WO2025157100A1 - Reporting method and apparatus, receiving method and apparatus, and terminals and network-side device - Google Patents

Abstract

A reporting method and apparatus, a receiving method and apparatus, and terminals and a network-side device, which belong to the technical field of communications. The reporting method in the embodiments comprises: a terminal reporting a first measurement report of RRM measurement to a network-side device, wherein the first measurement report comprises a prediction result of the terminal performing RRM measurement prediction on the basis of an AI unit.

Description

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

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Patent Application No. 202410100482.1 filed on January 24, 2024, and the contents of the above-mentioned Chinese patent application disclosure are hereby incorporated by reference in their entirety as a part of this application.

Technical Field

The present application belongs to the field of communication technology, and specifically relates to a reporting method, a receiving method, an apparatus, a terminal, and a network-side device.

Background Art

With the development of artificial intelligence (AI) technology, AI models have been applied to communication systems, such as AI-based channel state information (CSI) prediction, radio resource management (RRM) prediction, and event prediction. Currently, terminals report RRM measurement results after performing actual measurements. However, the reporting of AI-based RRM prediction results in AI-assisted mobility enhancement is unclear.

Summary of the Invention

The embodiments of the present application provide a reporting method, a receiving method, an apparatus, a terminal, and a network-side device, which can solve the problem in the related art of how to report the RRM prediction results based on AI, which is still unclear.

In a first aspect, a reporting method is provided, which is executed by a terminal, and the method includes:

The terminal reports a first measurement report of the radio resource management RRM measurement to the network side device;

The first measurement report includes a prediction result of the RRM measurement prediction performed by the terminal based on the artificial intelligence AI unit.

In a second aspect, a receiving method is provided, which is performed by a network-side device, and the method includes:

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

The first measurement report includes a prediction result of the RRM measurement prediction performed by the terminal based on the AI unit.

In a third aspect, a reporting device is provided, comprising:

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

The first measurement report includes a prediction result of the RRM measurement prediction performed by the device based on the AI unit.

In a fourth aspect, a receiving device is provided, comprising:

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

The first measurement report includes a prediction result of the RRM measurement prediction performed by the terminal based on the AI unit.

In a fifth aspect, a terminal is provided, comprising a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.

In a sixth aspect, a terminal is provided, including a processor and a communication interface, wherein the communication interface is configured to report a first measurement report of an RRM measurement to a network-side device;

The first measurement report includes a prediction result of the RRM measurement prediction performed by the device based on the AI unit.

In the seventh aspect, a network side device is provided, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the second aspect are implemented.

In an eighth aspect, a network-side device is provided, including a processor and a communication interface, wherein the communication interface is configured to receive a first measurement report of an RRM measurement reported by a terminal;

The first measurement report includes a prediction result of the RRM measurement prediction performed by the terminal based on the AI unit.

In the ninth aspect, a readable storage medium is provided, on which a program or instruction is stored. 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.

In the tenth aspect, a wireless communication system is provided, comprising: a terminal and a network side device, wherein 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.

In the eleventh aspect, a chip is provided, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the method as described in the first aspect, or to implement the method as described in the second aspect.

In the twelfth aspect, a computer program/program product is provided, which 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 to implement the steps of the method described in the second aspect.

In an embodiment of the present application, the terminal is able to report a first measurement report of the RRM measurement to the network side device, where the first measurement report includes a prediction result of the RRM measurement prediction performed by the terminal based on the AI unit, thereby stipulating 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, defines a reporting method for the prediction result, so that the network side device can promptly obtain the prediction result, which helps to improve the communication performance between the terminal and the network side device.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a block diagram of a wireless communication system to which embodiments of the present application may be applied;

FIG2 is a flowchart of a reporting method provided in an embodiment of the present application;

FIG3 is a flow chart of a receiving method provided in an embodiment of the present application;

FIG4 is a structural diagram of a reporting device provided in an embodiment of the present application;

FIG5 is a structural diagram of a receiving device provided in an embodiment of the present application;

FIG6 is a structural diagram of a communication device provided in an embodiment of the present application;

FIG7 is a structural diagram of a terminal provided in an embodiment of the present application;

FIG8 is a structural diagram of a network-side device provided in an embodiment of the present application.

DETAILED DESCRIPTION

The following will be combined with the accompanying drawings in the embodiments of this application to clearly describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, 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 generally of the same type, and do not limit the number of objects, for example, the first object can be one or more. In addition, "or" in this application represents at least one of the connected objects. For example, "A or B" covers three options, namely, Option 1: including A but not including B; Option 2: including B but not including A; Option 3: including both A and B. The character "/" generally indicates that the objects associated before and after are in an "or" relationship.

The term "indication" in this application can be either a direct indication (or explicit indication) or an indirect indication (or implicit indication). A direct indication can be understood as the sender explicitly informing the receiver of specific information, the operation to be performed, or the requested result, etc. in the instruction sent; an indirect indication can be understood as the receiver determining the corresponding information based on the instruction sent by the sender, or making a judgment and determining the operation to be performed or the requested result, etc. based on the judgment result.

It is worth noting 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, but 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 for the systems and radio technologies mentioned above as well as for other systems and radio technologies. The following description describes a New Radio (NR) system for example purposes, and NR terminology is used in most of the following description, but these technologies can also be applied to systems other than NR systems, such as ^6th Generation (6G) communication systems.

FIG1 shows a block diagram of a wireless communication system applicable to embodiments of the present application. 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 computer (Tablet Personal Computer), a laptop computer (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 (Wearable Device), a flight vehicle, a vehicle user equipment (VUE), a ship-borne equipment, a pedestrian user equipment (PUE), a smart home (home appliances with wireless communication functions, such as refrigerators, televisions, washing machines or furniture, etc.), a game console, a personal computer (PC), an ATM or a self-service machine and other terminal-side devices. Wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. Among them, the vehicle-mounted device can also be called a vehicle-mounted terminal, a vehicle-mounted controller, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip or a vehicle-mounted unit, etc. It should be noted that the specific type of the terminal 11 is not limited in the embodiment of the present application. The network side device 12 may include an access network device or a core network device, wherein the access network device may also be called a radio access network (Radio Access Network, RAN) device, a radio access network function or a radio access network unit. The access network device may include a base station, a wireless local area network (Wireless Local Area Network, WLAN) access point (Access Point, AP) or a wireless fidelity (Wireless Fidelity, WiFi) node, etc. Among them, the base station can be called 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, base The Basic Service Set (BSS), Extended Service Set (ESS), home Node B (HNB), home evolved Node B, transmission reception point (TRP) or other appropriate terms in the relevant field, as long as the same technical effect is achieved, the base station is not limited to specific technical vocabulary. It should be noted that in the embodiments of the present application, only the base station in the NR system is introduced as an example, and the specific type of the base station is not limited.

