI / ML model that generates the prediction result.

[0301] As an example, the ID refers to: IDentify, to prove.

[0302] As an example, the ID refers to: IDentification, identity proof.

[0303] As an example, the ID refers to: IDentity, identity or identifier.

[0304] As an example, the ID refers to: IDentifier, identifier.

[0305] As an example, the ID refers to: InDex, index.

[0306] As an example, the ID of the AI / ML includes the FunctionalityID corresponding to the AI / ML model.

[0307] As an example, the ID of the first model includes the entity ID corresponding to the AI / ML model.

[0308] As an example of this example, the step S640 is attached Figure 5 before the step S520 described above.

[0309] As an example of this example, the step S640 is attached Figure 5 after the step S520 described above.

[0310] As an example of this example, the step S630 is attached Figure 5 before the step S510 described above.

[0311] Example 7

[0312] Example 7 exemplifies the third flowchart for transmission between the first node and the second node according to an example of the present application, as shown in the attachment Figure 7 shown. In the attachment Figure 7 The first node U5 communicates with the second node N6 via a wireless link. It should be noted that the order in this example does not limit the signal transmission order and implementation order in the present application; in the case of no conflict, the examples, sub-examples, and subsidiary examples in Example 7 can be applied to Example 5 and Example 6; conversely, in the case of no conflict, any example, sub-example, and subsidiary example in Example 5 and Example 6 can be applied to Example 7.

[0313] For the first node U5, the first signaling is received in step S750.

[0314] For the second node N6, the first signaling is sent in step S760.

[0315] In Embodiment 7, the first signaling is a feedback for the first information block.

[0316] As an embodiment, the first signaling is transmitted through physical layer signaling.

[0317] As an embodiment, the physical layer channel occupied by the first signaling includes the PDCCH (Physical Downlink Control Channel).

[0318] As an embodiment, the physical layer channel occupied by the first signaling includes the PDSCH (Physical Downlink Shared Channel).

[0319] As an embodiment, the first signaling includes DCI (Downlink Control Information).

[0320] As an embodiment, the first signaling is transmitted through MAC CE.

[0321] As an embodiment, the first signaling is transmitted through RRC signaling.

[0322] As an embodiment, the first signaling indicates that the first information block is correctly received.

[0323] As an embodiment, the first signaling is used to confirm that the duration of the target time window in the time domain depends on the prediction result of the measurement of the target cell generated by the first node before the target time window.

[0324] As an embodiment, the first signaling enables the first time value.

[0325] As an embodiment, the first signaling is used to determine that the duration of the target time window is equal to the first time value.

[0326] As an embodiment, step S760 is before step S520 described in Appendix Figure 5 and after step S640 described in Appendix Figure 6 as described.

[0327] As an embodiment, step S760 is in Appendix Figure 5After the step S520 described above.

[0328] As an embodiment, the step S750 is before the step S510 described in Figure 5 ; and before the step S630 described in Figure 6

[0329] Example 8

[0330] Embodiment 8 illustrates a schematic diagram of a target time window according to an embodiment of the present application, as shown in Figure 8 . In Figure 8 , the target time window occupies continuous time domain resources in the time domain.

[0331] As an embodiment, the target time window corresponds to the measurement period of the first node.

[0332] As an embodiment, the target time window corresponds to the measurement of the first node in the idle state.

[0333] As an embodiment, the target time window corresponds to the measurement of the first node in the inactive state.

[0334] As an embodiment, the target time window is used for at least one of evaluation, measurement, or detection of the target cell.

[0335] Example 9

[0336] Embodiment 9 illustrates a schematic diagram of the timing according to an embodiment of the present application, as shown in Figure 9 . In Figure 9 , the first node determines the prediction result at the first moment, sends the first information block at the second moment, receives the first signaling at the third moment, and starts to perform at least M1 measurements on the target cell in the target time window at the fourth moment. Among them, the sending of the first information block at the second moment is optional, and the receiving of the first signaling at the third moment is optional.

[0337] As an embodiment, the first node determines that the duration of the target time window is equal to the first time value at the first moment.

[0338] As an embodiment, the first node determines that the duration of the target time window is equal to the first time value at the third moment.

