Prediction processing method and device and readable storage medium

Abstract

The invention discloses a prediction processing method and device and a readable storage medium, relates to the technical field of communication, and aims to reduce measurement of a terminal on signal resources. The method comprises the steps that prediction information is acquired, and prediction is executed through an artificial intelligence / machine learning AI / ML model according to the prediction information; wherein the prediction information comprises one or more of the following items: time domain information; frequency domain information; reference signal information; frequency information; and beam information. According to the embodiment of the invention, an effect of automatically predicting the signal resource by the terminal can be realized.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a predictive processing method, apparatus and readable storage medium. Background Technology

[0002] Artificial intelligence (AI) technology has been increasingly widely applied in modern society. In the medical field, AI can be used for medical image analysis, diagnosis, and prediction; in the financial field, it can be used for risk management, fraud detection, and credit assessment; and in manufacturing, it can be used for intelligent manufacturing and intelligent management. Furthermore, many other fields are also applying AI technology, such as autonomous driving, smart homes, game development, and education.

[0003] In existing technologies, terminals can perform measurements on SSB and CSI-RS. For SSB measurements, network devices can configure the resource locations for SSB measurements, such as frequency information, period, and the starting position within the period. The terminal performs the measurement based on this SSB configuration information. For CSI-RS measurements, the network side configures the frequency domain location, period, etc., of the CSI-RS, and the terminal performs CSI-RS measurements based on this configuration information.

[0004] Traditional measurements require the network side to configure corresponding reference signals at the corresponding locations, such as SSB and CSI-RS, and the terminal to perform the measurement at the resource location indicated by the network side, resulting in high power consumption. Some technologies even require the terminal to stop some DL reception / UL transmission before performing the corresponding measurement, which will also cause problems such as reduced system throughput. Summary of the Invention

[0005] This application provides an information processing method, apparatus, and readable storage medium to reduce the terminal's measurement of signal resources.

[0006] In a first aspect, embodiments of this application provide a prediction processing method applied to a terminal, comprising:

[0007] Obtain prediction information, and perform predictions based on the prediction information using artificial intelligence / machine learning (AI / ML) models;

[0008] The prediction information includes one or more of the following:

[0009] Time domain information; frequency domain information; reference signal information; frequency information; beam information.

[0010] Optionally, the time-domain information includes one or more of the following:

[0011] Periodic information and first time offset; second time offset; bit mapping information; measurement reduction rate.

[0012] Optionally, the prediction information may also include one or more of the following: the prediction object; the index number of the prediction object; and the reporting information of the prediction result.

[0013] Optionally, the prediction information is included within the first information, and prediction is performed based on the first information, wherein the first information further includes:

[0014] Optionally, performing the prediction based on the first information includes:

[0015] The prediction time is determined based on the measurement information and the time domain information; wherein the time domain information is the period information and the first time offset.

[0016] Optionally, performing the prediction based on the first information includes:

[0017] The predicted time is determined based on the measurement information and the second time offset; wherein the second time offset represents the time offset between the first measurement time and the predicted time in the measurement information.

[0018] Optionally, performing the prediction based on the first information includes:

[0019] The predicted time is determined based on the second measurement time and the bit mapping information.

[0020] Optionally, performing the prediction based on the first information includes:

[0021] The prediction time is determined based on the second measurement time and the measurement reduction rate.

[0022] Optionally, performing the prediction based on the first information includes:

[0023] The predicted time is determined based on the second measurement time and the beam information.

[0024] Optionally, the second measurement time is configured on the network side.

[0025] Optionally, performing predictions via artificial intelligence / machine learning (AI / ML) models includes:

[0026] AI predictions are performed using artificial intelligence / machine learning (AI / ML) models to obtain predictive measurement results.

[0027] Secondly, embodiments of this application also provide a prediction processing method applied to a network device, comprising:

[0028] Send prediction information, which is used to perform predictions through an artificial intelligence / machine learning (AI / ML) model;

[0029] The prediction information includes one or more of the following:

[0030] Time domain information; frequency domain information; reference signal information; frequency information; beam information.

[0031] Optionally, the time-domain information includes one or more of the following: periodic information and a first time offset; a second time offset; bit mapping information; and a measurement reduction rate.

[0032] Optionally, the prediction information may also include one or more of the following: the prediction object; the index number of the prediction object; and the reporting information of the prediction result.

[0033] Optionally, the method further includes: sending measurement information, the measurement information being used to indicate information measured by the terminal; wherein the measurement information includes:

[0034] The first measurement time is used to represent the measurement time in the measurement information;

[0035] The second measurement time is used to represent the measurement time configured on the network side.

