Method and apparatus in node used for wireless communication measurement

Abstract

The invention discloses a method and an apparatus in a node used for wireless communication measurement. A first node receives a first information block that configures a first measurement for mobility, the number of computing power units occupied by the first measurement being equal to a first value; processing the first measurement only when the first value is not greater than the number of remaining computing power units of the first node; the number of the remaining computing power units is equal to the sum of the total computing power units of the first node and the number of occupied computing power units; the total computing power unit number of the first node depends on the number of first-class resources included in the first node and the number of second-class resources included in the corresponding first-class resources; any one first type of resources included in the first node at least comprises one second type of resources. The computing power distribution is optimized, so that resource waste is avoided, and the measurement reliability is ensured.

Description

Technical Field

[0001] The present application relates to a signal transmission method and apparatus in a wireless communication system, and particularly to a measurement method and apparatus. Background Art

[0002] The application scenarios of future wireless communication systems are becoming more and more diversified. In order to meet the different performance requirements of different scenarios, 3GPP (3rd Generation Partnership Project) is actively researching how to combine AI (Artificial Intelligence) / ML (Machine Learning) technologies with mobile communications. Among them, the main application scenarios involve network automation, optimizing resource allocation, and improving the service continuity of mobile users, etc.

[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 measurement, Inter-Frequency NR (New Radio) measurement, Inter-RAT 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.

[0004] Meanwhile, AI / ML can achieve load balancing and energy savings by analyzing the measured and predicted resource status information (such as the physical resource block utilization rate of neighboring cells and serving cells, the number of active UEs, etc.); in addition, AI / ML can also optimize the radio resource management strategy by predicting the movement trajectory of UEs, enabling the network to make resource allocation and handover decisions according to the expected movement path of UEs. Mobility management based on AI / ML not only helps improve the accuracy and efficiency of handovers, but also reduces handover interruption time and enhances the user experience. In the future, 3GPP will further deepen the application of AI / ML in mobility management, promote the deep integration of AI / ML and communication networks, so as to improve the intelligence level of the network, optimize mobility management, improve spectrum and energy efficiency, and provide users with more stable and efficient mobile communication services. Summary of the Invention

[0005] In the current Release-18 standard, clear definitions of resource occupancy and corresponding priority designs are given for the processing of physical layer channel information, that is, when the remaining CPU (Central Processing Unit) resources are not enough to meet the number of CPUs required for channel processing, the processing of the corresponding channel information will be abandoned. The processing of physical layer channel information itself has relatively high requirements for latency. In the future, when AI / ML is used for measurements related to mobility management, since the requirements for hardware and resource occupancy in AI / ML computing and reasoning are more stringent, it is a problem that needs to be considered how to reasonably define the computing power or resources of AI / ML.

[0006] In view of the above problems, the present application discloses a solution. It should be noted that although a large number of embodiments of the present application are directed to AI / ML, the present application is also applicable to other solutions, such as traditional channel information reporting solutions. In addition, adopting a unified solution in different scenarios (including but not limited to AI / ML-based solutions and traditional information reporting solutions) also helps to reduce hardware complexity and cost. Without conflict, the embodiments and features in the first node of the present application can be applied to the second node, and vice versa. Without conflict, the embodiments and features in the embodiments of the present application can be combined with each other arbitrarily.

[0007] In particular, the explanations of the terms, nouns, functions, and variables in this 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 3GPP (the 3rd Generation Partnership Project). If necessary, TS38.211, TS38.212, TS38.213, TS38.214, TS38.215, TS38.300, TS38.304, TS38.305, TS38.321, TS38.331, TS37.355, and TS38.423 in the 3GPP technical standards can be referred to for assisting in understanding this application.

[0008] As an example, the explanations of the terms in this application refer to the definitions in the TS38 series of the 3GPP specification protocol.

[0009] As an example, the explanations of the terms in this application refer to the definitions in the TS37 series of the 3GPP specification protocol.

[0010] As an example, the explanations of the terms in this application refer to the definitions in the Rel-17 version of the 3GPP specification protocol.

[0011] As an example, the explanations of the terms in this application refer to the definitions in the Rel-18 version of the 3GPP specification protocol.

[0012] This application discloses a method for a first node for wireless communication measurement, which includes:

[0013] Receiving a first information block, where the first information block is configured for a first measurement of mobility, and the number of computing power units occupied by the first measurement for mobility is equal to a first value;

[0014] Processing the first measurement for mobility only when the first value is not greater than the number of remaining computing power units of the first node;

[0015] Wherein, the number of remaining computing power units of the first node is equal to the total number of computing power units of the first node minus the number of computing power units that have been occupied; the total number of computing power units of the first node depends on the number of first-type resources included in the first node and the number of second-type resources included in the corresponding first-type resources; any one of the first-type resources included in the first node includes at least one second-type resource.

[0016] As an example, the problems to be solved by this application include: the problem of the computing power allocation principle corresponding to high-level measurements.

[0017] As an example, the problems to be solved by this application include: when AI / ML computing power is used for high-level measurements and predictions, how to reasonably allocate computing power to improve performance.

[0018] As an example, the characteristics of the above method include: defining the corresponding occupied computing power quantity for measurements for mobility management, and then when AI / ML is used for measurements and predictions for mobility management, reasonably allocating resources and ensuring that the base station and the terminal can be consistent in understanding the situation of computing power occupancy.

[0019] As an example, the characteristics of the above method include: reasonably allocating AI / ML computing power to improve the overall performance of the system.

[0020] According to one aspect of this application, the above method is characterized in that the first type of resource corresponds to the processing units included in the first node, the second type of resource corresponds to one process supported by one processing unit included in the first node; the total number of computing power units of the first node corresponds to the total number of processes supported by all the processing units included in the first node.

[0021] As an example, the characteristics of the above method include: having the ability of parallel computing and parallel processing for AI / ML computing, defining AI / ML computing power as the first type of resource and the second type of resource, and defining the minimum unit of one computing power as one of the second type of resources, so as to further make full use of the parallel processing ability of AI / ML and ensure the optimal use of resources.

[0022] According to one aspect of this application, the above method is characterized in that the first value depends on the ID of the first model, the first model is used for the first measurement for mobility, and the first model is based on AI / ML.

[0023] As an example, the characteristics of the above method include: defining different occupied computing power quantities for different models. The model with more occupied computing power has better performance but also occupies more resources. Thus, a better balance is achieved between performance and overhead, and more importantly, while improving performance, reasonable use of resources is also taken into account.

[0024] According to one aspect of this application, the above method is characterized by including:

[0025] Receiving a second information block, the second information block is configured for the second measurement for mobility, and the number of computing power units occupied by the second measurement for mobility is equal to the second value;

[0026] Among them, the priority targeted by the second measurement is lower than the priority targeted by the first measurement; the first value is not greater than the number of remaining computing power units of the first node; the second value is not greater than the number of remaining computing power units of the first node; the sum of the first value and the second value is greater than the number of remaining computing power units of the first node; the second measurement for mobility is not processed.

[0027] As an embodiment, the characteristics of the above method include: defining different priorities for different measurements, and then when the computing power resources are insufficient, abandoning the measurements and reports with lower priorities to save resources.

[0028] As an embodiment, the characteristics of the above method include: the base station and the terminal keep the definition of priorities consistent, so that the base station can know which measurement with a lower priority is abandoned, to avoid incorrect reception of measurement reports on the base station side.

[0029] According to one aspect of the present application, the above method is characterized in that the first measurement for mobility is one measurement in a first type of measurement set, and the first type of measurement set includes at least one of Intra-frequency measurement, Inter-frequency measurement, and Inter-RAT measurement.

[0030] According to one aspect of the present application, the above method is characterized in that the first measurement for mobility is one measurement in a second type of measurement set, and the second type of measurement set includes at least one of cell selection, cell reselection, and cell handover.

[0031] According to one aspect of the present application, the above method is characterized in that the first value is a positive integer greater than 1, and the first node determines to process the first measurement for mobility; the first node does not assume that the computing power units occupied by the first measurement for mobility are located in two different first type of resources.

[0032] As an embodiment, the characteristics of the above method include: a measurement will not simultaneously occupy the second type of resources in two first type of resources, thus avoiding the need for interaction between two first type of resources for AI / ML calculation for one measurement, to improve the calculation efficiency and avoid introducing excessive complexity.

[0033] According to one aspect of the present application, the above method is characterized by including;

[0034] Send a first information set;

[0035] Among them, the first information set indicates the quantity of the first type of resources included in the first node, and the quantity of the second type of resources included in the corresponding first type of resources.

[0036] As an embodiment, the features of the above method include: The first node reports the computing power quantity to help the network side reasonably configure measurements and avoid scenarios of computing power waste and insufficient computing power.

[0037] According to one aspect of the present application, the above method is characterized in that it includes:

[0038] Receive a third information block;

[0039] Among them, the first value depends on the indication of the third information block.

[0040] As an embodiment, the ID is Identify.