[0339] Example 10

[0340] Embodiment 10 illustrates a schematic diagram of the timing relationship according to another embodiment of the present application, as shown in​Figure 10 As shown. In the appendix Figure 10 Among them, the first node determines at the first moment that the duration of the target time window used for the measurement of the target cell in the first time length is equal to the first time value.

[0341] As an embodiment, the first time length includes the target time window.

[0342] As an embodiment, the prediction result of the first node is used to determine the first time length.

[0343] As a sub - embodiment of this embodiment, the prediction result of the first node determines the start moment of the first time length.

[0344] As a sub - embodiment of this embodiment, the prediction result of the first node determines the end moment of the first time length.

[0345] As a sub - embodiment of this embodiment, the prediction result of the first node determines the duration of the first time length.

[0346] As a sub - embodiment of this embodiment, the first information block indicates the start moment of the first time length.

[0347] As a sub - embodiment of this embodiment, the first information block indicates the end moment of the first time length.

[0348] As a sub - embodiment of this embodiment, the first information block indicates the duration of the first time length.

[0349] As an embodiment, the duration of the target time window used by the first node for the measurement of the target cell after the first time length is equal to the second time value.

[0350] Example 11

[0351] Embodiment 11 exemplifies a schematic diagram of the RAN - domain AI / ML function deployment according to an embodiment of the present application, as shown in the appendix Figure 11 As shown. In the appendix Figure 11 Among them, the gNB can be replaced by network devices such as eNB, or 6G base stations, etc.

[0352] In Embodiment 11, the management of the ML inference functions of multiple base stations is completed by the RAN - domain management function 1102, that is, data interaction is performed with the RAN - domain MnS (Management Service) consumer / cross - domain management 1101 (as shown in the appendix Figure 11as shown by the dashed arrow in). The RAN domain ML training function 1103 is located in the RAN domain management function 1102; while the ML inference function is located in the base station, that is, the AI / ML inference function 1104 is located in the gNB 1105, the AI / ML inference function 1106 is located in the gNB 1107,....

[0353] AI / ML related functions include ML training function (also known as AI training, or AI / ML training), ML testing function, ML inference function (also known as AI inference, or AI / ML inference), etc. 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 running of executable files; or through a combination of software and hardware, such as accelerating specific computing units through hardware to improve the operation speed or save power consumption.

[0354] 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 (Management Data Analytic 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).

[0355] 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.

[0356] Similarly, the ML testing function can also be deployed in a cross-domain management system or a domain-specific management system.

[0357] Optionally, the management of the ML inference function can also be completed by the base station itself, that is, each base station can independently interact with the RAN domain MnS consumer / cross-domain management 1101 for data.

[0358] It should be noted that Embodiment 11 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.

[0359] As an embodiment, a gNB (or base station) in Embodiment 11 is the second node of the present application.

[0360] Example 12

[0361] Embodiment 12 illustrates a schematic diagram of the AI / ML function deployment of a UE according to an embodiment of the present application, as shown in the appendix Figure 12 shown. In the appendix Figure 12 RAN domain ML training function 1204 is optional.

[0362] The UE function 1203 is deployed in the first node of the present application. The UE function 1203 includes an AI / ML inference function 1205; the AI / ML inference function 1205 uses an ML model (also called an AI model) for inference; an ML model usually needs to be trained before being used for AI / ML inference.

[0363] As an embodiment, the UE function 1203 includes a RAN domain ML training function 1204. The RAN domain ML training function 1204 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.

[0364] The above embodiments can reduce the complexity of the base station, or save the radio interface resources caused by reporting training data; however, the above embodiments impose relatively high requirements on the processing capabilities of the UE side.

[0365] Optionally, the UE function 1203 further includes a CN domain ML training function ( Figure 12 not included in the figure).

[0366] Optionally, the UE function 1203 further includes an AI / ML deployment function- Figure 12is not included and is used for loading the ML model and data.

[0367] As an embodiment, the first node indicates whether it supports the ML training function (RAN domain or CN domain) through capability reporting, and the capability reporting is RRC signaling or NAS (Non-Access Stratum) signaling.