[0036] Thirdly, embodiments of this application also provide a predictive processing apparatus, characterized in that it is applied to a terminal and includes: a memory, a transceiver, and a processor.

[0037] A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs from the memory and performing the following operations:

[0038] Obtain prediction information, and perform predictions based on the prediction information using artificial intelligence / machine learning (AI / ML) models;

[0039] The prediction information includes one or more of the following: time domain information; frequency domain information; reference signal information; frequency information; beam information.

[0040] Fourthly, embodiments of this application also provide a prediction processing apparatus applied to a network device, comprising: a memory, a transceiver, and a processor.

[0041] A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs from the memory and performing the following operations:

[0042] Send prediction information, which is used to perform predictions through an artificial intelligence / machine learning (AI / ML) model;

[0043] The prediction information includes one or more of the following: time domain information; frequency domain information; reference signal information; frequency information; beam information.

[0044] Fifthly, embodiments of this application also provide a prediction processing apparatus applied to a terminal, comprising:

[0045] The first acquisition unit is used to acquire prediction information;

[0046] The first prediction unit is used to perform predictions based on the prediction information using an artificial intelligence / machine learning (AI / ML) model.

[0047] The prediction information includes one or more of the following: time domain information; frequency domain information; reference signal information; frequency information; beam information.

[0048] Sixthly, embodiments of this application also provide a prediction processing apparatus, applied to a network device, comprising:

[0049] A sending unit is used to send prediction information, which is used to perform predictions through an artificial intelligence / machine learning (AI / ML) model.

[0050] The prediction information includes one or more of the following: time domain information; frequency domain information; reference signal information; frequency information; beam information.

[0051] In a seventh aspect, embodiments of this application also provide a processor-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the predictive processing method described above.

[0052] In this embodiment of the application, the prediction is performed by an artificial intelligence / machine learning (AI / ML) model based on the predicted information, which can achieve the effect of automatic prediction of signal resources by the terminal. Attached Figure Description

[0053] Figure 1 This is a flowchart of a predictive processing method provided in this application;

[0054] Figure 2 This is a schematic diagram of Embodiment 1 of the predictive processing method provided in this application;

[0055] Figure 3 This is a schematic diagram of Embodiment 2 of the predictive processing method provided in this application;

[0056] Figure 4 This is a schematic diagram of Embodiment 3 of the predictive processing method provided in this application;

[0057] Figure 5This is a schematic diagram of Embodiment 4 of the predictive processing method provided in this application;

[0058] Figure 6 This is a schematic diagram of Embodiment 5 of the predictive processing method provided in this application;

[0059] Figure 7 This is a flowchart of another predictive processing method provided in this application;

[0060] Figure 8 This is a structural diagram of a predictive processing device provided in an embodiment of this application;

[0061] Figure 9 This is a structural diagram of another predictive processing device provided in the embodiments of this application;

[0062] Figure 10 This is an internal structural diagram of a predictive processing device provided in an embodiment of this application. Detailed Implementation

[0063] In the embodiments of this application, the term "and / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0064] In the embodiments of this application, the term "multiple" refers to two or more, and other quantifiers are similar.

[0065] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0066] This application provides a prediction processing method and apparatus, relating to the field of communication technology, to reduce the measurement of signal resources by the terminal. The method includes: acquiring prediction information, and performing prediction using an artificial intelligence / machine learning (AI / ML) model based on the prediction information; wherein the prediction information includes one or more of the following: time-domain information; frequency-domain information; reference signal information; frequency information; and beam information. This application embodiment can achieve the effect of automatic prediction of signal resources by the terminal.

[0067] The method and apparatus are based on the same concept of the application. Since the methods and apparatus solve problems in similar ways, the implementation of the apparatus and methods can refer to each other, and the repeated parts will not be described again.

[0068] In existing technology measurements, the terminal can perform measurements on SSB and CSI-RS.

[0069] For SSB measurements, the network side can configure the resource locations for SSB measurements, such as the period and the starting position within the period. The terminal performs the measurement based on this SSB configuration information.

[0070] For CSI-RS measurements, the network side configures the frequency domain location, period, etc. of CSI-RS, and the terminal performs CSI-RS measurements based on this configuration information.

[0071] Traditional measurements require the network side to configure corresponding reference signals at specific locations, such as SSB or CSI-RS, and the terminal to perform the measurement at the location indicated by the network side, resulting in high power consumption. Some measurements even require gap configuration, necessitating the terminal to stop some DL reception / UL transmission before performing the corresponding measurement, which also leads to a decrease in system throughput. In the embodiments adopted in this invention, artificial intelligence / machine learning (AI / ML) models are used to predict measurement results, thereby achieving automatic prediction of signal resources by the terminal.

