Information reporting method and apparatus, terminal, and network side device

Abstract

The present application relates to the technical field of communications, and discloses an information reporting method and apparatus, a terminal, and a network side device. The information reporting method in embodiments of the present application comprises: a terminal receiving first information from a network side device, the first information being used for providing configuration information related to an artificial intelligence (AI) unit; and sending second information to the network side device on the basis of the first information, the second information being used for assisting the network side device in determining an AI unit available to the terminal.

Description

Information reporting methods, devices, terminals and network-side equipment

[0001] Cross-references to related applications

[0002] This application claims priority to Chinese Patent Application No. 202510376011.8, filed in China on March 27, 2025, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application belongs to the field of communication technology, specifically relating to an information reporting method, apparatus, terminal, and network-side equipment. Background Technology

[0004] Artificial intelligence (AI) has been widely applied across various fields. Integrating AI into wireless communication networks to significantly improve technical indicators such as throughput, latency, and user capacity is a crucial task for future wireless communication networks. AI-based mobility enhancement can include radio resource management (RRM) measurement prediction and measurement event prediction. However, the applicability of AI-based mobility enhancement in the lifecycle management of on-device AI units has not been considered for various use cases. Therefore, how to enable the network to determine available on-device AI units is a pressing issue that needs to be addressed. Summary of the Invention

[0005] This application provides an information reporting method, apparatus, terminal, and network-side device, which can solve the problem of how to enable the network to determine the available terminal-side AI unit.

[0006] Firstly, an information reporting method is provided, executed by a terminal, the method comprising:

[0007] The terminal receives first information from the network-side device, the first information being used to provide configuration information related to the AI ​​unit;

[0008] The terminal sends second information to the network-side device based on the first information; wherein the second information is used to assist the network-side device in determining the artificial intelligence (AI) unit available to the terminal.

[0009] Secondly, an information reporting method is provided, executed by a network-side device, the method comprising:

[0010] The network-side device sends first information to the terminal, the first information being used to provide configuration information related to the AI ​​unit;

[0011] The network-side device receives second information from the terminal; wherein the second information is determined based on the first information, and the second information is used to assist the network-side device in determining the AI ​​units available to the terminal.

[0012] Thirdly, an information reporting device is provided, applied to a terminal, including:

[0013] The first receiving module is used to receive first information from the network-side device, wherein the first information is used to provide configuration information related to the AI ​​unit;

[0014] The first sending module is used to send second information to the network-side device based on the first information; wherein the second information is used to assist the network-side device in determining the AI ​​unit available to the terminal.

[0015] Fourthly, an information reporting device is provided, applied to network-side equipment, including:

[0016] The second sending module is used to send first information to the terminal, the first information being used to provide configuration information related to the AI ​​unit;

[0017] The second receiving module is configured to receive second information from the terminal; wherein the second information is determined based on the first information, and the second information is used to assist the network-side device in determining the AI ​​units available to the terminal.

[0018] Fifthly, an information reporting device is provided, the device being configured to perform the steps of the method described in the first aspect, or to implement the steps of the method described in the second aspect.

[0019] In a sixth aspect, a terminal is provided, the terminal including a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the method as described in the first aspect.

[0020] In a seventh aspect, a terminal is provided, including a processor and a communication interface, wherein the communication interface is used to receive first information from a network-side device, the first information being used to provide configuration information related to an AI unit; and to send second information to the network-side device based on the first information; the second information being used to assist the network-side device in determining the artificial intelligence (AI) unit available in the terminal.

[0021] Eighthly, a network-side device is provided, the network-side device including a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the method as described in the second aspect.

[0022] In a ninth aspect, a network-side device is provided, including a processor and a communication interface, wherein the communication interface is used to send first information to a terminal, the first information being used to provide configuration information related to an AI unit; and to receive second information from the terminal; the second information is determined based on the first information, and the second information is used to assist the network-side device in determining the AI ​​units available to the terminal.

[0023] In a tenth aspect, a readable storage medium is provided, on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect, or implement the steps of the method described in the second aspect.

[0024] Eleventhly, a wireless communication system is provided, comprising: a terminal and a network-side device, wherein the terminal is configured to perform the steps of the method described in the first aspect, and the network-side device is configured to perform the steps of the method described in the second aspect.

[0025] In a twelfth aspect, a chip is provided, the chip including a processor and a communication interface coupled to the processor, the processor being configured to run a program or instructions 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.

[0026] In a thirteenth 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 as described in the first aspect, or to implement the steps of the method as described in the second aspect.

[0027] The solution provided in this application allows the network-side device to determine the AI ​​units available to the terminal, thereby enabling the network-side device to provide reasonable configurations related to the AI ​​units, reducing signaling overhead and interaction latency, and avoiding the signaling overhead and interaction latency caused by the network-side device blindly providing AI unit configurations. Attached Figure Description

[0028] Figure 1 shows a block diagram of a wireless communication system that can be applied to an embodiment of this application;

[0029] Figure 2A is a schematic diagram of a neural network according to an embodiment of this application;

[0030] Figure 2B is a schematic diagram of the neurons of the neural network in an embodiment of this application;

[0031] Figure 3 is a flowchart of an information reporting method provided in an embodiment of this application;

[0032] Figure 4 is a flowchart of the prediction availability reporting process in an embodiment of this application.

[0033] Figure 5 is a flowchart of another information reporting method provided in an embodiment of this application;

[0034] Figure 6 is a schematic diagram of the structure of an information reporting device provided in an embodiment of this application;

[0035] Figure 7 is a schematic diagram of another information reporting device provided in an embodiment of this application;

[0036] Figure 8 is a schematic diagram of the structure of a communication device provided in an embodiment of this application;

[0037] Figure 9 is a schematic diagram of the structure of a terminal provided in an embodiment of this application;

[0038] Figure 10 is a schematic diagram of the structure of a network-side device provided in an embodiment of this application. Detailed Implementation

[0039] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0040] The terms "first," "second," etc., used in this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same class, not limited in number; for example, the first object can be one or more. Furthermore, "or" in this application indicates at least one of the connected objects. For example, the scope of protection for "A or B" covers at least three scenarios: Scenario 1: including A but not B; Scenario 2: including B but not A; Scenario 3: including both A and B. In addition, the terms "A and / or B," "at least one of A and B," and "at least one of A or B" also cover at least the above three scenarios. The character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0041] The term "instruction" in this application can be either a direct instruction (or explicit instruction) or an indirect instruction (or implicit instruction). A direct instruction can be understood as the sender explicitly informing the receiver of specific information, the required operation, or the requested result in the instruction sent. An indirect instruction 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 required operation or requested result based on the judgment result.

[0042] It is worth noting that the technologies described in this application are not limited to Long Term Evolution (LTE) / LTE-Advanced (LTE-A) systems, 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 this application are often used interchangeably, and the described technologies can be used with the systems and radio technologies mentioned above, as well as with other systems and radio technologies. The following description describes New Radio (NR) systems for illustrative purposes, and the term NR is used in most of the following description; however, these technologies can also be applied to systems other than NR systems, such as 6th generation (6G) radio systems. th Generation 6G communication system.