[0368] As an embodiment, the ML model and related metadata are loaded by the first node from a network device or a remote server.

[0369] Optionally, the UE function 1203 is an MnS producer, providing data to the CN domain MnF (Management Function), and / or the RAN domain MnF, and / or the cross-domain management system 1201 for management or analysis (as shown by the double arrow 1202).

[0370] Optionally, the UE function 1203 is an MnS consumer, loading data from the CN domain MnF, and / or the RAN domain MnF, and / or the cross-domain management system 1201 for AI / ML-related management, such as management data requests, ML model activation, and / or ML training, etc. (as shown by the double arrow 1202).

[0371] As an embodiment, the measurement result of the first measurement in this application is obtained through the inference of the AI / ML inference function 1205.

[0372] As an embodiment, the ML model is based on NN (Neural Networks).

[0373] As an embodiment, the ML model is based on ANN (Artificial Neural Networks).

[0374] As an embodiment, the ML model is based on CNN (Convolutional Neural Networks).

[0375] As an embodiment, the ML model is based on the LLM (Large Language Model) architecture.

[0376] As an embodiment, the ML model is based on the Transformer architecture.

[0377] As an example, the ML model is based on LSTM (Long Short-Term Memory).

[0378] As an example, the ML model is based on MLP (MultiLayer Perceptron).

[0379] As an example, the ML model is based on GAN (Generative Adversarial Nets).

[0380] As an example, the ML model is based on a lightweight neural network.

[0381] As a sub-example of this example, the lightweight neural network includes one or more of MobileNet, ShuffleNet, and SqueezeNet.

[0382] Example 13

[0383] Example 13 illustrates a schematic diagram of a processing system based on artificial intelligence or machine learning according to an example of the present application, as shown in the appendix Figure 13 shown. In the appendix Figure 13 shown, the processing system based on artificial intelligence or machine learning includes a first processor, a second processor, a third processor, and a fourth processor.

[0384] In Example 13, the first processor sends a first data set to the second processor and a second data set to the third processor; the second processor generates a target first type of parameter group according to the first data set, and the second processor sends the generated target first type of parameter group to the third processor; the third processor processes the second data set using the target first type of parameter group to obtain a first type of output. Optionally, the third processor sends the first type of output to the fourth processor. In the appendix Figure 13 shown, the first type of feedback and the second type of feedback are optional; the second processor includes an ML training function; the third processor includes an ML inference function.

[0385] As an example, the fourth processor includes an ML testing function.

[0386] As an example, the fourth processor includes performance monitoring / evaluation of the ML model.

[0387] As an example, the third processor sends a first type of feedback to the second processor; the first type of feedback is used to trigger recalculation or update of the target first type of parameter group, that is, to trigger ML initial training or ML retraining.

[0388] As an example, the fourth processor sends a second type of feedback to the first processor; 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.

[0389] As an example, the third processor belongs to the first node, and the fourth processor belongs to the second node.

[0390] As an example, the third processor belongs to the first node.

[0391] As an example, the first data set includes training data.

[0392] As an example, the second processor is used to train an ML model, and the trained model is described by the target first type of parameter group.

[0393] As an example, the second processor belongs to the first node; the above method avoids transferring the first data set to the second node.

[0394] As an example, the second processor belongs to the second node in this application; the above method supports joint training and optimizes system performance.

[0395] As an example, the second processor belongs to the core network; the above method supports full-network joint training and further optimizes system performance.

[0396] As an example, the second data set includes inference data.

[0397] As an example, the third 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.

[0398] As an example, the output of the third processor includes the measurement result of the first measurement.

[0399] As an example, the M computing resource sets depend on the third processor.

[0400] As an example, the ID of the first model in this application includes the ID of the first data set corresponding to the first model.

[0401] As an example, the third 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.

[0402] As an example, 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 second processor recalculates the target first type of parameter group.

[0403] As an example, 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.

[0404] As an example, the target first type of parameter group includes one or more of: a convolution kernel (kernel), a pooling kernel (poolcore), a pooling function, an activation function, parameters of the pooling function, or parameters of the activation function.

