Transmission method and device, terminal and network side equipment

Abstract

The invention discloses a transmission method and device, a terminal and network side equipment, and belongs to the technical field of communication, and the transmission method comprises the steps that the terminal sends first information to the network side equipment, and the first information is used for indicating an adjustment suggestion of the terminal for a first artificial intelligence AI unit.

Description

Technical Field

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

[0002] With the development of Artificial Intelligence (AI) technology, AI models (also referred to as AI units) have been applied to communication systems. Currently, in a communication system, for the model monitoring of an AI model, it is usually mainly based on the terminal reporting monitoring metrics or monitoring data, and the network side adjusts the AI model according to the monitoring metrics or monitoring data reported by multiple terminals. However, the AI model adjusted by the network side in this way is not applicable to all terminals, resulting in poor applicability of the adjustment method of the AI model. Summary of the Invention

[0003] Embodiments of this application provide a transmission method, apparatus, terminal, and network-side device, which can solve the problem of poor applicability of the adjustment method for an AI model in related technologies.

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

[0005] The terminal sends first information to a network-side device, where the first information is used to indicate an adjustment suggestion of the terminal for a first Artificial Intelligence (AI) unit.

[0006] In a second aspect, a transmission method is provided, which is executed by a network-side device. The method includes:

[0007] The network-side device receives the first information sent by the terminal, where the first information is used to indicate an adjustment suggestion of the terminal for a first AI unit.

[0008] In a third aspect, a transmission apparatus is provided. The apparatus includes:

[0009] A first sending module, configured to send first information to a network-side device, where the first information is used to indicate an adjustment suggestion of the apparatus for a first AI unit.

[0010] In a fourth aspect, a transmission apparatus is provided. The apparatus includes:

[0011] A second receiving module, configured to receive the first information sent by the terminal, where the first information is used to indicate an adjustment suggestion of the terminal for a first AI unit.

[0012] In a fifth aspect, a terminal is provided. The terminal includes a processor and a memory. The memory stores a program or instruction that can run on the processor. When the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.

[0013] In a sixth aspect, a terminal is provided, including a processor and a communication interface, where the communication interface is configured to send first information to a network-side device, and the first information is used to indicate an adjustment suggestion of the terminal for a first AI unit.

[0014] In a seventh aspect, a network-side device is provided. The network-side device includes a processor and a memory. The memory stores a program or instructions that can be run on the processor. When the program or instructions are executed by the processor, the steps of the method described in the second aspect are implemented.

[0015] In an eighth aspect, a network-side device is provided, including a processor and a communication interface. The communication interface is configured to receive first information sent by a terminal, and the first information is used to indicate an adjustment suggestion of the terminal for a first AI unit.

[0016] In a ninth aspect, a readable storage medium is provided. A program or instructions are stored on the readable storage medium. When the program or instructions are executed by a processor, the steps of the method described in the first aspect are implemented, or the steps of the method described in the second aspect are implemented.

[0017] In a tenth aspect, a wireless communication system is provided, including: a terminal and a network-side device. The terminal can be configured to execute the steps of the method described in the first aspect, and the network-side device can be configured to execute the steps of the method described in the second aspect.

[0018] In an eleventh aspect, a chip is provided. The chip includes a processor and a communication interface. The communication interface is coupled to the processor, and the processor is configured to run a program or instructions to implement the method described in the first aspect, or to implement the method described in the second aspect.

[0019] In a twelfth aspect, a computer program / program product is provided. The computer program / program product is stored in a storage medium, and the program / program product is executed by at least one processor to implement the steps of the method described in the first aspect or the second aspect.

[0020] In an embodiment of the present application, the terminal sends first information to the network-side device, and the first information is used to indicate an adjustment suggestion of the terminal for a first AI unit. In this way, the adjustment of the first AI unit by the network-side device is based on the adjustment suggestion reported by the terminal, making the adjustment of the first AI unit more suitable for the operation scenario of the terminal and effectively improving the applicability of the adjustment of the AI unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A block diagram of a wireless communication system to which an embodiment of the present application can be applied is shown;

[0022] Figure 2 is a flowchart of a transmission method provided by an embodiment of the present application;

[0023] Figure 3 is a flowchart of another transmission method provided by an embodiment of the present application;