[0041] As an embodiment, the ID is Identification.

[0042] As an embodiment, the ID is Identity.

[0043] As an embodiment, the ID is Identifier.

[0044] As an embodiment, the ID is identity.

[0045] As an embodiment, the ID is identification.

[0046] As an embodiment, the ID includes an AI / ML model ID.

[0047] As an embodiment, the ID includes an AI / ML function ID.

[0048] As an embodiment, the ID includes an AI / ML entity ID.

[0049] As an embodiment, the first node is a user equipment.

[0050] As an embodiment, the first node is a terminal.

[0051] The present application discloses a method in a second node for wireless communication measurement, which includes:

[0052] Send a first information block, the first information block is configured for the first measurement of mobility, and the quantity of computing power units occupied by the first measurement for mobility is equal to the first value;

[0053] Among them, the recipient of the first information block includes a first node; only when the first value is not greater than the number of remaining computing power units of the first node, the first node processes the first measurement for mobility; the number of remaining computing power units of the first node is equal to the total number of computing power units of the first node minus the number of occupied computing power units; the total number of computing power units of the first node depends on the number of first-type resources included in the first node and the number of second-type resources included in the corresponding first-type resources; any one of the first-type resources included in the first node includes at least one second-type resource.

[0054] According to an aspect of the present application, the above method is characterized in that the first-type resources correspond to the processing units included in the first node, the second-type resources correspond to a process supported by one of the processing units included in the first node; the total number of computing power units of the first node corresponds to the total number of processes supported by all the processing units included in the first node.

[0055] According to an aspect of the present application, the above method is characterized in that the first value depends on the ID of the first model, the first model is used for the first measurement for mobility, and the first model is AI / ML-based.

[0056] According to an aspect of the present application, the above method is characterized by including:

[0057] Sending a second information block, the second information block is configured for a second measurement for mobility, and the number of computing power units occupied by the second measurement for mobility is equal to the second value;

[0058] Among them, the priority of the second measurement is lower than the priority of the first measurement; the first value is not greater than the number of remaining computing power units of the first node; the second value is not greater than the number of remaining computing power units of the first node; the sum of the first value and the second value is greater than the number of remaining computing power units of the first node; the second measurement for mobility is not processed by the first node.

[0059] According to an aspect of the present application, the above method is characterized in that the first measurement for mobility is one of a first type of measurement set, and the first type of measurement set includes at least one of Intra-frequency measurement, Inter-frequency measurement, and Inter-RAT measurement.

[0060] According to one aspect of the present application, the above method is characterized in that the first measurement for mobility is one measurement in a second type of measurement set, and the second type of measurement set includes at least one of cell selection, cell reselection, and cell handover.

[0061] According to one aspect of the present application, the above method is characterized in that the first value is a positive integer greater than 1, and the first node determines to process the first measurement for mobility; the first node does not assume that the computing power units occupied by the first measurement for mobility are located in two different first type of resources.

[0062] According to one aspect of the present application, the above method is characterized by including:

[0063] Receiving a first information set;

[0064] Wherein, the first information set indicates the number of first type of resources included in the first node and the number of second type of resources included in the corresponding first type of resources.

[0065] According to one aspect of the present application, the above method is characterized by including:

[0066] Sending a third information block;

[0067] Wherein, the first value depends on the indication of the third information block.

[0068] As an embodiment, the second node is a base station.

[0069] As an embodiment, the second node is an eNB.

[0070] As an embodiment, the second node is a gNB.

[0071] The present application discloses a device for a first node in wireless communication measurement, which includes:

[0072] A first receiver, which receives a first information block, and the first information block is configured for a first measurement for mobility, and the number of computing power units occupied by the first measurement for mobility is equal to a first value;

[0073] The first receiver processes the first measurement for mobility only when the first value is not greater than the number of remaining computing power units of the first node;

[0074] Among them, the number of remaining computing power units of the first node is equal to the total number of computing power units of the first node minus the number of occupied computing power units; the total number of computing power units of the first node depends on the number of first - type resources included in the first node and the number of second - type resources included in the corresponding first - type resources; any one of the first - type resources included in the first node includes at least one second - type resource.

[0075] This application discloses a device for a second node in wireless communication measurement, which includes:

[0076] A second transmitter that sends a first information block, where the first information block is configured for a first measurement of mobility, and the number of computing power units occupied by the first measurement for mobility is equal to a first value;

[0077] Among them, the receiver of the first information block includes a first node; only when the first value is not greater than the number of remaining computing power units of the first node, the first node processes the first measurement for mobility; the number of remaining computing power units of the first node is equal to the total number of computing power units of the first node minus the number of occupied computing power units; the total number of computing power units of the first node depends on the number of first - type resources included in the first node and the number of second - type resources included in the corresponding first - type resources; any one of the first - type resources included in the first node includes at least one second - type resource.

[0078] As an embodiment, compared with traditional solutions, this application has the following advantageous but not limited advantages:

[0079] This application supports applying AI / ML to high - layer measurements. Considering that the time required for AI / ML calculations for high - layer measurements is long and there is a problem of large computational consumption, different computing power overheads are defined for different measurements to reasonably allocate computing power resources;

[0080] Define first - type resources and second - type resources to match the different requirements of AI / ML calculations for computing speed, storage space, and storage speed, as well as the wide use of parallel computing, so as to make full use of computing power resources;

[0081] Establish a consensus between the base station and the terminal to ensure the reliable transmission of reported information;

[0082] Support the integration of AI and communication, improve the adaptability and intelligence level of the communication system, and thus enhance the performance, efficiency, and user experience of the communication system. Description of the Drawings

[0083] 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:

[0084] Figure 1 The flowchart of the first node transmission according to an embodiment of the present application is shown;

[0085] Figure 2 The schematic diagram of the network architecture according to an embodiment of the present application is shown;

[0086] Figure 3 The schematic diagram of the embodiment of the radio protocol architecture of the user plane and the control plane according to an embodiment of the present application is shown;

[0087] Figure 4 The schematic diagram of the first communication device and the second communication device according to an embodiment of the present application is shown;

[0088] Figure 5 The flowchart of the transmission between the first node and the second node according to an embodiment of the present application is shown;

[0089] Figure 6 The flowchart of the transmission between the first node and the second node according to another embodiment of the present application is shown;

[0090] Figure 7 The flowchart of the transmission between the first node and the second node according to still another embodiment of the present application is shown;

[0091] Figure 8 The schematic diagram of the first type of resource and the second type of resource according to an embodiment of the present application is shown;

[0092] Figure 9 The schematic diagram of the first measurement according to an embodiment of the present application is shown;

[0093] Figure 10 The schematic diagram of the occupancy of the first type of resource according to an embodiment of the present application is shown;

[0094] Figure 11 The schematic diagram of the RAN domain AI / ML function deployment according to an embodiment of the present application is shown;

[0095] Figure 12 The schematic diagram of the UE's AI / ML function deployment according to an embodiment of the present application is shown;

[0096] Figure 13 The schematic diagram of the processing system based on artificial intelligence or machine learning according to an embodiment of the present application is shown;

[0097] Figure 14 A schematic diagram of artificial intelligence or machine learning according to an embodiment of the present application is shown;

[0098] Figure 15 A structural block diagram of a processing device in a first node according to an embodiment of the present application is shown;

[0099] Figure 16 A structural block diagram of a processing device in a second node according to an embodiment of the present application is shown. Detailed implementation manners

[0100] The technical solutions of the present application will be further described in detail below with reference to 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. Considering aspects such as performance, flexibility, complexity, overhead, and compatibility, those skilled in the art have the motivation to flexibly combine the embodiments in different drawings without conflict, including but not limited to the embodiments in Figure 1 the drawings and the embodiments in Figure 5 to Figure 14 the drawings, the embodiments in Figure 5 the drawings and the embodiments in Figure 6 to Figure 14 the drawings, and so on.

[0101] Example 1

[0102] Embodiment 1 exemplifies a flowchart of transmission by a first node according to an embodiment of the present application, as shown in Figure 1 the drawings. In Figure 1 the drawings, each block represents a step. In particular, the order of the steps in the block does not represent a specific temporal sequence between the steps.

[0103] The first node receives a first information block in step 101, the first information block being configured for a first measurement of mobility, and the number of computing power units occupied by the first measurement for mobility being equal to a first value; in step 102, the first measurement for mobility is processed only when the first value is not greater than the number of remaining computing power units of the first node;

[0104] In Embodiment 1, the number of remaining computing power units of the first node is equal to the total number of computing power units of the first node minus the number of computing power units already occupied; the total number of computing power units of the first node depends on the number of first-class resources included in the first node and the number of second-class resources included in the corresponding first-class resources; any one of the first-class resources included in the first node includes at least one second-class resource.

[0105] As an example, the first information block is transmitted via RRC (Radio Resource Control) signaling.

[0106] As an example, the first information block includes one or more RRC IEs (Information Elements).

[0107] As an example, the first information block includes one or more fields in an RRC IE.

