RRM measurement processing method and device, terminal and network side equipment

Abstract

The invention discloses an RRM measurement processing method and device, a terminal and network side equipment, and belongs to the technical field of communication, and the RRM measurement processing method comprises the steps that the terminal carries out RRM measurement prediction based on an artificial intelligence AI unit, and obtains a prediction result; the terminal triggers RRM measurement reporting or condition switching under the condition of determining that a prediction event is satisfied according to the prediction result; wherein the prediction event is an event predicted 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 method and apparatus for RRM measurement processing, 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 to 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 reports the RRM measurement results. However, in AI-assisted mobility enhancement, it is not yet clear how to measure and report or perform conditional handover on the RRM prediction results based on AI. Summary of the Invention

[0003] Embodiments of this application provide a method and apparatus for RRM measurement processing, a terminal, and a network-side device, which can solve the problem in the related art that it is not yet clear how to measure and report or perform conditional handover on the RRM prediction results based on AI.

[0004] In a first aspect, a method for RRM measurement processing is provided, which is executed by a terminal. The method includes:

[0005] The terminal performs RRM measurement prediction based on an AI unit to obtain a prediction result;

[0006] When the terminal determines that a prediction event is satisfied according to the prediction result, it triggers RRM measurement reporting or conditional handover;

[0007] Wherein, the prediction event is an event predicted by the terminal based on the AI unit.

[0008] In a second aspect, a method for RRM measurement processing is provided, which is executed by a network-side device. The method includes:

[0009] The network-side device sends a measurement configuration for RRM measurement to the terminal, and at least one reporting configuration of the measurement configuration includes a prediction event;

[0010] Wherein, the prediction event is an event predicted by the terminal based on the AI unit, and the terminal is used to trigger RRM measurement reporting or conditional handover when the prediction result obtained by performing RRM measurement prediction based on the AI unit satisfies the prediction event.

[0011] In a third aspect, a device for RRM measurement processing is provided, including:

[0012] A prediction module, configured to perform RRM measurement prediction based on the AI unit to obtain a prediction result;

[0013] A trigger module, configured to trigger RRM measurement reporting or condition switching when it is determined according to the prediction result that a prediction event is satisfied;

[0014] Wherein, the prediction event is an event predicted by the device based on the AI unit.

[0015] In a fourth aspect, a device for processing RRM measurement is provided, including:

[0016] A sending module, configured to send a measurement configuration for RRM measurement to a terminal, and at least one reporting configuration of the measurement configuration includes a prediction event;

[0017] Wherein, the prediction event is an event predicted by the terminal based on the AI unit, and the terminal is configured to trigger RRM measurement reporting or condition switching when a prediction result obtained by performing RRM measurement prediction according to the AI unit satisfies the prediction event.

[0018] In a fifth aspect, a terminal is provided, which includes a processor and a memory, and 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.

[0019] In a sixth aspect, a terminal is provided, including a processor and a communication interface. The processor is configured to perform RRM measurement prediction based on the AI unit to obtain a prediction result, and trigger RRM measurement reporting or condition switching when it is determined according to the prediction result that a prediction event is satisfied; wherein, the prediction event is an event predicted by the terminal based on the AI unit.

[0020] In a seventh aspect, a network-side device is provided, which includes a processor and a memory, and 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.

[0021] In an eighth aspect, a network-side device is provided, including a processor and a communication interface. The communication interface is configured to send a measurement configuration for RRM measurement to a terminal, and at least one reporting configuration of the measurement configuration includes a prediction event; the prediction event is an event predicted by the terminal based on the AI unit, and the terminal is configured to trigger RRM measurement reporting or condition switching when a prediction result obtained by performing RRM measurement prediction according to the AI unit satisfies the prediction event.

[0022] In a ninth aspect, a readable storage medium is provided, on which a program or instructions are stored, and when the program or instructions are 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] In a tenth aspect, a wireless communication system is provided, including: a terminal and a network-side device, where 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] In an eleventh aspect, a chip is provided, which includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run a program or instructions 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 the program / program product is executed by at least one processor to implement the steps of the method described in the first aspect or the second aspect.

[0026] In the embodiments of the present application, when the terminal performs RRM measurement prediction based on the AI unit and obtains a prediction result, and determines that the prediction event is satisfied according to the prediction result, the terminal triggers RRM measurement reporting or conditional handover, which helps to reduce the handover delay. Furthermore, it also stipulates how the terminal triggers measurement reporting or conditional handover according to the prediction result of the AI-based RRM measurement prediction in AI-assisted mobility enhancement, effectively standardizes the behavior of the terminal side, and helps 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 method for processing RRM measurement provided by the embodiments of the present application;

[0029] Figure 3a is one of the schematic diagrams of the scenario of a method for processing RRM measurement provided by the embodiments of the present application;

[0030] Figure 3b is another schematic diagram of the scenario of a method for processing RRM measurement provided by the embodiments of the present application;

[0031] Figure 4 is a flowchart of another method for processing RRM measurement provided by the embodiments of the present application;

[0032] Figure 5It is a structural diagram of an RRM measurement processing device provided by an embodiment of the present application;

[0033] Figure 6 It is a structural diagram of another RRM measurement processing device provided by an embodiment of the present application;

[0034] Figure 7 It is a structural diagram of a communication device provided by an embodiment of the present application;

[0035] Figure 8 It is a structural diagram of a terminal provided by an embodiment of the present application;

[0036] Figure 9 It is a structural diagram of a network-side device provided by an embodiment of the present application. Detailed implementation manners

[0037] Next, the technical solutions in the embodiments of the present application will be clearly described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, rather than 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.

[0038] The terms "first", "second", etc. in the present 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 the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are usually of the same category, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "or" in the present 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 related objects before and after are in an "or" relationship.

[0039] The term "indication" in the present 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 tells the receiver 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.

[0040] It should be noted that the technology described in the embodiments of the present application is not limited to the Long Term Evolution (LTE) / LTE-Advanced (LTE-A) system, 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 the present application are often used interchangeably, and the described technology can be used in the above-mentioned systems and radio technologies, as well as other systems and radio technologies. The following description describes the New Radio (NR) system for illustrative purposes, and the NR terminology is used 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 th Generation, 6G) communication system.