[0024] Figure 4 is a flowchart of another transmission method provided by an embodiment of the present application;

[0025] Figure 5 is a structural diagram of a transmission device provided by an embodiment of the present application;

[0026] Figure 6 is a structural diagram of another transmission device provided by an embodiment of the present application;

[0027] Figure 7 is a structural diagram of a communication device provided by an embodiment of the present application;

[0028] Figure 8 is a structural diagram of a terminal provided by an embodiment of the present application;

[0029] Figure 9 is a structural diagram of a network - side device provided by an embodiment of the present application. Detailed implementation manners

[0030] Next, the technical solutions in the embodiments of the present application will be clearly described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope protected by the present application.

[0031] The terms "first", "second", etc. in the present application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such terms can be interchanged under appropriate circumstances so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first" and "second" are usually of the same category, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "or" in the present application means at least one of the connected objects. For example, "A or B" covers three scenarios, namely, Scenario 1: including A and not including B; Scenario 2: including B and not including A; Scenario 3: including both A and B. The character " / " generally indicates an "or" relationship between the associated objects before and after.

[0032] The term "indication" in this application can be either a direct indication (or an explicit indication) or an indirect indication (or an implicit indication). Among them, a direct indication can be understood as that the sender clearly tells the receiver specific information, operations to be performed, request results, etc. in the sent indication; an indirect indication can be understood as that the receiver determines the corresponding information according to the indication sent by the sender, or makes a judgment and determines the operations to be performed or request results, etc. according to the judgment result.

[0033] It should be noted that the technology described in the embodiments of this application is not limited to the Long Term Evolution (LTE) / LTE-Advanced (LTE-A) system, and can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency-Division Multiple Access (SC-FDMA), or other systems. The terms "system" and "network" in the embodiments of this application are often used interchangeably, and the described technology can be used in the systems and radio technologies mentioned above, as well as in other systems and radio technologies. The following description describes the New Radio (NR) system for example purposes, and uses NR terms in most of the following descriptions, but these technologies can also be applied to systems other than the NR system, such as the 6th Generation (6G) communication system. th Generation, 6G) communication system.

[0034] Figure 1A block diagram of a wireless communication system to which embodiments of the present application can be applied is shown. The wireless communication system includes a terminal 11 and a network-side device 12. Among them, the terminal 11 can be a mobile phone, a tablet personal computer, a laptop computer, a notebook computer, a personal digital assistant (PDA), a handheld computer, a netbook, an ultra-mobile personal computer (UMPC), a mobile internet device (MID), an augmented reality (AR), a virtual reality (VR) device, a robot, a wearable device, a flight vehicle, a vehicle user equipment (VUE), a shipborne device, a pedestrian user equipment (PUE), a smart home (home devices with wireless communication functions, such as refrigerators, TVs, washing machines, or furniture, etc.), a game console, a personal computer (PC), a teller machine, or a self-service machine, etc., which are terminal-side devices. Wearable devices include: smart watches, smart bracelets, smart earphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart ankle chains, etc.), smart wristbands, smart clothing, etc. Among them, the vehicle user equipment can also be referred to as a vehicle terminal, a vehicle controller, a vehicle module, a vehicle component, a vehicle chip, or a vehicle unit, etc. It should be noted that the specific type of the terminal 11 is not limited in the embodiments of the present application. The network-side device 12 can include an access network device or a core network device. Among them, the access network device can also be referred to as a radio access network (RAN) device, a radio access network function, or a radio access network unit. The access network device can include a base station, a wireless local area network (WLAN) access point (AP), or a wireless fidelity (WiFi) node, etc.Among them, the base station can be referred to as Node B (NB), Evolved Node B (eNB), the next generation Node B (gNB), New Radio Node B (NR Node B), access point, Relay Base Station (RBS), Serving Base Station (SBS), Base Transceiver Station (BTS), radio base station, radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), home Node B (HNB), home evolved Node B, Transmission Reception Point (TRP), or some other suitable term in the field. As long as the same technical effect is achieved, the base station is not limited to specific technical terms. It should be noted that in the embodiments of this application, only the base station in the NR system is taken as an example for introduction, and the specific type of the base station is not limited.

[0035] For better understanding, the following explains the related concepts that may be involved in the embodiments of this application.