[0043] Figure 1 shows a block diagram of a wireless communication system applicable to an embodiment of this application. The wireless communication system includes a terminal 11 and a network-side device 12. The terminal 11 can also be referred to as User Equipment (UE), and can be a mobile phone, tablet computer, laptop computer, notebook computer, personal digital assistant (PDA), handheld computer, netbook, ultra-mobile personal computer (UMPC), mobile internet device (MID), augmented reality (AR), virtual reality (VR) device, robot, wearable device, flight vehicle, vehicle user equipment (VUE), shipboard equipment, pedestrian user equipment (PUE), smart home (home devices with wireless communication capabilities, such as refrigerators, televisions, washing machines, or furniture), game console, personal computer (PC), ATM, or self-service machine, etc. Wearable devices include: smartwatches, smart bracelets, smart earphones, smart glasses, smart jewelry (smart bracelets, smart chains, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. Among these, in-vehicle devices can also be referred to as in-vehicle terminals, in-vehicle controllers, in-vehicle modules, in-vehicle components, in-vehicle chips, or in-vehicle units, etc. Furthermore, in addition to the terminals described above, terminal 11 can also be a chip within a terminal, such as a modem chip, a system-on-chip (SoC), etc. It should be noted that the specific type of terminal 11 is not limited in the embodiments of this application.

[0044] Network-side equipment 12 may include access network equipment or core network equipment. Access network equipment may also be referred to as Radio Access Network (RAN) equipment, radio access network function, or radio access network unit. Access network equipment may include base stations, wireless local area network (WLAN) access points (APs), or wireless Fidelity (WiFi) nodes, etc. Among them, base stations can be referred to as Node B (NB), Evolved Node B (eNB), Next Generation Node B (gNB), New Radio Node B (NR Node B), Access Point, Relay Base Station (RBS), Serving Base Station (SBS), Base Transceiver Station (BTS), Radio Base Station, Radio Transceiver, Basic Service Set (BSS), Extended Service Set (ESS), Home Node B (HNB), Home Evolved Node B, Transmit / Receive Point (TRP), Non-Terrestrial Network (NTN) equipment (such as satellite or high altitude platform stations). The term "base station" can be any suitable term in the field, such as "station" or any other appropriate term in the relevant field, as long as the same technical effect is achieved. The term "base station" is not limited to specific technical terms. It should be noted that the embodiments of this application only use the base station in the NR system as an example for introduction, and do not limit the specific type of base station.

[0045] Optionally, the model in the embodiments of this application can be understood as an AI model. AI models can be implemented in various ways, such as neural networks, decision trees, support vector machines, Bayesian classifiers, etc. This application uses neural networks as an example for illustration, but does not limit the specific type of AI model.

[0046] Optionally, a neural network in this embodiment of the application may be as shown in Figure 2A, including an input layer, a hidden layer, and an output layer, wherein the input of the input layer is X1, X2...X... nThe output of the corresponding output layer is Y. A neural network consists of neurons, as shown in Figure 2B. Here, a1, a2…a… K σ is the input to the neuron, w is the weight (or multiplicative coefficient), b is the bias (or additive coefficient), and σ(.) is the activation function. Common activation functions include, but are not limited to, the sigmoid function, the hyperbolic tangent tanh function, the rectified linear unit (ReLU), etc.

[0047] Optionally, the parameters of the neural network can be optimized using gradient optimization algorithms. Gradient optimization algorithms are a class of algorithms that minimize or maximize an objective function (or loss function), which is often a mathematical combination of model parameters and data. For example, given the model input data X and its corresponding label Y, a neural network model f(.) is constructed. After constructing the model, the predicted output f(x) can be obtained based on the input data x, and the difference between the predicted value and the true value (f(x)-Y) can be calculated; this is the loss function. The purpose of training a neural network model is to find suitable weights w and biases b that minimize the value of the corresponding loss function. The smaller the loss value, the closer the model is to the reality.

[0048] Optionally, the optimization algorithms used in the embodiments of this application are mostly based on the error back propagation (BP) algorithm. The basic idea of ​​the BP algorithm is that the learning process consists of two processes: forward propagation of the signal and backward propagation of the error. During forward propagation, the input sample is introduced from the input layer, processed layer by layer by the hidden layers, and then transmitted to the output layer. If the actual output of the output layer does not match the expected output, the process switches to the error back propagation stage. Error back propagation involves propagating the output error back to the input layer layer by layer through the hidden layers in a certain form, and distributing the error to all units in each layer, thereby obtaining the error signal of each unit. This error signal serves as the basis for correcting the weights of each unit. This process of adjusting the weights of each layer through forward propagation of the signal and backward propagation of the error is repeated continuously. The process of continuously adjusting the weights is the learning and training process of the model. This process continues until the error of the model output is reduced to an acceptable level, or until the preset number of learning iterations is reached.

[0049] Optionally, the optimization algorithms used in the model in this application embodiment may include, but are not limited to: Gradient Descent, Stochastic Gradient Descent (SGD), mini-batch gradient descent, Momentum, Nesterov (the inventor's name, specifically stochastic gradient descent with momentum), Adagrad (Adaptive Gradient Descent), Adadelta, RMSprop (root mean square propagation), Adam (Adaptive Moment Estimation), etc. During error backpropagation, these optimization algorithms calculate the gradient by taking the derivative / partial derivative of the error / loss obtained from the loss function with respect to the current neuron, adding the learning rate, previous gradients / partial derivatives / derivatives, etc., and then passing the gradient to the previous layer.

[0050] Optionally, the AI ​​unit described in this application may also be referred to as an AI model, machine learning (ML) model, ML unit, AI structure, AI function, AI characteristic, machine learning model, neural network, neural network function, neural network functionality, etc.; or, the AI ​​unit may also refer to a processing unit capable of implementing specific algorithms, formulas, processing flows, capabilities, etc. related to AI; or, the AI ​​unit may be a processing method, algorithm, function, module, or unit for a specific dataset; or, the AI ​​unit may be a processing method, algorithm, function, module, or unit running on AI / ML related hardware such as a graphics processing unit (GPU), neural processing unit (NPU), tensor processing unit (TPU), or application-specific integrated circuit (ASIC), etc., and this application does not specifically limit this. Optionally, the specific dataset may include the input and / or output of the AI ​​unit.

[0051] Optionally, the identifier of the AI ​​unit in this application may be an AI model identifier, an AI structure identifier, an AI algorithm identifier, or an identifier of a specific dataset associated with the AI ​​unit, or an identifier of a specific scenario, environment, channel characteristics 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 make any specific limitations on this.

[0052] An AI function is a feature of an AI algorithm. An AI function can correspond to one or more AI units.

[0053] In practical applications, the performance of AI functions is inextricably linked to the dataset. If there is a significant difference between the training and testing datasets, the model trained on the training dataset will struggle to achieve good performance on the testing dataset. An ideal scenario is that the training, testing, and actual processing datasets have excellent consistency; under this condition, the trained model will perform well. However, in the actual dataset construction process, various practical constraints mean that the training and testing datasets may not perfectly match the datasets used for inference or prediction in real-world scenarios, potentially leading to a decline in inference performance.

[0054] AI-based mobility enhancement can include Radio Resource Management (RRM) measurement prediction and measurement event prediction. The RRM measurement prediction can include time-domain prediction, frequency-domain prediction, and spatial-domain prediction. Specifically, time-domain RRM measurement prediction predicts the RRM measurement result at a future time / time period for a given frequency point based on historical RRM measurement results; frequency-domain RRM measurement prediction predicts the RRM measurement result at another frequency point based on the RRM measurement results of one frequency point. Simulation results show that cluster-based prediction methods (e.g., inputting measurement values ​​from multiple cells (at the same or different frequencies) and outputting a predicted value for one cell) significantly improve the accuracy of frequency prediction.

[0055] The measurement event prediction can include direct measurement event prediction and indirect measurement event prediction. For direct measurement event prediction, the output of the AI ​​unit is whether a measurement event will occur or the probability of the event occurring within a future period. For indirect measurement event prediction, the output of the AI ​​unit is the future RRM measurement prediction result, and post-processing is performed based on the predicted RRM measurement result to determine whether the measurement event will occur at a future point in time.

