Reporting method and device, receiving method and device, terminal and network side equipment

Abstract

The invention discloses a reporting method and device, a receiving method and device, a terminal and network side equipment, and belongs to the technical field of communication, and the reporting method comprises the steps that the terminal reports a first measurement report of RRM measurement to the network side equipment; wherein the first measurement report comprises a prediction result of RRM measurement prediction performed by the terminal based on an AI unit.

Description

Technical Field

[0001] This application belongs to the field of communication technologies, and particularly relates to a reporting method, a receiving method, a device, a terminal, and a network-side device. Background Art

[0002] With the development of Artificial Intelligence (AI) technology, AI models have been able to be applied in communication systems, such as AI-based Channel State Information (CSI) prediction, Radio Resource Management (RRM) prediction, and event prediction. Currently, after a terminal performs actual RRM measurements, it will report the RRM measurement results. However, in AI-assisted mobility enhancement, how to report the RRM prediction results based on AI is currently unclear. Summary of the Invention

[0003] Embodiments of this application provide a reporting method, a receiving method, a device, a terminal, and a network-side device, which can solve the problem in related technologies that it is unclear how to report the RRM prediction results based on AI.

[0004] In a first aspect, a reporting method is provided, which is executed by a terminal. The method includes:

[0005] The terminal reports a first measurement report of Radio Resource Management (RRM) measurement to a network-side device;

[0006] Wherein, the first measurement report includes a prediction result of the terminal's RRM measurement prediction based on an Artificial Intelligence (AI) unit.

[0007] In a second aspect, a receiving method is provided, which is executed by a network-side device. The method includes:

[0008] The network-side device receives a first measurement report of RRM measurement reported by a terminal;

[0009] Wherein, the first measurement report includes a prediction result of the terminal's RRM measurement prediction based on an AI unit.

[0010] In a third aspect, a reporting device is provided, including:

[0011] A reporting module, configured to report a first measurement report of RRM measurement to a network-side device;

[0012] Wherein, the first measurement report includes a prediction result of the device's RRM measurement prediction based on an AI unit.

[0013] Fourth aspect, a receiving device is provided, including:

[0014] A receiving module, configured to receive a first measurement report of RRM measurement reported by a terminal;

[0015] Wherein, the first measurement report includes a prediction result of RRM measurement prediction by the terminal based on an AI unit.

[0016] Fifth aspect, a terminal is provided, which includes a processor and a memory. The memory stores a program or instruction that can run on the processor. When the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.

[0017] Sixth aspect, a terminal is provided, including a processor and a communication interface. The communication interface is configured to report a first measurement report of RRM measurement to a network-side device;

[0018] Wherein, the first measurement report includes a prediction result of RRM measurement prediction by the device based on an AI unit.

[0019] Seventh aspect, a network-side device is provided, which includes a processor and a memory. The memory stores a program or instruction that can run on the processor. When the program or instruction is executed by the processor, the steps of the method described in the second aspect are implemented.

[0020] Eighth aspect, a network-side device is provided, including a processor and a communication interface. The communication interface is configured to receive a first measurement report of RRM measurement reported by a terminal;

[0021] Wherein, the first measurement report includes a prediction result of RRM measurement prediction by the terminal based on an AI unit.

[0022] Ninth aspect, a readable storage medium is provided. A program or instruction is stored on the readable storage medium. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented, or the steps of the method described in the second aspect are implemented.

[0023] Tenth aspect, a wireless communication system is provided, including: a terminal and a network-side device. The terminal can be used to execute the steps of the method described in the first aspect, and the network-side device can be used to execute the steps of the method described in the second aspect.

[0024] Eleventh aspect, a chip is provided. The chip includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is configured to run a program or instruction to implement the method described in the first aspect, or to implement the method described in the second aspect.

[0025] In a twelfth aspect, a computer program / program product is provided. The computer program / program product is stored in a storage medium and is executed by at least one processor to implement the steps of the method described in the first aspect or the steps of the method described in the second aspect.

[0026] In the embodiments of the present application, the terminal can report a first measurement report of RRM measurement to the network-side device. The first measurement report includes the prediction result of the terminal's RRM measurement prediction based on the AI unit. Furthermore, it is stipulated that in AI-assisted mobility enhancement, the terminal reports the prediction result of RRM measurement prediction based on the AI unit to the network-side device through the first measurement report, that is, the reporting method of the prediction result is defined, enabling the network-side device to timely learn the prediction result and helping to improve the communication performance between the terminal and the network-side device. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a block diagram of a wireless communication system to which the embodiments of the present application can be applied;

[0028] Figure 2 is a flowchart of a reporting method provided by the embodiments of the present application;

[0029] Figure 3 is a flowchart of a receiving method provided by the embodiments of the present application;

[0030] Figure 4 is a structural diagram of a reporting device provided by the embodiments of the present application;

[0031] Figure 5 is a structural diagram of a receiving device provided by the embodiments of the present application;

[0032] Figure 6 is a structural diagram of a communication device provided by the embodiments of the present application;

[0033] Figure 7 is a structural diagram of a terminal provided by the embodiments of the present application;

[0034] Figure 8 is a structural diagram of a network-side device provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] Next, the technical solutions in the embodiments of the present application will be clearly described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0036] The terms "first", "second", etc. in this application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such terms can be interchanged under appropriate circumstances, so that the embodiments of this application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same category, and do not limit the number of objects. For example, the first object can be one or multiple. In addition, "or" in this application means at least one of the connected objects. For example, "A or B" covers three scenarios, namely, Scenario 1: including A and not including B; Scenario 2: including B and not including A; Scenario 3: including both A and B. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0037] The term "indication" in this application can be either a direct indication (or an explicit indication) or an indirect indication (or an implicit indication). Among them, a direct indication can be understood as that the sender clearly informs the receiver of specific information, operations to be performed, or request results, etc. in the sent indication; an indirect indication can be understood as that the receiver determines the corresponding information according to the indication sent by the sender, or makes a judgment and determines the operations to be performed or request results, etc. according to the judgment result.

[0038] It is worth pointing out that the technology described in the embodiments of this application is not limited to Long Term Evolution (LTE) / LTE-Advanced (LTE-A) systems, and can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency-Division Multiple Access (SC-FDMA), or other systems. The terms "system" and "network" in the embodiments of this application are often used interchangeably, and the described technology can be used not only in the above-mentioned systems and radio technologies, but also in other systems and radio technologies. The following description describes the New Radio (NR) system for example purposes, and uses NR terms in most of the following descriptions, but these technologies can also be applied to systems other than the NR system, such as the 6th generation (6 thGeneration, 6G) communication system.

[0039] Figure 1A block diagram of a wireless communication system to which embodiments of the present application can be applied is shown. The wireless communication system includes a terminal 11 and a network-side device 12. Among them, the terminal 11 can be a mobile phone, a tablet personal computer, a laptop computer, a notebook computer, a personal digital assistant (PDA), a handheld computer, a netbook, an ultra-mobile personal computer (UMPC), a mobile internet device (MID), an augmented reality (AR), a virtual reality (VR) device, a robot, a wearable device, a flight vehicle, a vehicle user equipment (VUE), a shipborne device, a pedestrian user equipment (PUE), a smart home (home devices with wireless communication functions, such as refrigerators, TVs, washing machines, or furniture, etc.), a game console, a personal computer (PC), a teller machine, or a self-service machine, etc., which are terminal-side devices. Wearable devices include: smart watches, smart bracelets, smart earphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart ankle chains, etc.), smart wristbands, smart clothing, etc. Among them, the vehicle user equipment can also be referred to as a vehicle terminal, a vehicle controller, a vehicle module, a vehicle component, a vehicle chip, or a vehicle unit, etc. It should be noted that the specific type of the terminal 11 is not limited in the embodiments of the present application. The network-side device 12 can include an access network device or a core network device. Among them, the access network device can also be referred to as a radio access network (RAN) device, a radio access network function, or a radio access network unit. The access network device can include a base station, a wireless local area network (WLAN) access point (AP), or a wireless fidelity (WiFi) node, etc.Among them, the base station can be referred to as Node B (NB), Evolved Node B (eNB), the next generation Node B (gNB), New Radio Node B (NR Node B), access point, Relay Base Station (RBS), Serving Base Station (SBS), Base Transceiver Station (BTS), radio base station, radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), home Node B (HNB), home evolved Node B, Transmission Reception Point (TRP), or some other suitable term in the art. As long as the same technical effect is achieved, the base station is not limited to specific technical terms. It should be noted that in the embodiments of the present application, only the base station in the NR system is taken as an example for introduction, and the specific type of the base station is not limited.

[0040] To better understand the technical solution of the present application, the following explains the related concepts involved in the embodiments of the present application.

[0041] Artificial Intelligence (AI):

[0042] AI has currently been widely applied in various fields. Incorporating AI into wireless communication networks to significantly improve technical indicators such as throughput, latency, and user capacity is an important task for future wireless communication networks. There are various implementation methods for the AI module, such as neural networks, decision trees, support vector machines, Bayesian classifiers, etc. The present application takes neural networks as an example for illustration, but does not limit the specific type of the AI module.