In order to better understand the technical solution of the present application, the relevant concepts involved in the embodiments of the present application are explained below.

Artificial Intelligence (AI):

AI has been widely applied in various fields. Integrating AI into wireless communication networks to significantly improve technical indicators such as throughput, latency, and user capacity is a key task for future wireless communication networks. AI modules can be implemented in a variety of ways, such as neural networks, decision trees, support vector machines, and Bayesian classifiers. This application uses neural networks as an example, but does not limit the specific type of AI module.

It should be noted that the AI unit/AI model (Model) described in this application may also be referred to as an AI unit, AI model, machine learning (ML) model, ML unit, AI structure, AI function, AI feature, machine learning model, neural network, neural network function, neural network function, etc., or the AI unit/AI model may also refer to a processing unit that can implement specific algorithms, formulas, processing procedures, 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 GPU, NPU, TPU, ASIC, etc., and this application does not make specific restrictions on this. Optionally, the specific data set includes the input and/or output of the AI unit/AI model.

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 an identifier of a specific data set associated with the AI unit/AI model, or an identifier of a specific scenario, environment, channel feature, or device related to the AI/ML, or an identifier of a function, feature, capability, or module related to the AI/ML. This application does not specifically limit this.

AI functionality: An AI algorithm function, which may include multiple AI models.

RRM measurement reporting:

The measurement configuration mainly consists of the measurement object, reporting configuration and measurement identifier (ID).

Measurement Object: such as the frequency point to be measured;

Report Config: includes reporting criteria (periodic/event-triggered), reference signal type (such as Synchronization Signal and PBCH block (SSB)/Channel State Information Reference Signal (CSI-RS)), measurement reporting quantity (such as 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.

Measurement ID: 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.

The reporting configuration can include event-triggered reporting. The events defined in NR are shown in Table 1 below:

Table 1

Taking Event A3 as an example, the meanings of the parameters for the entry and exit conditions of Event A3 are as follows:

Mn: Neighboring cell measurement result, without considering any offset;

Ofn: Neighboring cell measurement object specific offset;

Ocn: neighboring cell-level specific offset;

Mp: SpCell (primary serving cell) measurement result, without considering any offset;

Ofp: SpCell measurement object specific offset;

Ocp: SpCell cell-level specific offset;

Hys: hysteresis parameter of the event;

Off: The offset parameter of the event.

It should be noted that the meanings of other parameters involved in the above Table 1 can be referred to related technologies and will not be repeated here.

If the reporting type is event-triggered, in order to avoid frequent reporting or ping-pong switching, 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 within the timeToTrigger time meets the entry conditions of the event, the measurement report is triggered.

For conditional handover, the UE uses a cell that meets the conditions as a triggering cell and selects one of the triggering cells to perform conditional reconfiguration.

The relevant technology has specified how the terminal performs RRM measurements and how to trigger the reporting of RRM measurement results. However, in AI-assisted mobility enhancement, it is currently unclear how to report the AI-based RRM prediction results.

The following, in conjunction with the accompanying drawings, describes in detail the reporting method, apparatus, terminal, network-side equipment, etc. provided in the embodiments of the present application through some embodiments and their application scenarios.

Please refer to Figure 2, which is a flowchart of a reporting method provided in an embodiment of the present application, wherein the method is applied to a terminal. As shown in Figure 2, the method includes the following steps:

Step 201: The terminal reports a first measurement report of RRM measurement to a network-side device, wherein the first measurement report includes a prediction result of RRM measurement prediction performed by the terminal based on an AI unit.

In an 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 performed by the terminal based on the AI unit. The prediction result is obtained by the terminal through the AI unit to perform RRM measurement prediction, rather than the actual RRM measurement result. For example, the prediction result is the result of the terminal performing RRM measurement prediction through the AI unit at a certain moment or time period in the future.

It should be noted that the terminal can report the prediction result in real time. For example, after the terminal obtains the prediction result through the first measurement report after performing RRM measurement prediction based on the AI unit; or the terminal can report the prediction result once every preset time period. This application does not make specific restrictions on this.

In an embodiment of the present application, the terminal is able to report a first measurement report of the RRM measurement to the network side device, where the first measurement report includes a prediction result of the RRM measurement prediction performed by the terminal based on the AI unit, thereby stipulating 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, defines a reporting method for the prediction result, so that the network side device can promptly obtain the prediction result, which helps to improve the communication performance between the terminal and the network side device.

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

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

(2) a cell signal quality of the first cell predicted by the terminal based on the AI unit;

(3) a predicted time corresponding to the beam quality or cell signal quality of the first cell predicted by the terminal based on the AI unit, the predicted time including at least one moment or at least one time period; wherein the at least one moment is at least one moment in the future, and the at least one time period is at least one time period in the future;

(4) The terminal predicts at least one target cell ID based on the AI unit. The cell ID may be the target cell's physical cell identifier (PCI), NR cell global identifier (NR CGI), frequency + PCI, or a configuration ID associated with the target cell configuration;

(5) a handover time of at least one target cell predicted by the terminal based on the AI unit, such as an optimal handover time;

(6) a first indication, the first indication being used to indicate that a first condition is satisfied, the first condition being an entry condition or an exit condition of a measurement event predicted by the terminal based on the AI unit, wherein the measurement event may be an event as listed in Table 1; for example, the measurement event is an A1 event, and the first indication is an indication that the entry condition or the exit condition of the A1 event is satisfied, as predicted by the terminal based on the AI unit;

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

(8) a second indication, where the second indication is used to indicate that a radio link failure will occur, as predicted by the terminal based on the AI unit. That is, the second indication is an indication that a radio link failure will occur, as predicted by the terminal based on the AI unit.

(9) the time of occurrence of the wireless link failure predicted by the terminal based on the AI unit;

(10) a third indication, the third indication being used to indicate that a handover failure occurs when the terminal switches to the target cell based on the prediction by the AI unit. That is, the third indication is an indication that a handover failure occurs when the terminal switches to the target cell based on the prediction by the AI unit.

(11) the moment when the terminal fails to switch to the target cell based on the prediction of the AI unit;

The first cell includes at least one of the following: a source cell, at least one neighboring cell, at least one candidate switching cell, and at least one target cell to be switched.