[0041] Figure 1Block diagram of a wireless communication system to which embodiments of the present application can be applied. 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 appliances 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. 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 this 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.

[0042] To better understand the technical solution of this application, the following explains the related concepts involved in the embodiments of this application.

[0043] Artificial Intelligence (AI):

[0044] 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. This application takes neural networks as an example for illustration, but does not limit the specific type of the AI module.

[0045] It should be noted that the AI unit / AI model described in this application can 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. Or the AI unit / AI model can also refer to a processing unit that can implement specific algorithms, formulas, processing flows, capabilities, etc. related to AI. Or the AI unit / AI model can be a processing method, algorithm, function, module, or unit for a specific data set. Or the AI unit / AI model can 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.

[0046] In addition, the identifier of the AI unit / AI model can 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 feature, 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.

[0047] AI functionality: That is, an AI algorithm function, and the AI functionality may include multiple AI Models.

[0048] RRM measurement reporting:

[0049] The measurement configuration mainly consists of a measurement object, a reporting configuration, and a measurement identifier (Identifier, ID).

[0050] Measurement Object: That is, the frequency point to be measured;

[0051] 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.;

[0052] 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.

[0053] Event-triggered reporting can be included in the reporting configuration. The events defined in NR are shown in Table 1 below:

[0054] Table 1

[0055]

[0056]

[0057] Taking Event A3 as an example, the meanings of the parameters for the entry condition and departure condition of Event A3 are as follows:

[0058] Mn: Measurement result of the neighboring cell, without considering any offset;

[0059] Ofn: Neighboring cell measurement object specific offset;

[0060] Ocn: Neighboring cell cell-level specific offset;

[0061] Mp: Measurement result of the SpCell (primary serving cell), without considering any offset;

[0062] Ofp: SpCell measurement object specific offset;

[0063] Ocp: SpCell cell-level specific offset;

[0064] Hys: Hysteresis parameter of the event;

[0065] Off: Offset parameter of the event.

[0066] It should be noted that the meanings of other parameters involved in Table 1 above can be referred to the related technologies, which will not be elaborated here.

[0067] 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.

[0068] For conditional handover, conditional handover means that the network pre-configures multiple candidate cells for the UE, and the UE evaluates the execution conditions of the candidate cells and switches to the corresponding cell when the conditions are met. The execution conditions may include one or two trigger conditions.

[0069] Taking the parameters in NR as an example: condReconfigId indicates the conditional reconfiguration ID; condExecutionCond is used to configure the execution conditions for the change of the primary cell (PCell), and condRRCReconfig is used to configure the configuration parameters of the target master cell group (MCG). The three parameters correspond to a group of candidate cell configurations, which are used for conditional reconfiguration of a candidate PCell.

[0070] It should be noted that the execution conditions are the measurement events configured in the reporting configuration associated with MeasurementID. For conditional handover, the measurement events support condEventA3, condEventA4 or condEventA5, and their judgment conditions are the same as those of A3, A4, and A5 above. If the L3 filtered signal quality of one or more candidate cells satisfies the entry condition of the event within the timeToTrigger, the UE takes the cell that meets the conditions as the trigger cell and selects an execution condition reconfiguration in the trigger cell.

[0071] In the related technologies, how the terminal performs RRM measurements and how to trigger the reporting of RRM measurement results have been specified. However, in the mobility enhancement based on AI assistance, it is not clear how to perform measurement reporting or conditional handover on the RRM prediction results based on AI.

[0072] Next, in conjunction with the accompanying drawings, through some embodiments and their application scenarios, the RRM measurement processing method, device, terminal, network-side device, etc. provided by the embodiments of the present application will be described in detail.

[0073] Please refer to Figure 2 ,Figure 2 This is a flowchart of an RRM measurement processing method provided by an embodiment of the present application. The method is applied to a terminal. As Figure 2 shown, the method includes the following steps:

[0074] Step 201: The terminal performs RRM measurement prediction based on the AI unit to obtain a prediction result.

[0075] In an embodiment of the present application, the terminal performing RRM measurement prediction based on the AI unit may mean that the terminal performs RRM measurement prediction on a future moment or a certain time period through the AI unit to obtain a prediction result. The prediction result is the result inferred by the AI unit, rather than the actual RRM measurement result.

[0076] Optionally, the RRM measurement prediction performed by the terminal based on the AI unit may be to predict the beam quality or cell signal quality of the serving cell / neighbor cell / target cell at a future moment or time period, or to predict the handover moment of the target cell, or to predict the moment when the entry condition or exit condition of the measurement event is satisfied, etc.

[0077] Step 202: When the terminal determines that a prediction event is satisfied according to the prediction result, it triggers RRM measurement reporting or conditional handover;

[0078] Among them, the prediction event is an event predicted by the terminal based on the AI unit. It should be noted that the prediction event can also be understood as a measurement event predicted by the terminal based on the AI unit, including the events described in Table 1 above, such as the predicted A1 event, the predicted A2 event, etc.

[0079] In an embodiment of the present application, after the terminal performs RRM measurement prediction based on the AI unit and obtains a prediction result, it determines whether the prediction event is satisfied according to the prediction result. For example, assume that the terminal predicts the cell signal quality of the neighbor cell and the serving cell in a future time period based on the AI unit, and obtains the cell signal quality of the neighbor cell and the serving cell in the future time period (i.e., the prediction result) respectively. If the cell signal quality of the neighbor cell in the future time period is always greater than the cell signal quality of the serving cell in the future time period, it is considered that the prediction result satisfies the prediction event. In this case, the terminal can trigger RRM measurement reporting or conditional handover. Among them, triggering RRM measurement reporting means that the terminal reports an RRM measurement report, and the measurement report includes the prediction result and / or the RRM measurement result; triggering conditional handover means that the terminal switches to the corresponding cell when the execution conditions of the configured candidate cells are met.

[0080] It should be noted that the satisfaction of the predicted event refers to the satisfaction of the entry condition or the departure condition of the predicted event, that is, the satisfaction of the entry condition or the departure condition of the measurement event predicted by the terminal based on the AI unit. For example, for the A1 event (EventA1), if the cell signal quality of the serving cell predicted by the AI unit meets the preset threshold, that is, it meets the entry condition of the predicted event (the predicted A1 event).