[0043] It should be noted that the AI unit / AI model described in this application may also be referred to as an AI unit, an AI model, a machine learning (ML) model, an ML unit, an AI structure, an AI function, an AI feature, a machine learning model, a neural network, a neural network function, a neural network capability, etc. Alternatively, the AI unit / AI model may also refer to a processing unit capable of implementing specific algorithms, formulas, processing flows, capabilities, etc. related to AI. Or the AI unit / AI model may be a processing method, algorithm, function, module, or unit for a specific data set. Or the AI unit / AI model may be a processing method, algorithm, function, module, or unit running on AI / ML-related hardware such as GPUs, NPUs, TPUs, ASICs, etc. This application does not make specific limitations in this regard. Optionally, the specific data set includes the input and / or output of the AI unit / AI model.

[0044] In addition, the identifier of the AI unit / AI model may be an AI model identifier, an AI structure identifier, an AI algorithm identifier, or the identifier of a specific data set associated with the AI unit / AI model, or the identifier of a specific scenario, environment, channel characteristic, device related to AI / ML, or the identifier of a function, feature, capability, or module related to AI / ML. This application does not make specific limitations in this regard.

[0045] AI functionality: That is, an AI algorithm function, and the AI functionality may include multiple AI Models.

[0046] RRM measurement reporting:

[0047] The measurement configuration mainly consists of a measurement object, a reporting configuration, and a measurement identifier (Identifier, ID).

[0048] Measurement Object: That is, the frequency point to be measured;

[0049] Report Config: It includes reporting criteria (periodic / event-triggered), reference signal types (such as Synchronization Signal and PBCH block (SSB) / Channel State Information Reference Signal (CSI-RS)), measurement reporting quantities (for example, any combination of Reference Signal Received Power (RSRP) / Reference Signal Received Quality (RSRQ) / signal-to-noise and interference ratio (SINR)), whether to report beam measurement results, the maximum number of reportable beams, etc.;

[0050] Measurement ID: It is used to associate a measurement object with a reporting configuration. A measurement object can be associated with multiple reporting configurations, and a reporting configuration can be associated with multiple measurement objects.

[0051] Event-triggered reporting can be included in the reporting configuration. The events defined in NR are shown in Table 1 below:

[0052] Table 1

[0053]

[0054] Taking Event A3 as an example, the meanings of the parameters for the entry condition and departure condition of Event A3 are as follows:

[0055] Mn: Measurement result of the neighboring cell, without considering any offset;

[0056] Ofn: Neighboring cell measurement object specific offset;

[0057] Ocn: Neighboring cell cell-level specific offset;

[0058] Mp: Measurement result of the SpCell (primary serving cell), without considering any offset;

[0059] Ofp: SpCell measurement object specific offset;

[0060] Ocp: SpCell cell-level specific offset;

[0061] Hys: Hysteresis parameter of the event;

[0062] Off: Offset parameter of the event.

[0063] It should be noted that the meanings of other parameters involved in Table 1 above can be referred to the related art and will not be elaborated here.

[0064] If the reporting type is event-triggered reporting, in order to avoid frequent reporting or ping-pong handover, the base station configures a trigger time (timeToTrigger) parameter for each event. If the layer 3 (L3) filtered signal quality of one or more candidate cells satisfies the entry condition of the event within the timeToTrigger time, a measurement report is triggered.

[0065] For conditional handover, the UE uses the cell that meets the conditions as the trigger cell and selects one to perform conditional reconfiguration in the trigger cell.

[0066] In the related art, it has been stipulated how the terminal performs RRM measurement and how to trigger the reporting of RRM measurement results. However, in the mobility enhancement based on AI assistance, it is not clear how to report the RRM prediction results based on AI.

[0067] Next, in conjunction with the accompanying drawings, through some embodiments and their application scenarios, the reporting method, device, terminal, network-side device, etc. provided by the embodiments of the present application will be described in detail.

[0068] Please refer to Figure 2 , Figure 2 which is a flowchart of a reporting method provided by an embodiment of the present application, and the method is applied to a terminal.

[0069] As Figure 2 shown, the method includes the following steps:

[0070] Step 201, the terminal reports a first measurement report of RRM measurement to the network-side device, where the first measurement report includes a prediction result of the RRM measurement prediction by the terminal based on the AI unit.

[0071] In the embodiment of the present application, the first measurement report reported by the terminal to the network-side device includes a prediction result of the RRM measurement prediction by the terminal based on the AI unit. The prediction result is obtained by the terminal through RRM measurement prediction by the AI unit, rather than the actual RRM measurement result. For example, the prediction result is the result of the terminal predicting the RRM measurement at a future moment or a certain time period through the AI unit.

[0072] It should be noted that the terminal can report the prediction result in real time, that is, the terminal reports the prediction result through the first measurement report immediately after obtaining the prediction result of the RRM measurement prediction based on the AI unit; or it can also report the prediction result once every preset time period, and the present application does not make a specific limitation on this.

[0073] In an embodiment of the present application, the terminal can report a first measurement report of RRM measurement to a network-side device. The first measurement report includes a prediction result of the terminal's RRM measurement prediction based on an AI unit. Furthermore, it is thus stipulated that in AI-assisted mobility enhancement, the terminal reports the prediction result of the RRM measurement prediction based on the AI unit to the network-side device through the first measurement report, that is, the reporting method of the prediction result is defined, enabling the network-side device to timely learn the prediction result, which helps improve the communication performance between the terminal and the network-side device.

[0074] Optionally, the prediction result includes at least one of the following:

[0075] (1) The beam quality of the first cell predicted by the terminal based on the AI unit;

[0076] (2) The cell signal quality of the first cell predicted by the terminal based on the AI unit;

[0077] (3) The prediction time corresponding to the beam quality or cell signal quality of the first cell predicted by the terminal based on the AI unit, where the prediction time includes at least one moment or the at least one time period; wherein, the at least one moment is at least one future moment, and the at least one time period is at least one future time period;

[0078] (4) The cell IDs of at least one target cell predicted by the terminal based on the AI unit. The cell ID can be a physical cell identifier (PCI) of the target cell, an NR cell global identifier (NR CGI), a frequency point + PCI, etc., or a configuration ID associated with the target cell configuration;

[0079] (5) The handover moment of at least one target cell predicted by the terminal based on the AI unit, such as the optimal handover moment;

[0080] (6) A first indication for indicating that a first condition is satisfied. The first condition is the entry condition or departure condition of a measurement event predicted by the terminal based on the AI unit, where the measurement event can be an event listed in Table 1; for example, if the measurement event is an A1 event, the first indication is also an indication that the terminal predicts based on the AI unit to satisfy the entry condition or departure condition of the A1 event;

[0081] (7) The moment when the first condition is satisfied, that is, the moment when the entry condition or departure condition of the measurement event predicted by the terminal based on the AI unit is satisfied;

[0082] (8) A second indication, which is used to indicate that a radio link failure predicted by the AI unit of the terminal will occur, that is, the second indication is an indication that a radio link failure predicted by the AI unit of the terminal will occur;

[0083] (9) The occurrence time of the radio link failure predicted by the AI unit of the terminal;

[0084] (10) A third indication, which is used to indicate that a handover to a target cell predicted by the AI unit of the terminal fails, that is, the third indication is an indication that a handover to a target cell predicted by the AI unit of the terminal fails;

[0085] (11) The time when the handover to the target cell predicted by the AI unit of the terminal fails;

[0086] Wherein, the first cell includes at least one of the following: a source cell, at least one neighboring cell, at least one candidate handover cell, and at least one target cell to be handed over.

[0087] It should be noted that the time corresponding to the prediction result (such as the above prediction time, handover time, occurrence time, etc.) can be represented in the following ways:

[0088] The system frame corresponding to the prediction result (such as the reference system frame (System frame number, SFN)), with a value range of 0 to 1023, that is, 10 bits (bit);

[0089] A part of the bits intercepted from the system frame corresponding to the prediction result;

[0090] The subframe or time slot (slot) where the prediction result is located in the corresponding system frame;

[0091] The reference time corresponding to the prediction result, and the reference time can be represented by hours, minutes, seconds, milliseconds, and microseconds;

[0092] The time difference between the prediction result and the current reporting time. Similarly, the time difference can be the difference in system frame numbers, or the difference in time slots, or the absolute time difference, etc.

[0093] Optionally, in the above prediction result, the beam quality or the cell signal quality of the (first cell) predicted by the AI unit of the terminal is characterized by the difference between the predicted value and the reference value, and the predicted value is the beam quality or the cell signal quality predicted by the AI unit of the terminal.

[0094] Exemplarily, taking the first cell as the source cell, the terminal predicts the cell signal quality of the source cell at a certain future moment based on the AI unit, and obtains the cell signal quality of the source cell at a certain future moment. In this case, the prediction result obtained by the terminal based on the AI unit can be the difference between the predicted value and the reference value, that is, the terminal reports the difference, rather than directly reporting the predicted value. It should be noted that the reference value can be a value pre-agreed between the terminal and the network-side device. For example, the reference value is the cell signal quality actually measured for the source cell in the first measurement report reported by the terminal. Among them, the current measurement report can refer to the measurement report obtained by the terminal through RRM measurement. In the embodiments of the present application, the terminal characterizes the beam quality or cell signal quality of the first cell predicted based on the AI unit by reporting the difference, and the network-side device can obtain the predicted value based on the difference and the reference value. Compared with directly reporting the predicted value, reporting the difference can help save the reporting overhead of the terminal.