It should be noted that the time corresponding to the prediction result (such as the above-mentioned prediction time, switching time, occurrence time, etc.) can be expressed in the following ways:

The system frame corresponding to the prediction result (e.g., reference system frame number (SFN)), ranging from 0 to 1023, such as indicated by 10 bits;

A portion of bits intercepted from the system frame corresponding to the prediction result;

The subframe or time slot where the prediction result is located in the corresponding system frame;

The reference time corresponding to the prediction result, which can be expressed in hours, minutes, seconds, milliseconds, and microseconds;

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 time slot difference, or the absolute time difference, etc.

Optionally, in the above prediction results, the beam quality (of the first cell) 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.

Exemplarily, taking the first cell as the source cell as an example, the terminal predicts the cell signal quality of the source cell at a certain moment in the future based on the AI unit, and obtains the cell signal quality of the source cell at a certain moment in the future. In this case, the prediction result obtained by the terminal based on the AI unit may be the difference between the predicted value and the reference value, and the terminal may report the difference instead of directly reporting the predicted value. It should be noted that the reference value may be a value pre-agreed upon by the terminal and the network-side device, for example, the reference value is the cell signal quality actually measured by the source cell in the first measurement report reported by the terminal. The current measurement report may refer to a measurement report obtained by the terminal performing RRM measurement. In an embodiment of the present application, the terminal characterizes the beam quality or cell signal quality of the first cell predicted by the terminal based on the AI unit by reporting the difference. 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.

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

(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;

(2) The first predicted value of each cell in the first measurement report. It can be understood that the terminal can predict the beam quality or cell signal quality of each cell at several future moments or a certain time period based on the AI unit, and obtain multiple predicted beam qualities or predicted cell signal qualities. The first predicted value is the first predicted beam quality or predicted cell signal quality of each cell obtained by the terminal based on the RRM measurement prediction performed by the AI unit;

(3) the maximum predicted value for each cell in the first measurement report, such as the maximum predicted beam quality or predicted cell signal quality for each cell among the predicted values obtained by the terminal based on the RRM measurement prediction performed by the AI unit;

(4) the measurement value of the second cell in the first measurement report;

(5) the first predicted value of the second cell in the first measurement report;

(6) the maximum predicted value of the second cell in the first measurement report;

(7) the maximum measurement value in the first measurement report, such as the maximum beam quality or maximum cell signal quality among the actual beam qualities or actual cell signal qualities obtained by the terminal through RRM measurement;

(8) the maximum predicted value in the first measurement report, such as the maximum predicted beam quality among the predicted beam qualities 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 qualities;

The second cell is any one of the first cells (such as a source cell or a neighboring cell, etc.), and the measurement value is the beam quality or cell signal quality actually measured by the terminal.

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

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

(b) The fifth indication in the measurement configuration sent by the network side device, such as the network side device sending the fifth indication to the terminal, is used to indicate which second cell is, so that the terminal can determine which cell's measurement value or the first predicted value or the maximum predicted value the reference value should correspond to, to ensure that the terminal obtains the difference between the reference value and the predicted value obtained by performing RRM measurement prediction based on the AI unit, and thus reports the difference.

Optionally, the method further includes:

The terminal receives a first configuration sent by a network-side device, where the first configuration includes a first threshold;

Wherein, when the difference between the terminal's predicted value for the third cell and the terminal's measured value for the third cell is less than the first threshold, the prediction result does not include the terminal's predicted value for the third cell; or,

When a difference between a predicted value of a third cell by the terminal at a first time and a predicted value of the third cell at a second time is less than the first threshold, the predicted value of the third cell by the terminal at the second time is not included in the prediction result, and the second time is later than the first time.

The third cell is any one of the first cells, and the measurement value is an actual measurement value obtained by the terminal performing RRM measurement.

For example, taking the third cell as the source cell, if the difference between the predicted beam quality obtained by the terminal based on the AI unit to perform RRM measurement on the source cell and the actual beam quality obtained by the terminal to perform RRM measurement on the source cell is less than the first threshold, the terminal does not report the predicted beam quality.

Alternatively, the terminal obtains a first predicted beam quality based on the RRM measurement prediction of the source cell by the AI unit at a first time (for example, a certain moment), and obtains a second predicted beam quality based on the RRM measurement prediction of the source cell by 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.

In an embodiment of the present application, the network side device configures the first threshold for the terminal, thereby instructing the terminal to report the prediction results obtained by RRM measurement prediction based on the AI unit, as well as which prediction results can be reported and which prediction results do not need to be reported. For example, when the prediction result does not change much compared to the measurement result or the previous prediction result, it does not need to be reported, thereby helping to save the terminal's reporting overhead.

Optionally, the first measurement report reported by the terminal also includes at least one of the following:

(1) the measurement results of the RRM measurement performed by the terminal, such as the aforementioned measurement values;

(2) a sixth indication, where the sixth indication is used to indicate that there is no valid prediction result. In this case, the terminal may not report the prediction result but only report the sixth indication. The network-side device can learn from the sixth indication that the RRM measurement prediction performed by the terminal based on the AI unit has not obtained a valid prediction result.

(3) the inference accuracy (also called prediction accuracy) of the AI unit in performing RRM measurement predictions;

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

The reasoning accuracy may include at least one of the following:

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

Mean square error (MSE) between the prediction result and the measurement result;

The root mean square error (RSME) between the prediction result and the measurement result;

The cosine similarity between the prediction result and the measurement result.

In an embodiment of the present application, the first measurement report reported by the terminal also includes at least one of the items described above, so that the network-side device can also obtain the measurement results of the RRM measurement performed by the terminal, so that the terminal can report the actual measurement results of the RRM measurement and the prediction results of the RRM measurement prediction based on the AI unit to the network-side device through the first measurement report, so that the network-side device can refer to the measurement results and the prediction results at the same time, thereby helping the network-side device to make more accurate signal quality judgments and switching decisions for the terminal. Alternatively, the first measurement report reported by the terminal includes the reasoning accuracy of the RRM measurement prediction performed by the AI unit and/or the credibility or confidence of the prediction result, so that the network-side device can directly obtain the reasoning accuracy and/or the credibility or confidence of the prediction result, and the network-side device can determine the reliability of the prediction result based on this information, which helps the network-side device to make more accurate signal quality judgments and switching decisions for the terminal.

Optionally, in an embodiment of the present application, the method further includes:

The terminal receives a second configuration sent by the network-side device, 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 first measurement report includes the prediction result.