[0036] Artificial intelligence has currently been widely applied in various fields. There are various implementation methods for the AI module, such as neural networks, decision trees, support vector machines, Bayesian classifiers, etc. This application takes neural networks as an example for illustration, but does not limit the specific type of the AI module.

[0037] The parameters of the neural network are optimized through optimization algorithms. An optimization algorithm is a type of algorithm that can help us minimize or maximize the objective function (sometimes also called the loss function). And the objective function is often a mathematical combination of model parameters and data. For example, given data X and its corresponding label Y, we construct a neural network model f(.). After having the model, according to the input x, we can obtain the predicted output f(x), and can calculate the gap between the predicted value and the true value (f(x) - Y), which is the loss function. Our goal is to find the appropriate weights (multiplicative coefficients) and biases (additive coefficients) to minimize the value of the above loss function. The smaller the loss value, the closer our AI model is to the real situation.

[0038] Currently, the common optimization algorithms are basically based on the error Back Propagation (BP) algorithm. The basic idea of the BP algorithm is that the learning process consists of two processes: the forward propagation of signals and the backward propagation of errors. During forward propagation, the input samples are passed from the input layer, processed layer by layer through each hidden layer, and then passed to the output layer. If the actual output of the output layer does not match the expected output, it enters the backward propagation stage of errors. Error backpropagation is to pass the output error back layer by layer through the hidden layer to the input layer in a certain form, and distribute the error to all units in each layer, so as to obtain the error signals of each layer of units. This error signal is used as the basis for correcting the weights of each unit. This process of adjusting the weights of each layer in the forward propagation of signals and the backward propagation of errors is carried out cyclically. The process of continuously adjusting the weights is also the learning and training process of the network. This process continues until the error of the network output is reduced to an acceptable level, or until a pre-set number of learning times is reached.

[0039] Generally speaking, according to different types of problems to be solved, the selected AI algorithms and the adopted AI models are also different. At present, the main method to improve the 5G network performance with the help of AI is to enhance or replace the existing algorithms or processing modules through algorithms and AI models based on neural networks. In specific scenarios, algorithms and AI models based on neural networks can achieve better performance than those based on deterministic algorithms. Commonly used neural networks include deep neural networks, convolutional neural networks, and recurrent neural networks, etc. With the help of existing AI tools, the construction, training, and verification of neural networks can be realized.

[0040] Replacing the modules in the existing system through AI / Machine Learning (ML) methods can effectively improve the system performance. For example, for Channel State Information (CSI) prediction, the historical CSI is input into the AI model, and the AI model analyzes the time-domain change characteristics of the channel and outputs the future CSI. By monitoring and analyzing the system performance, there will be a very large performance gain in CSI prediction compared with the scheme without prediction. At the same time, different future moments for prediction will result in different prediction accuracies.

[0041] Although the CSI prediction based on AI can obtain certain gains, there are also certain disadvantages. The most important one is that the generalization of the AI-based scheme is poor. When the inference environment is quite different from the training environment, the performance of the AI model will become very poor, that is, there will be a mismatch. For example, the CSI prediction AI model trained with the channel of a terminal moving at a speed of 30 km / h will have very poor performance in the terminal moving scenario at 60 km / h. Therefore, it is necessary to monitor the actual inference performance of CSI prediction and trigger a series of adjustment measures according to the monitoring results.

[0042] Model monitoring can be based on the implementation of various metrics, such as monitoring based on input / output data characteristics or distributions, monitoring based on intermediate model outputs (error metrics, accuracy metrics), monitoring based on final performance results, monitoring based on comparison results with other solutions, etc.

[0043] Monitoring based on the distribution of model inputs or outputs: Statistical calculations are performed on the input information or output information of a running model (a model being inferred, an activated model) to obtain the distribution information of the model's input information or output information (such as mean, mean vector, mean matrix, variance, variance vector, variance matrix, covariance, covariance vector, covariance matrix, maximum value, maximum value vector, maximum value matrix, minimum value, minimum value vector, minimum value matrix). There is also a range of applicable input or output distribution information when the model is trained. The terminal compares the calculated distribution information with the applicable distribution information range of the model to obtain the monitoring result. Alternatively, the terminal reports the calculated distribution information to the network side, and the network side compares the received distribution information with the applicable distribution information range of the model to obtain the monitoring result.