[0108] As an example, the name of the RRC signaling for transmitting the first information block includes CSI (Channel State Information).

[0109] As an example, the name of the RRC signaling for transmitting the first information block includes CSI-RS (Channel State Information Reference Signal).

[0110] As an example, the name of the RRC signaling for transmitting the first information block includes Report.

[0111] As an example, the name of the RRC signaling for transmitting the first information block includes Config.

[0112] As an example, the name of the RRC signaling for transmitting the first information block includes Meas.

[0113] As an example, the name of the RRC signaling for transmitting the first information block includes Measurement.

[0114] As an example, the name of the RRC signaling for transmitting the first information block includes Object.

[0115] As an example, the first information block includes a CSI-ReportConfig IE.

[0116] As an example, the first information block includes a CSI-ReportSubConfig IE.

[0117] As an example, the first information block includes a ServingCellConfig IE.

[0118] As an example, the first information block includes one or more fields in the ServingCellConfig IE.

[0119] As an example, the first information block includes the CSI-MeasConfig IE.

[0120] As an example, the first information block includes one or more fields in the CSI-MeasConfig IE.

[0121] As an example, the first information block includes the CSI-SemiPersistentOnPUSCH-TriggerStateList IE.

[0122] As an example, the first information block includes one or more fields in the CSI-SemiPersistentOnPUSCH-TriggerStateList IE.

[0123] As an example, the first information block includes the CSI-SemiPersistentOnPUSCH-TriggerState IE.

[0124] As an example, the first information block includes one or more fields in the CSI-SemiPersistentOnPUSCH-TriggerState IE.

[0125] As an example, the first information block includes the CSI-ReportSubConfigTriggerList IE.

[0126] As an example, the first information block includes one or more fields in the CSI-ReportSubConfigTriggerList IE.

[0127] As an example, the first information block includes the CSI-ReportConfig IE.

[0128] As an example, the first information block includes one or more fields in the CSI-ReportConfig IE.

[0129] As an example, the first information block includes the CSI-ReportSubConfig IE.

[0130] As an example, the first information block includes one or more fields in the CSI-ReportSubConfig IE.

[0131] As an example, the first information block includes one or more fields in the CSI-AperiodicTriggerStateList IE.

[0132] As an example, the first information block includes one or more fields in the CSI-IM-Resource IE.

[0133] As an example, the first information block includes one or more fields in the CSI-IM-ResourceSet IE.

[0134] As an example, the first information block includes one or more fields in the CSI-ResourceConfig IE.

[0135] As an example, the first information block includes one or more fields in the CSI-RS-ResourceConfigMobility IE.

[0136] As an example, the first information block includes one or more fields in the NZP-CSI-RS-Resource IE.

[0137] As an example, the first information block includes one or more fields in the NZP-CSI-RS-ResourceSet IE.

[0138] As an example, the first information block includes one or more fields in the MeasConfig IE.

[0139] As an example, the first information block includes one or more fields in the MeasObject IE.

[0140] As an example, the meaning that the first information block is configured for the first measurement for mobility includes: the first information block is configured for the reference signal of the first measurement for mobility.

[0141] As an example, the meaning that the first information block is configured for the first measurement for mobility includes: the first information block is configured for the reporting of the measurement result of the first measurement for mobility.

[0142] As an example, the meaning that the first information block is configured for the first measurement for mobility includes: the first information block is configured for the condition for triggering the reporting of the first measurement for mobility.

[0143] As an example, the configuration of the first information block for the first measurement of mobility means that the first information block is configured for the AI / ML model for the first measurement of mobility.

[0144] As an example, the candidates for the first measurement include measurements for BM (Beam Management).

[0145] As an example, the candidates for the first measurement include measurements for RLF (Radio Link Failure).

[0146] As an example, the candidates for the first measurement include measurements for positioning.

[0147] As an example, the calculation capability unit is a Calculation Unit.

[0148] As an example, the calculation capability unit is a Process Unit.

[0149] As an example, the calculation capability unit is a Calculation Element.

[0150] As an example, the calculation capability unit is a Process Element.

[0151] As an example, the calculation capability unit is an APU.

[0152] As an example, the calculation capability unit is a CPU.

[0153] As an example, the calculation capability unit is an AI-PU.

[0154] As an example, the first numerical value is a positive integer.

[0155] As an example, the calculation capability unit corresponds to the minimum computing resources required for the first node to perform a measurement for mobility.

[0156] As an example, the first measurement for mobility is prediction-based.

[0157] As an example, the first measurement for mobility is generated based on AI / ML.

[0158] As an example, the first measurement for mobility is generated based on an AI / ML model.

[0159] As an example, the number of remaining computing power units of the first node refers to: the number of remaining computing power units of the first node when the first measurement for mobility receives the first information block configuration.

[0160] As an example, the number of remaining computing power units of the first node refers to: the number of remaining computing power units of the first node before processing the first measurement for mobility.

[0161] As an example, the number of remaining computing power units of the first node refers to: the number of remaining computing power units of the first node before starting the first measurement for mobility.

[0162] As an example, processing the first measurement for mobility includes: receiving a reference signal configured for the first measurement for mobility.

[0163] As an example, processing the first measurement for mobility includes: determining a measurement result of the first measurement based on the reception of a reference signal configured for the first measurement for mobility.

[0164] As a sub - example of this example, the first node implements the relevant determination of the measurement result of the first measurement based on the reception of a reference signal configured for the first measurement for mobility.

[0165] As a sub - example of this example, the first node determines the measurement result of the first measurement based on baseband algorithms according to the reception of a reference signal configured for the first measurement for mobility.

[0166] As a sub - example of this example, the first node determines the measurement result of the first measurement based on AI / ML models and algorithms according to the reception of a reference signal configured for the first measurement for mobility.

[0167] As an example, processing the first measurement for mobility includes: calculating a measurement result of the first measurement based on the reception of a reference signal configured for the first measurement for mobility.

[0168] As a sub - example of this example, the first node implements the relevant calculation of the measurement result of the first measurement based on the reception of a reference signal configured for the first measurement for mobility.

[0169] As a sub - example of this example, the first node calculates the measurement result of the first measurement based on baseband algorithms according to the reception of a reference signal configured for the first measurement for mobility.

[0170] As a sub - embodiment of this embodiment, the first node calculates the measurement result of the first measurement based on an AI / ML model and algorithm according to the reception of the reference signal configured for the first measurement for mobility.

[0171] As an embodiment, the processing of the first measurement for mobility includes: predicting the reception result of the reference signal configured for the first measurement for mobility, and predicting the measurement result of the first measurement.

[0172] As a sub - embodiment of this embodiment, the first node implements relevant prediction of the reception result of the reference signal configured for the first measurement for mobility, and predicts the measurement result of the first measurement.

[0173] As a sub - embodiment of this embodiment, the first node predicts the reception result of the reference signal configured for the first measurement for mobility based on a baseband algorithm, and predicts the measurement result of the first measurement.

[0174] As a sub - embodiment of this embodiment, the first node predicts the reception result of the reference signal configured for the first measurement for mobility based on an AI / ML model and algorithm, and predicts the measurement result of the first measurement.

[0175] As an embodiment, the first node includes M first - type resources. Given that the given first - type resource is the i - th first - type resource among the M first - type resources, the number of second - type resources included in the given first - type resource is O i , the total number of computing power units of the first node is equal to where the value range of i is from 1 to M.

[0176] As an embodiment, the first node includes M first - type resources, and any one of the M first - type resources includes M1 second - type resources, where M1 is a positive integer greater than 1.

[0177] As an embodiment, the first node includes M first - type resources, and the number of second - type resources included in at least two of the M first - type resources is different.

[0178] As an embodiment, the first - type resource is a processing unit of the first node.

[0179] As an embodiment, the first - type resource is the APU of the first node.

[0180] As an embodiment, the first - type resource is the CPU of the first node.

[0181] As an example, the first type of resource is the AI-PU of the first node.

[0182] As an example, the second type of resource included in the first type of resource is a process.

[0183] As an example, the second type of resource included in the first type of resource is a storage unit.

[0184] As an example, the second type of resource included in the first type of resource is a computing unit.

[0185] As an example, the second type of resource included in the first type of resource is a storage unit and a computing unit.

[0186] As an example, the first type of resource is the APU Set of the first node.

[0187] As an example, the second type of resource included in the first type of resource is an APU.

[0188] As an example, the first type of resource is the CPU Set of the first node.

[0189] As an example, the second type of resource included in the first type of resource is a CPU.

[0190] As an example, the first type of resource is the AI-PU Set of the first node.

[0191] As an example, the second type of resource included in the first type of resource is an AI-PU.

[0192] Example 2

[0193] Embodiment 2 exemplifies a schematic diagram of a network architecture according to an embodiment of the present application, as shown in the appendix Figure 2 as follows.