[0072] See Figure 1 , Figure 1 This is a flowchart of a prediction processing method provided in an embodiment of this application, such as... Figure 1 As shown, it includes the following steps:

[0073] Step 101: Obtain prediction information;

[0074] Step 102: Based on the predicted information, perform prediction using an artificial intelligence / machine learning (AI / ML) model.

[0075] The prediction information includes one or more of the following: time-domain information; frequency-domain information; reference signal information; frequency information; beam information. The prediction information also includes: the prediction object; the index number of the prediction object; and the reporting information of the prediction result.

[0076] The time-domain information further includes one or more of the following: periodic information and a first time offset; a second time offset; bit mapping information; measurement reduction rate, and other types of information. The first measurement time represents the measurement time in the measurement information; the second measurement time represents the measurement time configured on the network side.

[0077] Furthermore, in embodiments of the present invention, prediction can also be performed based on the first information (including measurement information and prediction information), wherein the measurement information is used to indicate information measured by the terminal.

[0078] Example 1:

[0079] In an embodiment of the present invention, the prediction information may indicate the prediction period and a first time offset, thereby performing prediction through an artificial intelligence / machine learning (AI / ML) model to obtain the prediction time. Specific steps include:

[0080] Step 1-1: The terminal obtains the first information. The first information may be configuration information sent by the network side, such as through RRC (Radio Resource Control) reconfiguration, RRCSetup (RRC establishment), or RRCResume (RRC recovery) messages.

[0081] Step 1-2: The terminal obtains the prediction information through the first information.

[0082] The terminal may obtain the prediction information through the first information in one of the following two ways:

[0083] Method 1: The first information includes measurement object information, which may include measurement information and prediction information.

[0084] Method 2: The first information includes the prediction information, and the prediction information includes the index information of the measurement object.

[0085] Optionally, the measurement object is indicated by measurement object information, whereby the first information indicates the measurement object information. Optionally, the measurement object is used to indicate same-frequency or different-frequency measurements applied to a reference signal, and the reference signal may be SSB, CSI-RS, PSS / SSS, CRS, etc., which are not limited here.

[0086] In this embodiment, the measurement information may include the time-domain and / or frequency-domain location of the measurement, such as indicating the measurement period and / or time-domain offset, and may also include the measurement reporting period, which is not limited here. The measurement information is the information actually used in the measurement, including the reference signal position, such as the period and time offset; the notification method can be SMTC, for example, the following executed code:

[0087]

[0088] As described above, the period and offset can be notified via periodicityAndOffset; and the position of CSI-RS can be notified via information such as slotConfig in SI-RS-Resource-Mobility.

[0089] Figure 2 This is a schematic diagram of the first embodiment of the prediction processing method provided in this application. See also: Figure 2In an embodiment of this application, optionally, the prediction information is information predicted by an AI / ML model, including: the prediction period of the AI / ML model and / or a first offset. For example... Figure 2 For example, suppose the measurement information indicates a period of 20 time units and an offset of 5 time units. These time units can be time slots, subframes, milliseconds, seconds, etc., without restriction. It should be noted that... Figure 2 The time units for the period and offset can be the same or different; there are no restrictions here.

[0090] like Figure 2 The prediction information can indicate periodicity and a first time offset. For example, the periodicity is 20 time units, and the first time offset is 15 time units.

[0091] Alternatively, it can be implemented as follows:

[0092]

[0093] In this configuration, the terminal needs to perform measurements based on the resource location indicated by the measurement information, obtain the measurement results, and output the results predicted by the AI / ML model at the location indicated by the prediction information, based on the measurement results.

[0094] Steps 1-3: The terminal can predict the results based on the actual measurement results using AI / ML models, perform subsequent operations, and determine whether the measurement event is triggered or the measurement results are reported.

[0095] In this step, the measurement event may include one or more of the following events, which are not limited here:

[0096] A1 event: The signal quality of the serving cell is below the specified threshold;

[0097] A2 event: The signal quality of the serving cell is below the specified threshold.

[0098] A3 event: The neighboring cell signal quality is higher than SpCell by a specified offset;

[0099] A4 event: The signal quality of the neighboring cell is higher than the specified threshold;

[0100] A5 event: The signal quality of SpCell is below a certain threshold, while that of a neighboring cell is above another threshold; A6 event: The signal quality of a neighboring cell is higher than that of SCell by a specified offset;

[0101] B1: Inter RAT neighbor cell signal quality is higher than a specified threshold;

[0102] B2: The signal quality of PCell is below a certain threshold, while the Inter RAT neighbor cell is above another threshold;

[0103] I1: Interference exceeds a specified threshold value;

[0104] And V2X-related events, etc.