[0056] To address the issue of how to enable the network to determine available terminal-side AI units, this application provides a method for terminals to report information that assists the network in obtaining information about the availability of AI units, thereby enabling the network to provide reasonable AI unit configurations and reduce signaling overhead and interaction latency.

[0057] In this embodiment of the application, inference performance can be understood as prediction performance, and the two have the same meaning.

[0058] The information reporting method, apparatus, terminal, and network-side equipment provided in this application will be described in detail below with reference to the accompanying drawings and through some embodiments and application scenarios.

[0059] Please refer to Figure 3, which is a flowchart of an information reporting method provided in an embodiment of this application. The method is executed by a terminal. As shown in Figure 3, the method includes the following steps:

[0060] Step 31: The terminal receives first information from the network-side device, the first information being used to provide configuration information related to the artificial intelligence (AI) unit;

[0061] Step 33: The terminal sends second information to the network-side device based on the first information; the second information is used to assist the network-side device in determining the AI ​​units available to the terminal.

[0062] In this embodiment, the first information is used to trigger the terminal to send the second information. The first information may be partial prediction configuration information.

[0063] The second information can be understood as auxiliary information, which can help network-side devices determine the AI ​​units available to the terminal.

[0064] In some embodiments, the first information may be provided by the network-side device through other configurations (such as OtherConfig) in a Radio Resource Control (RRC) Reconfiguration message. The second information may be carried through UE assistance information (such as UEAssistanceInformation) messages sent by the terminal.

[0065] In some embodiments, the first information may be sent based on the capabilities of AI units supported by the terminal.

[0066] The solution provided in this application allows the network-side device to determine the AI ​​units available to the terminal, thereby enabling the network-side device to provide reasonable configurations related to the AI ​​units, reducing signaling overhead and interaction latency, and avoiding the signaling overhead and interaction latency caused by the network-side device blindly providing AI unit configurations.

[0067] Optionally, after sending the second information to the network-side device as described above, the reporting method in this application embodiment may further include:

[0068] The terminal receives a first configuration from the network-side device; wherein the first configuration is a configuration for the available AI units, and the first configuration is determined based on the second information. This allows the terminal to receive appropriate AI unit configurations from the network side, thereby reducing signaling overhead and interaction latency.

[0069] Optionally, the first configuration may include at least one of the following:

[0070] Measurement configuration; for example, this measurement configuration is the measurement configuration of the input frequency points of the available AI units, such as including measurement period, measurement event, measurement signal, etc.

[0071] Measurement gap; This measurement gap can be understood as a period of time configured by the network for the terminal, during which measurements can be performed, but the measurement behavior of the terminal is not restricted; for example, this measurement gap is the measurement interval of the input frequency points of the available AI unit;

[0072] The prediction configuration of the available AI units; this prediction configuration can also be an inference configuration.

[0073] Optionally, the prediction configuration may include at least one of the following:

[0074] ① Predict the frequency of the output; for example, the frequency of the predicted output can be configured by the object being measured;

[0075] ② The number of prediction instances; for example, the prediction instance corresponds to the cell-level or beam-level prediction quality at a certain time point, such as the reference signal received power (RSRP) and the signal to interference plus noise ratio (SINR);

[0076] ③ The time interval of the prediction instance; for example, the time interval of the prediction instance represents the length of time between two adjacent predictions.

[0077] ④ The cycle for triggering prediction;

[0078] ⑤ Events that trigger prediction; for example, the event could be that the quality of the current serving cell is better than that of neighboring cells by a threshold, or that the quality of the current serving cell exceeds a threshold, etc.

[0079] With the above prediction configuration, the terminal can perform the corresponding prediction operation.

[0080] In this embodiment of the application, the first information may include at least one of the following:

[0081] Output frequency information;

[0082] First prediction window length;

[0083] First preference information, which is used to indicate to the network-side device the expected prediction result at a future time point;

[0084] The first associated ID is used to characterize (e.g., implicitly characterize) the base station feature information associated with the input-output information of the AI ​​unit.

[0085] The contents of the first information will be explained below.

[0086] (1) Output frequency information

[0087] Optionally, the output frequency information corresponds to the AI ​​unit used for inter-frequency prediction. That is, the output frequency information specifically refers to the output frequency information of the AI ​​unit corresponding to the inter-frequency prediction use case. The output frequency information indicates at which frequency the prediction quality of the output in the inter-frequency prediction use case is obtained. It can be understood that the inter-frequency prediction is frequency domain RRM prediction.

[0088] It should be noted that for inter-frequency prediction, the generalization performance of the AI ​​unit for the frequency points of the model input-output is poor. The network-side device can configure the frequency point information to be predicted (i.e., the output frequency point information) to trigger the terminal to report the input frequency point information of the corresponding available AI unit, and then configure the measurement or measurement interval for the terminal at the corresponding frequency point based on the input frequency point information.

[0089] In some embodiments, if a terminal reports that it supports frequency domain RRM prediction, the network-side device can send the output frequency point information to the terminal.

[0090] Optionally, when the first information includes the output frequency information, the second information may include at least one of the following:

[0091] (a) A first indication, wherein the first indication is used to indicate whether the output frequency point information is available; with the aid of this first indication, the network-side device can determine the availability of the output frequency point information it sends, and then when it is determined that the output frequency point information is available, it can determine the corresponding available AI unit based on the output frequency point information, thereby providing configuration for the available AI unit and avoiding signaling redundancy caused by directly sending predictive configuration when the AI ​​unit is unavailable;

[0092] (b) Input frequency information, which corresponds to the output frequency information; thereby, the network-side device can determine the available AI units based on the relationship between the input and output frequency points, and provide the available AI units with measurements corresponding to the input information (e.g., measurements or measurement intervals at the input frequency points), avoiding signaling redundancy caused by directly issuing predictive configurations when the AI ​​units are unavailable.

[0093] In some embodiments, when the second information includes a first indication, the first information is sent via an RRC reconfiguration message, and the first indication is sent to the network-side device via an RRC reconfiguration completion message.

[0094] Optionally, when the output frequency information is associated with the first AI unit, the first indication is further used to indicate whether the first AI unit is available, so that the network can directly determine whether the first AI unit is available; and / or, the input frequency information is associated with the first AI unit. The association could be that the output frequency information is the output frequency information of the first AI unit.

[0095] Optionally, the output frequency information may include information about one or more frequency points, with the information of one output frequency point corresponding to the information of one or more input frequency points. For example, the output frequency points may form a frequency point list, and each output frequency point may correspond to a frequency point list (i.e., including one or more input frequency points).

[0096] In some embodiments, the output frequency point may include one frequency point (e.g., Fout1) or multiple frequency points (e.g., Fout1, ..., Fouty), indicating that the network expects to obtain RRM prediction values ​​for one or more frequency points. The input frequency point corresponding to the output frequency point information may be one or more frequency points, and these multiple frequency points correspond to cluster-based prediction methods.

[0097] For example, the correspondence between output frequency points and input frequency points can be as follows: output frequency point Fout1 corresponds to input frequency points Fin1 and Fin3, output frequency point Fout2 corresponds to input frequency point Fin4, and output frequency point Fouty corresponds to input frequency points Fin2, Fin4, ..., Finx.

[0098] In some embodiments, after receiving input frequency information, the network-side device can provide configuration for the AI ​​units available on the terminal side based on the input frequency information.

[0099] (2) First prediction window length

[0100] In this embodiment, the first prediction window length can be understood as the prediction window length configured by the network. For example, the first prediction window length is the prediction window length of the AI ​​unit used for temporal RRM prediction / measurement event prediction.