[0194] Appendix Figure 2Described is a 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 systems. The network architectures of LTE, LTE-A, 5G system, 5G-Advanced, and future 6G systems 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, a RAN (Next Generation Radio Access Network) 202, a core network 210, an HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and an 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 that provide circuit switching services. The RAN 202 includes Node B 203 and other nodes 204. Node 203 provides user and control plane protocol termination towards 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 for UE 201 to the core network 210; 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 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 device. Those skilled in the art may also refer to UE 201 as a mobile station, subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable term. Node 203 is connected to the core network 210 via the S1 / NG interface.The core network 210 includes MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MME / AMF / SMF 214, S-GW (Service Gateway) / UPF (User Plane Function) 212, and P-GW (Packet Date Network Gateway) / UPF 213. MME / AMF / SMF 211 is a control node that processes signaling between the UE 201 and the 5G-CN / EPC 210. Generally, MME / AMF / SMF 211 provides bearer and connection management. All user IP (Internet Protocol) packets are transmitted through S-GW / UPF 212, and S-GW / UPF 212 itself is connected to P-GW / UPF 213. The P-GW provides UE IP address allocation and other functions. P-GW / UPF 213 is connected to the Internet service 230. The Internet service 230 includes operator-corresponding Internet protocol services, specifically including the Internet, intranet, IMS (IP Multimedia Subsystem), and packet switching services.

[0195] As an embodiment, the first node described in the present application includes the UE 201.

[0196] As an embodiment, the second node described in the present application includes the node 203.

[0197] As an embodiment, the node 203 is a macro cell base station.

[0198] As an embodiment, the node 203 is a micro cell base station.

[0199] As an embodiment, the node 203 is a pico cell base station.

[0200] As an embodiment, the node 203 is a femtocell.

[0201] As an embodiment, the node 203 is a base station device that supports large delay differences.

[0202] As an embodiment, the node 203 is an aerial platform device.

[0203] As an embodiment, the node 203 is a satellite device.

[0204] As an embodiment, the node 203 is a test device (such as a transceiver that simulates some functions of a base station, a signaling tester).

[0205] As an embodiment, the UE 201 includes a mobile phone.

[0206] As an embodiment, the UE 201 is a vehicle including an automobile.

[0207] 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.

[0208] 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.

[0209] As an embodiment, the radio link between the node 203 and the UE 201 includes a cellular network link.

[0210] As an embodiment, the node 203 and the UE 201 are connected through the Uu air interface.

[0211] As an embodiment, the receiver of the first information block in this application includes the UE 201.

[0212] As an embodiment, the sender of the first information block in this application includes the node 203.

[0213] As an embodiment, the receiver of the second information block in this application includes the UE 201.

[0214] As an embodiment, the sender of the second information block in this application includes the node 203.

[0215] As an embodiment, the sender of the first information set in this application includes the UE 201.

[0216] As an embodiment, the receiver of the first information set in this application includes the node 203.

[0217] As an embodiment, the receiver of the third information block in this application includes the UE 201.

[0218] As an example, the sender of the third information block in the present application includes the node 203.

[0219] As an example, the UE 201 supports AI / ML models for high-layer measurements and high-layer reporting.

[0220] As an example, the node 203 supports AI / ML models for high-layer measurements and high-layer reporting.

[0221] As an example, the UE 201 supports the 5G system.

[0222] As an example, the node 203 supports the 5G system.

[0223] As an example, the UE 201 supports at least the 6G system.

[0224] As an example, the node 203 supports at least the 6G system.

[0225] Example 3

[0226] 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 shown.

[0227] 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 V2X (Vehicle to Everything), in-vehicle device or in-vehicle communication module) and a second node device (gNB, UE or RSU in V2X, in-vehicle device or in-vehicle communication module), or between two UEs, using three layers: Layer 1 (L1), Layer 2 (L2), and Layer 3 (L3). L1 is the lowest layer and implements various PHY (Physical layer) signal processing functions. L1 will be referred to as 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 of 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 radio 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.).

[0228] As an embodiment, the Figure 3 radio protocol architecture in is applicable to the first node in this application.

[0229] As an embodiment, the Figure 3 radio protocol architecture in is applicable to the second node in this application.

[0230] As an embodiment, the first information block is generated at the RRC 306.

[0231] As an embodiment, the first information block is generated at the MAC 302 or the MAC 352.

[0232] As an embodiment, the second information block is generated at the RRC 306.

[0233] As an embodiment, the second information block is generated at the MAC 302 or the MAC 352.

[0234] As an embodiment, the first information set is generated at the RRC 306.

[0235] As an embodiment, the first information set is generated at the MAC 302 or the MAC 352.

[0236] As an example, the third information block is generated at the RRC 306.

[0237] As an example, the third information block is generated at the MAC 302 or the MAC 352.

[0238] Example 4

[0239] Embodiment 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 appendix. Figure 4 Appendix 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.

[0240] 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.

[0241] 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.

[0242] 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 for 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 space 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.

[0243] 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 that is provided to the receive processor 456. The receive processor 456 and the multi-antenna receive processor 458 perform various signal processing functions of 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 is recovered in the multi-antenna receive processor 458 after multi-antenna detection for any parallel streams destined for the second communication device 450. 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 de-interleaves 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 channels, 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.

[0244] 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 an upper layer data packet 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. 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 a multi-carrier / single-carrier symbol stream, and after passing through an analog precoding / beamforming operation in the multi-antenna transmit processor 457, provides it 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.

[0245] 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 a radio frequency signal through its corresponding antenna 420, converts the received radio frequency signal into a baseband signal, and provides the baseband signal to a multi-antenna receive processor 472 and a receive processor 470. The receive processor 470 and the multi-antenna receive processor 472 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 the transport and logical channels, packet reassembly, decryption, header decompression, and control signal processing to recover the upper layer data packet from the second communication device 450. The upper layer data packet 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.

[0246] As an example, the second communication device 450 includes: at least one processor and at least one memory, where the at least one memory includes 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 at least receives a first information block, where the first information block is configured for a first measurement for mobility, and the number of computing power units occupied by the first measurement for mobility is equal to a first value; only when the first value is not greater than the number of remaining computing power units of the first node, process the first measurement for mobility; the number of remaining computing power units of the first node is equal to the total number of computing power units of the first node minus the number of computing power units that have been occupied; the total number of computing power units of the first node depends on the number of first type resources included in the first node and the number of second type resources included in the corresponding first type resources; any one of the first type resources included in the first node includes at least one second type resource.

[0247] As an example, the second communication device 450 includes: a memory storing a computer-readable instruction program, where the computer-readable instruction program generates actions when executed by at least one processor, and the actions include: receiving a first information block, where the first information block is configured for a first measurement for mobility, and the number of computing power units occupied by the first measurement for mobility is equal to a first value; only when the first value is not greater than the number of remaining computing power units of the first node, process the first measurement for mobility.

[0248] As an example, the first communication device 410 includes: at least one processor and at least one memory, where the at least one memory includes 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 at least sends a first information block, where the first information block is configured for a first measurement for mobility, and the number of computing power units occupied by the first measurement for mobility is equal to a first value; the recipient of the first information block includes a first node; only when the first value is not greater than the number of remaining computing power units of the first node, the first node processes the first measurement for mobility; the number of remaining computing power units of the first node is equal to the total number of computing power units of the first node minus the number of computing power units that have been occupied; the total number of computing power units of the first node depends on the number of first type resources included in the first node and the number of second type resources included in the corresponding first type resources; any one of the first type resources included in the first node includes at least one second type resource.

[0249] As an example, the first communication device 410 includes: a memory storing a computer-readable instruction program, which generates actions when executed by at least one processor, and the actions include: sending a first information block configured for a first measurement of mobility, and the number of computing power units occupied by the first measurement for mobility is equal to a first value.

[0250] As an example, the first node in this application includes the second communication device 450.

[0251] As an example, the second node in this application includes the first communication device 410.

[0252] 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 send the first information block 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 information block in this application.

[0253] As an example, 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 process the first measurement for mobility only when the first value is not greater than the number of remaining computing power units of the first node.

[0254] 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 send the second information block 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 second information block in this application.

[0255] 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 third information block described in the present 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 third information block described in the present application.

[0256] 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 set described in the present 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 set described in the present application.

[0257] Example 5

[0258] Example 5 illustrates a flowchart of transmission between a first node and a second node according to an embodiment of the present application, as shown in the appendix Figure 5 shown. In the appendix Figure 5 shown, the first node U1 communicates with the second node N2 via a wireless link, and the steps in block F50 are optional. It should be specifically noted that the order in this embodiment does not limit the signal transmission order and the implementation order in the present application.

[0259] For the first node U1, the first information set is transmitted in step S510; the first information block is received in step S511; and the first measurement for mobility is processed in step S512.

[0260] For the second node N2, the first information set is received in step S520; and the first information block is transmitted in step S521.