[0105] In Embodiment 1 of the present invention, the prediction information may indicate the period and / or first time offset of the AI ​​prediction, thereby performing the prediction through the artificial intelligence / machine learning AI / ML model, obtaining the prediction time of the prediction result, and obtaining the prediction result.

[0106] Example 2:

[0107] In an embodiment of the present invention, the prediction information may indicate a second offset between the predicted time position and the first measurement information.

[0108] Step 2-1: The terminal obtains the first information. The first information may be configuration information sent by the network side, such as through RRC reconfiguration, RRCSetup, or RRCResume messages.

[0109] Step 2-2: The terminal obtains the prediction information through the first information.

[0110] The terminal may obtain the prediction information through the first information in one of the following two ways:

[0111] Method 1: The first information includes measurement object information, which includes measurement information and prediction information;

[0112] Method 2: The first information includes the prediction information, which includes the index information of the measurement object. Optionally, the measurement object is indicated by measurement object information, and the first information indicates the measurement object information. Optionally, the measurement object is used to indicate same-frequency or different-frequency measurements applied to a reference signal. The reference signal can be SSB, CSI-RS, PSS / SSS, CRS, etc., which are not limited here. The measurement information can include the time-domain and / or frequency-domain location of the measurement, such as indicating the measurement period and / or time offset, and can also include the measurement reporting period, which are not limited here either.

[0113] The measurement information is the information actually used in the measurement, including the first measurement time and / or pilot information. The first measurement time indicates the measurement period and time offset, etc. The notification method can be SMTC, for example, the following executed code:

[0114]

[0115] As described above, the period and offset of the first measurement moment can be notified via periodicityAndOffset; and the position of CSI-RS can be notified via information such as slotConfig in SI-RS-Resource-Mobility.

[0116] Figure 3 This is a schematic diagram of Embodiment 2 of the prediction processing method provided in this application. See also... Figure 3 In an embodiment of this application, optionally, the prediction information is information predicted by the AI / ML model, including: the time position (prediction time) predicted by the AI / ML model and the second time offset between the first measurement time; the measurement information is information actually measured, including the first measurement time and / or pilot position, such as the first measurement time, period and time offset, etc., and the notification method can be the same as the above-mentioned SMTC method.

[0117] like Figure 3 As shown, for example, the second time offset is 10 time units, which means the offset is 10 time units away from the first measurement time.

[0118] The notification method can be:

[0119]

[0120] In an embodiment of the present invention, the terminal performs a measurement based on a first measurement time indicated by the measurement information, and performs a prediction using an AI / ML model based on a second time offset.

[0121] Steps 2-3: Based on the actual measurement results, the terminal can use the AI / ML model to predict the results and perform subsequent operations to determine whether the measurement event is triggered or the measurement results are reported. Other specific details in this step are the same as in steps 1-3 and will not be repeated here.

[0122] Example 3:

[0123] In an embodiment of the present invention, the prediction information may indicate bit mapping information and a second period, wherein the second period may be an AI prediction period and / or an actual measurement period.

[0124] Step 3-1: The terminal obtains the first information. The first information may be configuration information sent by the network-side device, such as through RRC reconfiguration, RRCSetup, or RRCResume messages.

[0125] Step 3-2: The terminal obtains the prediction information through the first information.

[0126] The terminal may obtain the prediction information through the first information in one of the following two ways:

[0127] Method 1: The first information includes measurement object information, which may include measurement information and prediction information.

[0128] Method 2: The first information includes the prediction information, and the prediction information includes the index information of the measurement object.

[0129] Optionally, the measurement object is indicated by measurement object information, whereby the first information indicates the measurement object information. The measurement object is used to indicate same-frequency or different-frequency measurements applied to a reference signal. The reference signal can be SSB, CSI-RS, PSS / SSS, CRS, etc., and is not limited here.

[0130] The measurement information may include the time domain and / or frequency domain position of the reference signal, such as the period and / or time offset of the reference signal, and may also include the measurement reporting period, which is not limited here.

[0131] The measurement information refers to the information actually used in the measurement, including pilot position, such as period and time offset. The notification method can be SMTC, for example, the following executed code:

[0132]

[0133]

[0134] As described above, the period and offset can be notified via periodicityAndOffset; and the position of CSI-RS can be notified via information such as slotConfig in SI-RS-Resource-Mobility.