[0081] In the embodiments of the present application, when the terminal performs RRM measurement prediction based on the AI unit to obtain a prediction result, and determines that the predicted event is satisfied according to the prediction result, the terminal triggers RRM measurement reporting or conditional handover, which helps to reduce the handover delay. Furthermore, it also stipulates how the terminal triggers measurement reporting or conditional handover according to the prediction result of the AI-based RRM measurement prediction in AI-assisted mobility enhancement, effectively standardizes the behavior of the terminal side, and helps to improve the communication performance between the terminal and the network-side device.

[0082] Optionally, the terminal determines that the predicted event is satisfied according to the prediction result, including any one of the following:

[0083] When the prediction result continuously satisfies the predicted event within the first time, the terminal determines that the predicted event is satisfied;

[0084] When the measurement result of the RRM measurement continuously satisfies the measurement event within the second time, and the prediction result continuously satisfies the predicted event within the third time, the terminal determines that the predicted event is satisfied.

[0085] For example, in one implementation, the terminal determines whether the predicted event is satisfied according to the prediction result, which means that the terminal determines whether the prediction result continuously satisfies the predicted event (the entry condition or the departure condition) within the first time, so as to determine whether the predicted event is satisfied. If the prediction result continuously satisfies the predicted event within the first time, it is determined that the predicted event is satisfied. For example, the predicted A1 event is satisfied.

[0086] Specifically, by analogy with the existing measurement events, the difference is that the existing measurement events are judged whether the measurement events are satisfied based on the measurement results of the serving cell and / or neighboring cell RRM measurements, while the predicted events in the present application are judged whether the predicted events are satisfied based on the prediction results of the serving cell and / or neighboring cells. For example, for Event A3, if the measurement result of the neighboring cell is always higher than the measurement result of the serving cell by a preset threshold within the T1 event, the A3 measurement event is satisfied; for the Event A3 (predicted event) predicted by the terminal based on the AI unit, if the prediction result of the neighboring cell at the same moment is always higher than the prediction result of the serving cell by a preset threshold within the T1 time, the A3 predicted event is satisfied. As Figure 3aAs shown, the A3 measurement event first meets the entry condition at time t0 and always meets the entry condition within time T1. Then the terminal triggers a measurement report at time t0 + T1. The A3 prediction event meets the entry condition at or before time t0. According to the prediction result, it always meets the entry condition from time t0 to t0 + T1. Therefore, if the prediction event is met at or before time t0, the terminal triggers a measurement report at time t0.

[0087] Alternatively, in another implementation, the terminal determines whether the prediction event is met based on whether the measurement result of RRM measurement continuously meets the measurement event within a second time and whether the prediction result of RRM measurement prediction based on the AI unit continuously meets the prediction event within a third time.

[0088] Exemplarily, the terminal evaluates whether the measurement event is continuously met based on the measurement result of RRM measurement within time T2. If it is continuously met within time T2, the terminal evaluates whether the prediction event is continuously met within time T3 based on the prediction result of RRM measurement prediction based on the AI unit. If it is continuously met within time T3, the terminal triggers a measurement report. For example, for Event A3, if the measurement result of the neighboring cell is always higher than the measurement result of the serving cell by a preset threshold within the time to trigger, then the A3 measurement event is met. For the predicted Event A3, if the measurement result of the neighboring cell is always higher than the measurement result of the serving cell by a first threshold within time T2, and the predicted result of the neighboring cell at the same moment is always higher than the measurement result of the serving cell by a second threshold within time T3, then the A3 prediction event is met. As Figure 3b As shown, for the A3 measurement event, if it first meets the entry condition at time t0 and continuously meets the entry condition during the time to trigger, the terminal triggers a measurement report at time t0 + T. For the A3 prediction event, if the A3 prediction event first meets the entry condition at time t0, the terminal determines based on the measurement result of RRM measurement that the signal quality of the neighboring cell is always higher than the signal quality of the serving cell by a first threshold from time t0 to t0 + T2. The terminal enables the RRM measurement prediction of the AI unit at or before time t0 + T2. According to the prediction result, it determines that the signal quality of the neighboring cell is always higher than the signal quality of the serving cell by a second threshold from t0 + T2 to t0 + T3. If the terminal determines that the prediction event is met at time t0 + T2, it triggers a measurement report. Wherein, T = T2 + T3, and the first threshold and the second threshold may be the same or different.

[0089] In the embodiments of the present application, the terminal can determine whether the prediction result obtained by predicting RRM measurement based on the AI unit meets the prediction event based on the above two methods, so as to determine whether to trigger measurement reporting or conditional handover. Furthermore, in the mobility enhancement based on AI assistance, the terminal can perform measurement reporting or conditional handover earlier according to the prediction event, which helps to reduce the handover delay and improve the communication performance between the terminal and the network-side device.

[0090] Optionally, the method further includes:

[0091] The terminal receives a first configuration sent by the network-side device and determines the second time and the third time according to the first configuration.

[0092] That is to say, the second time and the third time are determined by the terminal according to the first configuration sent by the network-side device. For example, the first configuration includes the second time and the third time, that is, the second time and the third time are directly configured by the network-side device to the terminal, so that the terminal can then determine whether the prediction result meets the prediction event according to the second time and the third time, which helps to implement the terminal measurement reporting or conditional handover. For example, when the measurement result of the RRM measurement continuously meets the measurement event within the second time, and the prediction result of the RRM measurement prediction based on the AI unit continuously meets the prediction event within the third time, the terminal determines that the prediction event is met, and then the terminal can trigger measurement reporting or conditional handover to reduce the handover delay.

[0093] It should be noted that the first configuration may also be a reporting configuration sent by the network-side device for RRM measurement.

[0094] Optionally, the first configuration includes any one of the following:

[0095] The second time and the third time;

[0096] The second time and the sum or difference between the second time and the third time;

[0097] The third time and the sum or difference between the third time and the second time.

[0098] Exemplarily, the first configuration includes the second time and the sum or difference between the second time and the third time, so that the terminal can calculate the third time. Or, the first configuration includes the third time and the sum or difference between the second time and the third time, and the terminal can also calculate the second time. In the embodiments of the present application, the first configuration including any one of the above can make the configuration of the second time and the third time by the network-side device more flexible.