[0261] In Embodiment 5, the first information block configures the first measurement for mobility, and the number of computing power units occupied by the first measurement for mobility is equal to a first value; the first node processes the first measurement for mobility only when the first value is not greater than the number of remaining computing power units of the first node; the number of remaining computing power units of the first node is equal to the total number of computing power units of the first node minus the number of computing power units that have been occupied; the total number of computing power units of the first node depends on the number of first type resources included in the first node and the number of second type resources included in the corresponding first type resources; any one of the first type resources included in the first node includes at least one second type resource; the first information set indicates the number of first type resources included in the first node and the number of second type resources included in the corresponding first type resources.

[0262] As an embodiment, the first node U1 is the first node in this application.

[0263] As an embodiment, the second node N2 is the second node in this application.

[0264] As an embodiment, the air interface between the second node N2 and the first node U1 includes the wireless interface between the base station device and the user equipment.

[0265] As an embodiment, the air interface between the second node N2 and the first node U1 includes the wireless interface between the relay node device and the user equipment.

[0266] As an embodiment, the air interface between the second node N2 and the first node U1 includes the wireless interface between user equipment and user equipment.

[0267] As an embodiment, the second node N2 and the first node U1 communicate through the Uu interface.

[0268] As an embodiment, the second node N2 is the serving cell maintaining base station of the first node U1.

[0269] As an embodiment, step S512 further includes receiving a reference signal configured for the first measurement.

[0270] As an embodiment, step S512 further includes generating a measurement report according to the measurement result of the first measurement.

[0271] As an embodiment, step S512 further includes sending the measurement report generated according to the measurement result of the first measurement.

[0272] As an embodiment, the step S512 further includes determining that the first value is not greater than the number of remaining computing power units of the first node.

[0273] As an embodiment, the subsequent steps of the step S521 further include determining that the first value is not greater than the number of remaining computing power units of the first node.

[0274] As an embodiment, the subsequent steps of the step S521 further include receiving a measurement report generated according to the measurement result of the first measurement.

[0275] As an embodiment, the first information set includes RRC IE.

[0276] As an embodiment, the first information set belongs to the capability information of the first node.

[0277] As an embodiment, the first information set includes the capability information of the first node.

[0278] As an embodiment, the first information set includes one or more capability parameters of the first node.

[0279] As an embodiment, the first information set includes one or more fields in one or more UE (user equipment) capability IE (information element).

[0280] As an embodiment, the first information set includes one or more parameters in one or more UE (user equipment) capability IE.

[0281] As an embodiment, after receiving a UE capability enquiry (UECapabilityEnquiry) from the network, the first node transmits the capability information of the first node, and the first information set belongs to the capability information of the first node.

[0282] As an embodiment, the capability information of the first node includes UECapabilityInformation.

[0283] As an embodiment, the capability information of the first node includes the radio access capability of the first node.

[0284] Typically, the first type of resources corresponds to the processing units included in the first node, and the second type of resources corresponds to a process supported by one of the processing units included in the first node; the total number of computing power units of the first node corresponds to the total number of processes supported by all the processing units included in the first node.

[0285] Typically, the first value depends on the ID of the first model, which is used for the first measurement for mobility, and the first model is AI / ML-based.

[0286] As an embodiment, the first value varies according to the IDs of different models.

[0287] As an embodiment, the ID of the first model is used to determine the first value.

[0288] As an embodiment, the first model is one of K1 candidate models, the K1 candidate models respectively correspond to K1 candidate values, and the first value is the candidate value corresponding to the first model among the K1 candidate values; K1 is a positive integer greater than 1.

[0289] As a sub-embodiment of this embodiment, any one of the K1 candidate values is a positive integer.

[0290] As an embodiment, the ID of the first model includes Model ID.

[0291] As an embodiment, the ID of the first model includes Functionality ID.

[0292] As an embodiment, the first information block configures the first model.

[0293] As an embodiment, the first information block indicates the first model used for the first measurement for mobility.

[0294] As an embodiment, the meaning that the first model is AI / ML-based includes: the first model is used for AI / ML.

[0295] As an embodiment, the meaning that the first model is AI / ML-based includes: the first model is used for prediction.

[0296] Typically, the first value is a positive integer greater than 1, and the first node determines to process the first measurement for mobility; the first node does not assume that the computing power units occupied by the first measurement for mobility are located in two different first types of resources.

[0297] As an example, the first node assumes that the computing power unit occupied by the first measurement for mobility is located in a first type of resource.

[0298] As an example, for a given first type of resource, when the quantity of the second type of resource remaining in the given first type of resource is less than the first value, the given first type of resource is not occupied by the first measurement.

[0299] Example 6

[0300] Embodiment 6 exemplifies a flowchart of transmission between a first node and a second node according to another embodiment of the present application, as shown in the appendix. Figure 6 As shown. In the appendix Figure 6 As shown, communication is performed between the first node U3 and 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.

[0301] For the first node U3, a second information block is received in step S530.

[0302] For the second node N4, a second information block is sent in step S540.

[0303] In Embodiment 6, the second information block is configured for the second measurement for mobility, the number of computing power units occupied by the second measurement for mobility is equal to the second value; the priority targeted by the second measurement is lower than the priority targeted by the first measurement; the first value is not greater than the number of remaining computing power units of the first node; the second value is not greater than the number of remaining computing power units of the first node; the sum of the first value and the second value is greater than the number of remaining computing power units of the first node; the second measurement for mobility is not processed.

[0304] As an example, the subsequent steps of step S530 further include determining that the second measurement for mobility is not processed.

[0305] As an example, the subsequent steps of step S530 further include abandoning the second measurement for mobility.

[0306] As an example, the subsequent steps of step S540 further include determining that the second measurement for mobility is not processed.

[0307] As an example, the second information block is transmitted via RRC signaling.

[0308] As an example, the second information block includes one or more RRC IEs.

[0309] As an example, the second information block includes one or more fields in an RRC IE.

[0310] As an example, the name of the RRC signaling for transmitting the second information block includes CSI.

[0311] As an example, the name of the RRC signaling for transmitting the second information block includes CSI-RS.

[0312] As an example, the name of the RRC signaling for transmitting the second information block includes Report.

[0313] As an example, the name of the RRC signaling for transmitting the second information block includes Config.

[0314] As an example, the name of the RRC signaling for transmitting the second information block includes Meas.

[0315] As an example, the name of the RRC signaling for transmitting the second information block includes Measurement.

[0316] As an example, the name of the RRC signaling for transmitting the second information block includes Object.

[0317] As an example, the second information block includes a CSI-ReportConfig IE.

[0318] As an example, the second information block includes a CSI-ReportSubConfig IE.

[0319] As an example, the second information block includes a ServingCellConfig IE.

[0320] As an example, the second information block includes one or more fields in a ServingCellConfig IE.

[0321] As an example, the second information block includes a CSI-MeasConfig IE.

[0322] As an example, the second information block includes one or more fields in a CSI-MeasConfig IE.

[0323] As an example, the second information block includes a CSI-SemiPersistentOnPUSCH-TriggerStateList IE.

[0324] As an example, the second information block includes one or more fields in the CSI-SemiPersistentOnPUSCH-TriggerStateList IE.

[0325] As an example, the second information block includes the CSI-SemiPersistentOnPUSCH-TriggerState IE.

[0326] As an example, the second information block includes one or more fields in the CSI-SemiPersistentOnPUSCH-TriggerState IE.

[0327] As an example, the second information block includes the CSI-ReportSubConfigTriggerList IE.

[0328] As an example, the second information block includes one or more fields in the CSI-ReportSubConfigTriggerList IE.

[0329] As an example, the second information block includes the CSI-ReportConfig IE.

[0330] As an example, the second information block includes one or more fields in the CSI-ReportConfig IE.

[0331] As an example, the second information block includes the CSI-ReportSubConfig IE.

[0332] As an example, the second information block includes one or more fields in the CSI-ReportSubConfig IE.

[0333] As an example, the second information block includes one or more fields in the CSI-AperiodicTriggerStateList IE.

[0334] As an example, the second information block includes one or more fields in the CSI-IM-Resource IE.

[0335] As an example, the second information block includes one or more fields in the CSI-IM-ResourceSet IE.

[0336] As an example, the second information block includes one or more fields in the CSI-ResourceConfig IE.

[0337] As an example, the second information block includes one or more fields in the CSI-RS-ResourceConfigMobility IE.

[0338] As an example, the second information block includes one or more fields in the NZP-CSI-RS-Resource IE.

[0339] As an example, the second information block includes one or more fields in the NZP-CSI-RS-ResourceSet IE.

[0340] As an example, the second information block includes one or more fields in the MeasConfig IE.

[0341] As an example, the second information block includes one or more fields in the MeasObject IE.

[0342] As an example, the meaning that the second information block is configured for the second measurement for mobility includes: the second information block is configured for the reference signal of the second measurement for mobility.

[0343] As an example, the meaning that the second information block is configured for the second measurement for mobility includes: the second information block is configured for reporting the measurement result of the second measurement for mobility.

[0344] As an example, the meaning that the second information block is configured for the second measurement for mobility includes: the second information block configures the condition for triggering the reporting of the second measurement for mobility.