[0135] Figure 4 This is a schematic diagram of Embodiment 3 of the prediction processing method provided in this application. See also... Figure 4 In an embodiment of this application, optionally, the prediction information is information predicted by an AI / ML model, including: bit mapping information and / or a second period, where the second period may be an AI prediction period and / or an actual measurement period. In this step, the terminal obtains information about performing measurements and performing AI / ML model predictions based on the second measurement time indicated by the measurement information (which represents the actual measurement time configured by the network side) and the bit mapping information.

[0136] like Figure 4As shown, the measurement information notification period and / or offset allow the terminal to perform measurements based on the measurement information. The prediction information notification includes a bitmask, which can be in bitmap form. The terminal can calculate the resource location predicted by the AI / ML model using the bitmask. For example, if the measurement information notification period is 50ms and the bitmap length is 4 (assumed to be 0111), the network side can optionally notify the AI ​​prediction period, which is 10ms in this embodiment. Thus, the terminal can assume that there are 5 points within 50ms, of which 2 are the actual measured points and 3 are locations that need to be predicted by the AI ​​(= bitmap length - 1). Therefore, the terminal can obtain the AI / ML model predicted locations.

[0137] Step 3-3: Based on the actual measurement results, the terminal can predict the results using an AI / ML model, execute subsequent operations, and determine whether the measurement event is triggered or the measurement results are reported. Other specific details in this step are the same as in Steps 1-3 and will not be repeated here.

[0138] Example 4:

[0139] In an embodiment of the present invention, the prediction information may indicate the time-domain measurement reduction rate and a third period, wherein the third period may be the AI ​​prediction period and / or the actual measurement period.

[0140] Step 4-1: The terminal obtains the first information. The first information may be configuration information sent by the network side, such as through RRC reconfiguration, RRCSetup, or RRCResume messages.

[0141] Step 4-2: The terminal obtains the prediction information through the first information.

[0142] The terminal may obtain the prediction information through the first information in one of the following two ways:

[0143] Method 1: The first information includes measurement object information, which may include measurement information and prediction information.

[0144] Method 2: The first information includes the prediction information, and the prediction information includes the index information of the measurement object.

[0145] Optionally, the measurement object is indicated by measurement object information, whereby the first information indicates the measurement object information. Optionally, the measurement object is used to indicate same-frequency or different-frequency measurements applied to a reference signal, and the reference signal may be SSB, CSI-RS, PSS / SSS, CRS, etc., which are not limited here.

[0146] The measurement information may include the time domain and / or frequency domain location of the measurement, such as the measurement period and / or time offset, and may also include the measurement reporting period, which is not limited here.

[0147] The measurement information refers to the information actually used in the measurement, including pilot position, such as period and time offset. The notification method can be SMTC, for example, the following executed code:

[0148]

[0149] As described above, the period and offset can be notified via periodicityAndOffset; and the position of CSI-RS can be notified via information such as slotConfig in SI-RS-Resource-Mobility. Figure 5 This is a schematic diagram of Embodiment 4 of the prediction processing method provided in this application. See also... Figure 5 In an embodiment of this application, optionally, the prediction information is information predicted by an AI / ML model, including: a prediction information notification time-domain measurement reduction rate, for example, 3 / 4, and / or a third period, wherein the third period can be the AI ​​prediction period and / or the actual measurement period, for example, in this embodiment, it is the AI ​​prediction period with a size of 10ms. As shown in the figure below, if the period of the first measurement information notification is 50ms and the AI ​​prediction period is 10ms, which can be configured on the network side, the measurement reduction rate is 3 / 4. The terminal can determine that through a measurement result (for example, the second measurement time, which represents a certain moment of the actual measurement configured on the network side), the terminal can predict three prediction results, thereby obtaining the predicted location.

[0150] Step 4-3: Based on the actual measurement results, the terminal can predict the results using an AI / ML model, execute subsequent operations, and determine whether the measurement event is triggered or the measurement results are reported. The other specific details in this step are the same as in steps 1-3, and will not be repeated here.

[0151] Example 5:

[0152] In an embodiment of the present invention, the prediction information may indicate beam prediction information.

[0153] Step 5-1: The terminal obtains the first information. The first information may be configuration information sent by the network side, such as through RRC reconfiguration, RRCSetup, or RRCResume messages.

[0154] Step 5-2: The terminal obtains measurement prediction information through the first information.

[0155] The terminal may obtain the prediction information through the first information in one of the following two ways:

[0156] Method 1: The first information includes object information, and the measurement object information may include measurement information and prediction information.