[0101] It should be noted that for time-domain RRM prediction and measurement event prediction, the prediction window length of the AI ​​unit may affect the model's inference performance. Specifically, if the prediction window length configured by the network is greater than the prediction window length of the AI ​​unit, the inference performance of the AI ​​unit may decrease, leading the terminal to consider the AI ​​unit unusable. The network-side device can configure a maximum prediction window length, triggering the terminal to report the availability of the corresponding prediction window length / AI unit. If the AI ​​unit is unavailable, the terminal can report the maximum available prediction window length for that AI unit, allowing the network to provide the terminal with a suitable prediction configuration (i.e., prediction window length). The inference performance can be understood as prediction performance.

[0102] In some embodiments, if a terminal reports that it supports temporal RRM prediction, the network-side device may send the first prediction window length to the terminal.

[0103] Optionally, when the first information includes the first prediction window length, the second information may include at least one of the following:

[0104] (c) A second indication, wherein the second indication is used to indicate whether the first prediction window length is available; with the aid of this second indication, the network-side device can determine the availability of its configured first prediction window length, and then, when it is determined that the first prediction window length is available, it can determine the corresponding available AI unit based on the first prediction window length, thereby providing configuration for the available AI unit and avoiding signaling redundancy caused by directly issuing prediction configuration when the AI ​​unit is unavailable;

[0105] (d) Second prediction window length, wherein the second prediction window length is the maximum prediction window length available to the terminal; thereby, the network-side device can determine the available AI units based on the maximum prediction window length available to the terminal, thereby providing configuration for the available AI units and avoiding signaling redundancy caused by directly issuing prediction configurations when the AI ​​units are unavailable.

[0106] In some embodiments, when the second information includes a second indication, the first information is sent via an RRC reconfiguration message, and the second indication is sent to the network-side device via an RRC reconfiguration completion message.

[0107] In some embodiments, the first prediction window length and / or the second prediction window length may be expressed as: number of prediction instances * time interval of prediction instances.

[0108] Optionally, when the first prediction window length is associated with the second AI unit, the second indication is further used to indicate whether the second AI unit is available, so that the network can directly determine whether the second AI unit is available; and / or, the second prediction window length is associated with the second AI unit. The association could be: the first prediction window length is the prediction window length of the second AI unit.

[0109] For example, assuming the second AI unit corresponds to temporal RRM prediction and the first prediction window length is 400ms, the second indication can indicate that temporal RRM prediction is unavailable when the prediction window length is 400ms.

[0110] For example, assuming the second AI unit corresponds to measurement event prediction and the second prediction window length is 320ms, then the second prediction window length means that the available prediction window length for the terminal for measurement event prediction is 320ms.

[0111] In some embodiments, the network-side device may provide a prediction configuration for the AI ​​unit available on the terminal side based on at least one of a second indication and a second prediction window length.

[0112] (3) First preference information

[0113] In this embodiment, the first preference information is used to indicate to the network-side device the expected prediction result at a future time point. For example, the first preference information can be represented as a certain time, which characterizes a future point in time where the prediction result is expected. The first preference information can be used to determine whether the terminal supports the AI ​​unit corresponding to the indirect measurement event prediction.

[0114] Optionally, the prediction result is the result of the measurement event prediction. For example, the prediction result may be a prediction of the triggering of the measurement event or a prediction of the departure of the measurement event.

[0115] In some embodiments, if a terminal reports that it supports the prediction of measurement events (such as direct and / or indirect measurement event prediction), the network-side device may send the first preference information to the terminal.

[0116] Optionally, when the first information includes the first preference information, the second information includes a third indication, which indicates whether the third AI unit is available, and the third AI unit supports outputting the prediction results for the future time point. This allows network-side devices to determine the availability of the third AI unit and, when the availability is determined, provide appropriate configuration, avoiding signaling redundancy caused by directly issuing prediction configurations when the AI ​​unit is unavailable.

[0117] In some embodiments, when the second information includes a third indication, the first information is sent via an RRC reconfiguration message, and the third indication is sent to the network-side device via an RRC reconfiguration completion message.

[0118] In some embodiments, the third AI unit is used to measure event prediction. Further, the third AI unit is used to directly measure event prediction.

[0119] In some embodiments, the network-side device can provide predictive configurations for AI units available on the terminal side based on a received third instruction.

[0120] (4) First association identifier

[0121] In this embodiment, the first association identifier is used to characterize (e.g., implicitly characterize) the base station feature information associated with the input-output information of the AI ​​unit. This base station feature information includes, but is not limited to, beam, cell, and other related features. The first association identifier can be understood as an association identifier configured in the network.

[0122] It should be noted that for spatial RRM prediction, the prediction performance of the AI ​​unit is limited by the base station's characteristic information. Network-side equipment can configure association identifiers to trigger the terminal to report the availability of the corresponding association identifier / AI unit. If the AI ​​unit is unavailable, the terminal can report the association identifier for which the corresponding AI unit is available, thus allowing the network to provide the terminal with appropriate prediction configurations.

[0123] In some embodiments, if a terminal reports that it supports spatial RRM prediction, the network-side device may send the first association identifier to the terminal.

[0124] The first association identifier may include one or more association identifiers.

[0125] Optionally, when the first information includes the first association identifier, the second information may include at least one of the following:

[0126] (e) Fourth indication, the fourth indication is used to indicate whether the first association identifier is available; with the help of this fourth indication, the network side device can determine the availability of the first association identifier, and when it is determined that the first association identifier is available, it can determine the corresponding available AI unit according to the first association identifier, thereby providing configuration for the available AI unit and avoiding signaling redundancy caused by directly issuing predictive configuration when the AI ​​unit is unavailable;

[0127] (f) A second association identifier, wherein the second association identifier is an association identifier available to the terminal; the second association identifier may include one or more association identifiers; thereby enabling the network-side device to determine the available AI unit based on the association identifier available to the terminal, thereby providing configuration for the available AI unit and avoiding signaling redundancy caused by directly issuing predictive configuration when the AI ​​unit is unavailable.

[0128] In some embodiments, when the second information includes a fourth indication, the first information is sent via an RRC reconfiguration message, and the fourth indication is sent to the network-side device via an RRC reconfiguration completion message.

[0129] Optionally, when the first association identifier is associated with the fourth AI unit, the fourth indication is further used to indicate whether the fourth AI unit is available, so that the network can directly determine whether the fourth AI unit is available; and / or, the second association identifier is associated with the fourth AI unit. The above association can be: the first association identifier is the association identifier of the fourth AI unit. The fourth AI unit can be used for spatial RRM prediction.

[0130] For example, assuming the fourth AI unit corresponds to spatial RRM prediction, the fourth indication can indicate that spatial RRM prediction is unavailable when corresponding to the first association identifier.

[0131] For example, assuming the fourth AI unit corresponds to spatial RRM prediction, the second association identifier indicates the available association identifier corresponding to the spatial RRM prediction.

[0132] In some embodiments, the network-side device may provide predictive configuration for AI units available on the terminal side based on at least one of a fourth indication and a second association identifier.

[0133] In this embodiment of the application, the first information is sent according to the capabilities of the AI ​​unit supported by the terminal; the capabilities of the AI ​​unit supported by the terminal may include, but are not limited to, at least one of the following:

[0134] - The terminal supports time-domain prediction; for example, the terminal supports time-domain RRM prediction.

[0135] - The terminal supports frequency domain prediction; for example, the terminal supports frequency domain RRM prediction; or, for example, the terminal supports RRM prediction based on multiple input frequency points / multiple cells.

[0136] - The terminal supports spatial prediction; for example, the terminal supports spatial RRM prediction.

[0137] - The terminal supports measurement event prediction; for example, the measurement event prediction includes direct and / or indirect measurement event prediction; the terminal's support for measurement event prediction may include the terminal's support for direct and / or indirect measurement event prediction.