[0095] Optionally, the reference value includes at least one of the following:

[0096] (1) The measurement value of each cell in the first measurement report, where the measurement value refers to the actual measurement value obtained by the terminal through RRM measurement, that is, the actual beam quality or actual cell signal quality obtained by the terminal through RRM measurement;

[0097] (2) The first predicted value of each cell in the first measurement report. Understandably, the terminal can predict the beam quality or cell signal quality of each cell at a certain future moment or a certain time period based on the AI unit, that is, multiple predicted beam qualities or predicted cell signal qualities will be obtained. The first predicted value is the first predicted beam quality or predicted cell signal quality of each cell predicted by the terminal based on the AI unit for RRM measurement;

[0098] (3) The maximum predicted value of each cell in the first measurement report, that is, among the predicted values obtained by the terminal based on the AI unit for RRM measurement, the maximum predicted beam quality or predicted cell signal quality of each cell;

[0099] (4) The measurement value of the second cell in the first measurement report;

[0100] (5) The first predicted value of the second cell in the first measurement report;

[0101] (6) The maximum predicted value of the second cell in the first measurement report;

[0102] (7) The maximum measurement value in the first measurement report, that is, the maximum beam quality or the maximum cell signal quality among the actual beam quality or the actual cell signal quality obtained by the terminal through RRM measurement;

[0103] (8) The maximum predicted value in the first measurement report, that is, the maximum predicted beam quality among the predicted beam quality obtained by the terminal through RRM measurement prediction based on the AI unit, or the maximum predicted cell signal quality among the predicted cell signal quality;

[0104] Wherein, the second cell is any one of the first cells (such as the source cell or the neighbor cell, etc.), and the measurement value is the beam quality or the cell signal quality actually measured by the terminal.

[0105] Optionally, the second cell is indicated by at least one of the following:

[0106] (a) The fourth indication in the first measurement report, that is, the terminal indicates the second cell in the reported first measurement report through the fourth indication, so that the network-side device can know which the second cell is, to ensure that the network-side device can determine which cell's measurement value, first predicted value or maximum predicted value the reference value corresponds to, so as to ensure that the network-side device accurately obtains the predicted value obtained by the terminal through RRM measurement prediction based on the AI unit according to the reference value;

[0107] (b) The fifth indication in the measurement configuration sent by the network-side device, that is, the network-side device sends the fifth indication to the terminal, and the fifth indication is used to indicate which the second cell is, so that the terminal can determine which cell's measurement value, first predicted value or maximum predicted value the reference value should correspond to, to ensure that the terminal obtains the difference between the two according to the reference value and the predicted value obtained by the terminal through RRM measurement prediction based on the AI unit, so as to report the difference.

[0108] Optionally, the method further includes:

[0109] The terminal receives a first configuration sent by the network-side device, and the first configuration includes a first threshold;

[0110] Wherein, when the difference between the predicted value of the terminal for the third cell and the measured value of the terminal for the third cell is less than the first threshold, the predicted result does not include the predicted value of the terminal for the third cell; or,

[0111] When the difference between the predicted value of the terminal for the third cell at the first time and the predicted value of the terminal for the third cell at the second time is less than the first threshold, the predicted result does not include the predicted value of the terminal for the third cell at the second time, and the second time is after the first time;

[0112] Among them, the third cell is any one of the first cells, and the measurement value is the actual measurement value obtained by the terminal through RRM measurement.

[0113] Exemplarily, taking the third cell as the source cell as an example, if the difference between the predicted beam quality obtained by the terminal through RRM measurement prediction of the source cell based on the AI unit and the actual beam quality obtained by the terminal through RRM measurement of the source cell is less than the first threshold, the terminal does not report the predicted beam quality.

[0114] Alternatively, the first predicted beam quality obtained by the terminal through RRM measurement prediction of the source cell based on the AI unit at a first time (for example, a certain moment), the second predicted beam quality obtained by the terminal through RRM measurement prediction of the source cell based on the AI unit at a second time (a time after the first time). If the difference between the second predicted beam quality and the first predicted beam quality is less than the first threshold, the terminal does not report the second predicted beam quality.

[0115] In the embodiments of the present application, the network-side device configures the first threshold for the terminal, thereby limiting the reporting behavior of the terminal for the prediction results obtained through RRM measurement prediction based on the AI unit, that is, limiting which prediction results can be reported and which prediction results do not need to be reported. For example, when the prediction results do not change much compared with the measurement results or compared with the previous prediction results, they do not need to be reported, which helps to save the reporting overhead of the terminal.

[0116] Optionally, the first measurement report reported by the terminal further includes at least one of the following:

[0117] (1) The measurement result of the terminal's RRM measurement, such as the aforementioned measurement value;

[0118] (2) A sixth indication, which is used to indicate that there is no valid prediction result. In this case, the terminal can not report the prediction result and only report the sixth indication. The network-side device can also know from this sixth indication that the RRM measurement prediction based on the AI unit by the terminal does not obtain a valid prediction result;

[0119] (3) The inference accuracy (also called prediction accuracy) of the RRM measurement prediction by the AI unit;

[0120] (4) The credibility or confidence level of the prediction result, for example, it can be a probability value.

[0121] Among them, the inference accuracy may include at least one of the following:

[0122] The sum of squares due to error (SSE) between the predicted result and the measured result;

[0123] The Mean Square Error (MSE) between the predicted result and the measured result;

[0124] The Root Mean Square Error (RMSE) between the predicted result and the measured result;

[0125] The cosine similarity between the predicted result and the measured result.

[0126] In the embodiment of the present application, the first measurement report reported by the terminal further includes at least one of the above, so that the network-side device can also obtain the measurement result of the terminal's RRM measurement. Thus, the terminal can report the measurement result of the actual RRM measurement and the predicted result of the RRM measurement predicted based on the AI unit to the network-side device through the first measurement report, enabling the network-side device to refer to both the measurement result and the predicted result simultaneously, which helps the network-side device to make a more accurate signal quality judgment and handover decision for the terminal. Alternatively, the first measurement report reported by the terminal includes the inference accuracy of the RRM measurement prediction by the AI unit and / or the credibility or confidence level of the predicted result, so that the network-side device can directly obtain the inference accuracy and / or the credibility or confidence level of the predicted result. The network-side device can determine the reliability of the predicted result based on this information, which helps the network-side device to make a more accurate signal quality judgment and handover decision for the terminal.

[0127] Optionally, in the embodiment of the present application, the method further includes:

[0128] The terminal receives a second configuration sent by the network-side device, and the second configuration includes a second threshold;

[0129] Wherein, when the inference accuracy of the RRM measurement prediction by the AI unit is greater than or equal to the second threshold, the first measurement report includes the predicted result.

[0130] In the embodiment of the present application, the terminal, according to the second threshold configured by the network-side device, will report the predicted result of the RRM measurement prediction based on the AI unit only when the inference accuracy of the RRM measurement prediction by the AI unit is greater than or equal to the second threshold. If the inference accuracy is less than the second threshold, the terminal may not report the predicted result, which helps to save the reporting overhead of the terminal.

[0131] Optionally, each of the AI units corresponds to a second threshold, or each AI function corresponds to a second threshold. Exemplarily, the terminal may perform RRM measurement prediction through multiple AI units, and the network-side device may configure a second threshold for each AI unit, and these second thresholds may be the same or different. Alternatively, one or more AI units of the terminal may correspond to multiple AI functions, and the network-side device may configure a second threshold for each AI function, and these second thresholds may be the same or different. In this way, the terminal determines whether to report the prediction result for each AI unit or each AI function, which is more helpful to standardize the reporting behavior of the terminal.

[0132] Optionally, in the embodiment of the present application, when the first condition is met, the first measurement report reported by the terminal includes the prediction result;

[0133] The first condition includes at least one of the following:

[0134] The first object is associated with a seventh indication, where the seventh indication is used to instruct the terminal to perform RRM measurement prediction based on the AI unit;

[0135] The first object is associated with a first identifier, where the first identifier is an identifier of the AI unit or an AI function identifier;

[0136] The reporting configuration of the RRM measurement is associated with a first event, where the first event is an event that the terminal evaluates whether an event is satisfied according to the prediction result;

[0137] The first object includes at least one of the following: measurement configuration, measurement identifier, measurement object, and reporting configuration. It can be understood that the RRM measurement report of the terminal includes a measurement configuration for the RRM measurement report, and the measurement configuration includes a measurement object, a reporting configuration, and a measurement identifier.

[0138] Exemplarily, the terminal determines whether the first measurement report needs to carry the prediction result according to whether the measurement identifier that triggers the measurement report or the measurement object associated with the measurement identifier and the reporting configuration are associated with the seventh indication. For example, if the measurement object is associated with the seventh indication, the first measurement report reported by the terminal carries the prediction result.