[0345] As an example, the meaning that the second information block is configured for the second measurement for mobility includes: the second information block is configured for the AI / ML model of the second measurement for mobility.

[0346] As an example, the first information block and the second information block are two RRC IEs respectively.

[0347] As an example, the first information block and the second information block are two fields in one RRC IE respectively.

[0348] As an example, the first measurement and the second measurement are different.

[0349] As a sub-example of this example, the first measurement and the second measurement are respectively for different Objects.

[0350] As a sub - embodiment of this embodiment, the first measurement and the second measurement employ different reference signal resources.

[0351] As a sub - embodiment of this embodiment, the first measurement and the second measurement are respectively directed to different frequency bands.

[0352] As a sub - embodiment of this embodiment, the first measurement and the second measurement are respectively for different purposes.

[0353] As a sub - embodiment of this embodiment, the first measurement and the second measurement are respectively directed to different cells.

[0354] As a sub - embodiment of this embodiment, the first measurement and the second measurement are respectively directed to different RATs.

[0355] As an embodiment, the second value is a positive integer.

[0356] As an embodiment, the meaning that the priority targeted by the second measurement is lower than the priority targeted by the first measurement includes: in the case of resource constraints, compared with the second measurement, the first measurement is preferentially processed.

[0357] As an embodiment, the meaning that the priority targeted by the second measurement is lower than the priority targeted by the first measurement includes: in the case of resource constraints, the second measurement is abandoned and the first measurement is retained.

[0358] As an embodiment, the meaning that the priority targeted by the second measurement is lower than the priority targeted by the first measurement includes: the value of the priority targeted by the second measurement is greater than the value of the priority targeted by the first measurement.

[0359] As an embodiment, the meaning that the priority targeted by the second measurement is lower than the priority targeted by the first measurement includes: the value of the priority targeted by the second measurement is less than the value of the priority targeted by the first measurement.

[0360] As an embodiment, the priority targeted by the first measurement is configured by higher - layer signaling.

[0361] As an embodiment, the priority targeted by the second measurement is configured by higher - layer signaling.

[0362] As an embodiment, the priority targeted by the first measurement depends on the measurement type of the first measurement.

[0363] As an embodiment, the priority targeted by the first measurement depends on the measurement target of the first measurement.

[0364] As an embodiment, the priority for the first measurement depends on the frequency band for which the first measurement is made.

[0365] As an embodiment, the priority for the second measurement depends on the measurement type of the first measurement.

[0366] As an embodiment, the priority for the second measurement depends on the measurement objective of the first measurement.

[0367] As an embodiment, the priority for the second measurement depends on the frequency band for which the first measurement is made.

[0368] As an embodiment, the meaning that the second measurement for mobility is not processed includes: the second measurement is abandoned.

[0369] As an embodiment, step S530 is not earlier than step S511 in Embodiment 5.

[0370] As an embodiment, step S540 is not earlier than step S521 in Embodiment 5.

[0371] Example 7

[0372] Embodiment 7 exemplifies a flowchart of transmission between a first node and a second node according to another embodiment of the present application, as shown in the appendix Figure 7 shown. In the appendix Figure 7 , communication is carried out between the first node U5 and the second node N6 through 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 the present application.

[0373] For the first node U5, a third information block is received in step S550.

[0374] For the second node N6, a third information block is sent in step S560.

[0375] In Embodiment 7, the first numerical value depends on the indication of the third information block.

[0376] As an embodiment, the third information block includes an RRC IE.

[0377] As an embodiment, the third information block includes one or more fields in an RRC IE.

[0378] As an embodiment, the third information block configures the first model.

[0379] As an embodiment, the third information block indicates the ID of the first model.

[0380] As an example, the third information block indicates the first numerical value.

[0381] As an example, the ID of the first model indicated by the third information block is used to determine the first numerical value.

[0382] As an example, the first numerical value depends on the ID of the first model indicated by the third information block.

[0383] As an example, the step S550 is located after the step S511 in Example 5.

[0384] As an example, the step S550 is located before the step S511 in Example 5.

[0385] As an example, the step S560 is located after the step S521 in Example 5.

[0386] As an example, the step S560 is located before the step S521 in Example 5.

[0387] Example 8

[0388] Example 8 exemplifies a schematic diagram of a first type of resource and a second type of resource according to an embodiment of the present application, as shown in the appendix Figure 8 shown. In the appendix Figure 8 , the first node includes M first type of resources, where M is a positive integer greater than 1; any one of the M first type of resources includes at least one second type of resource.

[0389] As an example, the total number of all the second type of resources located among the M first type of resources included in the first node corresponds to the quantity of the maximum computing power of the first node.

[0390] As an example, the quantity of the first type of resources included in the first node corresponds to the number of simultaneously enabled AI / ML models supported by the first node.

[0391] As an example, the quantity of the first type of resources included in the first node corresponds to the number of simultaneously enabled AI / ML functionality supported by the first node.

[0392] As an example, the quantity of the first type of resources included in the first node corresponds to the number of simultaneously enabled AI / ML computing modules supported by the first node.

[0393] Example 9

[0394] Embodiment 9 exemplifies a schematic diagram of a first measurement according to the present application, as shown in the appendix Figure 9 shown. In the appendix Figure 9 the first measurement is one of a plurality of candidate measurements, and the plurality of candidate measurements are all for higher layer measurements.

[0395] As an embodiment, at least one of the plurality of candidate measurements is for mobility.

[0396] As an embodiment, at least one of the plurality of candidate measurements is for RLM (Radio Link Monitoring).

[0397] As an embodiment, at least one of the plurality of candidate measurements is for RRM (Radio Resource Management).

[0398] As an embodiment, the second measurement in the present application is one of the plurality of candidate measurements.

[0399] As an embodiment, the plurality of candidate measurements are all for UEs in the connected state.

[0400] As an embodiment, the plurality of candidate measurements are all for UEs in the idle state.

[0401] Example 10

[0402] Embodiment 10 exemplifies a schematic diagram of a first type of resource occupancy according to an embodiment of the present application, as shown in the appendix Figure 10 shown. In the appendix Figure 10 the resources filled with slashes in the figure are the unoccupied second type of resources, and the resources not filled with slashes are the occupied second type of resources.

[0403] In Embodiment 10, the first node includes a first type of resource #1 and a first type of resource #2. The first type of resource #1 only includes one unoccupied second type of resource, and the first type of resource #2 includes not less than 2 second type of resources; the first value is not greater than the number of remaining computing power units of the first node, and the first value is equal to 2; the remaining second type of resources in the first type of resource #1 are not occupied by the first measurement, and the remaining second type of resources in the first type of resource #2 are occupied by the first measurement.

[0404] As an embodiment, the above description is a non-limiting description of the following features:

[0405] The first numerical value is a positive integer greater than 1, and the first node determines to process the first measurement for mobility; the first node does not assume that the computing power units occupied by the first measurement for mobility are located in two different first-type resources.

[0406] Example 11

[0407] Embodiment 11 exemplifies a schematic diagram of RAN domain AI / ML function deployment according to an embodiment of the present application, as shown in the appendix Figure 11 shown. In the appendix Figure 11 shown, the gNB can be replaced with, for example, an eNB, or a network device such as a 6G base station.

[0408] 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 by the dotted arrows in the appendix Figure 11 shown). 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,....

[0409] AI / ML-related functions include an ML training function (also referred to as AI training, or AI / ML training), an ML testing function, an ML inference function (also referred to as AI inference, or AI / ML inference), and so on. The ML training function, the ML testing function, and the ML inference function can be independently deployed 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 it can be achieved through a combination of software and hardware, such as accelerating specific computing units through hardware to improve the operation speed or save power consumption.

[0410] 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, for the ML training function of MDA (Management Data Analytics), it can be deployed in the MDAF (Management Data Analytic Function); for the ML training of network data analysis, it can be deployed in the NWDAF (NetWork Data Analytics Function), that is, the ML training function is the MTLF (Model Training Logical Function).

[0411] 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.

[0412] Similarly, the ML testing function can also be deployed in a cross-domain management system or a domain-specific management system.

[0413] 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.

[0414] 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 in 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.

[0415] As an embodiment, one gNB (or base station) in Embodiment 11 is the second node of the present application.

[0416] Example 12

[0417] 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 the RAN domain ML training function 1204 is optional.

[0418] The UE function 1203 is deployed in the first node of this application. The UE function 1203 includes an AI / ML inference function 1205; the AI / ML inference function 1205 uses an ML model (also known as an AI model) for inference; an ML model usually needs to be trained before being used for AI / ML inference.

[0419] 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 an 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.

[0420] 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 place relatively high requirements on the processing capabilities on the UE side.

[0421] Optionally, the UE function 1203 further includes a CN domain ML training function ( Figure 12 not included in).

[0422] Optionally, the UE function 1203 further includes an AI / ML deployment function - Figure 12 not included in, for loading the ML model and data.