[0044] Monitoring based on intermediate results (error metrics, accuracy metrics) calculated from model outputs: The output information of the model is compared with its corresponding true value information to calculate intermediate results such as the error or accuracy of the model output. The terminal compares this intermediate result with a preset threshold to obtain the monitoring result. Alternatively, the terminal reports the calculated intermediate result to the network side, and the network side compares the received intermediate result with the preset threshold to obtain the monitoring result.

[0045] Monitoring based on final performance results: The terminal or the network side statistics or calculates the performance of the current communication system, such as throughput, spectral efficiency, signal-to-noise and interference ratio (SINR), signal-to-noise ratio (SNR), bit error rate, block error rate, packet loss rate, transmission rate (up / down), peak rate (up / down), etc. The terminal or the network side compares the current communication system performance with a preset threshold to obtain the monitoring result. If the terminal statistics or calculates the current communication system performance, it can also report this communication system performance to the network side.

[0046] Monitoring based on comparison results with other solutions: The terminal compares the intermediate results or final performance results obtained based on the model with the intermediate results or final performance results obtained by other solutions to obtain the monitoring results. Alternatively, the terminal reports the intermediate results or final performance results obtained based on the model and the intermediate results or final performance results obtained by other solutions to the network side, and the network side compares the intermediate results or final performance results obtained based on the model with the intermediate results or final performance results obtained by other solutions to obtain the monitoring results.

[0047] Currently, in a communication system, for the monitoring of an AI unit, it is usually mainly based on the terminal reporting monitoring metrics or monitoring data, and the network side adjusts the AI unit according to the monitoring metrics or monitoring data reported by multiple terminals. However, the AI unit adjusted by the network side in this way is not applicable to all terminals, resulting in poor applicability of the adjustment method of the AI unit.

[0048] It should be noted that the AI unit described in this application can also be referred to as an AI model, AI structure, etc., or the AI unit can also refer to a processing unit that can implement specific algorithms, formulas, processing flows, capabilities, etc. related to AI, or the AI unit can be a processing method, algorithm, function, module or unit for a specific data set, or the AI unit can be a processing method, algorithm, function, module or unit running on AI-related hardware such as GPU, NPU, TPU, ASIC, etc. This application does not make specific limitations on this. Optionally, the specific data set includes the input and / or output of the AI unit.

[0049] Optionally, the identifier of the AI unit can be an AI model identifier, an AI structure identifier, an AI algorithm identifier, a functionality ID, a physical identifier, a logical identifier, a global identifier, a local identifier, or the identifier of a specific data set associated with the AI unit, or the identifier of a specific scenario, environment, channel characteristic, device related to the AI, or the identifier of a function, feature, capability or module related to the AI. This application does not make specific limitations on this.

[0050] Next, with reference to the accompanying drawings, through some embodiments and their application scenarios, the transmission method, device, communication equipment, etc. provided by the embodiments of this application will be described in detail.

[0051] Please refer to Figure 2 , Figure 2 which is a flowchart of a transmission method provided by an embodiment of this application. As Figure 2 shown, the method includes the following steps:

[0052] Step 201, the terminal sends first information to the network side device, and the first information is used to indicate the adjustment suggestion for the first AI unit.

[0053] Among them, the first AI unit may refer to a specific AI unit. Optionally, the terminal may determine the first AI unit through the identifier of the AI unit, so that the network-side device can know which AI unit the first information is directed to.

[0054] Exemplarily, the first AI unit is the AI unit operated (or used) by the terminal. Further, the terminal may determine an adjustment suggestion for adjusting the first AI unit according to the operation status of the first AI unit. For example, the model parameters that need to be adjusted for the first AI unit, the monitoring configuration of the first AI unit, etc., or it may also include a suggestion on whether to adjust the first AI unit, such as keeping the first AI unit running with the current configuration, switching the first AI unit, etc. The terminal generates the first information based on these adjustment suggestions and sends the first information to the network-side device, that is, sends the adjustment suggestions of the terminal for the first AI unit to the network-side device, which helps the network-side device to make a decision on whether to adjust the first AI unit and how to adjust it according to the adjustment suggestions.