[0157] Method 2: The first information includes the prediction information, and the prediction information includes the index information of the measurement object. Optionally, the first information indicates the measurement object information. Optionally, the measurement object information is used to indicate the measurement object, and the measurement object is used to indicate the same-frequency or different-frequency measurement applied to the reference signal. The reference signal can be SSB, CSI-RS, PSS / SSS, CRS, etc., which are not limited here. The measurement information can include the time domain and / or frequency domain position of the reference signal, for example, indicating the measurement period and / or time offset, etc., and can also include the measurement reporting period, which are not limited here either.

[0158] The measurement information is the information used in the actual measurement, including SSB beam information or CSI-RS beam information, such as a beam bitmap. The measurement information may also include a second measurement time, which represents a certain moment of the actual measurement configured on the network side.

[0159] Figure 6 This is a schematic diagram of Embodiment 5 of the prediction processing method provided in this application. See also... Figure 6 In an embodiment of this application, optionally, the prediction information is information predicted by an AI / ML model, including: first prediction information notifying beam prediction information, such as bitmap information. Figure 6 As shown, if the beam pattern notified by the first measurement information is 10101010 (counter-clockwise rotation, with the leftmost bit corresponding to the leftmost beam), the beam prediction information is 01010101 (counter-clockwise rotation, with the leftmost bit corresponding to the leftmost beam). After mapping, it can be known that the measured beams are indices 0, 2, 4, and 6, while the predicted beams are indices 1, 3, 5, and 7, thus obtaining the prediction result.

[0160] Step 4-3: Based on the measured R results, the terminal can predict the results using an AI / ML model, perform subsequent operations, and determine whether the measurement event is triggered or the measurement results are reported. The other specific details in this step are the same as in steps 1-3 and will not be repeated here.

[0161] In an embodiment of the present invention, this application also provides a prediction processing method. Figure 7 This is a flowchart of another predictive processing method provided in this application, such as... Figure 7 As shown, it is applied to network devices, including:

[0162] Step 701: Send prediction information, which is used to perform predictions through an artificial intelligence / machine learning (AI / ML) model;

[0163] The prediction information includes one or more of the following: time domain information; frequency domain information; reference signal information; frequency information; beam information.

[0164] The time-domain information includes one or more of the following: periodic information and a first time offset; a second time offset; bit mapping information; and measurement reduction rate.

[0165] Optionally, the prediction information may further include: the prediction object; the index number of the prediction object; and the reporting information of the prediction result.

[0166] Optionally, the method further includes: sending measurement information, the measurement information being used to indicate information measured by the terminal; wherein the measurement information includes:

[0167] The first measurement time is used to represent the measurement time in the measurement information;

[0168] The second measurement time is used to represent the measurement time configured on the network side.

[0169] Optionally, the second measurement time includes: the Radio Resource Management (RRM) measurement time configured on the network side.

[0170] In embodiments of the present invention, the prediction information and measurement information may be configuration information sent by network-side devices, such as through RRC reconfiguration, RRCSetup (RRC establishment), and RRCResume (RRC recovery) messages.

[0171] The prediction information can indicate various information predicted by AI, and through the first or second measurement time in the measurement information, the prediction is performed by the artificial intelligence / machine learning (AI / ML) model to obtain the prediction time of the prediction result.

[0172] The above embodiments describe the service performance monitoring method of this application. The following embodiments will further describe the corresponding devices in conjunction with the accompanying drawings.

[0173] In an embodiment of the present invention, Figure 8 This is a structural diagram of a predictive processing device provided in an embodiment of this application, as shown below. Figure 8 As shown, this application embodiment provides a prediction processing device 800, applied to a terminal, including:

[0174] The first acquisition unit 801 is used to acquire prediction information;

[0175] The first prediction unit 802 is used to perform predictions based on the prediction information using an artificial intelligence / machine learning (AI / ML) model.

[0176] The prediction information includes one or more of the following:

[0177] Time domain information;

[0178] Frequency domain information;

[0179] Reference signal information;

[0180] Frequency information;

[0181] Beam information.

[0182] It should be noted that the apparatus provided in this application embodiment can implement all the method steps implemented in the method embodiment applied to the terminal, and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0183] In one embodiment of the present invention Figure 9 This is a structural diagram of another predictive processing apparatus provided in an embodiment of this application. For example... Figure 9 As shown, this application provides a prediction processing apparatus 900, applied to a network device, comprising:

[0184] The sending unit 901 is used to send prediction information, which is used to perform prediction through an artificial intelligence / machine learning (AI / ML) model.

[0185] The prediction information includes one or more of the following:

[0186] Time domain information; frequency domain information; reference signal information; frequency information; beam information.

[0187] It should be noted that the apparatus provided in this application embodiment can implement all the method steps implemented in the method embodiment applied to the first network element, and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0188] It should be noted that the division of units in the embodiments of this application is illustrative and only represents one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units.