[0139] For another example, the terminal determines whether the first measurement report needs to carry the prediction result according to the measurement identifier that triggers the measurement report or the measurement object associated with the measurement identifier and whether the reporting configuration is associated with the identifier of the AI unit or the AI function identifier. For example, if the measurement object is associated with the identifier of the AI unit, the first measurement report reported by the terminal carries the prediction result.

[0140] Alternatively, if the reporting configuration is associated with a first event, where the first event is an event for which the terminal evaluates whether the event is satisfied according to the prediction result, in this case, the prediction result is carried in the first measurement report reported by the terminal. Wherein, the event refers to the measurement event as described above, such as A1 event, A2 event, etc., and whether the event is satisfied refers to whether the entry condition or exit condition corresponding to the measurement event is satisfied.

[0141] In the embodiments of the present application, the terminal can determine whether the prediction result needs to be carried in the first measurement report according to whether the first condition is satisfied, that is, determine whether the prediction result needs to be reported. In this way, it is more helpful to standardize the reporting behavior of the terminal for the prediction result.

[0142] Optionally, the seventh indication is further used to indicate the prediction type of the terminal for RRM measurement prediction based on the AI unit. Wherein, the prediction type includes target cell prediction or beam prediction, RRM measurement prediction, measurement event prediction, etc., and the terminal can select the corresponding AI unit for RRM measurement prediction according to the prediction type, and the identifier of the AI unit corresponding to the prediction type or the AI function identifier can be indicated by the network side device or determined according to the protocol pre - definition.

[0143] To better understand the technical solution of the present application, the following will be specifically described through several specific embodiments.

[0144] Embodiment 1:

[0145] The terminal carries the prediction result of RRM measurement prediction based on the AI unit in the reported first measurement report. The prediction result includes, in addition to the predicted value (the beam quality or cell signal quality of the first cell predicted by the terminal based on the AI unit), the difference between the predicted value and the reference value. The reference value can be at least one of the following:

[0146] (1) The measured value / first predicted value (i.e., the predicted value closest to the current time) / maximum predicted value of each cell in the first measurement report;

[0147] (2) The measured value / first predicted value / maximum predicted value of the second cell in the first measurement report. The second cell is any one of the first cells (the definition of the first cell is as described above), and the second cell is indicated by the terminal to the network side device in the first measurement report;

[0148] (3) The maximum measured value / maximum predicted value of all cells in the first measurement report.

[0149] If the reference value is the maximum predicted value of a certain cell, the terminal needs to indicate in the first measurement report which measurement result the reference value corresponds to. For example, the measurement result can be associated with an identifier, and when the identifier exists, it means that the current measurement result is the reference value.

[0150] In one implementation, the first measurement report needs to report the measurement values of cells A and B and their predicted values for the next 1 second and 2 seconds, and the values are shown in Table 2 below:

[0151] Table 2

[0152] Measured value Predicted value for the next 1 s Predicted value for the next 2 s Cell A A0 A1 A2 Cell B B0 B1 B2

[0153] Exemplarily, Case 1-1: If the reference value is the measurement value of each cell, the reference values are A0 of cell A and B0 of cell B respectively. In this case, the reported values are shown in Table 3 below:

[0154] Table 3

[0155] Measured value Predicted value for the next 1 s Predicted value for the next 2 s Cell A A0 (A0 - A1) or (A1 - A0) (A0 - A2) or (A2 - A0) Cell B B0 (B0 - B1) or (B1 - B0) (B0 - B2) or (B2 - B0)

[0156] Case 1-2: If the reference value is the first predicted value of each cell, the reference values are A1 of cell A and B1 of cell B respectively. In this case, the reported values are shown in Table 4 below:

[0157] Table 4

[0158] Measured value Predicted value for the next 1 s Predicted value for the next 2 s Cell A A0 A1 (A1 - A2) or (A2 - A1) Cell B B0 B1 (B1 - B2) or (B2 - B1)

[0159] Case 1-3: If the reference value is the maximum predicted value of each cell, and at this time A2 > A1, B2 > B1, then the reference values are A2 of cell A and B2 of cell B respectively. In this case, the reported values are shown in Table 5 below:

[0160] Table 5

[0161] Measured value Predicted value for the next 1 s Predicted value for the next 2 s Cell A A0 (A1 - A2) or (A2 - A1) A2 Cell B B0 (B1 - B2) or (A0 - B1) B2

[0162] Case 1-4: If the reference value is the first predicted value of the second cell, and at this time the second cell is cell A, then the reference value is A0 of cell A. In this case, the reported values are shown in Table 6 below:

[0163] Table 6

[0164] Measured value Predicted value for the next 1 s Predicted value for the next 2 s Cell A A0 (A0 - A1) or (A1 - A0) (A0 - A2) or (A2 - A0) Cell B B0 (B1 - A0) or (A0 - B1) (B2 - A0) or (A0 - B2)

[0165] Case 1-5: If the reference value is the maximum predicted value of the second cell, and at this time the second cell is cell A and A2 > A1, then the reference value is A2 of cell A. In this case, the reported values are shown in Table 7 below:

[0166] Table 7

[0167] Measured value Predicted value for the next 1 s Predicted value for the next 2 s Cell A A0 (A1 - A2) or (A2 - A1) A2 Cell B B0 (B1 - A2) or (A2 - B1) (B2 - A2) or (A2 - B2)

[0168] Case1-6: The reference value is the maximum measured value of all cells. If A0 > B0, the reference value is A0. The reported values are as shown in Table 8 below:

[0169] Table 8

[0170] Measured value Predicted value for the next 1 s Predicted value for the next 2 s Cell A A0 (A0 - A1) or (A1 - A0) (A0 - A2) or (A2 - A0) Cell B B0 (B1 - A0) or (A0 - B1) (B2 - A0) or (A0 - B2)

[0171] Case1-7: The reference value is the maximum predicted value of all cells. If A1 > A2, B1, B2, the reference value is A1. The reported values are as shown in Table 9 below:

[0172] Table 9

[0173] Measured value Predicted value for the next 1 s Predicted value for the next 2 s Cell A A0 A1 (A1 - A2) or (A2 - A1) Cell B B0 (B1 - A1) or (A1 - B1) (B2 - A1) or (A1 - B2)

[0174] It should be noted that the measured value and the predicted value are both values after quantization processing.

[0175] Embodiment 2:

[0176] The terminal determines whether the first measurement report to be reported includes a prediction result according to whether the measurement configuration is associated with an AI unit or an AI function.

[0177] Among them, the way that the measurement configuration is associated with an AI unit or an AI function can be as follows:

[0178] Method 1: The measurement configuration / measurement identifier / measurement object / reporting configuration is associated with a prediction indication (i.e., the above seventh indication).

[0179] Among them, the prediction indication is used to indicate that the measurement report associated with the measurement identifier / measurement object / reporting configuration needs to carry the prediction result;

[0180] When the measurement configuration is associated with a prediction indication, all first measurement reports triggered by the terminal need to carry the prediction result;

[0181] The first measurement report associated with the measurement object or the reporting configuration refers to the measurement report carrying the measurement ID associated with the measurement object or the reporting configuration;

[0182] The prediction indication can also indicate the prediction type. The prediction type includes target cell / beam prediction, RRM prediction, measurement event prediction, etc. The terminal selects the corresponding AI unit for prediction / inference according to the prediction type. The AI unit identifier or function identifier corresponding to the prediction type is indicated by the network-side device or determined according to protocol predefinition.

[0183] Method 2: The measurement configuration / measurement identifier / measurement object / reporting configuration is associated with the identifier of the AI unit or the AI function identifier.

[0184] Among them, the identifier of the AI unit or the AI function identifier is used to instruct the terminal that the prediction result needs to be carried in the measurement report associated with the measurement ID / measurement object / reporting configuration;

[0185] The terminal uses the AI unit corresponding to the indicated AI unit or AI function identifier to obtain the prediction result. When the reporting condition is met, the prediction result is carried in the first measurement report associated with the measurement identifier / measurement object / reporting configuration associated with the identifier of the AI unit or the AI function identifier.

[0186] Preferably, the identifier of the associated AI unit or the AI function identifier is carried in the measurement object.

[0187] Method 3: The reporting configuration is associated with a prediction event (i.e., the above-mentioned first event).

[0188] If the reporting configuration is associated with event-triggered reporting and the event is a prediction event that triggers measurement reporting, the terminal evaluates whether the prediction event is satisfied according to the prediction result and triggers measurement reporting when it is satisfied.

[0189] The difference between the prediction event and the existing measurement event is that the measurement event determines whether to trigger measurement reporting or conditional handover based on the measurement results of the serving cell and / or neighboring cells, while the prediction event determines whether to trigger measurement reporting or conditional handover based on the prediction results of the serving cell and / or neighboring cells.

[0190] For example, for Event A3, if the measurement result of the neighboring cell is always higher than a certain threshold than the measurement result of the serving cell within the TTT time, then the A3 measurement event is satisfied; for the predicted Event A3, if the predicted result of the neighboring cell at the same moment is always higher than a certain threshold than the predicted result of the serving cell within the TTT time, then the A3 prediction event is satisfied.