[0138] It should be noted that the above predictions can be either cell-level or beam-level predictions, and there is no limitation on either.

[0139] The capabilities of the AI ​​units supported by the terminal can be reported by the terminal to the network-side device based on network requests. If the network has sent a capability request, but the terminal does not report a certain capability, it indicates that the terminal does not support that capability. For example, if the terminal does not report that it supports time-domain prediction, it indicates that the terminal does not support time-domain prediction; if the terminal does not report that it supports frequency-domain prediction, it indicates that the terminal does not support frequency-domain prediction; if the terminal does not report that it supports spatial-domain prediction, it indicates that the terminal does not support spatial-domain prediction; if the terminal does not report that it supports direct and / or indirect measurement event prediction, it indicates that the terminal does not support direct and / or indirect measurement event prediction.

[0140] Please refer to Figure 4. The reporting process for predicted availability in this embodiment may include the following steps:

[0141] Step 1: The base station gNB sends a UE capability request (e.g., UECapabilityEnqiry) message to the UE, requesting the UE to provide the capabilities of the AI ​​units it supports, wherein the capability may include at least one of the following: 1) whether it supports time-domain RRM prediction; 2) whether it supports frequency-domain RRM prediction; 3) whether it supports spatial-domain RRM prediction; 4) whether it supports direct and / or indirect measurement event prediction.

[0142] Step 2: The UE sends a capability (such as UECapability) message to report the capabilities of the AI ​​units it supports.

[0143] Step 3: Based on the capabilities of the AI ​​units supported by the UE, the gNB provides the UE with first information through other configurations (such as OtherConfig) in the RRC Reconfiguration message. The first information is used to trigger the terminal to send second information. The first information is as described above and will not be repeated here.

[0144] Step 4: Based on the first information, the UE sends the second information to the gNB through a UE Assistance Information (such as UE Assistance Information) message or an RRC Reconfiguration Complete message. The second information is used to assist the gNB in ​​determining the available AI units. The second information is as described above and will not be repeated here.

[0145] Step 5: Based on the second information, the gNB determines the available AI units and sends an RRC reconfiguration (such as RRCReconfiguration) message to the UE, which carries a first configuration. The first configuration is used to configure the available AI units to report prediction / predicted results.

[0146] Please refer to Figure 5, which is a flowchart of an information reporting method provided in an embodiment of this application. The method is executed by a network-side device. As shown in Figure 5, the method includes the following steps:

[0147] Step 51: The network-side device sends first information to the terminal, the first information being used to provide configuration information related to the AI ​​unit;

[0148] Step 52: The network-side device receives second information from the terminal; the second information is determined based on the first information, and the second information is used to assist the network-side device in determining the AI ​​units available to the terminal.

[0149] In this embodiment, the first information is used to trigger the terminal to send the second information. The first information may be partial prediction configuration information.

[0150] The second information can be understood as auxiliary information, which can help network-side devices determine the AI ​​units available to the terminal.

[0151] In some embodiments, the first information may be provided by the network-side device through other configurations (such as OtherConfig) in an RRC Reconfiguration message. The second information may be carried through a UE assistance information (such as UEAssistanceInformation) message sent by the terminal or an RRC ReconfigurationComplete message.

[0152] In some embodiments, the first information may be sent based on the capabilities of AI units supported by the terminal.

[0153] The solution provided in this application allows the network-side device to determine the AI ​​units available to the terminal, thereby enabling the network-side device to provide reasonable configurations related to the AI ​​units, reducing signaling overhead and interaction latency, and avoiding the signaling overhead and interaction latency caused by the network-side device blindly providing AI unit configurations.

[0154] Optionally, the reporting method in the embodiments of this application may further include:

[0155] The network-side device sends a first configuration to the terminal. This first configuration is for the available AI units and is determined based on the second information. This allows the terminal to receive appropriate AI unit configurations from the network side, thereby reducing signaling overhead and interaction latency.

[0156] Optionally, the first configuration may include at least one of the following:

[0157] Measurement configuration; for example, this measurement configuration is the measurement configuration of the input frequency points of the available AI units, such as including measurement period, measurement event, measurement signal, etc.

[0158] Measurement gap; for example, this measurement gap is the measurement interval of the input frequency points of the available AI unit;

[0159] The prediction configuration of the available AI units; this prediction configuration can also be an inference configuration.

[0160] In this embodiment of the application, the first information may include at least one of the following:

[0161] Output frequency information;

[0162] First prediction window length;

[0163] First preference information, which is used to indicate to the network-side device the expected prediction result at a future time point;

[0164] The first associated ID is used to characterize (e.g., implicitly characterize) the base station feature information associated with the input-output information of the AI ​​unit.

[0165] Optionally, the output frequency information corresponds to the AI ​​unit used for inter-frequency prediction. That is, the output frequency information specifically refers to the output frequency information of the AI ​​unit corresponding to the inter-frequency prediction use case. The output frequency information indicates at which frequency the prediction quality of the output in the inter-frequency prediction use case is obtained. It can be understood that the inter-frequency prediction is frequency domain RRM prediction.

[0166] Optionally, when the first information includes the output frequency information, the second information includes at least one of the following:

[0167] The first indication is used to indicate whether the output frequency point information is available. With the help of this first indication, the network-side device can determine the availability of the output frequency point information it sends. Then, when it is determined that the output frequency point information is available, it can determine the corresponding available AI unit based on the output frequency point information, thereby providing the available AI unit with the measurement corresponding to the input information (e.g., measurement on the input frequency point or measurement interval), avoiding signaling redundancy caused by directly sending the prediction configuration when the AI ​​unit is unavailable.

[0168] Input frequency point information corresponds to output frequency point information; thereby, network-side devices can determine available AI units based on the relationship between input and output frequency points, thus providing configuration for available AI units and avoiding signaling redundancy caused by directly issuing predictive configurations when AI units are unavailable.

[0169] Optionally, when the output frequency information is associated with the first AI unit, the first indication is also used to indicate whether the first AI unit is available, and / or that the input frequency information is associated with the first AI unit.

[0170] Optionally, the output frequency information includes information about one or more frequency points, and the information of one output frequency point corresponds to the information of one or more input frequency points. For example, the output frequency points can form a frequency point list, and each output frequency point can correspond to a frequency point list (i.e., including one or more input frequency points).

[0171] Optionally, when the first information includes the first prediction window length, the second information may include at least one of the following:

[0172] The second indication is used to indicate whether the first prediction window length is available. With the help of this second indication, the network-side device can determine the availability of its configured first prediction window length. Then, when it is determined that the first prediction window length is available, it can determine the corresponding available AI unit based on the first prediction window length, thereby providing configuration for the available AI unit and avoiding signaling redundancy caused by directly issuing prediction configuration when the AI ​​unit is unavailable.

[0173] The second prediction window length is the maximum prediction window length available to the terminal. This allows the network-side device to determine the available AI units based on the maximum prediction window length available to the terminal, thereby providing configuration for the available AI units and avoiding signaling redundancy caused by directly issuing prediction configurations when the AI ​​units are unavailable.

[0174] Optionally, when the first prediction window length is associated with the second AI unit, the second indication is also used to indicate whether the second AI unit is available, and / or that the second prediction window length is associated with the second AI unit.

[0175] Optionally, when the first information includes the first preference information, the second information includes a third indication, which indicates whether the third AI unit is available, and the third AI unit supports outputting the prediction results for the future time point. This allows network-side devices to determine the availability of the third AI unit and, when the availability is determined, provide appropriate configuration, avoiding signaling redundancy caused by directly issuing prediction configurations when the AI ​​unit is unavailable.