[0405] As an example, the target first type of parameter group includes one or more of: a convolution kernel size, a number of convolution layers, a convolution stride, a pooling kernel size, a pooling kernel stride, a pooling function, an activation function, or a number of feature maps.

[0406] Example 14

[0407] Example 14 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of the present application, as shown in the appendix Figure 14 In the appendix Figure 14 shown. In the appendix

[0408] As an example, the first operation includes AI / ML training, the second operation includes AI / ML testing, the third operation includes AI / ML emulation, the fourth operation includes AI / ML entity loading, and the fifth operation includes AI / ML inference.

[0409] As an example, the first phase includes a training phase, the second phase includes an emulation phase, the third phase includes a deployment phase, and the fourth phase includes an inference phase.

[0410] As an example, the first stage includes AI / ML model training.

[0411] As an example, the first stage includes AI / ML model training and AI / ML testing.

[0412] As an example, the AI / ML model training includes initial training and re-training of one or a group of AI / ML entities.

[0413] As an example, the AI / ML model training depends on training data.

[0414] As an example, the AI / ML model training includes AI / ML entity validation.

[0415] As an example, the AI / ML entity validation is used to evaluate the performance of the AI / ML entity.

[0416] As an example, the AI / ML entity validation depends on validation data.

[0417] As an example, if the result of the AI / ML entity validation does not meet the expectation, the AI / ML model will be re-trained.

[0418] As an example, the AI / ML testing includes testing the validated AI / ML entity to evaluate the performance of the trained AI / ML model.

[0419] As an example, if the result of the AI / ML testing meets the expectation, the AI / ML entity proceeds to the next stage; otherwise, the AI / ML model will be re-trained.

[0420] As an example, the AI / ML testing depends on test data.

[0421] As an example, the second stage includes AI / ML simulation, and the AI / ML simulation performs inference of the AI / ML entity in a simulation environment.

[0422] As an example, the AI / ML simulation estimates the performance of the AI / ML entity inference in a simulation environment before using the AI / ML entity.

[0423] As an example, the second stage is optional.

[0424] As an example, the third stage includes AI / ML entity loading, which is to obtain a trained AI / ML entity to achieve the desired AI / ML inference function.

[0425] As an example, the third stage is optional.

[0426] As an example, when the training function and the inference function are co-located, the third stage is no longer required.

[0427] As an example, the fourth stage includes AI / ML inference.

[0428] Example 15

[0429] Example 15 illustrates a structural block diagram of a processing device in a first node according to an embodiment of the present application, as shown in the appendix Figure 15 shown. In the appendix Figure 15 shown, the processing device 1500 in the first node includes a first receiver 1501 and a first transmitter 1502, where the first transmitter 1502 is optional.

[0430] In Example 15, the first receiver 1501 makes at least M1 measurements for the target cell within a target time window, where M1 is a positive integer.

[0431] In Example 15, the duration of the target time window in the time domain depends on whether the first node generates a prediction result for the measurement of the target cell before the target time window; the prediction result depends on the AI / ML model.

[0432] As an example, when the first node generates a prediction result for the measurement of the target cell before the target time window, the duration of the target time window is equal to a first time value; when the first node does not generate a prediction result for the measurement of the target cell before the target time window, the duration of the target time window is equal to a second time value; the first time value and the second time value are different.

[0433] As an example, the processing device of the first node includes:

[0434] The first transmitter 1502, which sends a first information block;

[0435] wherein, the first information block includes the prediction result of the measurement of the target cell generated by the first node.

[0436] As an example, the first time value is equal to the product of a first integer and a first reference time value, where the first integer depends on the prediction result or the first reference time value depends on the prediction result, and the first reference time depends on the length of the discontinuous reception period of the first node.

[0437] As an example, the target integer depends on the indication of the first information block, and the first time value depends on the target integer and the length of the discontinuous reception period of the first node.

[0438] As an example, the target cell includes at least one of the serving cell of the first node, an Intra-Frequency cell, an Inter-Frequency cell, and an Inter-RAT cell.

[0439] As an example, the first information block includes the confidence interval of the prediction result.

[0440] As an example, the first receiver 1501 receives a first signaling; the first signaling is a feedback for the first information block.