In an embodiment of the present application, the terminal reports the prediction result of the RRM measurement prediction based on the AI unit only when the reasoning accuracy of the RRM measurement prediction performed by the AI unit is greater than or equal to the second threshold according to the second threshold configured by the network side device. If the reasoning accuracy is less than the second threshold, the terminal may not report the prediction result, thereby helping to save the reporting overhead of the terminal.

Optionally, each AI unit corresponds to a second threshold, or each AI function corresponds to a second threshold. For example, 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 in standardizing the reporting behavior of the terminal.

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;

The first condition includes at least one of the following:

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;

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;

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

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, reporting configuration, and measurement identifier.

Exemplarily, the terminal determines whether the first measurement report needs to carry the prediction result based on 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.

For another example, the terminal determines whether the first measurement report needs to carry the prediction result based on 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 AI unit identifier or the AI function identifier. For example, if the measurement object is associated with the AI unit identifier, the first measurement report reported by the terminal carries the prediction result.

Alternatively, if the reporting configuration is associated with a first event, the first event is an event in which the terminal evaluates whether an event is satisfied based on a prediction result. In this case, the first measurement report reported by the terminal carries the prediction result. The event refers to the measurement event described above, such as an A1 event, an 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.

In the embodiment of the present application, the terminal can determine whether the first measurement report needs to carry the prediction result, that is, whether the prediction result needs to be reported, based on whether the first condition is met. This is more helpful to standardize the terminal's reporting behavior for the prediction result.

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

In order to better understand the technical solution of the present application, several specific embodiments are provided below for detailed description.

Example 1:

The terminal carries the prediction result of the 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 may be at least one of the following:

(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;

(2) the measured value/first predicted value/maximum predicted value of the second cell in the first measurement report, where 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 device in the first measurement report;

(3) The maximum measured value/maximum predicted value among all cells in the first measurement report.

If the reference value is the maximum predicted value of a 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. When the identifier exists, it means that the current measurement result is the reference value.

In one embodiment, the first measurement report needs to report the measured values of cell A and cell B and their predicted values for the next 1s and 2s, as shown in Table 2 below:

Table 2

For example, in Case 1-1, the reference value is the measured value of each cell. The reference values are A0 for cell A and B0 for cell B. The reported values are shown in Table 3 below:

Table 3

Case 1-2: The reference value is the first predicted value for each cell. The reference values are A1 for cell A and B1 for cell B. The reported values are shown in Table 4 below:

Table 4

Case 1-3: The reference value is the maximum predicted value for each cell. If A2>A1 and B2>B1, then the reference values are A2 for cell A and B2 for cell B, respectively. The reported values are shown in Table 5 below:

Table 5

Case 1-4: The reference value is the first predicted value of the second cell. If the second cell is cell A, then the reference value is A0 of cell A. The reported values are shown in Table 6 below:

Table 6

Case 1-5: The reference value is the maximum predicted value of the second cell. If the second cell is cell A, and A2>A1, then the reference value is A2 of cell A. The reported values are shown in Table 7 below:

Table 7

Case 1-6: The reference value is the maximum measured value of all cells. If A0>B0, the reference value is A0. The reported values are shown in Table 8 below:

Table 8

Case 1-7: 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 shown in Table 9 below:

Table 9

It should be noted that the measured values and predicted values are all quantized values.

Example 2:

The terminal determines whether the first measurement report reported includes the prediction result according to whether the measurement configuration is associated with the AI unit or AI function.

The measurement configuration may be associated with an AI unit or AI function in the following ways:

Method 1: measurement configuration/measurement identity/measurement object/reporting configuration associated prediction indication (ie, the seventh indication mentioned above).

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;

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

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

The prediction indication may also indicate a prediction type, which 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 the protocol pre-definition.

Method 2: Measurement configuration/measurement identifier/measurement object/reporting configuration associated with the AI unit identifier or AI function identifier.

The AI unit identifier or AI function identifier is used to indicate to the terminal that the measurement report associated with the measurement ID/measurement object/reporting configuration needs to carry the prediction result;

The terminal uses the indicated AI unit or the AI unit corresponding to the AI function identifier to obtain a 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 AI unit identifier or the AI function identifier.

Preferably, the measurement object carries an identifier of an associated AI unit or an AI function identifier.

Method 3: Reporting the configuration-related prediction event (ie, the first event mentioned above).

If the reporting configuration is associated with event-triggered reporting, and the event is a predicted event that triggers measurement reporting, the terminal evaluates whether the predicted event is met based on the prediction result, and triggers measurement reporting if it is met.

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

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

It should be noted that the above three methods can be used in combination. For example, for method 1 and method 3, the measurement configuration/measurement identifier/measurement object/reporting configuration can also be associated with the AI unit identifier or AI function identifier;

When the measurement configuration/measurement identifier/measurement object/reporting configuration is not associated with the AI unit identifier or AI function identifier, the predicted AI unit or AI function to be used can be determined according to the protocol pre-definition, or the UE and the network can obtain it through interaction before obtaining the measurement configuration.

The present application also provides a receiving method. Please refer to Figure 3, which is a flow chart of a receiving method provided by the present application, and the method is applied to a network-side device. As shown in Figure 3, the method includes the following steps:

Step 301: A network-side device receives a first measurement report of an RRM measurement reported by a terminal; wherein the first measurement report includes a prediction result of an RRM measurement prediction performed by the terminal based on an AI unit.

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

The terminal predicts a beam quality of the first cell based on the AI unit;

A cell signal quality of the first cell predicted by the terminal based on the AI unit;

A predicted 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 predicted time includes at least one moment or the at least one time period;

A cell ID of at least one target cell predicted by the terminal based on the AI unit;

The terminal predicts a switching time of at least one target cell based on the AI unit;

A first indication, where the first indication is used to indicate that a first condition is satisfied, where the first condition is an entry condition or an exit condition of a measurement event predicted by the terminal based on the AI unit;

The moment when the first condition is met;

a second indication, where the second indication is used to indicate to the terminal that a radio link failure will occur based on a prediction by the AI unit;

The terminal predicts the occurrence time of the wireless link failure based on the AI unit;

A third indication, the third indication being used to indicate that a handover failure occurs to the target cell predicted by the AI unit;

The moment when the terminal fails to switch to the target cell based on the prediction of the AI unit;

The first cell includes at least one of the following: a source cell, at least one neighboring cell, at least one candidate switching cell, and at least one target cell to be switched.

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.