[0176] Optionally, the prediction result is the result of the measurement event prediction. For example, the prediction result may be a prediction of the triggering of the measurement event or a prediction of the departure of the measurement event.

[0177] Optionally, when the first information includes the first association identifier, the second information includes at least one of the following:

[0178] The fourth indication is used to indicate whether the first association identifier is available. With the help of this fourth indication, the network-side device can determine the availability of the first association identifier. Then, when it is determined that the first association identifier is available, the corresponding available AI unit can be determined according to the first association identifier, thereby providing configuration for the available AI unit and avoiding signaling redundancy caused by directly issuing predictive configuration when the AI ​​unit is unavailable.

[0179] The second association identifier is an association identifier available to the terminal; thereby, the network-side device can determine the available AI unit based on the association identifier available to the terminal, and thus provide configuration for the available AI unit, avoiding signaling redundancy caused by directly issuing predictive configuration when the AI ​​unit is unavailable.

[0180] Optionally, if the first association identifier is associated with the fourth AI unit, the fourth indication is also used to indicate whether the fourth AI unit is available, and / or, the second association identifier is associated with the fourth AI unit.

[0181] Optionally, the first information is sent based on the capabilities of the AI ​​units supported by the terminal. The network-side device sending the first information to the terminal may include:

[0182] The network-side device sends first information to the terminal based on the capabilities of the AI ​​units supported by the terminal; wherein the capabilities of the AI ​​units supported by the terminal include at least one of the following:

[0183] The terminal supports time-domain prediction;

[0184] The terminal supports frequency domain prediction;

[0185] The terminal supports spatial prediction.

[0186] The terminal supports measurement event prediction.

[0187] The information reporting method provided in this application can be executed by an information reporting device. This application uses an information reporting device executing the information reporting method as an example to illustrate the information reporting device provided in this application.

[0188] This application provides an information reporting device. As an example, the information reporting device can be a communication device or a component within a communication device, such as a chip. The communication device can be a terminal, a network-side device, or a server, etc. Exemplarily, the terminal can be, but is not limited to, the type of terminal 11 listed above, and the network-side device can be, but is not limited to, the type of network-side device 12 listed above. This application does not impose specific limitations.

[0189] The information reporting device includes a receiving module, a transmitting module, and a processing module. These modules can be implemented in software or hardware. When implemented in hardware, the processing module can be implemented by a processor. For example, the processor can include general-purpose processors, special-purpose processors, etc., such as central processing units (CPUs), microprocessors, digital signal processors (DSPs), artificial intelligence (AI) processors, graphics processing units (GPUs), application-specific integrated circuits (ASICs), network processors (NPs), field-programmable gate arrays (FPGAs), or other programmable logic devices, gate circuits, transistors, discrete hardware components, etc. The receiving and transmitting modules can be implemented by a communication interface, which can include one or more of the following: transceivers, pins, circuits, buses, radio frequency units, etc.

[0190] Specifically, referring to Figure 6, when the information reporting device is a terminal or a component within a terminal, the information reporting device 60 includes:

[0191] The first receiving module 61 is used to receive first information from the network-side device, the first information being used to provide configuration information related to the AI ​​unit;

[0192] The first sending module 62 is used to send second information to the network-side device according to the first information; the second information is used to assist the network-side device in determining the AI ​​unit available to the terminal.

[0193] Optionally, the first information includes at least one of the following:

[0194] Output frequency information;

[0195] First prediction window length;

[0196] First preference information, which is used to indicate the network-side device's expectation of obtaining the prediction result at a future time point;

[0197] The first association identifier is used to characterize the base station feature information associated with the input-output information of the AI ​​unit.

[0198] Optionally, the output frequency information corresponds to the AI ​​unit used for inter-frequency prediction.

[0199] Optionally, when the first information includes the output frequency information, the second information includes at least one of the following:

[0200] A first indication, wherein the first indication is used to indicate whether the output frequency point information is available;

[0201] Input frequency information, which corresponds to the output frequency information.

[0202] Optionally, when the output frequency information is associated with the first AI unit, the first indication is also used to indicate whether the first AI unit is available, and / or that the input frequency information is associated with the first AI unit.

[0203] Optionally, the input frequency information includes information about one or more frequency points.

[0204] Optionally, when the first information includes a first prediction window length, the second information includes at least one of the following:

[0205] A second indication is used to indicate whether the first prediction window length is available;

[0206] The second prediction window length is the maximum prediction window length available to the terminal.

[0207] Optionally, when the first prediction window length is associated with the second AI unit, the second indication is also used to indicate whether the second AI unit is available, and / or that the second prediction window length is associated with the second AI unit.

[0208] Optionally, when the first information includes the first preference information, the second information includes a third indication, the third indication being used to indicate whether a third AI unit is available, the third AI unit supporting the output of the prediction results for the future time point.

[0209] Optionally, the prediction result is the result of the prediction of the measurement event.

[0210] Optionally, when the first information includes the first association identifier, the second information includes at least one of the following:

[0211] A fourth indication is used to indicate whether the first associated identifier is available;

[0212] The second association identifier is an association identifier available to the terminal.

[0213] Optionally, if the first association identifier is associated with the fourth AI unit, the fourth indication is also used to indicate whether the fourth AI unit is available, and / or, the second association identifier is associated with the fourth AI unit.

[0214] Optionally, the first receiving module 61 is further configured to: receive a first configuration from a network-side device, the first configuration being a configuration for the available AI unit.

[0215] Optionally, the first configuration includes at least one of the following:

[0216] Measurement configuration;

[0217] Measurement interval;

[0218] The prediction configuration of the available AI units.

[0219] Optionally, the first information is sent based on the capabilities of the AI ​​units supported by the terminal; wherein the capabilities of the AI ​​units supported by the terminal include at least one of the following:

[0220] The terminal supports time-domain prediction;

[0221] The terminal supports frequency domain prediction;

[0222] The terminal supports spatial prediction.

[0223] The terminal supports measurement event prediction.

[0224] The information reporting device 60 provided in this application embodiment can implement the various processes implemented in the method embodiment shown in FIG3 and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0225] Referring to Figure 7, when the information reporting device is a network-side device or a component within a network-side device, the information reporting device 70 includes:

[0226] The second sending module 71 is used to send first information to the terminal, the first information being used to provide configuration information related to the AI ​​unit;

[0227] The second receiving module 72 is used to receive second information from the terminal; the second information is determined based on the first information, and the second information is used to assist the network-side device in determining the AI ​​units available to the terminal.

[0228] Optionally, the first information includes at least one of the following:

[0229] Output frequency information;

[0230] First prediction window length;

[0231] First preference information, which is used to indicate the network-side device's expectation of obtaining the prediction result at a future time point;

[0232] The first association identifier is used to characterize the base station feature information associated with the input-output information of the AI ​​unit.

[0233] Optionally, the output frequency information corresponds to the AI ​​unit used for inter-frequency prediction.

[0234] Optionally, when the first information includes the output frequency information, the second information includes at least one of the following:

[0235] A first indication, wherein the first indication is used to indicate whether the output frequency point information is available;

[0236] Input frequency information, which corresponds to the output frequency information.

[0237] Optionally, when the output frequency information is associated with the first AI unit, the first indication is also used to indicate whether the first AI unit is available, and / or that the input frequency information is associated with the first AI unit.

[0238] Optionally, the input frequency information includes information about one or more frequency points.

[0239] Optionally, when the first information includes a first prediction window length, the second information includes at least one of the following:

[0240] A second indication is used to indicate whether the first prediction window length is available;

[0241] The second prediction window length is the maximum prediction window length available to the terminal.