[0099] Optionally, in the embodiments of the present application, before the terminal performs RRM measurement prediction based on the AI unit, the method further includes:

[0100] The terminal receives a measurement configuration for RRM measurement sent by a network-side device, and at least one reporting configuration of the measurement configuration includes the prediction event.

[0101] It can be understood that the measurement configuration for RRM measurement generally includes a reporting configuration, a measurement object, and a measurement ID. Furthermore, the terminal can better perform RRM measurement and measurement reporting according to the measurement configuration. Among them, the measurement configuration may include multiple reporting configurations.

[0102] In the embodiments of the present application, at least one reporting configuration of the measurement configuration includes the prediction event, that is, the network-side device can configure the terminal to perform RRM measurement prediction based on the AI unit, and can configure what event the event for RRM measurement prediction is, such as the prediction of an A1 event, the prediction of an A3 event, etc. Furthermore, the network-side device configures the prediction event in at least one reporting configuration of the measurement configuration, so as to better standardize the behavior of the terminal side, so that the terminal can perform RRM measurement prediction based on the AI unit for the prediction event, obtain a prediction result, and trigger RRM measurement reporting or conditional handover when it is determined that the prediction event is satisfied according to the prediction result.

[0103] Optionally, when the terminal determines that the prediction event is satisfied according to the prediction result and triggers conditional handover, it includes any one of the following:

[0104] When at least one measurement event and at least one prediction event related to conditional handover are included in at least one reporting configuration of the measurement configuration, the terminal determines that at least one measurement event is satisfied according to the measurement result of the RRM measurement, and determines that at least one prediction event is satisfied according to the prediction result, and then triggers conditional handover;

[0105] When at least one prediction event is included in at least one reporting configuration of the measurement configuration, the terminal triggers conditional handover when it determines that all prediction events are satisfied according to the prediction result.

[0106] For example, in one implementation, if at least one measurement event related to conditional handover and at least one prediction event related to conditional handover are included in at least one reporting configuration of the measurement configuration, the terminal evaluates whether at least one measurement event is satisfied according to the measurement result of the RRM measurement, evaluates whether at least one prediction event is satisfied according to the prediction result of the RRM measurement prediction based on the AI unit, and when both the measurement event and the prediction event are satisfied, the terminal triggers conditional handover.

[0107] Alternatively, in another implementation, if at least one reporting configuration in the measurement configuration includes at least one prediction event related to conditional handover, the terminal evaluates whether the at least one prediction event is satisfied according to the prediction result predicted by the RRM measurement based on the AI unit. When all the prediction events are satisfied, the terminal triggers a conditional handover.

[0108] It should be noted that the satisfaction of the measurement event means that the measurement result satisfies the entry condition or the exit condition of the measurement event. For example, for an A3 event, if the measurement result of the neighboring cell is always higher than the preset threshold than the measurement result of the serving cell within T1 time, the A3 measurement event is satisfied. The specific implementation of the satisfaction of the prediction event is as described above and will not be elaborated here.

[0109] In the embodiment of the present application, the terminal can also determine whether to trigger a conditional handover according to the content configured by the reporting configuration, thereby better standardizing the behavior of the terminal to perform a conditional handover.

[0110] Optionally, when at least one reporting configuration in the measurement configuration includes a prediction event, the reporting configuration including the prediction event is associated with a first measurement identifier, and the first measurement identifier is a measurement identifier associated with the execution condition of the conditional handover.

[0111] It can be understood that the conditional handover includes corresponding execution conditions, and the execution conditions of the conditional handover can be associated with corresponding measurement identifiers. For example, different execution conditions are associated with different measurement identifiers, so as to distinguish the execution conditions and be able to represent the content of the execution conditions. In the embodiment of the present application, the reporting configuration including the prediction event is associated with the measurement identifier (i.e., the first measurement identifier) associated with the execution condition of the conditional handover. Therefore, it can also represent that the prediction event included in the reporting configuration is related to the conditional handover. In this case, the first measurement identifier can also instruct the terminal to determine whether to trigger a conditional handover according to the content configured by the reporting configuration, thereby more helping to standardize the behavior of the terminal side.

[0112] Optionally, at least one of the first measurement identifiers is associated with the candidate cell configuration corresponding to the same conditional handover, and one first measurement identifier is associated with one prediction event. It can be understood that one first measurement identifier is associated with one prediction event, and the candidate cell corresponding to the same conditional handover can be configured to be associated with multiple first measurement identifiers, that is, associated with multiple prediction events. In this case, the terminal triggers a conditional handover only when the prediction result obtained by predicting the RRM measurement based on the AI unit determines that all prediction events are satisfied.

[0113] Optionally, the reporting configuration or the measurement identification configuration sent by the network side device further includes a first identification, where the first identification is the identification of the AI unit or the AI function identification.

[0114] Optionally, in this case, the terminal performs RRM measurement prediction based on the AI unit to obtain a prediction result, including:

[0115] The terminal performs RRM measurement prediction according to the AI unit corresponding to the first identification to obtain a prediction result.

[0116] In the embodiments of the present application, the network side device carries the first identification in the reporting configuration or the measurement identification configuration, where the first identification is the identification of the AI unit or the AI function identification. Furthermore, the terminal can perform RRM measurement prediction using the AI unit corresponding to the first identification. In this way, the network side device can configure the terminal to use the corresponding AI unit for RRM measurement prediction based on the first identification, enabling both the network side device and the terminal to clearly know which AI unit is used for RRM measurement prediction, which helps with information synchronization between the network side device and the terminal.

[0117] Optionally, in the embodiments of the present application, the method further includes:

[0118] The terminal receives the conditional reconfiguration sent by the network side device for conditional handover;

[0119] When the terminal determines that the prediction event is satisfied according to the prediction result, the terminal triggers conditional handover, including:

[0120] The terminal triggers conditional handover according to the conditional reconfiguration and when it determines that the prediction event is satisfied according to the prediction result.

[0121] It can be understood that when the network side device sends the conditional reconfiguration for conditional handover to the terminal, the terminal needs to determine whether to trigger conditional handover according to the conditional reconfiguration and whether the prediction result obtained by performing RRM measurement prediction based on the AI unit satisfies the prediction event. Among them, the relevant parameters of the conditional reconfiguration can refer to related technologies.