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

a measurement value of each cell in the first measurement report;

a first predicted value for each cell in the first measurement report;

a maximum predicted value of each cell in the first measurement report;

a measurement value of the second cell in the first measurement report;

a first predicted value of the second cell in the first measurement report;

a maximum predicted value of the second cell in the first measurement report;

a maximum measurement value in the first measurement report;

a maximum predicted value in the first measurement report;

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.

Optionally, the second cell is indicated by a fourth indication in the first measurement report as follows; or,

The method further comprises:

The network-side device sends a measurement configuration to the terminal, where the measurement configuration includes a fifth indication for indicating the second cell.

Optionally, the method further includes:

The network-side device sends a first configuration to the terminal, where the first configuration includes a first threshold;

Wherein, when the difference between the terminal's predicted value for the third cell and the terminal's measured value for the third cell is less than the first threshold, the prediction result does not include the terminal's predicted value for the third cell; or,

When a difference between a predicted value of a third cell by the terminal at a first time and a predicted value of the third cell at a second time is less than the first threshold, the predicted value of the third cell by the terminal at the second time is not included in the prediction result, and the second time is later than the first time.

The third cell is any one of the first cells.

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

a measurement result of RRM measurement performed by the terminal;

a sixth indication, the sixth indication being used to indicate that there is no valid prediction result;

The inference accuracy of the AI unit in performing RRM measurement predictions;

The credibility or confidence of the prediction result.

Optionally, the method further includes:

The network-side device sends a second configuration to the 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 first measurement report includes the prediction result.

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

It should be noted that the receiving method provided in the embodiment of the present application corresponds to the reporting method on the aforementioned terminal side. The relevant concepts and specific implementation processes involved in the embodiment of the present application can be referred to the description in the terminal side method embodiment, and will not be repeated in this embodiment.

In an embodiment of the present application, a network-side device receives a first measurement report of an RRM measurement reported by a terminal. The first measurement report includes a prediction result of the RRM measurement prediction performed by the device based on the AI unit, thereby stipulating 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, defines a reporting method for the prediction result, so that the network-side device can obtain the prediction result in a timely manner, which helps to improve the communication performance between the terminal and the network-side device.

The reporting method provided in the embodiment of the present application can be executed by a reporting device. In the embodiment of the present application, the reporting device provided in the embodiment of the present application is described by taking the reporting method performed by the reporting device as an example.

Please refer to FIG4 , which is a structural diagram of a reporting device provided in an embodiment of the present application. As shown in FIG4 , the reporting device 400 includes:

A reporting module 401 is configured to report a first measurement report of an RRM measurement to a network-side device;

The first measurement report includes a prediction result of the RRM measurement prediction performed by the device based on the AI unit.

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

The device predicts a beam quality of the first cell based on the AI unit;

The device predicts a cell signal quality of the first cell based on the AI unit;

The device predicts, based on the AI unit, a predicted time corresponding to the beam quality or cell signal quality of the first cell, where the predicted time includes at least one moment or the at least one time period;

The device predicts a cell identification ID of at least one target cell based on the AI unit;

The device predicts a switching time of at least one target cell based on the AI unit;

a first indication, the first indication being used to indicate that a first condition is satisfied, the first condition being an entry condition or an exit condition of a measurement event predicted by the device based on the AI unit;

The moment when the first condition is met;

a second indication, the second indication being used to indicate to the apparatus that a radio link failure will occur based on a prediction by the AI unit;

The device predicts the occurrence time of wireless link failure based on the AI unit;

a third indication, the third indication being used to indicate that a handover to a target cell predicted by the AI unit by the apparatus fails;

The device predicts a handover failure to the target cell based on the AI unit;

The first cell includes at least one of the following: a source cell, at least one neighboring cell, at least one candidate switching cell, and at least one target cell to be switched.

Optionally, the beam quality or the cell signal quality predicted by the device 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 device based on the AI unit.

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

a measurement value of each cell in the first measurement report;

a first predicted value for each cell in the first measurement report;

a maximum predicted value of each cell in the first measurement report;

a measurement value of the second cell in the first measurement report;

a first predicted value of the second cell in the first measurement report;

a maximum predicted value of the second cell in the first measurement report;

a maximum measurement value in the first measurement report;

a maximum predicted value in the first measurement report;

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 device.

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

a fourth indication in the first measurement report;

The fifth indication in the measurement configuration sent by the network side device.

Optionally, the device further comprises:

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

Wherein, 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 value of the device for the third cell is not included in the prediction result; or,

When a difference between a predicted value of a third cell by a device at a first time and a predicted value of the third cell at a second time is less than the first threshold, the predicted value of the third cell by the device at the second time is not included in the prediction result, and the second time is later than the first time.

The third cell is any one of the first cells.

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

a measurement result of RRM measurement performed by the apparatus;

a sixth indication, the sixth indication being used to indicate that there is no valid prediction result;

The inference accuracy of the AI unit in performing RRM measurement predictions;

The credibility or confidence of the prediction result.

Optionally, the device further comprises:

a receiving module, configured to receive a second configuration sent by a network-side device, 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 first measurement report includes the prediction result.

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

Optionally, when the first condition is met, the first measurement report includes the prediction result;

The first condition includes at least one of the following:

The first object is associated with a seventh indication, the seventh indication being used to instruct the apparatus to perform RRM measurement prediction based on the AI unit;

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;

The reporting configuration of the RRM measurement is associated with a first event, the first event being an event that the apparatus evaluates based on the prediction result as to whether an event is satisfied;

The first object includes at least one of the following: measurement configuration, measurement identifier, measurement object, and reporting configuration.

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

In an embodiment of the present application, the device is capable of reporting a first measurement report of the RRM measurement to the network side device, where the first measurement report includes a prediction result of the RRM measurement prediction performed by the device based on the AI unit, thereby stipulating that in AI-assisted mobility enhancement, the prediction result of the RRM measurement prediction performed based on the AI unit is reported 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 obtain the prediction result in a timely manner.

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 it can be other devices other than a terminal. For example, the terminal can include but is not limited to the types of terminals 11 listed above, and other devices can be servers, network attached storage (NAS), etc., which are not specifically limited in the embodiment of the present application.

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

The receiving method provided in the embodiment of the present application can be executed by a receiving device. In the embodiment of the present application, the receiving device provided in the embodiment of the present application is described by taking the receiving method executed by the receiving device as an example.

Please refer to FIG5 , which is a structural diagram of a receiving device provided in an embodiment of the present application. As shown in FIG5 , the receiving device 500 includes:

The receiving module 501 is configured to receive a first measurement report of an RRM measurement reported by a terminal;

The first measurement report includes a prediction result of the RRM measurement prediction performed by the terminal based on the AI unit.