[0441] As an example, the first information block includes the ID of the AI / ML model that generates the prediction result.

[0442] As an example, the first node 1500 is a user equipment.

[0443] As an example, the first node 1500 is a terminal.

[0444] As an example, the first receiver 1501 includes at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, the data source 467} in Embodiment 4.

[0445] As an example, the first transmitter 1502 includes at least one of {the antenna 452, the transmitter 454, the transmitting processor 468, the multi-antenna transmitting processor 457, the controller / processor 459, the memory 460, the data source 467} in Embodiment 4.

[0446] Example 16

[0447] Embodiment 16 illustrates a structural block diagram of a processing device in a second node according to an embodiment of the present application, as shown in the attached Figure 16 shown. In the attached Figure 16In [description], the processing device 1600 in the second node includes a second transmitter 1601 and a second receiver 1602, where the second receiver 1602 is optional.

[0448] In Embodiment 16, the second transmitter 1601 is configured as a reference signal for measurement of the target cell.

[0449] In Embodiment 16, the receiver of the reference signal includes a first node that performs at least M1 measurements on the target cell within a target time window, where M1 is a positive integer; the duration of the target time window in the time domain depends on whether the first node generates a prediction result for the measurement of the target cell before the target time window; the prediction result depends on an AI / ML model.

[0450] As an embodiment, when the first node generates a prediction result for the measurement of the target cell before the target time window, the duration of the target time window is equal to a first time value; when the first node does not generate a prediction result for the measurement of the target cell before the target time window, the duration of the target time window is equal to a second time value; the first time value and the second time value are different.

[0451] As an embodiment, the processing device of the second node includes:

[0452] The second receiver 1602 that receives a first information block;

[0453] where the first information block includes the prediction result for the measurement of the target cell generated by the first node.

[0454] As an embodiment, the first time value is equal to the product of a first integer and a first reference time value, where the first integer depends on the prediction result or the first reference time value depends on the prediction result, and the first reference time depends on the length of the discontinuous reception period of the first node.

[0455] As an embodiment, a target integer depends on the indication of the first information block, and the first time value depends on the target integer and the length of the discontinuous reception period of the first node.

[0456] As an embodiment, the target cell includes at least one of the serving cell of the first node, an Intra-Frequency cell, an Inter-Frequency cell, and an Inter-RAT cell.

[0457] As an embodiment, the first information block includes a confidence interval of the prediction result.

[0458] As an example, the second transmitter 1601 transmits a first signaling; the first signaling is feedback for the first information block.

[0459] As an example, the first information block includes the ID of the AI / ML model that generates the prediction result.

[0460] As an example, the second node 1600 includes a base station device.

[0461] As an example, the second node 1600 includes a core network device.

[0462] As an example, the second node 1600 includes a network device.

[0463] As an example, the second transmitter 1601 includes at least one of {the antenna 420, the transmitter 418, the transmit processor 416, the multi-antenna transmit processor 471, the controller / processor 475, the memory 476} in Embodiment 4.

[0464] As an example, the second receiver 1602 includes at least one of {the antenna 420, the receiver 418, the receive processor 470, the multi-antenna receive processor 472, the controller / processor 475, the memory 476} in Embodiment 4.

[0465] 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 the form of hardware or in the form of a software functional module. This application is not limited to any specific form of 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 aircraft, aircraft, small aircraft, mobile phones, tablet computers, notebooks, vehicle-mounted communication devices, transportation means, vehicles, RSU, wireless sensors, network cards, Internet of Things terminals, RFID (Radio Frequency Identification) terminals, NB-IoT (Narrow Band Internet of Things) terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, network cards, vehicle-mounted communication devices, low-cost mobile phones, low-cost tablet computers, and other wireless communication devices. The base station or system equipment in this application includes, but is not limited to, macrocell base stations, microcell base stations, small cell base stations, home base stations, relay base stations, eNB (evolved Node B), gNB, TRP, GNSS (Global Navigation Satellite System), relay satellites, satellite base stations, aerial base stations, RSU, drones, test equipment, such as transceiver devices or signaling testers that simulate some functions of base stations, and other wireless communication devices.