[0122] Optionally, in the embodiments of the present application, after triggering the RRM measurement report or conditional handover, the method further includes:

[0123] The terminal reports a first measurement report, where the first measurement report includes at least one of the following:

[0124] The prediction result;

[0125] The measurement result of the RRM measurement.

[0126] Understandably, during the communication process between the terminal and the network-side device, the terminal needs to perform RRM measurements and be able to report the measurement results of the RRM measurements. In the embodiments of the present application, the terminal can perform RRM measurement prediction based on the AI unit to obtain a prediction result. The terminal can report the prediction result together with or separately from the measurement result of the RRM measurement, or can also report only the prediction result, or only report the measurement result of the RRM measurement, thereby making the reporting behavior on the terminal side more flexible.

[0127] Please refer to Figure 4 , Figure 4 which is a flowchart of another RRM measurement processing method provided by the embodiments of the present application. The method is applied to the network-side device. As Figure 4 shown, the method includes the following steps:

[0128] Step 401, the network-side device sends a measurement configuration for RRM measurement to the terminal. At least one reporting configuration of the measurement configuration includes a prediction event;

[0129] wherein, the prediction event is an event predicted by the terminal based on the AI unit, and the terminal is used to trigger RRM measurement reporting or conditional handover when the prediction result obtained by predicting the RRM measurement according to the AI unit meets the prediction event.

[0130] Optionally, the reporting configuration including the prediction event is associated with a first measurement identifier, and the first measurement identifier is a measurement identifier associated with the execution condition of the conditional handover.

[0131] Optionally, at least one first measurement identifier is associated with the candidate cell configuration corresponding to the same conditional handover, and one first measurement identifier is associated with one prediction event.

[0132] Optionally, the reporting configuration or the measurement identifier configuration sent by the network-side device further includes a first identifier, and the first identifier is an identifier of the AI unit or an AI function identifier.

[0133] Optionally, the method further includes:

[0134] The network-side device sends a first configuration to the terminal, and the first configuration is used for the terminal to determine a second time and a third time. The second time is used for the terminal to determine whether the measurement result of the RRM measurement meets the measurement event, and the third time is used for the terminal to determine whether the prediction result meets the prediction event.

[0135] Optionally, the first configuration includes any one of the following:

[0136] The second time and the third time;

[0137] the second time, and the sum or difference between the second time and the third time;

[0138] the third time, and the sum or difference between the third time and the second time.

[0139] Optionally, the method further includes:

[0140] The network side device receives a first measurement result reported by a terminal, and the first measurement report includes at least one of the following:

[0141] the prediction result;

[0142] the measurement result of RRM measurement.

[0143] Optionally, the method further includes:

[0144] The network side device sends conditional reconfiguration for conditional handover to the terminal.

[0145] It should be noted that the method provided in the embodiments of the present application corresponds to the method applied to the terminal side above. The relevant concepts and specific implementation processes involved in the embodiments of the present application can refer to the description in the embodiments of the terminal side method, and will not be repeated in this embodiment.

[0146] In the embodiments of the present application, the network side device sends a measurement configuration for RRM measurement to the terminal. At least one reporting configuration of the measurement configuration includes a prediction event, and the prediction event is an event predicted by the terminal based on the AI unit. The terminal is used to trigger RRM measurement reporting or conditional handover when the prediction result obtained by predicting RRM measurement based on the AI unit satisfies the prediction event, which helps to reduce the handover delay of the terminal, and also stipulates how the terminal triggers measurement reporting or conditional handover according to the prediction result of AI-based RRM measurement prediction in AI-assisted mobility enhancement, effectively standardizing the behavior of the terminal side.

[0147] For the RRM measurement processing method provided in the embodiments of the present application, the execution subject may be an RRM measurement processing device. In the embodiments of the present application, taking the RRM measurement processing device executing the RRM measurement processing method as an example, the RRM measurement processing device provided in the embodiments of the present application is described.

[0148] Please refer to Figure 5 , Figure 5 which is a structural diagram of an RRM measurement processing device provided in the embodiments of the present application. As Figure 5 shown, the RRM measurement processing device 500 includes:

[0149] A prediction module 501, configured to perform RRM measurement prediction based on an AI unit to obtain a prediction result;

[0150] A triggering module 502, configured to trigger RRM measurement reporting or conditional handover when it is determined according to the prediction result that a prediction event is satisfied;

[0151] Wherein, the prediction event is an event predicted by the device based on the AI unit.

[0152] Optionally, the triggering module 502 is further configured to perform any of the following:

[0153] Determine that the prediction event is satisfied when the prediction result continuously satisfies the prediction event within a first period of time;

[0154] Determine that the prediction event is satisfied when the measurement result of the RRM measurement continuously satisfies the measurement event within a second period of time and the prediction result continuously satisfies the prediction event within a third period of time.

[0155] Optionally, the device further includes:

[0156] A first receiving module, configured to receive a first configuration sent by a network-side device and determine the second period of time and the third period of time according to the first configuration.

[0157] Optionally, the first configuration includes any of the following:

[0158] The second period of time and the third period of time;

[0159] The second period of time, and the sum or difference between the second period of time and the third period of time;

[0160] The third period of time, and the sum or difference between the third period of time and the second period of time.

[0161] Optionally, the device further includes:

[0162] A second receiving module, configured to receive a measurement configuration for RRM measurement sent by a network-side device, and at least one reporting configuration of the measurement configuration includes the prediction event.

[0163] Optionally, the triggering module 502 is further configured to perform any of the following:

[0164] When at least one reporting configuration of the measurement configuration includes at least one measurement event and at least one prediction event, trigger conditional handover when it is determined according to the measurement result of the RRM measurement that at least one measurement event is satisfied and it is determined according to the prediction result that at least one prediction event is satisfied;

[0165] When at least one reporting configuration of the measurement configuration includes at least one prediction event, trigger conditional handover when it is determined according to the prediction result that all prediction events are satisfied.

[0166] Optionally, the reporting configuration including the predicted event is associated with a first measurement identifier, and the first measurement identifier is a measurement identifier associated with the execution condition of the conditional handover.

[0167] Optionally, at least one of the first measurement identifiers is associated with the candidate cell configuration corresponding to the same conditional handover, and one first measurement identifier is associated with one predicted event.