[0191] It should be noted that the above three methods can be used in combination. That is, for Method 1 and Method 3, the measurement configuration / measurement identifier / measurement object / reporting configuration can also be associated with the identifier of the AI unit or the AI function identifier;

[0192] When the measurement configuration / measurement identifier / measurement object / reporting configuration is not associated with the identifier of the AI unit or the AI function identifier, the AI unit or AI function used for prediction can be determined according to the protocol predefinition, or obtained by the UE and the network through interaction before obtaining the measurement configuration.

[0193] The embodiment of the present application also provides a receiving method. Please refer to Figure 3 , Figure 3It is a flowchart of a receiving method provided by an embodiment of the present application, and the method is applied to a network-side device. As Figure 3 shown, the method includes the following steps:

[0194] Step 301, the network-side device receives a first measurement report of RRM measurement reported by the terminal; wherein, the first measurement report includes a prediction result of the terminal's RRM measurement prediction based on the AI unit.

[0195] Optionally, the prediction result includes at least one of the following:

[0196] The beam quality of the first cell predicted by the terminal based on the AI unit;

[0197] The cell signal quality of the first cell predicted by the terminal based on the AI unit;

[0198] The prediction time corresponding to the beam quality or cell signal quality of the first cell predicted by the terminal based on the AI unit, and the prediction time includes at least one moment or the at least one time period;

[0199] The cell ID of at least one target cell predicted by the terminal based on the AI unit;

[0200] The handover moment of at least one target cell predicted by the terminal based on the AI unit;

[0201] A first indication, the first indication is used to indicate that a first condition is satisfied, and the first condition is an entry condition or a departure condition of a measurement event predicted by the terminal based on the AI unit;

[0202] The moment when the first condition is satisfied;

[0203] A second indication, the second indication is used to indicate that a radio link failure predicted by the terminal based on the AI unit will occur;

[0204] The occurrence moment of the radio link failure predicted by the terminal based on the AI unit;

[0205] A third indication, the third indication is used to indicate that a handover to a target cell predicted by the terminal based on the AI unit fails;

[0206] The moment when a handover to a target cell predicted by the terminal based on the AI unit fails;

[0207] Wherein, the first cell includes at least one of the following: a source cell, at least one neighboring cell, at least one candidate handover cell, at least one target cell to be handed over.

[0208] Optionally, the terminal characterizes the beam quality or the cell signal quality predicted by the AI unit by the difference between the predicted value and the reference value, where the predicted value is the beam quality or the cell signal quality predicted by the terminal based on the AI unit.

[0209] Optionally, the reference value includes at least one of the following:

[0210] The measurement value of each cell in the first measurement report;

[0211] The first predicted value of each cell in the first measurement report;

[0212] The maximum predicted value of each cell in the first measurement report;

[0213] The measurement value of the second cell in the first measurement report;

[0214] The first predicted value of the second cell in the first measurement report;

[0215] The maximum predicted value of the second cell in the first measurement report;

[0216] The maximum measurement value in the first measurement report;

[0217] The maximum predicted value in the first measurement report;

[0218] Wherein, the second cell is any one of the first cells, and the measurement value is the beam quality or the cell signal quality actually measured by the terminal.

[0219] Optionally, the second cell is indicated by a fourth indication in the first measurement report as described below; or,

[0220] The method further includes:

[0221] The network side device sends a measurement configuration to the terminal, and the measurement configuration includes a fifth indication for indicating the second cell.

[0222] Optionally, the method further includes:

[0223] The network side device sends a first configuration to the terminal, and the first configuration includes a first threshold;

[0224] Wherein, when the difference between the predicted value of the terminal for the third cell and the measured value of the terminal for the third cell is less than the first threshold, the predicted result does not include the predicted value of the terminal for the third cell; or,

[0225] In the case that the difference between the predicted value of the third cell at the first time and the predicted value of the third cell at the second time by the terminal is less than the first threshold, the predicted result does not include the predicted value of the third cell at the second time by the terminal, and the second time is after the first time;

[0226] Wherein, the third cell is any one of the first cells.

[0227] Optionally, the first measurement report further includes at least one of the following:

[0228] The measurement result of the RRM measurement performed by the terminal;

[0229] A sixth indication, where the sixth indication is used to indicate that there is no valid predicted result;

[0230] The inference accuracy of the RRM measurement prediction performed by the AI unit;

[0231] The credibility or confidence level of the predicted result.

[0232] Optionally, the method further includes:

[0233] The network device sends a second configuration to the terminal, and the second configuration includes a second threshold;

[0234] Wherein, in the case that the inference accuracy of the RRM measurement prediction performed by the AI unit is greater than or equal to the second threshold, the predicted result is included in the first measurement report.

[0235] Optionally, each AI unit corresponds to a second threshold, or each AI function corresponds to a second threshold.

[0236] It should be noted that the receiving method provided in the embodiments of the present application corresponds to the foregoing reporting method on the terminal side. The relevant concepts and specific implementation processes involved in the embodiments of the present application may refer to the descriptions in the method embodiments on the terminal side, and will not be elaborated in this embodiment.

[0237] In the embodiments of the present application, the network device receives a first measurement report of the RRM measurement reported by the terminal, and the first measurement report includes the predicted result of the RRM measurement prediction based on the AI unit by the device. Furthermore, it is stipulated that in AI-assisted mobility enhancement, the terminal reports the predicted result of the RRM measurement prediction based on the AI unit to the network device through the first measurement report, that is, the reporting method of the predicted result is defined, so that the network device can timely obtain the predicted result, which helps to improve the communication performance between the terminal and the network device.

[0238] The reporting method provided by the embodiments of the present application may be executed by a reporting device. In the embodiments of the present application, taking the reporting device executing the reporting method as an example, the reporting device provided by the embodiments of the present application is described.

[0239] Please refer to Figure 4 , Figure 4 which is a structural diagram of a reporting device provided by the embodiments of the present application. As Figure 4 shown, the reporting device 400 includes:

[0240] A reporting module 401, configured to report a first measurement report of RRM measurement to a network-side device;

[0241] Wherein, the first measurement report includes a prediction result of the device based on the AI unit for RRM measurement prediction.

[0242] Optionally, the prediction result includes at least one of the following:

[0243] The beam quality of the first cell predicted by the device based on the AI unit;

[0244] The cell signal quality of the first cell predicted by the device based on the AI unit;

[0245] The prediction time corresponding to the beam quality or cell signal quality of the first cell predicted by the device based on the AI unit, where the prediction time includes at least one moment or the at least one time period;

[0246] The cell identification ID of at least one target cell predicted by the device based on the AI unit;

[0247] The handover moment of at least one target cell predicted by the device based on the AI unit;

[0248] A first indication, where the first indication is used to indicate that a first condition is satisfied, and the first condition is an entry condition or a departure condition of a measurement event predicted by the device based on the AI unit;

[0249] The moment when the first condition is satisfied;

[0250] A second indication, where the second indication is used to indicate that a radio link failure predicted by the device based on the AI unit will occur;

[0251] The occurrence moment of the radio link failure predicted by the device based on the AI unit;

[0252] A third indication, where the third indication is used to indicate that a handover to a target cell predicted by the device based on the AI unit fails;

[0253] The moment when the handover to the target cell predicted by the device based on the AI unit fails;

[0254] Among them, the first cell includes at least one of the following: a source cell, at least one neighboring cell, at least one candidate handover cell, and at least one target cell to be handed over.

[0255] Optionally, the device is characterized by the difference between the predicted value and the reference value based on the beam quality or the cell signal quality predicted by the AI unit, where the predicted value is the beam quality or the cell signal quality predicted by the device based on the AI unit.

[0256] Optionally, the reference value includes at least one of the following:

[0257] The measurement value of each cell in the first measurement report;

[0258] The first predicted value of each cell in the first measurement report;

[0259] The maximum predicted value of each cell in the first measurement report;

[0260] The measurement value of the second cell in the first measurement report;

[0261] The first predicted value of the second cell in the first measurement report;

[0262] The maximum predicted value of the second cell in the first measurement report;

[0263] The maximum measurement value in the first measurement report;

[0264] The maximum predicted value in the first measurement report;

[0265] Among them, the second cell is any one of the first cells, and the measurement value is the beam quality or the cell signal quality actually measured by the device.

[0266] Optionally, the second cell is indicated by at least one of the following:

[0267] The fourth indication in the first measurement report;

[0268] The fifth indication in the measurement configuration sent by the network-side device.

[0269] Optionally, the device further includes:

[0270] A receiving module, configured to receive a first configuration sent by a network-side device, where the first configuration includes a first threshold;

[0271] Among them, when the difference between the predicted value of the device for the third cell and the measured value of the device for the third cell is less than the first threshold, the predicted result does not include the predicted value of the device for the third cell; or,

[0272] When the difference between the predicted value of the third cell at the first time and the predicted value of the third cell at the second time by the device is less than the first threshold, the predicted result does not include the predicted value of the third cell by the device at the second time, and the second time is after the first time;

[0273] Wherein, the third cell is any one of the first cells.