[0466] Those skilled in the art should understand that the present invention can be implemented in other specific forms without departing from its core or basic characteristics. Therefore, the currently disclosed embodiments should be considered as descriptive rather than restrictive in any case. The scope of the invention is determined by the appended claims rather than the previous description, and all modifications within the equivalent meaning and scope thereof are considered to be included therein.

Claims

1. A method used in a terminal for wireless communication and artificial intelligence, characterized in that: include: Perform at least M1 measurements on the target cell in the target time window, where M1 is a positive integer; The duration of the target time window in the time domain depends on whether the terminal generates a prediction result of the measurement for the target cell before the target time window; and the prediction result depends on an AI / ML model.

2. The method according to claim 1, characterized in that When the terminal generates a prediction result of the measurement for the target cell before the target time window, the duration of the target time window is equal to a first time value; when the terminal does not generate a prediction result of the measurement for the target cell before the target time window, the duration of the target time window is equal to a second time value; the first time value and the second time value are different.

3. The method according to claim 1 or 2, characterized in that: include: Sending a first information block; The first information block includes the prediction result of the measurement for the target cell generated by the terminal.

4. The method according to claim 2 or 3, characterized in that: The first time value is equal to the product of a first integer and a first reference time value, the first integer depends on the prediction result or the first reference time value depends on the prediction result, and the first reference time depends on the length of the discontinuous reception cycle of the terminal.

5. The method according to claim 3 or 4, characterized in that: The target integer depends on an indication of the first information block, and the first time value depends on the target integer and a length of a discontinuous reception cycle of the terminal.

6. The method according to any one of claims 1 to 5, characterized in that: The target cell includes at least one of a serving cell, an Intra-Frequency cell, an Inter-Frequency cell, and an Inter-RAT cell of the terminal.

7. The method according to any one of claims 3 to 5, characterized in that: The first information block includes a confidence interval of the prediction result.

8. The method according to any one of claims 3 to 7, characterized in that include: receiving a first signaling; The first signaling is feedback for the first information block.

9. The method according to any one of claims 3 to 8, characterized in that: The first information block includes the ID of the AI / ML model that generated the prediction result.

10. 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 9.

11. A method used in a base station for wireless communication and artificial intelligence, characterized in that: include: Configuring a reference signal for measurement of a target cell; Among them, the receiver of the reference signal includes a terminal, and the terminal performs at least M1 measurements on the target cell in a target time window, where M1 is a positive integer; the duration of the target time window in the time domain depends on whether the terminal generates a prediction result of the measurement for the target cell before the target time window; and the prediction result depends on an AI / ML model.

12. The method according to claim 11, characterized in that When the terminal generates a prediction result of the measurement for the target cell before the target time window, the duration of the target time window is equal to a first time value; when the terminal does not generate a prediction result of the measurement for the target cell before the target time window, the duration of the target time window is equal to a second time value; the first time value and the second time value are different.

13. The method according to claim 11 or 12, characterized in that: include: receiving a first information block; The first information block includes the prediction result of the measurement for the target cell generated by the terminal.

14. The method according to claim 12 or 13, characterized in that The first time value is equal to the product of a first integer and a first reference time value, the first integer depends on the prediction result or the first reference time value depends on the prediction result, and the first reference time depends on the length of the discontinuous reception cycle of the terminal.

15. The method according to claim 13 or 14, characterized in that The target integer depends on an indication of the first information block, and the first time value depends on the target integer and a length of a discontinuous reception cycle of the terminal.

16. The method according to any one of claims 11 to 15, characterized in that The target cell includes at least one of a serving cell, an Intra-Frequency cell, an Inter-Frequency cell, and an Inter-RAT cell of the terminal.

17. The method according to any one of claims 13 to 15, characterized in that The first information block includes a confidence interval of the prediction result.

18. The method according to any one of claims 13 to 17, characterized in that include: Sending a first signaling; The first signaling is feedback for the first information block.

19. The method according to any one of claims 13 to 18, characterized in that The first information block includes the ID of the AI / ML model that generated the prediction result.

20. 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. The one or more processors call the computer instructions to enable the terminal to execute the method according to any one of claims 11 to 19.

Images