[0189] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0190] This application also provides a communication device, including: a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the information processing method described above.

[0191] In one exemplary embodiment, a prediction processing device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 10 As shown. The predictive processing device includes a processor 1000, a transceiver 1010, a memory 1010, and a bus interface. The processor 1000 and the memory 1010 are connected via the bus interface. The processor 1000 provides computational and control capabilities. The memory 1010 of the assisted positioning device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the assisted positioning device is used for exchanging information between the processor and external devices. The communication interface of the assisted positioning device is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a positioning method for a passive device. The display unit of the assisted positioning device is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the auxiliary positioning device can be a touch layer covering the display screen, or a button, trackball, or touchpad set on the housing of the auxiliary positioning device, or an external keyboard, touchpad, or mouse, etc.

[0192] The assisted positioning device involved in this application embodiment can be a base station, which may include multiple cells providing services to terminals. Depending on the specific application, the base station may also be called an access point, or a device in the access network that communicates with the wireless terminal device through one or more sectors on the air interface, or other names. The network device can be used to exchange received air frames with Internet Protocol (IP) packets, acting as a router between the wireless terminal device and the rest of the access network, where the rest of the access network may include an Internet Protocol (IP) communication network. The network device can also coordinate the attribute management of the air interface. For example, the network device involved in this application embodiment may be an evolved Node B (eNB or e-NodeB) in a long term evolution (LTE) system, a 5G base station (gNB) in a next generation system, or a Home evolved Node B (HeNB), relay node, femto, pico, network testing equipment, etc., and is not limited in this application embodiment. In some network architectures, network devices may include centralized unit (CU) nodes and distributed unit (DU) nodes, which may also be geographically separated.

[0193] Those skilled in the art will understand that Figure 10 The structures shown are merely block diagrams of some structures related to the solution of this application and do not constitute a limitation on the auxiliary positioning device to which the solution of this application is applied. Specific auxiliary positioning devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. This application also provides a computer program product, including computer instructions. When executed by a processor, these computer instructions implement the various processes of the above-described information processing method embodiments and achieve the same technical effects. To avoid repetition, further details are omitted here.

[0194] This application also provides a processor-readable storage medium storing a program. When executed by a processor, this program implements the various processes of the above-described information processing method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic storage (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs)).

[0195] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0196] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0197] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0198] These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0199] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of the embodiments of this application and their equivalents, then the embodiments of this application are also intended to include these modifications and variations.

Claims

1. A predictive processing method, characterized in that, Applied to terminals, including: Obtain prediction information, and perform predictions based on the prediction information using artificial intelligence / machine learning (AI / ML) models; The prediction information includes one or more of the following: Time domain information; Frequency domain information; Reference signal information; Frequency information; Beam information.

2. The method according to claim 1, characterized in that, The time-domain information includes one or more of the following: Periodic information and first time offset; Second time offset; Bit mapping information Measure the reduction rate.

3. The method according to claim 1, characterized in that, The prediction information also includes one or more of the following: Predicted object; The index number of the predicted object; Information on reporting prediction results.

4. The method according to claim 1, characterized in that, The prediction information is included within the first information, and prediction is performed based on the first information, wherein the first information further includes: Measurement information, which is used to indicate information measured by the terminal.

5. The method according to claim 4, characterized in that, The step of performing prediction based on the first information includes: The prediction time is determined based on the measurement information and the time domain information; wherein the time domain information is the period information and the first time offset.

6. The method according to claim 4, characterized in that, The step of performing prediction based on the first information includes: The predicted time is determined based on the measurement information and the second time offset; wherein the second time offset represents the time offset between the first measurement time and the predicted time in the measurement information.

7. The method according to claim 4, characterized in that, The step of performing prediction based on the first information includes: The predicted time is determined based on the second measurement time and the bit mapping information.

8. The method according to claim 4, characterized in that, The step of performing prediction based on the first information includes: The prediction time is determined based on the second measurement time and the measurement reduction rate.

9. The method according to claim 1, characterized in that, The step of performing prediction based on the first information includes: The predicted time is determined based on the second measurement time and the beam information.

10. The method according to any one of claims 7 to 9, characterized in that, The second measurement time is configured on the network side.

11. The method according to claim 1, characterized in that, Performing predictions using artificial intelligence / machine learning (AI / ML) models includes: AI predictions are performed using artificial intelligence / machine learning (AI / ML) models to obtain predictive measurement results.

12. A predictive processing method, characterized in that, Applied to network devices, including: Send prediction information, which is used to perform predictions through an artificial intelligence / machine learning (AI / ML) model; The prediction information includes one or more of the following: Time domain information; Frequency domain information; Reference signal information; Frequency information; Beam information.