[0055] In the embodiment of the present application, the terminal sends the first information to the network-side device, and the first information is used to indicate the adjustment suggestions of the terminal for the first AI unit. Further, the terminal can also obtain the adjustment suggestions according to its own operation status of the first AI unit, which helps the network-side device to make a decision on whether to adjust the first AI unit and how to adjust it according to the adjustment suggestions. In this way, it is also made that the adjustment of the first AI unit by the network-side device is based on the adjustment suggestions reported by the terminal, making the adjustment of the first AI unit more suitable for the operation scenario of the terminal, and making the adjusted first AI unit more suitable for running on the terminal, which helps to improve the inference performance of the first AI unit.

[0056] Optionally, before step 201, the method further includes:

[0057] The terminal receives configuration information sent by the network-side device, and the configuration information is used to instruct the terminal to report adjustment suggestions for the first AI unit.

[0058] In the embodiment of the present application, before the terminal sends the first information to the network-side device, the network-side device sends configuration information to the terminal, and the configuration information is used to instruct the terminal to report adjustment suggestions for the first AI unit, that is, the network-side device can inform the terminal through the configuration information which adjustment suggestions can be reported, so that the terminal can clarify which adjustment suggestions need to be collected according to the configuration information, which helps to better generate the first information.

[0059] Optionally, the first information or the configuration information includes at least one of the following adjustment suggestions:

[0060] Keep the first AI unit running with the current configuration, that is, the first AI unit may not be adjusted;

[0061] Switch to a second AI unit, where the second AI unit refers to an AI unit different from the first AI unit, and the first information or the configuration information may include an identifier for switching to the second AI unit;

[0062] Fallback to a non-AI mode, for example, the first AI unit may be stopped;

[0063] Turn off the function corresponding to the first AI unit. For example, if the first AI unit is an AI unit for CSI prediction, the CSI prediction function may be turned off. Another example is that if the first AI unit is an AI unit for CSI prediction in a specific scenario (such as a specific speed range), the CSI prediction function for the above specific scenario may be turned off, but the CSI prediction functions for other scenarios are not adjusted;

[0064] Adjust the parameters of the first AI unit;

[0065] Request a new AI unit;

[0066] Deregister the first AI unit;

[0067] Adjust the monitoring configuration of the first AI unit, where the monitoring configuration includes information related to the monitoring window (monitoring duration, number of monitoring samples, etc.), information on the triggering conditions for monitoring, information on the reporting conditions for monitoring results, etc.;

[0068] Request training for the first AI unit;

[0069] Request data collection for training the first AI unit;

[0070] Request multiple AI units to run in parallel or monitor multiple AI units, where the multiple AI units may include the first AI unit;

[0071] Adjust the auxiliary information in the input of the first AI unit. The auxiliary information is part of the input information of the first AI unit and can be of various types. For example, the auxiliary information of the first AI unit for CSI prediction may be speed information, location information, perception information, Doppler information, cell identification information, etc.

[0072] In the embodiments of the present application, the first information sent by the terminal to the network-side device includes at least one of the above adjustment suggestions. Further, the network-side device obtains these adjustment suggestions according to the first information reported by the terminal, which helps the network-side device make decisions on whether to adjust the first AI unit and how to adjust it according to the adjustment suggestions. In addition, the configuration information sent by the network-side device to the terminal may also include at least one of the above adjustment suggestions, so that the terminal can also clarify which adjustment suggestions need to be reported according to the configuration information, making the reporting behavior of the terminal more targeted.

[0073] Optionally, the first information or the configuration information further includes at least one of the following:

[0074] The reporting conditions of each adjustment suggestion in the first information or the configuration information, for example, if a certain adjustment suggestion or related index meets a preset threshold condition, it is determined to report a certain adjustment suggestion;

[0075] The reporting priorities of each adjustment suggestion in the first information or the configuration information, that is, which adjustment suggestions can be reported preferentially;

[0076] The reporting order of each adjustment suggestion in the first information or the configuration information, that is, the sequence of reporting of each adjustment suggestion, for example, which adjustment suggestions are reported first and which are reported later;

[0077] The dependency relationships between the adjustment suggestions in the first information or the configuration information, for example, which adjustment suggestions need to be reported together, such as the adjustment suggestions to cancel the first AI unit and roll back to the non-AI mode need to be reported together, and another example is that the request for training the first AI unit and the request for data collection for training the first AI unit need to be reported together, etc.