[0274] Optionally, the first measurement report further includes at least one of the following:

[0275] The measurement result of the RRM measurement performed by the device;

[0276] A sixth indication for indicating that there is no valid predicted result;

[0277] The inference accuracy of the RRM measurement prediction performed by the AI unit;

[0278] The credibility or confidence level of the predicted result.

[0279] Optionally, the device further includes:

[0280] A receiving module, configured to receive a second configuration sent by a network-side device, where the second configuration includes a second threshold;

[0281] Wherein, when the inference accuracy of the RRM measurement prediction performed by the AI unit is greater than or equal to the second threshold, the predicted result is included in the first measurement report.

[0282] Optionally, each AI unit corresponds to a second threshold, or each AI function corresponds to a second threshold.

[0283] Optionally, when a first condition is satisfied, the predicted result is included in the first measurement report;

[0284] Wherein, the first condition includes at least one of the following:

[0285] A first object is associated with a seventh indication for indicating that the device performs RRM measurement prediction based on an AI unit;

[0286] A first object is associated with a first identifier, and the first identifier is an identifier of the AI unit or an AI function identifier;

[0287] The reporting configuration of the RRM measurement is associated with a first event, and the first event is an event for which the device evaluates whether the event is satisfied according to the predicted result;

[0288] Wherein, the first object includes at least one of the following: measurement configuration, measurement identifier, measurement object, reporting configuration.

[0289] Optionally, the seventh indication is further used to indicate the prediction type of the RRM measurement prediction performed by the device based on the AI unit.

[0290] In an embodiment of the present application, the device can report a first measurement report of RRM measurement to a network-side device. The first measurement report includes the prediction result of the RRM measurement prediction performed by the device based on the AI unit. Furthermore, it stipulates that in AI-assisted mobility enhancement, the prediction result of the RRM measurement prediction based on the AI unit is reported to the network-side device through the first measurement report, that is, it defines the reporting method of the prediction result, so that the network-side device can timely learn the prediction result.

[0291] The reporting device in the embodiment of the present application can be an electronic device, such as an electronic device with an operating system, or a component in an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices other than the terminal. Exemplarily, the terminal can include, but is not limited to, the types of the terminal 11 listed above, and other devices can be a server, a Network Attached Storage (NAS), etc., which are not specifically limited in the embodiment of the present application.

[0292] The reporting device provided in the embodiment of the present application can implement Figure 2 each process implemented by the terminal in the method embodiment and achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0293] The receiving method provided in the embodiment of the present application, the execution subject can be a receiving device. In the embodiment of the present application, taking the receiving device executing the receiving method as an example, the receiving device provided in the embodiment of the present application is described.

[0294] Please refer to Figure 5 , Figure 5 which is a structural diagram of a receiving device provided in an embodiment of the present application. As Figure 5 shown, the receiving device 500 includes:

[0295] A receiving module 501, configured to receive a first measurement report of RRM measurement reported by a terminal;

[0296] Wherein, the first measurement report includes the prediction result of the RRM measurement prediction performed by the terminal based on the AI unit.

[0297] Optionally, the prediction result includes at least one of the following:

[0298] The beam quality of the first cell predicted by the terminal based on the AI unit;

[0299] The cell signal quality of the first cell predicted by the terminal based on the AI unit;

[0300] The prediction time corresponding to the beam quality or cell signal quality of the first cell predicted by the terminal based on the AI unit, where the prediction time includes at least one moment or the at least one time period;

[0301] The cell ID of at least one target cell predicted by the terminal based on the AI unit;

[0302] The handover moment of at least one target cell predicted by the terminal based on the AI unit;

[0303] A first indication, where the first indication is used to indicate that a first condition is satisfied, and the first condition is the entry condition or departure condition of the measurement event predicted by the terminal based on the AI unit;

[0304] The moment when the first condition is satisfied;

[0305] A second indication, where the second indication is used to indicate that a radio link failure predicted by the terminal based on the AI unit will occur;

[0306] The occurrence moment of the radio link failure predicted by the terminal based on the AI unit;

[0307] A third indication, where the third indication is used to indicate that a handover to a target cell predicted by the terminal based on the AI unit fails;

[0308] The moment when a handover to a target cell predicted by the terminal based on the AI unit fails;

[0309] Wherein, the first cell includes at least one of the following: a source cell, at least one neighboring cell, at least one candidate handover cell, and at least one target cell to be handed over.

[0310] Optionally, the beam quality or the cell signal quality predicted by the terminal based on the AI unit is characterized by the difference between the predicted value and the reference value, and the predicted value is the beam quality or the cell signal quality predicted by the terminal based on the AI unit.

[0311] Optionally, the reference value includes at least one of the following:

[0312] The measurement value of each cell in the first measurement report;

[0313] The first predicted value of each cell in the first measurement report;

[0314] The maximum predicted value of each cell in the first measurement report;

[0315] The measurement value of the second cell in the first measurement report;

[0316] The first predicted value of the second cell in the first measurement report;

[0317] The maximum predicted value of the second cell in the first measurement report;

[0318] The maximum measurement value in the first measurement report;

[0319] The maximum predicted value in the first measurement report;

[0320] Wherein, the second cell is any one of the first cells, and the measurement value is the beam quality or cell signal quality actually measured by the terminal.

[0321] Optionally, the second cell is indicated by a fourth indication in the first measurement report as described below; or,

[0322] The device further includes:

[0323] A first sending module, configured to send a measurement configuration to the terminal, where the measurement configuration includes a fifth indication for indicating the second cell.

[0324] Optionally, the device further includes:

[0325] A second sending module, configured to send a first configuration to the terminal, where the first configuration includes a first threshold;

[0326] Wherein, when the difference between the predicted value of the terminal for the third cell and the measured value of the terminal for the third cell is less than the first threshold, the predicted result does not include the predicted value of the terminal for the third cell; or,

[0327] When the difference between the predicted value of the terminal for the third cell at a first time and the predicted value of the terminal for the third cell at a second time is less than the first threshold, the predicted result does not include the predicted value of the terminal for the third cell at the second time, and the second time is after the first time;

[0328] Wherein, the third cell is any one of the first cells.

[0329] Optionally, the first measurement report further includes at least one of the following:

[0330] The measurement result of the terminal for RRM measurement;

[0331] A sixth indication, where the sixth indication is used to indicate that there is no valid predicted result;

[0332] The inference accuracy of the AI unit for RRM measurement prediction;

[0333] The credibility or confidence level of the predicted result.

[0334] Optionally, the device further includes:

[0335] A third sending module, configured to send a second configuration to the terminal, where the second configuration includes a second threshold;

[0336] Wherein, when the inference accuracy of the RRM measurement prediction by the AI unit is greater than or equal to the second threshold, the first measurement report includes the predicted result.

[0337] Optionally, each AI unit corresponds to one second threshold, or each AI function corresponds to one second threshold.

[0338] The receiving device provided in the embodiments of the present application can implement Figure 3 Each process implemented by the network-side device in the method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0339] As Figure 6 shown, the embodiments of the present application further provide a communication device 600, including a processor 601 and a memory 602. A program or instruction that can run on the processor 601 is stored on the memory 602. For example, when the communication device 600 is a terminal, when the program or instruction is executed by the processor 601, each step of the above reporting method embodiment is implemented, and the same technical effect can be achieved. When the communication device 600 is a network-side device, when the program or instruction is executed by the processor 601, each step of the above receiving method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be elaborated here.

[0340] The embodiments of the present application further provide a terminal, including a processor and a communication interface. The communication interface is coupled to the processor. The processor is configured to run a program or instruction to implement the steps in the method embodiment as Figure 2 shown. This terminal embodiment corresponds to the above terminal-side method embodiment. Each implementation process, implementation manner, and related concepts of the above method embodiment can be applied to this terminal embodiment, and the same technical effect can be achieved. Specifically, Figure 7 A schematic diagram of the hardware structure of a terminal for implementing the embodiments of the present application.

[0341] The terminal 700 includes, but is not limited to, at least some components such as a radio frequency unit 701, a network module 702, an audio output unit 703, an input unit 704, a sensor 705, a display unit 706, a user input unit 707, an interface unit 708, a memory 709, and a processor 710.

[0342] Those skilled in the art can understand that the terminal 700 may further include a power source (such as a battery) for supplying power to each component. The power source can be logically connected to the processor 710 through a power management system, so as to implement functions such as management of charging, discharging, and power consumption management through the power management system. Figure 7 The terminal structure shown does not limit the terminal. The terminal may include more or fewer components than shown in the figure, or combine some components, or have different component arrangements, which will not be elaborated here.

[0343] It should be understood that in the embodiments of the present application, the input unit 704 may include a Graphics Processing Unit (GPU) 7041 and a microphone 7042. The graphics processor 7041 processes the image data of still pictures or videos obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 706 may include a display panel 7061, and the display panel 7061 may be configured in the form of a liquid crystal display, an organic light-emitting diode, etc. The user input unit 707 includes at least one of a touch panel 7071 and other input devices 7072. The touch panel 7071 is also called a touch screen. The touch panel 7071 may include two parts: a touch detection device and a touch controller. The other input devices 7072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which will not be elaborated here.