13. The method according to claim 12, characterized in that, The time-domain information includes one or more of the following: Periodic information and first time offset; Second time offset; Bit mapping information; Measure the reduction rate.

14. The method according to claim 12, characterized in that, The prediction information also includes one or more of the following: Predicted object; The index number of the predicted object; Information on reporting prediction results.

15. The method according to claim 12, characterized in that, The method further includes: Sending measurement information, the measurement information being used to indicate information measured by the terminal; wherein, the measurement information includes: The first measurement time is used to represent the measurement time in the measurement information; The second measurement time is used to represent the measurement time configured on the network side.

16. A predictive processing apparatus, characterized in that, Applications in terminals include: memory, transceiver, and processor. A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs from the memory and performing the following operations: Obtain prediction information, and perform predictions based on the prediction information using artificial intelligence / machine learning (AI / ML) models; The prediction information includes one or more of the following: Time domain information; Frequency domain information; Reference signal information; Frequency information; Beam information.

17. The apparatus according to claim 16, characterized in that, The time-domain information includes one or more of the following: Periodic information and first time offset; Second time offset; Bit mapping information; Measure the reduction rate.

18. The apparatus according to claim 16, characterized in that, The prediction information also includes one or more of the following: Predicted object; The index number of the predicted object; Information on reporting prediction results.

19. The apparatus according to claim 16, characterized in that, The prediction information is included within the first information, and prediction is performed based on the first information, wherein the first information further includes: Measurement information, which is used to indicate information measured by the terminal.

20. The apparatus according to claim 19, characterized in that, The step of performing prediction based on the first information includes: The prediction time is determined based on the measurement information and the time domain information; wherein the time domain information is the period information and the first time offset.

21. The apparatus according to claim 19, characterized in that, The step of performing prediction based on the first information includes: The predicted time is determined based on the measurement information and the second time offset; wherein the second time offset represents the time offset between the first measurement time and the predicted time in the measurement information.

22. The apparatus according to claim 19, characterized in that, The step of performing prediction based on the first information includes: The second measurement time is used to determine the predicted time by mapping the bit information to the second measurement time.

23. The apparatus according to claim 19, characterized in that, The step of performing prediction based on the first information includes: The second measurement time and the measurement reduction rate are used to determine the prediction time.

24. The apparatus according to claim 16, characterized in that, The step of performing prediction based on the first information includes: The second measurement time and the beam information are used to determine the predicted time.

25. The method according to any one of claims 22 to 24, characterized in that, The second measurement time is configured on the network side.

26. The apparatus according to any one of claims 22 to 24, characterized in that, The second measurement time includes: The measurement time of the Radio Resource Management (RRM) configured on the network side.

27. The apparatus according to claim 16, characterized in that, Performing predictions using artificial intelligence / machine learning (AI / ML) models includes: AI predictions are performed using artificial intelligence / machine learning (AI / ML) models to obtain predictive measurement results.

28. A predictive processing apparatus, characterized in that, Applications in network devices, including: memory, transceivers, and processors. A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs from the memory and performing the following operations: Send prediction information, which is used to perform predictions through an artificial intelligence / machine learning (AI / ML) model; The prediction information includes one or more of the following: Time domain information; Frequency domain information; Reference signal information; Frequency information; Beam information.

29. The apparatus according to claim 28, characterized in that, The time-domain information includes one or more of the following: Periodic information and first time offset; Second time offset; Bit mapping information; Measure the reduction rate.

30. The apparatus according to claim 28, characterized in that, The method further includes: Sending measurement information, the measurement information being used to indicate information measured by the terminal; wherein, the measurement information includes: The first measurement time is used to represent the measurement time in the measurement information; The second measurement time is used to represent the measurement time configured on the network side.

31. A predictive processing apparatus, characterized in that, Applied to terminals, including: The first acquisition unit is used to acquire prediction information; The first prediction unit is used to perform predictions based on the prediction information using an artificial intelligence / machine learning (AI / ML) model. The prediction information includes one or more of the following: Time domain information; Frequency domain information; Reference signal information; Frequency information; Beam information.

32. A predictive processing apparatus, characterized in that, Applied to network devices, including: A sending unit is used to send prediction information, which is used to perform predictions through an artificial intelligence / machine learning (AI / ML) model. The prediction information includes one or more of the following: Time domain information; Frequency domain information; Reference signal information; Frequency information; Beam information.

33. A processor-readable storage medium, characterized in that, The processor-readable storage medium stores a program for causing the processor to perform the method as described in any one of claims 1 to 15.

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