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

The terminal predicts a beam quality of the first cell based on the AI unit;

A cell signal quality of the first cell predicted by the terminal based on the AI unit;

A predicted 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 predicted time includes at least one moment or the at least one time period;

A cell ID of at least one target cell predicted by the terminal based on the AI unit;

The terminal predicts a switching time of at least one target cell based on the AI unit;

A first indication, where the first indication is used to indicate that a first condition is satisfied, where the first condition is an entry condition or an exit condition of a measurement event predicted by the terminal based on the AI unit;

The moment when the first condition is met;

a second indication, where the second indication is used to indicate to the terminal that a radio link failure will occur based on a prediction by the AI unit;

The terminal predicts the occurrence time of the wireless link failure based on the AI unit;

A third indication, the third indication being used to indicate that a handover failure occurs to the target cell predicted by the AI unit;

The moment when the terminal fails to switch to the target cell based on the prediction of the AI unit;

The first cell includes at least one of the following: a source cell, at least one neighboring cell, at least one candidate switching cell, and at least one target cell to be switched.

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.

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

a measurement value of each cell in the first measurement report;

a first predicted value for each cell in the first measurement report;

a maximum predicted value of each cell in the first measurement report;

a measurement value of the second cell in the first measurement report;

a first predicted value of the second cell in the first measurement report;

a maximum predicted value of the second cell in the first measurement report;

a maximum measurement value in the first measurement report;

a maximum predicted value in the first measurement report;

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.

Optionally, the second cell is indicated by a fourth indication in the first measurement report as follows; or,

The device further comprises:

The first sending module is configured to send a measurement configuration to the terminal, where the measurement configuration includes a fifth indication for indicating the second cell.

Optionally, the device further comprises:

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

Wherein, when the difference between the terminal's predicted value for the third cell and the terminal's measured value for the third cell is less than the first threshold, the prediction result does not include the terminal's predicted value for the third cell; or,

When a difference between a predicted value of a third cell by the terminal at a first time and a predicted value of the third cell at a second time is less than the first threshold, the predicted value of the third cell by the terminal at the second time is not included in the prediction result, and the second time is later than the first time.

The third cell is any one of the first cells.

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

a measurement result of RRM measurement performed by the terminal;

a sixth indication, the sixth indication being used to indicate that there is no valid prediction result;

The inference accuracy of the AI unit in performing RRM measurement predictions;

The credibility or confidence of the prediction result.

Optionally, the device further comprises:

A third sending module, configured to send a second configuration to the 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 first measurement report includes the prediction result.

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

The receiving device provided in the embodiment of the present application can implement each process implemented by the network side device in the method embodiment of Figure 3 and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

As shown in Figure 6, an embodiment of the present application further provides a communication device 600, including a processor 601 and a memory 602. The memory 602 stores a program or instruction that can be run on the processor 601. For example, when the communication device 600 is a terminal, the program or instruction is executed by the processor 601 to implement the various steps of the above-mentioned reporting method embodiment and can achieve the same technical effect. When the communication device 600 is a network-side device, the program or instruction is executed by the processor 601 to implement the various steps of the above-mentioned receiving method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

The present application also provides a terminal including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to execute a program or instruction to implement the steps of the method embodiment shown in FIG2 . This terminal embodiment corresponds to the aforementioned terminal-side method embodiment, and the various implementation processes, implementation methods, and related concepts of the aforementioned method embodiment are applicable to this terminal embodiment and can achieve the same technical effects. Specifically, FIG7 is a schematic diagram of the hardware structure of a terminal implementing an embodiment of the present application.

The terminal 700 includes but is not limited to: 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 at least some of the components of the processor 710.

Those skilled in the art will appreciate that the terminal 700 may also include a power supply (such as a battery) to power various components. The power supply may be logically connected to the processor 710 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The terminal structure shown in FIG7 does not limit the terminal. The terminal may include more or fewer components than shown, or may combine certain components, or have different component arrangements, which will not be described in detail here.

It should be understood that in the embodiment of the present application, the input unit 704 may include a graphics processing unit (GPU) 7041 and a microphone 7042, and the graphics processor 7041 processes the image data of the static picture or video obtained by the image capture device (such as a camera) in the video capture mode or the 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 a touch panel 7071 and at least one of the 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. 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 repeated here.

In the embodiment of the present application, after receiving downlink data from a network-side device, the RF unit 701 may transmit the data to the processor 710 for processing. Furthermore, the RF unit 701 may send uplink data to the network-side device. Typically, the RF unit 701 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, and the like.

The memory 709 can be used to store software programs or instructions and 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. The first storage area may store an operating system, applications or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory 709 may include a volatile memory or a non-volatile memory. 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 random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM (DRRAM). The memory 709 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

Processor 710 may include one or more processing units. Optionally, processor 710 integrates an application processor and a modem processor. The application processor primarily handles operations related to the operating system, user interface, and application programs, while the modem processor primarily processes wireless communication signals, such as a baseband processor. It is understood that the modem processor may not be integrated into processor 710.

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 the prediction result of the RRM measurement prediction performed by the terminal based on the AI unit.

In an embodiment of the present application, the terminal is able to report a first measurement report of the RRM measurement to the network side device, where the first measurement report includes a prediction result of the RRM measurement prediction performed by the terminal based on the AI unit, thereby stipulating 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, defines a reporting method for the prediction result, so that the network side device can promptly obtain the prediction result, which helps to improve the communication performance between the terminal and the network side device.

It can be understood that the implementation process of each implementation method mentioned in this embodiment can refer to the relevant description of the above-mentioned reporting method embodiment and achieve the same or corresponding technical effects. To avoid repetition, it will not be repeated here.

The present application also provides a network-side device, including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to execute a program or instruction to implement the steps of the method embodiment shown in FIG3 . This network-side device embodiment corresponds to the aforementioned network-side device method embodiment, and each implementation process and implementation method of the aforementioned method embodiment are applicable to this network-side device embodiment and can achieve the same technical effects.

Specifically, embodiments of the present application also provide a network-side device. As shown in Figure 8, 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. Antenna 81 is connected to radio frequency device 82. In the uplink direction, radio frequency device 82 receives information via antenna 81 and sends the received information to baseband device 83 for processing. In the downlink direction, baseband device 83 processes the information to be transmitted and sends it to radio frequency device 82. Radio frequency device 82 processes the received information and then sends it through antenna 81.