[0344] In the embodiments of the present application, after the radio frequency unit 701 receives downlink data from a network-side device, it can be transmitted to the processor 710 for processing; in addition, the radio frequency unit 701 can send uplink data to the network-side device. Generally, the radio frequency unit 701 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, etc.

[0345] The memory 709 can be used to store software programs or instructions as well as various data. The memory 709 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data. Among them, the first storage area can store an operating system, application programs or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory 709 can include volatile memory or non-volatile memory. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDR SDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM), and a direct rambus random access memory (DRRAM). The memory 709 in the embodiments of the present application includes but is not limited to these and any other suitable types of memory.

[0346] The processor 710 may include one or more processing units; optionally, the processor 710 integrates an application processor and a modem processor. Among them, the application processor mainly processes operations related to the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communication signals, such as a baseband processor. It can be understood that the above modem processor may not be integrated into the processor 710 either.

[0347] Among them, the radio frequency unit 701 is used to report a first measurement report of RRM measurement to the network-side device; the first measurement report includes a prediction result of the terminal's RRM measurement prediction based on the AI unit.

[0348] In the embodiments of the present application, the terminal can report a first measurement report of RRM measurement to the network-side device. The first measurement report includes the prediction result of the RRM measurement prediction by the terminal based on the AI unit. Furthermore, it is stipulated that in AI-assisted mobility enhancement, the terminal reports the prediction result of the RRM measurement prediction based on the AI unit to the network-side device through the first measurement report, that is, the reporting method of the prediction result is defined, so that the network-side device can timely learn the prediction result, which helps to improve the communication performance between the terminal and the network-side device.

[0349] It can be understood that the implementation processes of the various implementation manners mentioned in this embodiment can refer to the relevant descriptions of the above reporting method embodiment and achieve the same or corresponding technical effects. To avoid repetition, they will not be elaborated here.

[0350] The embodiments of the present application further provide a network-side device, including a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the steps of the method embodiment as Figure 3 shown. This network-side device embodiment corresponds to the above network-side device method embodiment. The various implementation processes and implementation manners of the above method embodiment can be applied to this network-side device embodiment and can achieve the same technical effects.

[0351] Specifically, the embodiments of the present application further provide a network-side device. As Figure 8 shown, the network-side device 800 includes: an antenna 81, a radio frequency device 82, a baseband device 83, a processor 84, and a memory 85. The antenna 81 is connected to the radio frequency device 82. In the uplink direction, the radio frequency device 82 receives information through the antenna 81 and sends the received information to the baseband device 83 for processing. In the downlink direction, the baseband device 83 processes the information to be sent and sends it to the radio frequency device 82. The radio frequency device 82 processes the received information and then sends it out through the antenna 81.

[0352] The method executed by the network-side device in the above embodiments can be implemented in the baseband device 83, and the baseband device 83 includes a baseband processor.

[0353] The baseband device 83 may include, for example, at least one baseband board, and multiple chips are provided on the baseband board. As Figure 8 shown, one of the chips is, for example, a baseband processor, which is connected to the memory 85 through a bus interface to call the program in the memory 85 to execute the network device operations shown in the above method embodiments.

[0354] The network-side device may further include a network interface 86, and this interface is, for example, a Common Public Radio Interface (CPRI).

[0355] Specifically, the network - side device 800 according to the embodiment of the present invention further includes: instructions or programs stored in the memory 85 and executable on the processor 84. The processor 84 calls the instructions or programs in the memory 85 to execute Figure 5 the methods executed by the modules shown, and achieves the same technical effects. To avoid repetition, it will not be elaborated here.

[0356] The embodiment of the present application further provides a readable storage medium. Programs or instructions are stored on the readable storage medium. When the programs or instructions are executed by a processor, each process of the above - mentioned reporting method or receiving method embodiment is implemented, and the same technical effects can be achieved. To avoid repetition, it will not be elaborated here.

[0357] Among them, the processor is the processor in the terminal described in the above - mentioned embodiment. The readable storage medium includes computer - readable storage media, such as computer read - only memory ROM, random - access memory RAM, magnetic disks or optical discs, etc. In some examples, the readable storage medium can be a non - transient readable storage medium.

[0358] The embodiment of the present application further provides a chip. The chip includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement each process of the above - mentioned reporting method or receiving method embodiment, and the same technical effects can be achieved. To avoid repetition, it will not be elaborated here.

[0359] It should be understood that the chip mentioned in the embodiment of the present application can also be referred to as a system - on - chip, system chip, chip system or system - on - a - chip, etc.

[0360] The embodiment of the present application further provides a computer program / program product. The computer program / program product is stored in a storage medium. The computer program / program product is executed by at least one processor to implement each process of the above - mentioned reporting method or receiving method embodiment, and the same technical effects can be achieved. To avoid repetition, it will not be elaborated here.

[0361] The embodiment of the present application further provides a communication system, including: a terminal and a network - side device. The terminal can be used to execute the steps of the reporting method as described above, and the network - side device can be used to execute the steps of the receiving method as described above.

[0362] It should be noted that in this text, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising that element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted or combined. Additionally, the features described with reference to certain examples may be combined in other examples.

[0363] From the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of a computer software product plus a necessary general hardware platform, and of course, can also be implemented by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and includes several instructions for causing a terminal or a network-side device to execute the methods described in the various embodiments of the present application.

[0364] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms of embodiments without departing from the purpose of the present application and the scope protected by the claims. These embodiments are all within the protection scope of the present application.

Claims

1. A reporting method, characterized in that, Including: The terminal reports a first measurement report of radio resource management (RRM) measurements to the network-side device; Wherein, the first measurement report includes a prediction result of the RRM measurement prediction by the terminal based on an artificial intelligence (AI) unit.

2. The method according to claim 1, characterized in that, The prediction result includes at least one of the following: The beam quality of the first cell predicted by the terminal based on the AI unit; The cell signal quality of the first cell predicted by the terminal based on the AI unit; The prediction time corresponding to the beam quality or cell signal quality of the first cell predicted by the terminal based on the AI unit, where the prediction time includes at least one moment or the at least one time period; The cell identifier (ID) of at least one target cell predicted by the terminal based on the AI unit; The handover moment of at least one target cell predicted by the terminal based on the AI unit; A first indication, where the first indication is used to indicate that a first condition is satisfied, and the first condition is an entry condition or a departure condition of a measurement event predicted by the terminal based on the AI unit; The moment when the first condition is satisfied; A second indication, where the second indication is used to indicate that a radio link failure predicted by the terminal based on the AI unit will occur; The occurrence moment of the radio link failure predicted by the terminal based on the AI unit; A third indication, where the third indication is used to indicate that a handover to a target cell predicted by the terminal based on the AI unit fails; The moment when the handover to a target cell predicted by the terminal based on the AI unit fails; Wherein, the first cell includes at least one of the following: a source cell, at least one neighboring cell, at least one candidate handover cell, at least one target cell to be handed over.

3. The method according to claim 2, wherein The beam quality or cell signal quality predicted by the terminal based on the AI unit is characterized by the difference between the predicted value and the reference value, and the predicted value is the beam quality or cell signal quality predicted by the terminal based on the AI unit.

4. The method according to claim 3, wherein The reference value includes at least one of the following: The measurement value of each cell in the first measurement report; The first predicted value of each cell in the first measurement report; The maximum predicted value of each cell in the first measurement report; The measurement value of the second cell in the first measurement report; The first predicted value of the second cell in the first measurement report; The maximum predicted value of the second cell in the first measurement report; The maximum measurement value in the first measurement report; The maximum predicted value in the first measurement report; Wherein, the second cell is any one of the first cells, and the measurement value is the beam quality or cell signal quality actually measured by the terminal.

5. The method according to claim 4, characterized in that The second cell is indicated by at least one of the following: A fourth indication in the first measurement report; A fifth indication in the measurement configuration sent by the network-side device.

6. The method according to claim 4, wherein The method further includes: The terminal receives a first configuration sent by the network-side device, and the first configuration includes a first threshold; Wherein, when the difference between the predicted value of the terminal for the third cell and the measured value of the terminal for the third cell is less than the first threshold, the predicted value of the terminal for the third cell is not included in the prediction result; or, When the difference between the predicted value of the third cell at the first time and the predicted value of the third cell at the second time by the terminal is less than the first threshold, the predicted result does not include the predicted value of the third cell by the terminal at the second time, and the second time is after the first time; Wherein, the third cell is any one of the first cells.

7. The method according to any one of claims 1-6, characterized in that, The first measurement report further includes at least one of the following: The measurement result of the RRM measurement performed by the terminal; A sixth indication for indicating that there is no valid predicted result; The inference accuracy of the RRM measurement prediction performed by the AI unit; The credibility or confidence level of the predicted result.

8. The method according to any one of claims 1-7, characterized in that, The method further includes: The terminal receives a second configuration sent by the network side device, and the second configuration includes a second threshold; Wherein, when the inference accuracy of the RRM measurement prediction performed by the AI unit is greater than or equal to the second threshold, the predicted result is included in the first measurement report.

9. The method according to claim 8, characterized in that Each AI unit corresponds to a second threshold, or each AI function corresponds to a second threshold.