[0423] As an embodiment, the first node indicates whether it supports the ML training function (RAN domain or CN domain) through capability reporting. The capability reporting is RRC signaling or NAS (Non-Access Stratum) signaling.

[0424] As an embodiment, the ML model and related metadata are loaded by the first node from a network device or a remote server.

[0425] 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).

[0426] Optionally, the UE function 1203 is an MnS consumer, loading data for AI / ML-related management from the CN domain MnF and / or the RAN domain MnF and / or the cross-domain management system 1201, such as management data requests, ML model activation, and / or ML training, etc. (as shown by the double arrow 1202).

[0427] As an example, the second information block in this application is obtained through the inference of the AI / ML inference function 1105.

[0428] As an example, the reporting information of the beam management in this application is obtained through the inference of the AI / ML inference function 1105.

[0429] As an example, the AI / ML model is NN-based.

[0430] As an example, the AI / ML model is ANN-based.

[0431] As an example, the AI / ML model is CNN-based.

[0432] As an example, the AI / ML model is based on the LLM architecture.

[0433] As an example, the AI / ML model is based on the Transformer architecture.

[0434] As an example, the AI / ML model is LSTM-based.

[0435] As an example, the AI / ML model is MLP-based.

[0436] As an example, the AI / ML model is GAN-based.

[0437] As an example, the AI / ML model is based on a lightweight neural network.

[0438] As a sub-example of this example, the lightweight neural network includes one or more of MobileNet, ShuffleNet, and SqueezeNet.

[0439] Example 13

[0440] Example 13 illustrates a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of this application, as shown in the appendix Figure 13 shown. In the appendix Figure 13Among them, the processing system based on artificial intelligence or machine learning includes a first processor, a second processor, a third processor, and a fourth processor.

[0441] In Embodiment 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 by 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 Among them, 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.

[0442] As an embodiment, the fourth processor includes an ML testing function.

[0443] As an embodiment, the fourth processor includes the performance monitoring / evaluation of the ML model.

[0444] As an embodiment, the third processor sends the first type of feedback to the second processor; the first type of feedback is used to trigger the recalculation or update of the target first type of parameter group, that is, to trigger the initial ML training or ML retraining.

[0445] As an embodiment, the fourth processor sends the 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.

[0446] As an embodiment, the first processor generates the first data set and the second data set according to the measurement of the reference signal.

[0447] As an embodiment, the third processor belongs to the first node, and the fourth processor belongs to the second node.

[0448] As an embodiment, the first data set includes training data.

[0449] As an embodiment, the second processor is used to train the ML model, and the trained model is described by the target first type of parameter group.

[0450] As an embodiment, the second processor belongs to the first node; the above method avoids transmitting the first data set to the second node.

[0451] As an embodiment, the second processor belongs to the second node; the above method supports joint training and optimizes the system performance.

[0452] As an embodiment, the second processor belongs to the core network; the above method supports full-network joint training and further optimizes the system performance.

[0453] As an embodiment, the second data set includes inference data.

[0454] As an embodiment, the third processor belongs to the first node.

[0455] As an embodiment, 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.

[0456] As an embodiment, the second data set includes the first reference signal.

[0457] As an embodiment, the second data set includes L1 measurement results obtained by measuring the first reference signal.

[0458] As an embodiment, the second data set includes beam-level measurement results obtained by measuring the first reference signal.

[0459] As an embodiment, the second data set includes L1 prediction results generated by the first node based on the first reference signal.

[0460] As an embodiment, the second data set includes cell-level prediction results generated by the first node based on the first reference signal.

[0461] As an embodiment, the first type of output includes the second information block.

[0462] As an embodiment, the first type of output includes the reporting information of beam management.

[0463] As an embodiment, the first type of output includes whether to send the first information block.

[0464] As an embodiment, the first type of output includes the first threshold.

[0465] As an embodiment, the first type of output includes the second threshold.

[0466] As an embodiment, the first type of output includes the time predicted by the first node to trigger the first information block based on the first reference signal.

[0467] As an example, the first type of output includes the time when RLF occurs based on the first reference signal predicted by the first node.

[0468] 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.

[0469] 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 will recalculate the target first type of parameter group.

[0470] 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.

[0471] As an example, the target first type of parameter group includes one or more of: a convolution kernel, a pooling kernel, a pooling function, an activation function, parameters of the pooling function, or parameters of the activation function.

[0472] 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.

[0473] Example 14

[0474] Example 14 exemplifies 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 shown. In the appendix Figure 14 In it, the first operation and the second operation belong to the first stage, the third operation belongs to the second stage, the fourth operation belongs to the third stage, and the fifth operation belongs to the fourth stage; the arrowed lines indicate the order of the process.

[0475] 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.

[0476] 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.

[0477] As an example, the first phase includes AI / ML model training.

[0478] As an example, the first phase includes AI / ML model training and AI / ML testing.

[0479] As an example, the AI / ML model training includes the initial training and re-training of one or a group of AI / ML entities.

[0480] As an example, the AI / ML model training depends on training data.

[0481] As an example, the AI / ML model training includes AI / ML entity validation.

[0482] As an example, the AI / ML entity validation is used to evaluate the performance of the AI / ML entity.

[0483] As an example, the AI / ML entity validation depends on validation data.

[0484] 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.

[0485] As an example, the AI / ML testing includes testing the validated AI / ML entity to evaluate the performance of the trained AI / ML model.

[0486] As an example, if the result of the AI / ML testing meets the expectation, the AI / ML entity proceeds to the next phase; otherwise, the AI / ML model will be re-trained.

[0487] As an example, the AI / ML testing depends on test data.

[0488] As an example, the second phase includes AI / ML emulation, and the AI / ML emulation performs inference on the AI / ML entity in an emulation environment.

[0489] As an example, the AI / ML simulation estimates the performance of AI / ML entity inference in a simulation environment before using the AI / ML entity.

[0490] As an example, the second stage is optional.

[0491] As an example, the third stage includes AI / ML entity loading, which is to obtain a trained AI / ML entity to obtain the desired AI / ML inference function.

[0492] As an example, the third stage is optional.

[0493] As an example, when the training function and the inference function are co-located, the third stage is no longer required.

[0494] As an example, the fourth stage includes AI / ML inference.

[0495] Example 15

[0496] Example 15 illustrates a structural block diagram of a processing device in a first node according to an example of the present application, as shown in the appendix Figure 15 In the appendix Figure 15 shown. In the appendix

[0497] In Example 15, the first receiver 1501 receives a first information block, the first information block is configured for a first measurement of mobility, and the number of computing power units occupied by the first measurement for mobility is equal to a first value;

[0498] The first receiver 1501 processes the first measurement for mobility only when the first value is not greater than the number of remaining computing power units of the first node;

[0499] In Example 15, the number of remaining computing power units of the first node is equal to the total number of computing power units of the first node minus the number of computing power units that have been occupied; the total number of computing power units of the first node depends on the number of first-type resources included in the first node and the number of second-type resources included in the corresponding first-type resources; any one of the first-type resources included in the first node includes at least one second-type resource.

[0500] As an example, the first type of resources corresponds to the processing units included in the terminal, and the second type of resources corresponds to a process supported by one of the processing units included in the terminal; the total number of computing power units of the terminal corresponds to the total number of processes supported by all the processing units included in the terminal.

[0501] As an example, the first value depends on the ID of the first model, and the first model is used for the first measurement for mobility, and the first model is AI / ML-based.

[0502] As an example, the first receiver 1501 receives a second information block, and the second information block is configured for a second measurement for mobility, and the number of computing power units occupied by the second measurement for mobility is equal to the second value; the priority of the second measurement is lower than the priority of the first measurement; the first value is not greater than the remaining computing power units of the terminal; the second value is not greater than the remaining computing power units of the terminal; the sum of the first value and the second value is greater than the remaining computing power units of the terminal; the second measurement for mobility is not processed.

[0503] As an example, the first measurement for mobility is one of a first set of measurements, and the first set of measurements includes at least one of Intra-frequency measurements, Inter-frequency measurements, and Inter-RAT measurements.

[0504] As an example, the first measurement for mobility is one of a second set of measurements, and the second set of measurements includes at least one of cell selection, cell reselection, and cell handover.

[0505] As an example, the first value is a positive integer greater than 1, and the terminal determines to process the first measurement for mobility; the terminal does not assume that the computing power units occupied by the first measurement for mobility are located in two different first types of resources.

[0506] As an example, the first transmitter 1502 sends a first set of information; the first set of information indicates the number of the first type of resources included in the terminal and the number of the second type of resources included in the corresponding first type of resources.

[0507] As an example, the first receiver 1501 receives a second information block; the first value depends on the indication of the second information block.

[0508] As an example, the first node 1500 is a user equipment.

[0509] As an example, the first node 1500 is a terminal.

[0510] As an example, the first node 1500 is a relay node device.

[0511] 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.

[0512] 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.