The method executed by the network-side device in the above embodiment may be implemented in the baseband device 83 , which includes a baseband processor.

The baseband device 83 may include, for example, at least one baseband board, on which multiple chips are arranged, as shown in Figure 8, 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 and execute the network side device operations shown in the above method embodiment.

The network side device may also include a network interface 86, which is, for example, a Common Public Radio Interface (CPRI).

Specifically, the network side device 800 of the embodiment of the present application also includes: instructions or programs stored in the memory 85 and can be run on the processor 84. The processor 84 calls the instructions or programs in the memory 85 to execute the methods executed by each module shown in Figure 5 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned reporting method or receiving method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

The processor is the processor in the terminal described in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. In some examples, the readable storage medium may be a non-transitory readable storage medium.

An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned reporting method or receiving method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

An embodiment of the present application further provides a computer program/program product, which 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-mentioned reporting method or receiving method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

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

It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the 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 opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted or combined. In addition, the features described with reference to certain examples may be combined in other examples.

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

The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms of implementation methods without departing from the purpose of this application and the scope of protection of the claims. These implementation methods are all within the protection of this application.

Claims (36)

A reporting method, comprising: The terminal reports a first measurement report of the radio resource management RRM measurement to the network side device; The first measurement report includes a prediction result of the RRM measurement prediction performed by the terminal based on the artificial intelligence AI unit. The method according to claim 1, wherein the prediction result includes at least one of the following: The terminal predicts a beam quality of the first cell based on the AI unit; A cell signal quality of the first cell predicted by the terminal based on the AI unit; A predicted 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 predicted time includes at least one moment or the at least one time period; The terminal predicts at least one target cell ID based on the AI unit; The terminal predicts a switching time of at least one target cell based on the AI unit; A first indication, where the first indication is used to indicate that a first condition is satisfied, where the first condition is an entry condition or an exit condition of a measurement event predicted by the terminal based on the AI unit; The moment when the first condition is met; a second indication, where the second indication is used to indicate to the terminal that a radio link failure will occur based on a prediction by the AI unit; The terminal predicts the occurrence time of the wireless link failure based on the AI unit; A third indication, the third indication being used to indicate that a handover failure occurs to the target cell predicted by the AI unit; The moment when the terminal fails to switch to the target cell based on the prediction of the AI unit; The first cell includes at least one of the following: a source cell, at least one neighboring cell, at least one candidate switching cell, and at least one target cell to be switched. The method according to claim 2, wherein 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. The method according to claim 3, wherein the reference value includes at least one of the following: a measurement value of each cell in the first measurement report; a first predicted value for each cell in the first measurement report; a maximum predicted value of each cell in the first measurement report; a measurement value of the second cell in the first measurement report; a first predicted value of the second cell in the first measurement report; a maximum predicted value of the second cell in the first measurement report; a maximum measurement value in the first measurement report; a maximum predicted value in the first measurement report; 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. The method according to claim 4, wherein the second cell is indicated by at least one of the following: a fourth indication in the first measurement report; The fifth indication in the measurement configuration sent by the network side device. The method according to claim 4, wherein the method further comprises: The terminal receives a first configuration sent by a network-side device, where the first configuration includes a first threshold; Wherein, when the difference between the terminal's predicted value for the third cell and the terminal's measured value for the third cell is less than the first threshold, the prediction result does not include the terminal's predicted value for the third cell; or, When a difference between a predicted value of a third cell by the terminal at a first time and a predicted value of the third cell at a second time is less than the first threshold, the predicted value of the third cell by the terminal at the second time is not included in the prediction result, and the second time is later than the first time. The third cell is any one of the first cells. The method according to any one of claims 1 to 6, wherein the first measurement report further includes at least one of the following: a measurement result of RRM measurement performed by the terminal; a sixth indication, the sixth indication being used to indicate that there is no valid prediction result; The inference accuracy of the AI unit in performing RRM measurement predictions; The credibility or confidence of the prediction result. The method according to any one of claims 1 to 7, wherein the method further comprises: The terminal receives a second configuration sent by the network-side device, 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 first measurement report includes the prediction result. The method according to claim 8, wherein each of the AI units corresponds to one second threshold, or each of the AI functions corresponds to one second threshold. The method according to any one of claims 1 to 9, wherein, when a first condition is met, the first measurement report reported by the terminal includes the prediction result; The first condition includes at least one of the following: 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; 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; The reporting configuration of the RRM measurement is associated with a first event, where the first event is an event in which the terminal evaluates whether an event is satisfied according to the prediction result; The first object includes at least one of the following: measurement configuration, measurement identifier, measurement object, and reporting configuration. The method according to claim 10, wherein the seventh indication is further used to indicate a prediction type of RRM measurement prediction performed by the terminal based on the AI unit. A receiving method, comprising: The network-side device receives a first measurement report of the RRM measurement reported by the terminal; The first measurement report includes a prediction result of the RRM measurement prediction performed by the terminal based on the AI unit. The method according to claim 12, wherein the prediction result includes at least one of the following: The terminal predicts a beam quality of the first cell based on the AI unit; A cell signal quality of the first cell predicted by the terminal based on the AI unit; A predicted 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 predicted time includes at least one moment or the at least one time period; A cell ID of at least one target cell predicted by the terminal based on the AI unit; The terminal predicts a switching time of at least one target cell based on the AI unit; A first indication, where the first indication is used to indicate that a first condition is satisfied, where the first condition is an entry condition or an exit condition of a measurement event predicted by the terminal based on the AI unit; The moment when the first condition is met; a second indication, where the second indication is used to indicate to the terminal that a radio link failure will occur based on a prediction by the AI unit; The terminal predicts the occurrence time of the wireless link failure based on the AI unit; A third indication, the third indication being used to indicate that a handover failure occurs to the target cell predicted by the AI unit; The moment when the terminal fails to switch to the target cell based on the prediction of the AI unit; The first cell includes at least one of the following: a source cell, at least one neighboring cell, at least one candidate switching cell, and at least one target cell to be switched. The method according to claim 13, wherein the beam quality or the cell signal quality predicted by the terminal based on the AI unit is characterized by a difference between a predicted value and a reference value, and the predicted value is the beam quality or the cell signal quality predicted by the terminal based on the AI unit. The method