10. The method according to any one of claims 1-9, characterized in that, When a first condition is met, the predicted result is included in the first measurement report reported by the terminal; Wherein, the first condition includes at least one of the following: The first object is associated with a seventh indication for indicating that the terminal performs RRM measurement prediction based on the AI unit; The first object is associated with a first identifier, and the first identifier is the identifier of the AI unit or the AI function identifier; The reporting configuration of the RRM measurement is associated with a first event, and the first event is an event for the terminal to evaluate whether the event is satisfied according to the predicted result; Wherein, the first object includes at least one of the following: measurement configuration, measurement identifier, measurement object, reporting configuration.

11. The method according to claim 10, wherein The seventh indication is further used to indicate the prediction type of the RRM measurement prediction performed by the terminal based on the AI unit.

12. A receiving method, characterized in that, Including: The network side device receives a first measurement report of the RRM measurement reported by the terminal; Wherein, the predicted result of the RRM measurement prediction performed by the terminal based on the AI unit is included in the first measurement report.

13. The method according to claim 12, wherein The predicted result includes at least one of the following: The beam quality of the first cell predicted by the terminal based on the AI unit; The cell signal quality of the first cell predicted by the terminal based on the AI unit; The prediction time corresponding to the beam quality or cell signal quality of the first cell predicted by the terminal based on the AI unit, and the prediction time includes at least one moment or the at least one time period; The cell IDs of at least one target cell predicted by the terminal based on the AI unit; The handover moment of at least one target cell predicted by the terminal based on the AI unit; A first indication for indicating that the first condition is met, and the first condition is the entry condition or departure condition of the measurement event predicted by the terminal based on the AI unit; The moment when the first condition is met; A second indication for indicating that a radio link failure predicted by the terminal based on the AI unit will occur; The occurrence moment of the radio link failure predicted by the terminal based on the AI unit; The third indication, which is used to indicate that the handover to the target cell predicted by the AI unit of the terminal fails. The moment when the handover to the target cell predicted by the AI unit of the terminal fails. Wherein, the first cell includes at least one of the following: a source cell, at least one neighboring cell, at least one candidate handover cell, and at least one target cell to be handed over.

14. The method according to claim 13, wherein The beam quality or the cell signal quality predicted by the AI unit of the terminal is characterized by the difference between the predicted value and the reference value, and the predicted value is the beam quality or the cell signal quality predicted by the AI unit of the terminal.

15. The method according to claim 14, wherein The reference value includes at least one of the following: The measurement value of each cell in the first measurement report; The first predicted value of each cell in the first measurement report; The maximum predicted value of each cell in the first measurement report; The measurement value of the second cell in the first measurement report; The first predicted value of the second cell in the first measurement report; The maximum predicted value of the second cell in the first measurement report; The maximum measurement value in the first measurement report; The maximum predicted value in the first measurement report; Wherein, the second cell is any one of the first cells, and the measurement value is the beam quality or the cell signal quality actually measured by the terminal.

16. The method according to claim 15, wherein The second cell is indicated by a fourth indication in the first measurement report as described below; or The method further includes: The network-side device sends a measurement configuration to the terminal, and the measurement configuration includes a fifth indication for indicating the second cell.

17. The method according to claim 15, characterized in that, The method further includes: The network-side device sends a first configuration to the terminal, and the first configuration includes a first threshold. Wherein, when the difference between the predicted value of the terminal for the third cell and the measured value of the terminal for the third cell is less than the first threshold, the predicted result does not include the predicted value of the terminal for the third cell; or When the difference between the predicted value of the terminal for the third cell at the first time and the predicted value of the terminal for the third cell at the second time is less than the first threshold, the predicted result does not include the predicted value of the terminal for the third cell at the second time, and the second time is after the first time; Wherein, the third cell is any one of the first cells.

18. The method according to any one of claims 12 - 17, characterized in that, The first measurement report further includes at least one of the following: The measurement result of the terminal's RRM measurement; A sixth indication, which is used to indicate that there is no valid predicted result; The inference accuracy of the AI unit's RRM measurement prediction; The credibility or confidence level of the predicted result.

19. The method according to any one of claims 12 - 18, characterized in that, The method further includes: The network-side device sends a second configuration to the terminal, and the second configuration includes a second threshold. Wherein, when the inference accuracy of the AI unit's RRM measurement prediction is greater than or equal to the second threshold, the predicted result is included in the first measurement report.

20. The method according to claim 19, wherein Each AI unit corresponds to a second threshold, or each AI function corresponds to a second threshold.

21. A reporting device, characterized in that, Including: A reporting module, which is used to report a first measurement report of the RRM measurement to the network-side device. Wherein, the first measurement report includes a prediction result of the device performing RRM measurement prediction based on the AI unit.

22. The device according to claim 21, characterized in that, The prediction result includes at least one of the following: The beam quality of the first cell predicted by the device based on the AI unit; The cell signal quality of the first cell predicted by the device based on the AI unit; The prediction time corresponding to the beam quality or cell signal quality of the first cell predicted by the device based on the AI unit, where the prediction time includes at least one moment or the at least one time period; The cell identification ID of at least one target cell predicted by the device based on the AI unit; The handover moment of at least one target cell predicted by the device based on the AI unit; A first indication for indicating that a first condition is satisfied, where the first condition is an entry condition or a departure condition of a measurement event predicted by the device based on the AI unit; The moment when the first condition is satisfied; A second indication for indicating that a radio link failure will occur as predicted by the device based on the AI unit; The occurrence moment of the radio link failure predicted by the device based on the AI unit; A third indication for indicating that a handover to a target cell fails as predicted by the device based on the AI unit; The moment when a handover to a target cell fails as predicted by the device based on the AI unit; Wherein, the first cell includes at least one of the following: a source cell, at least one neighboring cell, at least one candidate handover cell, at least one target cell to be handed over.

23. The device according to claim 21 or 22, characterized in that, The first measurement report further includes at least one of the following: The measurement result of the device performing RRM measurement; A sixth indication for indicating that there is no valid prediction result; The inference accuracy of the AI unit performing RRM measurement prediction; The credibility or confidence level of the prediction result.

24. The device according to any one of claims 21 to 23, characterized in that, The device further includes: A receiving module for receiving a second configuration sent by a network-side device, where the second configuration includes a second threshold; Wherein, when the inference accuracy of the AI unit performing RRM measurement prediction is greater than or equal to the second threshold, the prediction result is included in the first measurement report.

25. A receiving device, characterized in that, Includes: A receiving module for receiving a first measurement report of RRM measurement reported by a terminal; Wherein, the first measurement report includes a prediction result of the terminal performing RRM measurement prediction based on the AI unit.

26. The device according to claim 25, characterized in that, The prediction result includes at least one of the following: The beam quality of the first cell predicted by the terminal based on the AI unit; The cell signal quality of the first cell predicted by the terminal based on the AI unit; The prediction time corresponding to the beam quality or cell signal quality of the first cell predicted by the terminal based on the AI unit, where the prediction time includes at least one moment or the at least one time period; The cell ID of at least one target cell predicted by the terminal based on the AI unit; The handover moment of at least one target cell predicted by the terminal based on the AI unit; A first indication for indicating that a first condition is satisfied, where the first condition is an entry condition or a departure condition of a measurement event predicted by the terminal based on the AI unit; The moment when the first condition is satisfied; A second indication for indicating that a radio link failure predicted by the AI unit will occur at the terminal; The occurrence time of the radio link failure predicted by the AI unit at the terminal; A third indication for indicating that a handover to a target cell predicted by the AI unit at the terminal fails; The time when the handover to the target cell predicted by the AI unit at the terminal fails; Wherein, the first cell includes at least one of the following: a source cell, at least one neighbor cell, at least one candidate handover cell, and at least one target cell to be handed over.

27. The device according to claim 25 or 26, characterized in that, The first measurement report further includes at least one of the following: The measurement result of the RRM measurement performed by the terminal; A sixth indication for indicating that there is no valid prediction result; The inference accuracy of the RRM measurement prediction performed by the AI unit; The credibility or confidence level of the prediction result.

28. The device according to claim 25 or 26 or 27, characterized in that, The apparatus further includes: A sending module, configured to send a second configuration to a terminal, where the second configuration includes a second threshold; Wherein, when the inference accuracy of the RRM measurement prediction performed by the AI unit is greater than or equal to the second threshold, the prediction result is included in the first measurement report.

29. A terminal, characterized in that, Including a processor and a memory, where the memory stores a program or instruction that can run on the processor, and when the program or instruction is executed by the processor, the steps of the reporting method according to any one of claims 1-11 are implemented.

30. A network-side device, characterized in that, Including a processor and a memory, where the memory stores a program or instruction that can run on the processor, and when the program or instruction is executed by the processor, the steps of the receiving method according to any one of claims 12-20 are implemented.

31. A readable storage medium, characterized in that, A program or instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, the steps of the reporting method according to any one of claims 1-11 are implemented, or the steps of the receiving method according to any one of claims 12-20 are implemented.

32. A computer program product, characterized in that, The program product is executed by at least one processor to implement the steps of the reporting method according to any one of claims 1-11, or to implement the steps of the receiving method according to any one of claims 12-20.

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