[0242] Optionally, when the first prediction window length is associated with the second AI unit, the second indication is also used to indicate whether the second AI unit is available, and / or that the second prediction window length is associated with the second AI unit.

[0243] Optionally, when the first information includes the first preference information, the second information includes a third indication, the third indication being used to indicate whether a third AI unit is available, the third AI unit supporting the output of the prediction results for the future time point.

[0244] Optionally, the prediction result is the result of the prediction of the measurement event.

[0245] Optionally, when the first information includes the first association identifier, the second information includes at least one of the following:

[0246] A fourth indication is used to indicate whether the first associated identifier is available;

[0247] The second association identifier is an association identifier available to the terminal.

[0248] Optionally, if the first association identifier is associated with the fourth AI unit, the fourth indication is also used to indicate whether the fourth AI unit is available, and / or, the second association identifier is associated with the fourth AI unit.

[0249] Optionally, the second sending module 71 is further configured to: send a first configuration to the terminal, the first configuration being a configuration for the available AI unit.

[0250] Optionally, the first configuration includes at least one of the following:

[0251] Measurement configuration;

[0252] Measurement interval;

[0253] The prediction configuration of the available AI units.

[0254] Optionally, the second sending module 71 is further configured to: send first information to the terminal according to the capabilities of the AI ​​units supported by the terminal; wherein the capabilities of the AI ​​units supported by the terminal include at least one of the following:

[0255] The terminal supports time-domain prediction;

[0256] The terminal supports frequency domain prediction;

[0257] The terminal supports spatial prediction.

[0258] The terminal supports measurement event prediction.

[0259] The information reporting device 70 provided in this application embodiment can implement the various processes implemented in the method embodiment shown in FIG5 and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0260] As shown in Figure 8, this application embodiment also provides a communication device 80, including a processor 81 and a memory 82. The memory 82 stores programs or instructions that can run on the processor 81. For example, when the communication device 80 is a terminal, the program or instructions executed by the processor 81 implement the various steps of the information reporting method embodiment shown in Figure 3 above, and achieve the same technical effect. When the communication device 80 is a network-side device, the program or instructions executed by the processor 81 implement the various steps of the information reporting method embodiment shown in Figure 5 above, and achieve the same technical effect. To avoid repetition, this will not be described again here.

[0261] This 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 used to run programs or instructions to implement the steps in the method embodiment shown in FIG3. This terminal embodiment corresponds to the above-described terminal-side method embodiment, and all implementation processes and methods of the above-described method embodiments can be applied to this terminal embodiment and can achieve the same technical effect. The terminal may be the information reporting device shown in FIG6.

[0262] Specifically, Figure 9 is a schematic diagram of the hardware structure of a terminal implementing an embodiment of this application.

[0263] The terminal 900 includes, but is not limited to, at least some of the following components: radio frequency unit 901, network module 902, audio output unit 903, input unit 904, sensor 905, display unit 906, user input unit 907, interface unit 908, memory 909, and processor 910.

[0264] Those skilled in the art will understand that the terminal 900 may also include a power supply (such as a battery) for powering various components. The power supply can be logically connected to the processor 900 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The terminal structure shown in Figure 9 does not constitute a limitation on the terminal. The terminal may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0265] It should be understood that, in this embodiment, the input unit 904 may include a graphics processor 9041 and a microphone 9042. The graphics processor 9041 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 906 may include a display panel 9061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 907 includes at least one of a touch panel 9071 and other input devices 9072. The touch panel 9071 is also called a touch screen. The touch panel 9071 may include a touch detection device and a touch controller. Other input devices 9072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be described in detail here.

[0266] In this embodiment, after receiving downlink data from the network-side device, the radio frequency unit 901 can transmit it to the processor 910 for processing; in addition, the radio frequency unit 901 can send uplink data to the network-side device. Typically, the radio frequency unit 901 includes, but is not limited to, antennas, amplifiers, transceivers, couplers, low-noise amplifiers, duplexers, etc.

[0267] The memory 909 can be used to store software programs or instructions, as well as various data. The memory 909 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 909 may include volatile memory or non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 909 in the embodiments of this application includes, but is not limited to, these and any other suitable types of memory.

[0268] Processor 910 may include one or more processing units; optionally, processor 910 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 910.

[0269] The radio frequency unit 901 is used to receive first information from the network-side device, the first information being used to provide configuration information related to the AI ​​unit; and to send second information to the network-side device based on the first information; the second information being used to assist the network-side device in determining the artificial intelligence AI unit available to the terminal 900.

[0270] It is understood that the implementation process of each implementation method mentioned in this embodiment can refer to the relevant description of the method embodiment shown in Figure 3, and achieve the same or corresponding technical effects. To avoid repetition, it will not be described again here.

[0271] This application also provides a network-side device, including a processor and a communication interface. The communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the steps of the method embodiment shown in FIG5. This network-side device embodiment corresponds to the above-described network-side device method embodiment. All implementation processes and methods of the above-described method embodiments can be applied to this network-side device embodiment and can achieve the same technical effect.

[0272] Specifically, this application embodiment also provides a network-side device, which can be the information reporting device shown in FIG. 7. As shown in FIG. 10, the network-side device 100 includes: an antenna 101, a radio frequency device 102, a baseband device 103, a processor 104, and a memory 105. The antenna 101 is connected to the radio frequency device 102. In the uplink direction, the radio frequency device 102 receives information through the antenna 101 and sends the received information to the baseband device 103 for processing. In the downlink direction, the baseband device 103 processes the information to be transmitted and sends it to the radio frequency device 102. The radio frequency device 102 processes the received information and transmits it through the antenna 101.

[0273] The method executed by the network-side device in the above embodiments can be implemented in the baseband device 103, which includes a baseband processor.

[0274] The baseband device 103 may include at least one baseband board, on which multiple chips are disposed, as shown in FIG10. One of the chips is, for example, a baseband processor, which is connected to the memory 105 via a bus interface to call the program or instructions in the memory 105 to execute the network-side device operation shown in the above method embodiment.

[0275] The network-side device may also include a network interface 106, such as a Common Public Radio Interface (CPRI).

[0276] The radio frequency device 102 is used to send first information to the terminal, the first information being used to provide configuration information related to the AI ​​unit; and to receive second information from the terminal; the second information is determined based on the first information, and the second information is used to assist the network-side device in determining the AI ​​units available to the terminal.

[0277] In addition, the network-side device 1000 of this application embodiment also includes: a program or instructions stored in the memory 105 and executable on the processor 104. The processor 104 calls the program or instructions in the memory 105 to execute the methods executed by each module shown in FIG7 and achieve the same technical effect. To avoid repetition, it will not be described in detail here.

[0278] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the information reporting method embodiments shown in Figure 3 or Figure 5 above, and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0279] The processor mentioned above is either the processor in the terminal described in the above embodiments or the processor in the network-side device. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk. In some examples, the readable storage medium may be a non-transient readable storage medium.

[0280] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described information reporting method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0281] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0282] This application also provides a computer program / program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the above-described information reporting method embodiments, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0283] This application embodiment also provides a communication system, including: a terminal and a network-side device, wherein the terminal can be used to execute the steps of the information reporting method shown in Figure 3 above, and the network-side device can be used to execute the steps of the information reporting method shown in Figure 5 above.

[0284] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0285] From the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of computer software products plus necessary general-purpose hardware platforms, and of course, they can also be implemented by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.), and the computer software product includes several instructions to cause the terminal or network-side device to execute the methods described in the various embodiments of this application.

[0286] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other implementations under the guidance of this application without departing from the spirit and scope of the claims. All of these implementations are within the protection scope of this application.