according to claim 14, wherein the reference value includes at least one of the following: a measurement value of each cell in the first measurement report; a first predicted value for each cell in the first measurement report; a maximum predicted value of each cell in the first measurement report; a measurement value of the second cell in the first measurement report; a first predicted value of the second cell in the first measurement report; a maximum predicted value of the second cell in the first measurement report; a maximum measurement value in the first measurement report; a maximum predicted value in the first measurement report; 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. The method according to claim 15, wherein the second cell is indicated by a fourth indication in the first measurement report as follows; or The method further comprises: The network-side device sends a measurement configuration to the terminal, where the measurement configuration includes a fifth indication for indicating the second cell. The method according to claim 15, wherein the method further comprises: The network-side device sends a first configuration to the terminal, where the first configuration includes a first threshold; Wherein, when the difference between the terminal's predicted value for the third cell and the terminal's measured value for the third cell is less than the first threshold, the prediction result does not include the terminal's predicted value for the third cell; or, When a difference between a predicted value of a third cell by the terminal at a first time and a predicted value of the third cell at a second time is less than the first threshold, the predicted value of the third cell by the terminal at the second time is not included in the prediction result, and the second time is later than the first time. The third cell is any one of the first cells. The method according to any one of claims 12 to 17, wherein the first measurement report further includes at least one of the following: a measurement result of RRM measurement performed by the terminal; a sixth indication, the sixth indication being used to indicate that there is no valid prediction result; The inference accuracy of the AI unit in performing RRM measurement predictions; The credibility or confidence of the prediction result. The method according to any one of claims 12 to 18, wherein the method further comprises: The network-side device sends a second configuration to the 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 first measurement report includes the prediction result. The method according to claim 19, wherein each of the AI units corresponds to one second threshold, or each of the AI functions corresponds to one second threshold. A reporting device, comprising: A reporting module, configured to report a first measurement report of the RRM measurement to a network-side device; The first measurement report includes a prediction result of the RRM measurement prediction performed by the device based on the AI unit. The apparatus according to claim 21, wherein the prediction result includes at least one of the following: The device predicts a beam quality of the first cell based on the AI unit; The device predicts a cell signal quality of the first cell based on the AI unit; The device predicts, based on the AI unit, a predicted time corresponding to the beam quality or cell signal quality of the first cell, where the predicted time includes at least one moment or the at least one time period; The device predicts a cell identification ID of at least one target cell based on the AI unit; The device predicts a switching time of at least one target cell based on the AI unit; a first indication, the first indication being used to indicate that a first condition is satisfied, the first condition being an entry condition or an exit condition of a measurement event predicted by the device based on the AI unit; The moment when the first condition is met; a second indication, the second indication being used to indicate to the apparatus that a radio link failure will occur based on a prediction by the AI unit; The device predicts the occurrence time of wireless link failure based on the AI unit; a third indication, the third indication being used to indicate that a handover to a target cell predicted by the AI unit by the apparatus fails; The device predicts a handover failure to the target cell based on the AI unit; The first cell includes at least one of the following: a source cell, at least one neighboring cell, at least one candidate switching cell, and at least one target cell to be switched. The apparatus according to claim 21 or 22, wherein the first measurement report further includes at least one of the following: a measurement result of RRM measurement performed by the apparatus; a sixth indication, the sixth indication being used to indicate that there is no valid prediction result; The inference accuracy of the AI unit in performing RRM measurement predictions; The credibility or confidence of the prediction result. The device according to any one of claims 21 to 23, wherein the device further comprises: a receiving module, configured to receive a second configuration sent by a network-side device, 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 first measurement report includes the prediction result. A receiving device, comprising: A receiving module, configured to receive a first measurement report of an RRM measurement reported by a terminal; The first measurement report includes a prediction result of the RRM measurement prediction performed by the terminal based on the AI unit. The apparatus according to claim 25, wherein the prediction result includes at least one of the following: The terminal predicts a beam quality of the first cell based on the AI unit; A cell signal quality of the first cell predicted by the terminal based on the AI unit; A predicted 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 predicted time includes at least one moment or the at least one time period; A cell ID of at least one target cell predicted by the terminal based on the AI unit; The terminal predicts a switching time of at least one target cell based on the AI unit; A first indication, where the first indication is used to indicate that a first condition is satisfied, where the first condition is an entry condition or an exit condition of a measurement event predicted by the terminal based on the AI unit; The moment when the first condition is met; a second indication, where the second indication is used to indicate to the terminal that a radio link failure will occur based on a prediction by the AI unit; The terminal predicts the occurrence time of the wireless link failure based on the AI unit; A third indication, the third indication being used to indicate that a handover failure occurs to the target cell predicted by the AI unit; The moment when the terminal fails to switch to the target cell based on the prediction of the AI unit; The first cell includes at least one of the following: a source cell, at least one neighboring cell, at least one candidate switching cell, and at least one target cell to be switched. The apparatus according to claim 25 or 26, wherein the first measurement report further includes at least one of the following: a measurement result of RRM measurement performed by the terminal; a sixth indication, the sixth indication being used to indicate that there is no valid prediction result; The inference accuracy of the AI unit in performing RRM measurement predictions; The credibility or confidence of the prediction result. The device according to claim 25, 26 or 27, wherein the device further comprises: a sending module, configured to send a second configuration to the 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 first measurement report includes the prediction result. A terminal comprises a processor and a memory, wherein the memory stores a program or instruction that can be 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 to 11 are implemented. A network-side device comprises a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the receiving method described in any one of claims 12 to 20 are implemented. A readable storage medium, wherein the readable storage medium stores a program or instruction, and when the program or instruction is executed by a processor, the steps of the reporting method according to any one of claims 1 to 11 are implemented, or the steps of the receiving method according to any one of claims 12 to 20 are implemented. A computer program product, wherein 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 to 11, or implement the steps of the receiving method according to any one of claims 12 to 20. A terminal comprises a processor and a communication interface, wherein the communication interface is used to report a first measurement report of an RRM measurement to a network side device; The first measurement report includes a prediction result of the RRM measurement prediction performed by the device based on the AI unit. A network-side device comprises a processor and a communication interface, wherein the communication interface is configured to receive a first measurement report of an RRM measurement reported by a terminal; The first measurement report includes a prediction result of the RRM measurement prediction performed by the terminal based on the AI unit. A wireless communication system comprises: a terminal and a network-side device, wherein the terminal can be used to perform the steps of the reporting method according to any one of claims 1 to 11, and the network-side device can be used to perform the steps of the receiving method according to any one of claims 12 to 20. A chip comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to run a program or instruction to implement the reporting method according to any one of claims 1 to 11, or to implement the receiving method according to any one of claims 12 to 20.