[0513] Example 16

[0514] Embodiment 16 illustrates a block diagram of a processing device in a second node according to an embodiment of the present application, as shown in the appendix Figure 16 shown. In the appendix Figure 16 shown, the processing device 1600 in the second node includes a second transmitter 1601 and a second receiver 1602.

[0515] In Embodiment 16, the second transmitter 1601 transmits a first information block, the first information block is configured for a first measurement of mobility, and the number of computing power units occupied by the first measurement for mobility is equal to a first value;

[0516] In Embodiment 16, the receiver of the first information block includes a first node; only when the first value is not greater than the number of remaining computing power units of the first node, the first node processes the first measurement for mobility; the number of remaining computing power units of the first node is equal to the total number of computing power units of the first node minus the number of computing power units that have been occupied; the total number of computing power units of the first node depends on the number of first type resources included in the first node and the number of second type resources included in the corresponding first type resources; at least one second type resource is included in any one of the first type resources included in the first node.

[0517] As an example, the first type of resources corresponds to the processing units included in the first node, and the second type of resources corresponds to a process supported by one of the processing units included in the first node; the total number of computing power units of the first node corresponds to the total number of processes supported by all the processing units included in the first node.

[0518] As an example, the first value depends on the ID of the first model, and the first model is used for the first measurement for mobility, and the first model is AI / ML-based.

[0519] As an example, the second transmitter 1601 sends a second information block, and the second information block is configured for the second measurement for mobility. The number of computing power units occupied by the second measurement for mobility is equal to the second value; the priority of the second measurement for mobility is lower than the priority of the first measurement for mobility; the first value is not greater than the remaining number of computing power units of the first node; the second value is not greater than the remaining number of computing power units of the first node; the sum of the first value and the second value is greater than the remaining number of computing power units of the first node; the second measurement for mobility is not processed by the first node.

[0520] As an example, the first measurement for mobility is one of a first set of measurements, and the first set of measurements includes at least one of Intra-frequency measurements, Inter-frequency measurements, and Inter-RAT measurements.

[0521] As an example, the first measurement for mobility is one of a second set of measurements, and the second set of measurements includes at least one of cell selection, cell reselection, and cell handover.

[0522] As an example, the first value is a positive integer greater than 1, and the first node determines to process the first measurement for mobility; the first node does not assume that the computing power units occupied by the first measurement for mobility are located in two different first types of resources.

[0523] As an example, the second receiver 1602 receives a first set of information; the first set of information indicates the number of the first type of resources included in the first node and the number of the second type of resources included in the corresponding first type of resources.

[0524] As an example, the second transmitter 1601 sends a third information block; the first value depends on the indication of the third information block.

[0525] As an example, the second node 1600 is a base station device.

[0526] As an example, the second node 1600 is a user equipment.

[0527] As an example, the second node 1600 is a TRP.

[0528] 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.

[0529] 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.

[0530] Those of ordinary skill in the art can understand that all or part of the steps in the above methods 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 in the above embodiments can also be implemented using one or more integrated circuits. Correspondingly, each module unit in the above embodiments can be implemented in a hardware form or in the form of a software functional module. This application is not limited to any specific form of the combination of software and hardware. The user equipment, terminal, and UE in this application include, but are not limited to, drones, communication modules on drones, remote control airplanes, aircraft, small airplanes, mobile phones, tablet computers, laptops, 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 the base station, and other wireless communication devices.

[0531] Those skilled in the art should understand that the present invention can be implemented in other specified forms without departing from its core or basic characteristics. Therefore, the currently disclosed embodiments should in any case be regarded as descriptive rather than restrictive. The scope of the invention is determined by the appended claims rather than the preceding description, and all modifications within the equivalent meaning and scope thereof are considered to be included therein.

Claims

1. A method in a terminal for wireless communication measurement, characterized in that include: receiving a first information block, where the first information block is configured for a first measurement of mobility, and the number of computing power units occupied by the first measurement of mobility is equal to a first value; processing the first measurement for mobility only if the first value is not greater than the number of remaining computing capacity units of the terminal; Among them, the remaining number of computing power units of the terminal is equal to the total number of computing power units of the terminal minus the number of computing power units that have been occupied; the total number of computing power units of the terminal depends on the number of first-category resources included in the terminal, and the corresponding number of second-category resources included in the first-category resources; any first-category resource included in the terminal includes at least one second-category resource.

2. The method according to claim 1, characterized in that The first type of resources corresponds to a processing unit included in the terminal, and the second type of resources corresponds to a process supported by a processing unit included in the terminal; the total number of computing power units of the terminal corresponds to the total number of processes supported by all processing units included in the terminal.

3. The method according to claim 1 or 2, characterized in that: The first value depends on an ID of a first model, the first model is used for the first measurement for mobility, and the first model is based on AI / ML.

4. The method according to any one of claims 1 to 3, characterized in that include: receiving a second information block, where the second information block is configured for a second measurement of mobility, and the number of computing capacity units occupied by the second measurement of mobility is equal to a second value; Among them, the priority targeted by the second measurement is lower than the priority targeted by the first measurement; the first value is not greater than the number of remaining computing power units of the terminal; the second value is not greater than the number of remaining computing power units of the terminal; the sum of the first value and the second value is greater than the number of remaining computing power units of the terminal; and the second measurement for mobility is not processed.

5. The method according to any one of claims 1 to 4, characterized in that: The first measurement for mobility is a measurement in a first type of measurement set, and the first type of measurement set includes at least one of Intra-frequency measurement, Inter-frequency measurement, and Inter-RAT measurement.

6. The method according to any one of claims 1 to 4, characterized in that: The first measurement for mobility is a measurement in a second type of measurement set, and the second type of measurement set includes at least one of cell selection, cell reselection, and cell handover.

7. The method according to any one of claims 1 to 6, characterized in that: The first value is a positive integer greater than 1, and the terminal determines to process the first measurement for mobility; the terminal does not assume that the computing power unit occupied by the first measurement for mobility is located in two different first-category resources.

8. The method according to any one of claims 1 to 7, characterized in that include: sending a first information set; The first information set indicates the number of first-category resources included in the terminal, and the corresponding number of second-category resources included in the first-category resources.

9. The method according to any one of claims 1 to 8, characterized in that include: receiving a second information block; The first value depends on the indication of the second information block.

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 in a base station for wireless communication measurement, characterized in that include: Sending a first information block, where the first information block is configured for a first measurement of mobility, and the number of computing capacity units occupied by the first measurement of mobility is equal to a first value; Among them, the recipient of the first information block includes a terminal; the terminal processes the first measurement for mobility only when the first value is not greater than the number of remaining computing power units of the terminal; the number of remaining computing power units of the terminal is equal to the total number of computing power units of the terminal minus the number of computing power units that have been occupied; the total number of computing power units of the terminal depends on the number of first-category resources included in the terminal, and the corresponding number of second-category resources included in the first-category resources; any one of the first-category resources included in the terminal includes at least one second-category resource.

12. The method according to claim 11, characterized in that The first type of resources corresponds to a processing unit included in the terminal, and the second type of resources corresponds to a process supported by a processing unit included in the terminal; the total number of computing power units of the terminal corresponds to the total number of processes supported by all processing units included in the terminal.

13. The method according to claim 11 or 12, characterized in that: The first value depends on an ID of a first model, the first model is used for the first measurement for mobility, and the first model is based on AI / ML.

14. The method according to any one of claims 11 to 13, characterized in that include: Sending a second information block, where the second information block is configured for a second measurement of mobility, and the number of computing capacity units occupied by the second measurement of mobility is equal to a second value; Among them, the priority targeted by the second measurement is lower than the priority targeted by the first measurement; the first value is not greater than the number of remaining computing power units of the terminal; the second value is not greater than the number of remaining computing power units of the terminal; the sum of the first value and the second value is greater than the number of remaining computing power units of the terminal; and the second measurement for mobility is not processed by the terminal.

15. The method according to any one of claims 11 to 14, characterized in that The first measurement for mobility is a measurement in a first type of measurement set, and the first type of measurement set includes at least one of Intra-frequency measurement, Inter-frequency measurement, and Inter-RAT measurement.

16. The method according to any one of claims 11 to 14, characterized in that The first measurement for mobility is a measurement in a second type of measurement set, and the second type of measurement set includes at least one of cell selection, cell reselection, and cell handover.

17. The method according to any one of claims 11 to 16, characterized in that The first value is a positive integer greater than 1, and the terminal determines to process the first measurement for mobility; the terminal does not assume that the computing power unit occupied by the first measurement for mobility is located in two different first-category resources.

18. The method according to any one of claims 11 to 17, characterized in that include: receiving a first information set; The first information set indicates the number of first-category resources included in the terminal, and the corresponding number of second-category resources included in the first-category resources.

19. The method according to any one of claims 11 to 18, characterized in that include: sending a second information block; The first value depends on the indication of the second information block.

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, and the one or more processors call the computer instructions to enable the base station to perform the method according to any one of claims 11 to 19.

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