[0168] Optionally, the reporting configuration or the measurement identifier configuration sent by the network side device further includes a first identifier, and the first identifier is an identifier of the AI unit or an AI function identifier.

[0169] Optionally, the prediction module 501 is further configured to:

[0170] Perform RRM measurement prediction according to the AI unit corresponding to the first identifier to obtain a prediction result.

[0171] Optionally, the device further includes:

[0172] A reporting module, configured to report a first measurement report, where the first measurement report includes at least one of the following:

[0173] The prediction result;

[0174] The measurement result of the RRM measurement.

[0175] Optionally, the device further includes:

[0176] A third receiving module, configured to receive a conditional reconfiguration for conditional handover sent by the network side device;

[0177] The trigger module 502 is further configured to:

[0178] According to the conditional reconfiguration and when it is determined that the predicted event is satisfied according to the prediction result, trigger a conditional handover.

[0179] In the embodiments of the present application, the device performs RRM measurement prediction based on the AI unit to obtain a prediction result, and when it is determined that the predicted event is satisfied according to the prediction result, triggers an RRM measurement report or a conditional handover, which helps to reduce the handover delay, and also specifies how to trigger a measurement report or a conditional handover according to the prediction result of the AI-based RRM measurement prediction in AI-assisted mobility enhancement.

[0180] The RRM measurement processing device in the embodiments of the present application may 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 may be a terminal or other devices other than the terminal. Exemplarily, the terminal may include, but is not limited to, the types of the terminal 11 listed above, and other devices may be a server, a Network Attached Storage (NAS), etc., which are not specifically limited in the embodiments of the present application.

[0181] The RRM measurement processing device provided in the embodiments of the present application can implement Figure 2 Each process implemented by the method embodiment and achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0182] Please refer to Figure 6 , Figure 6 which is a structural diagram of another RRM measurement processing device provided in the embodiments of the present application. As Figure 6 shown, the RRM measurement processing device 600 includes:

[0183] A sending module 601, configured to send a measurement configuration for RRM measurement to a terminal, where at least one reporting configuration of the measurement configuration includes a prediction event;

[0184] Wherein, the prediction event is an event predicted by the terminal based on the AI unit, and the terminal is used to trigger RRM measurement reporting or conditional handover when the prediction result obtained by predicting the RRM measurement according to the AI unit meets the prediction event.

[0185] Optionally, the reporting configuration including the prediction event is associated with a first measurement identifier, and the first measurement identifier is a measurement identifier associated with the execution condition of the conditional handover.

[0186] Optionally, at least one of the first measurement identifiers is associated with the candidate cell configuration corresponding to the same conditional handover, and one first measurement identifier is associated with one prediction event.

[0187] Optionally, the reporting configuration or the measurement identifier configuration sent by the network side device further includes a first identifier, and the first identifier is an identifier of the AI unit or an AI function identifier.

[0188] Optionally, the sending module 601 is further configured to:

[0189] Send a first configuration to the terminal, where the first configuration is used for the terminal to determine a second time and a third time, the second time is used for the terminal to determine whether the measurement result of the RRM measurement meets the measurement event, and the third time is used for the terminal to determine whether the prediction result meets the prediction event.

[0190] Optionally, the first configuration includes any one of the following:

[0191] the second time and the third time;

[0192] the second time, and the sum or difference between the second time and the third time;

[0193] the third time, and the sum or difference between the third time and the second time.

[0194] Optionally, the device further includes:

[0195] a receiving module, configured to receive a first measurement result reported by a terminal, where the first measurement report includes at least one of the following:

[0196] the prediction result;

[0197] the measurement result of RRM measurement.

[0198] Optionally, the sending module 601 is further configured to:

[0199] send a conditional reconfiguration for conditional handover to the terminal.

[0200] The RRM measurement processing device provided by the embodiments of the present application can implement Figure 4 each process implemented by the method embodiments, and achieve the same technical effects. To avoid repetition, details are not described herein again.

[0201] As Figure 7 shown, the embodiments of the present application further provide a communication device 700, including a processor 701 and a memory 702. A program or instruction that can run on the processor 701 is stored on the memory 702. For example, when the communication device 700 is a terminal, when the program or instruction is executed by the processor 701, each step of the above RRM measurement processing method embodiment is implemented, and the same technical effects can be achieved. When the communication device 700 is a network-side device, when the program or instruction is executed by the processor 701, each step of the above RRM measurement processing method embodiment is implemented, and the same technical effects can be achieved. To avoid repetition, details are not described herein again.

[0202] 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, and 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 and implementation manner of the above method embodiment can be applied to this terminal embodiment, and the same technical effects can be achieved. Specifically, Figure 8Schematic diagram of the hardware structure of a terminal according to an embodiment of the present application.

[0203] The terminal 800 includes, but is not limited to, at least some components such as a radio frequency unit 801, a network module 802, an audio output unit 803, an input unit 804, a sensor 805, a display unit 806, a user input unit 807, an interface unit 808, a memory 809, and a processor 810.

[0204] Those skilled in the art can understand that the terminal 800 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 810 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 8 The terminal structure shown does not limit the terminal. The terminal may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0205] It should be understood that in the embodiment of the present application, the input unit 804 may include a graphics processing unit (GPU) 8041 and a microphone 8042. The graphics processing unit 8041 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 806 may include a display panel 8061, and the display panel 8061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 807 includes at least one of a touch panel 8071 and other input devices 8072. The touch panel 8071 is also called a touch screen. The touch panel 8071 may include two parts: a touch detection device and a touch controller. The other input devices 8072 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.

[0206] In the embodiment of the present application, after receiving downlink data from a network side device, the radio frequency unit 801 can transmit it to the processor 810 for processing; in addition, the radio frequency unit 801 can send uplink data to the network side device. Generally, the radio frequency unit 801 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc.

[0207] The memory 809 can be used to store software programs or instructions and various data. The memory 809 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 may 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 809 may include a volatile memory or a non-volatile memory. Among them, the non-volatile memory may 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 may 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 809 in the embodiments of the present application includes but is not limited to these and any other suitable types of memories.

[0208] The processor 810 may include one or more processing units; optionally, the processor 810 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 810 either.