Claims

1. An information reporting method, comprising: The terminal receives first information from the network-side device, the first information being used to provide configuration information related to the artificial intelligence (AI) unit; The terminal sends second information to the network-side device based on the first information; wherein the second information is used to assist the network-side device in determining the AI ​​unit available to the terminal.

2. The method according to claim 1, wherein, The first information includes at least one of the following: Output frequency information; First prediction window length; First preference information, which is used to indicate the network-side device's expectation of obtaining the prediction result at a future time point; The first association identifier is used to characterize the base station feature information associated with the input-output information of the AI ​​unit.

3. The method according to claim 2, wherein, The output frequency information corresponds to the AI ​​unit used for inter-frequency prediction.

4. The method according to claim 2 or 3, wherein, When the first information includes the output frequency point information, the second information includes at least one of the following: A first indication, wherein the first indication is used to indicate whether the output frequency point information is available; Input frequency information, which corresponds to the output frequency information.

5. The method according to claim 4, wherein, When the output frequency information is associated with the first AI unit, the first indication is also used to indicate whether the first AI unit is available, and / or whether the input frequency information is associated with the first AI unit.

6. The method according to claim 4 or 5, wherein, The input frequency information includes information about one or more frequency points.

7. The method according to claim 2, wherein, When the first information includes the first prediction window length, the second information includes at least one of the following: A second indication is used to indicate whether the first prediction window length is available; The second prediction window length is the maximum prediction window length available to the terminal.

8. The method according to claim 7, wherein, When the first prediction window length is associated with the second AI unit, the second indication is also used to indicate whether the second AI unit is available, and / or whether the second prediction window length is associated with the second AI unit.

9. The method according to claim 2, wherein, When the first information includes the first preference information, the second information includes a third indication, which is used to indicate whether a third AI unit is available, and the third AI unit supports outputting the prediction results for the future time point.

10. The method according to claim 2 or 9, wherein, The prediction result is the result of predicting the measured event.

11. The method according to claim 2, wherein, When the first information includes the first associated identifier, the second information includes at least one of the following: A fourth indication is used to indicate whether the first associated identifier is available; The second association identifier is an association identifier available to the terminal.

12. The method according to claim 11, wherein, When the first association identifier is associated with the fourth AI unit, the fourth indication is also used to indicate whether the fourth AI unit is available, and / or, the second association identifier is associated with the fourth AI unit.

13. The method according to any one of claims 1 to 12, wherein the method further comprises: The terminal receives a first configuration from the network-side device, the first configuration being a configuration for the available AI unit.

14. The method according to claim 13, wherein, The first configuration includes at least one of the following: Measurement configuration; Measurement interval; The prediction configuration of the available AI units.

15. An information reporting method, comprising: The network-side device sends first information to the terminal, the first information being used to provide configuration information related to the AI ​​unit; The network-side device receives second information from the terminal; wherein the second information is determined based on the first information, and the second information is used to assist the network-side device in determining the AI ​​units available to the terminal.

16. The method according to claim 15, wherein, The first information includes at least one of the following: Output frequency information; First prediction window length; First preference information, which is used to indicate the network-side device's expectation of obtaining the prediction result at a future time point; The first association identifier is used to characterize the base station feature information associated with the input-output information of the AI ​​unit.

17. The method according to claim 16, wherein, The output frequency information corresponds to the AI ​​unit used for inter-frequency prediction.

18. The method according to claim 16 or 17, wherein, When the first information includes the output frequency point information, the second information includes at least one of the following: A first indication, wherein the first indication is used to indicate whether the output frequency point information is available; Input frequency information, which corresponds to the output frequency information.

19. The method according to claim 18, wherein, When the output frequency information is associated with the first AI unit, the first indication is also used to indicate whether the first AI unit is available, and / or whether the input frequency information is associated with the first AI unit.

20. The method of claim 16, wherein, When the first information includes the first prediction window length, the second information includes at least one of the following: A second indication is used to indicate whether the first prediction window length is available; The second prediction window length is the maximum prediction window length available to the terminal.

21. The method according to claim 20, wherein, When the first prediction window length is associated with the second AI unit, the second indication is also used to indicate whether the second AI unit is available, and / or whether the second prediction window length is associated with the second AI unit.

22. The method according to claim 16, wherein, When the first information includes the first preference information, the second information includes a third indication, which is used to indicate whether a third AI unit is available, and the third AI unit supports outputting the prediction results for the future time point.

23. The method according to claim 16, wherein, When the first information includes the first associated identifier, the second information includes at least one of the following: A fourth indication is used to indicate whether the first associated identifier is available; The second association identifier is an association identifier available to the terminal.

24. The method according to claim 23, wherein, When the first association identifier is associated with the fourth AI unit, the fourth indication is also used to indicate whether the fourth AI unit is available, and / or, the second association identifier is associated with the fourth AI unit.

25. The method according to any one of claims 15 to 24, wherein the method further comprises: The network-side device sends a first configuration to the terminal, the first configuration being a configuration for the available AI unit.

26. The method according to any one of claims 15 to 25, wherein, The network-side device sends first information to the terminal, including: The network-side device sends the first information to the terminal based on the capabilities of the AI ​​units supported by the terminal; The AI ​​unit supported by the terminal includes at least one of the following capabilities: The terminal supports time-domain prediction; The terminal supports frequency domain prediction; The terminal supports spatial prediction. The terminal supports measurement event prediction.

27. An information reporting device, comprising: The first receiving module is used to receive first information from the network-side device, wherein the first information is used to provide configuration information related to the AI ​​unit; The first sending module is used to send second information to the network-side device based on the first information; wherein the second information is used to assist the network-side device in determining the AI ​​unit available to the terminal.

28. The apparatus according to claim 27, wherein, The first information includes at least one of the following: Output frequency information; First prediction window length; First preference information, which is used to indicate the network-side device's expectation of obtaining the prediction result at a future time point; The first association identifier is used to characterize the base station feature information associated with the input-output information of the AI ​​unit.

29. The apparatus according to claim 28, wherein, When the first information includes the output frequency point information, the second information includes at least one of the following: A first indication, wherein the first indication is used to indicate whether the output frequency point information is available; Input frequency information, which corresponds to the output frequency information.

30. The apparatus according to claim 28, wherein, When the first information includes the first prediction window length, the second information includes at least one of the following: A second indication is used to indicate whether the first prediction window length is available; The second prediction window length is the maximum prediction window length available to the terminal.

31. The apparatus according to claim 28, wherein, When the first information includes the first preference information, the second information includes a third indication, which is used to indicate whether a third AI unit is available, and the third AI unit supports outputting the prediction results for the future time point.

32. The apparatus according to claim 28, wherein, When the first information includes the first associated identifier, the second information includes at least one of the following: A fourth indication is used to indicate whether the first associated identifier is available; The second association identifier is an association identifier available to the terminal.

33. An information reporting device, comprising: The second sending module is used to send first information to the terminal, the first information being used to provide configuration information related to the AI ​​unit; The second receiving module is configured to receive second information from the terminal; wherein the second information is determined based on the first information, and the second information is used to assist the network-side device in determining the AI ​​units available to the terminal.

34. The apparatus according to claim 33, wherein, The first information includes at least one of the following: Output frequency information; First prediction window length; First preference information, which is used to indicate the network-side device's expectation of obtaining the prediction result at a future time point; The first association identifier is used to characterize the base station feature information associated with the input-output information of the AI ​​unit.

35. A terminal comprising a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the information reporting method as described in any one of claims 1 to 14.

36. A network-side device, comprising a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the information reporting method as described in any one of claims 15 to 26.

37. A readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the information reporting method as described in any one of claims 1 to 14, or implement the steps of the information reporting method as described in any one of claims 15 to 26.

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