[0209] Among them, the processor 810 is used for:

[0210] Based on the AI unit, perform RRM measurement prediction to obtain a prediction result;

[0211] When it is determined that a prediction event is satisfied according to the prediction result, trigger RRM measurement reporting or conditional handover;

[0212] Among them, the prediction event is an event predicted by the device based on the AI unit.

[0213] In the embodiments of the present application, when the terminal performs RRM measurement prediction based on the AI unit to obtain a prediction result, and determines that a prediction event is satisfied according to the prediction result, the terminal triggers RRM measurement reporting or conditional handover, which helps to reduce the handover delay. Furthermore, it also stipulates how the terminal triggers measurement reporting or conditional handover according to the prediction result of the AI-based RRM measurement prediction in AI-assisted mobility enhancement, effectively standardizes the behavior of the terminal side, and helps to improve the communication performance between the terminal and the network-side device.

[0214] It can be understood that the implementation processes of the implementation manners mentioned in this embodiment can refer to the relevant descriptions of the above RRM measurement processing method embodiment and achieve the same or corresponding technical effects. To avoid repetition, they will not be elaborated here.

[0215] 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, and the processor is used to run programs or instructions to implement the steps of the method embodiment as Figure 4 shown. This network-side device embodiment corresponds to the above network-side device method embodiment. Each implementation process and implementation manner of the above method embodiment can be applied to this network-side device embodiment and can achieve the same technical effects.

[0216] Specifically, the embodiments of the present application further provide a network-side device. As Figure 9 shown, the network-side device 900 includes: an antenna 91, a radio frequency device 92, a baseband device 93, a processor 94, and a memory 95. The antenna 91 is connected to the radio frequency device 92. In the uplink direction, the radio frequency device 92 receives information through the antenna 91 and sends the received information to the baseband device 93 for processing. In the downlink direction, the baseband device 93 processes the information to be sent and sends it to the radio frequency device 92. After the radio frequency device 92 processes the received information, it is sent out through the antenna 91.

[0217] The method executed by the network-side device in the above embodiments can be implemented in the baseband device 93, and the baseband device 93 includes a baseband processor.

[0218] The baseband device 93 may include, for example, at least one baseband board, and a plurality of chips are arranged on the baseband board. As Figure 9 shown, one of the chips is, for example, a baseband processor, which is connected to the memory 95 through a bus interface to call the program in the memory 95 and execute the network device operations shown in the above method embodiments.

[0219] The network-side device may further include a network interface 96, and this interface is, for example, a Common Public Radio Interface (CPRI).

[0220] Specifically, the network-side device 900 in the embodiments of the present invention further includes: instructions or programs stored in the memory 95 and executable on the processor 94. The processor 94 calls the instructions or programs in the memory 95 to execute Figure 6 the methods executed by the modules shown, and achieves the same technical effects. To avoid repetition, they will not be elaborated here.

[0221] The embodiments of the present application further provide a readable storage medium. Programs or instructions are stored on the readable storage medium. When the programs or instructions are executed by a processor, the various processes of the above-described embodiments of the RRM measurement processing method are implemented, and the same technical effects can be achieved. To avoid repetition, they will not be elaborated here.

[0222] Wherein, the processor is the processor in the terminal described in the above embodiments. 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 may be a non-transitory readable storage medium.

[0223] The embodiments of the present application further provide 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 the various processes of the above-described embodiments of the RRM measurement processing method, and the same technical effects can be achieved. To avoid repetition, they will not be elaborated here.

[0224] It should be understood that the chip mentioned in the embodiments of the present application may also be referred to as a system-on-chip, system chip, chip system, or system-on-chip, etc.

[0225] The embodiments of the present application further provide 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 the various processes of the above-described embodiments of the RRM measurement processing method, and the same technical effects can be achieved. To avoid repetition, they will not be elaborated here.

[0226] The embodiments of the present application further provide a communication system, including: a terminal and a network-side device. The terminal can be used to execute the steps of the RRM measurement processing method as described above, and the network-side device can be used to execute the steps of the RRM measurement processing method as described above.

[0227] 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 not only includes 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 a..." 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.

[0228] 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 computer software products plus a necessary general hardware platform, and of course, can also be implemented by hardware. The computer software products are stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and include several instructions for causing a terminal or a network-side device to execute the methods described in various embodiments of the present application.

[0229] 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 method for processing radio resource management (RRM) measurements, characterized in that including: The terminal performs RRM measurement prediction based on an artificial intelligence (AI) unit to obtain a prediction result; When the terminal determines that a prediction event is satisfied according to the prediction result, the terminal triggers RRM measurement reporting or conditional handover; wherein the prediction event is an event predicted by the terminal based on the AI unit.

2. The method according to claim 1, wherein The terminal determines that the prediction event is satisfied according to the prediction result, including any one of the following: When the prediction result continuously satisfies the prediction event within a first time period, the terminal determines that the prediction event is satisfied; When the measurement result of RRM measurement continuously satisfies a measurement event within a second time period and the prediction result continuously satisfies the prediction event within a third time period, the terminal determines that the prediction event is satisfied.

3. The method according to claim 2, wherein The method further includes: The terminal receives a first configuration sent by a network-side device and determines the second time period and the third time period according to the first configuration.

4. The method according to claim 3, wherein The first configuration includes any one of the following: The second time period and the third time period; The second time period and the sum or difference between the second time period and the third time period; The third time period and the sum or difference between the third time period and the second time period.

5. The method according to any one of claims 1-4, characterized in that, Before the terminal performs RRM measurement prediction based on the AI unit, the method further includes: The terminal receives a measurement configuration for RRM measurement sent by a network-side device, and at least one reporting configuration of the measurement configuration includes the prediction event.

6. The method according to claim 5, wherein When the terminal determines that a prediction event is satisfied according to the prediction result and triggers conditional handover, it includes any one of the following: When at least one reporting configuration of the measurement configuration includes at least one measurement event and at least one prediction event, the terminal determines that at least one measurement event is satisfied according to the measurement result of RRM measurement and determines that at least one prediction event is satisfied according to the prediction result, and then triggers conditional handover; When at least one reporting configuration of the measurement configuration includes at least one prediction event, the terminal triggers conditional handover when it determines that all prediction events are satisfied according to the prediction result.

7. The method according to claim 6, wherein The reporting configuration including the prediction event is associated with a first measurement identifier, and the first measurement identifier is a measurement identifier associated with the execution condition of the conditional handover.

8. The method according to claim 7, wherein The candidate cell configuration corresponding to the same conditional handover is associated with at least one of the first measurement identifiers, and one first measurement identifier is associated with one prediction event.

9. The method according to claim 5, wherein The reporting configuration or the measurement identifier configuration sent by the network-side device further includes a first identifier, and the first identifier is an identifier of the AI unit or an AI function identifier.

10. The method according to claim 9, characterized in that, The terminal performs RRM measurement prediction based on the AI unit to obtain a prediction result, including: The terminal performs RRM measurement prediction according to the AI unit corresponding to the first identifier to obtain a prediction result.

11. The method according to any one of claims 1 to 10, characterized in that, After triggering RRM measurement reporting or conditional handover, the method further includes: The terminal reports a first measurement report, and the first measurement report includes at least one of the following: The prediction result; The measurement result of RRM measurement.

12. The method according to any one of claims 1-10, characterized in that, The method further includes: The terminal receives a conditional reconfiguration for conditional handover sent by a network-side device; When the terminal determines that a predicted event is met according to the prediction result, triggering condition switching includes: The terminal performs reconfiguration according to the condition, and when it determines that a predicted event is met according to the prediction result, triggers condition switching.

13. A method for processing RRM measurement, characterized in that, Including: The network side device sends a measurement configuration for RRM measurement to the terminal, and at least one reporting configuration of the measurement configuration includes a predicted event; Wherein, the predicted event is an event predicted by the terminal based on the AI unit, and the terminal is used to trigger RRM measurement reporting or condition switching when the prediction result obtained by predicting RRM measurement based on the AI unit meets the predicted event.

14. The method according to claim 13, characterized in that, The reporting configuration including the predicted event is associated with a first measurement identifier, and the first measurement identifier is a measurement identifier associated with the execution condition of the condition switching.

15. The method according to claim 14, wherein At least one of the first measurement identifiers is associated with the candidate cell configuration corresponding to the same condition switching, and one first measurement identifier is associated with one predicted event.

16. The method according to claim 13, characterized in that, The reporting configuration or the measurement identifier configuration sent by the network side device further includes a first identifier, and the first identifier is an identifier of the AI unit or an AI function identifier.

17. The method according to claim 13, wherein The method further includes: The network side device sends a first configuration to the terminal, and the first configuration is used for the terminal to determine a second time and a third time. The second time is used for the terminal to determine whether a measurement event is met according to the measurement result of the RRM measurement, and the third time is used for the terminal to determine whether the predicted event is met according to the predicted result.

18. The method according to claim 17, wherein The first configuration includes any one of the following: The second time and the third time; The second time and the sum or difference between the second time and the third time; The third time and the sum or difference between the third time and the second time.

19. The method according to any one of claims 13-18, characterized in that, The method further includes: The network side device receives a first measurement result reported by the terminal, and the first measurement report includes at least one of the following: The prediction result; The measurement result of the RRM measurement.

20. The method according to any one of claims 13-18, characterized in that The method further includes: The network side device sends a condition reconfiguration for condition switching to the terminal.

21. A RRM measurement processing device, characterized in that, Including: A prediction module, configured to perform RRM measurement prediction based on the AI unit to obtain a prediction result; A trigger module, configured to trigger RRM measurement reporting or condition switching when it is determined that a predicted event is met according to the prediction result; Wherein, the predicted event is an event predicted by the device based on the AI unit.

22. The device according to claim 21, wherein, The trigger module is further configured to perform any one of the following: When the prediction result continuously meets the predicted event within a first time, determine that the predicted event is met; When the measurement result of the RRM measurement continuously meets the measurement event within a second time, and the prediction result continuously meets the predicted event within a third time, determine that the predicted event is met.

23. The device according to claim 22, wherein The device further includes: A first receiving module, configured to receive the first configuration sent by the network side device and determine the second time and the third time according to the first configuration.

24. The device according to any one of claims 21-23, characterized in that, The device further includes: A second receiving module, configured to receive a measurement configuration for RRM measurement sent by a network-side device, where at least one reporting configuration of the measurement configuration includes the prediction event.

25. The device according to claim 24, characterized in that, The triggering module is further configured to perform any one of the following: When at least one measurement event and at least one prediction event are included in at least one reporting configuration of the measurement configuration, when it is determined according to the measurement result of the RRM measurement that at least one measurement event is satisfied and it is determined according to the prediction result that at least one prediction event is satisfied, trigger condition switching; When at least one prediction event is included in at least one reporting configuration of the measurement configuration, when it is determined according to the prediction result that all prediction events are satisfied, trigger condition switching.

26. A RRM measurement processing device, characterized in that, It includes: A sending module, configured to send a measurement configuration for RRM measurement to a terminal, where at least one reporting configuration of the measurement configuration includes a prediction event; Wherein, the prediction event is an event predicted by the terminal based on the AI unit, and the terminal is configured to trigger RRM measurement reporting or condition switching when the prediction result obtained by predicting the RRM measurement according to the AI unit satisfies the prediction event.

27. The device according to claim 26, characterized in that, The sending module is further configured to: Send a first configuration to the terminal, where the first configuration is used for the terminal to determine a second time and a third time, the second time is used for the terminal to determine whether at least one measurement event is satisfied according to the measurement result of the RRM measurement, and the third time is used for the terminal to determine whether the prediction event is satisfied according to the prediction result.

28. The device according to claim 26 or 27, characterized in that, The device further includes: A receiving module, configured to receive a first measurement result reported by the terminal, where the first measurement report includes at least one of the following: The prediction result; The measurement result of the RRM measurement.

29. A terminal, characterized in that, It includes 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 RRM measurement processing method described in any one of claims 1-12 are implemented.

30. A network-side device, characterized in that, It includes 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 RRM measurement processing method described in any one of claims 13-20 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 the processor, the steps of the RRM measurement processing method described in any one of claims 1-12 are implemented, or the steps of the RRM measurement processing method described in any one of claims 13-20 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 RRM measurement processing method described in any one of claims 1-12, or to implement the steps of the RRM measurement processing method described in any one of claims 13-20.

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