[0078] In the embodiments of the present application, by defining the reporting conditions, reporting priorities, reporting order or dependency relationships of each adjustment suggestion in the first information or the configuration information, the reporting of the adjustment suggestions is made more orderly.

[0079] Optionally, before the terminal sends the first information to the network-side device, the method further includes:

[0080] The terminal receives the second information sent by the network-side device, and the second information is used to indicate the monitoring type of the first AI unit by the terminal, and the monitoring type includes at least one of the following:

[0081] Monitoring based on monitoring metrics;

[0082] Monitoring based on raw data, where the raw data is the data used to calculate the monitoring metrics;

[0083] Monitoring based on terminal suggestions.

[0084] It should be noted that the monitoring based on monitoring metrics refers to the monitoring metrics obtained by schemes such as monitoring based on the input and / or output data characteristics or distributions of the AI unit, monitoring based on the intermediate results of the AI unit output, and monitoring based on the final performance results of the AI unit, such as accuracy metrics, error metrics, distribution metrics, scoring metrics, etc. These metrics can be one or a set of numerical values.

[0085] The monitoring based on the original data refers to the original data of the monitoring metrics obtained by schemes such as the terminal reporting the monitoring based on the input and / or output data characteristics or distributions of the AI unit, monitoring based on the intermediate results of the AI unit output, and monitoring based on the final performance results of the AI unit, such as the output of the first AI unit, the true value corresponding to the output of the first AI unit, etc.

[0086] The monitoring based on the terminal suggestion, that is, the monitoring of the adjustment suggestion reported by the terminal to the network-side device for the first AI unit as described above.

[0087] In the embodiment of the present application, the network-side device sends the second information to the terminal, which in turn enables the terminal to determine which monitoring type to adopt for the first AI unit based on the second information, facilitating the monitoring of the inference performance of the first AI unit by the terminal and also facilitating the terminal to collect relevant data during the operation of the first AI unit to obtain the monitoring data or adjustment suggestions that need to be reported.

[0088] Optionally, the terminal sending the first information to the network-side device includes:

[0089] The terminal generates the first information based on at least one of the second information, the configuration message, and the inference performance of the first AI unit;

[0090] The terminal sends the first information to the network-side device.

[0091] Exemplarily, when the terminal receives the second information and the configuration information sent by the network-side device, it can generate the first information based on at least one of the second information, the configuration message, and the inference performance of the first AI unit. For example, the terminal generates the first information based on the configuration information and the inference performance of the first AI unit, that is, generates the adjustment suggestion of the terminal for the first AI unit; or, when the second information includes the case based on the terminal suggestion, the terminal generates the first information based on the second information, the configuration message, and the inference performance of the first AI unit, that is, generates the adjustment suggestion of the terminal for the first AI unit. Among them, the inference performance of the first AI unit may include the inference performance monitoring metrics obtained by the terminal based on the operation of the first AI unit.

[0092] In the embodiments of the present application, the terminal can generate first information based on at least one of the second information, the configuration message, and the inference performance of the first AI unit, that is, generate an adjustment suggestion for the first AI unit by the terminal, thereby clarifying the generation method of the adjustment suggestion.

[0093] Optionally, the sending of the second information and the configuration information satisfies any one of the following:

[0094] The second information and the configuration information are carried in the same information for sending. For example, the second information and the configuration information can be two contents or two sub-informations in one information. Furthermore, the second information and the configuration information are sent together, which helps to save transmission resources;

[0095] The second information and the configuration information are carried in the same signaling for sending. For example, the second information and the configuration information are two independent informations, but can be carried in the same signaling for sending, which also helps to save transmission resources;

[0096] The second information and the configuration information are respectively carried in different signallings for sending, that is, the second information and the configuration information are two independent informations and are respectively carried in different signallings for sending.

[0097] Optionally, before the terminal receives the second information sent by the network-side device, the method further includes at least one of the following:

[0098] The terminal sends first capability information to the network-side device, and the first capability information is used to indicate the monitoring type of the AI unit supported by the terminal;

[0099] The terminal sends second capability information to the network-side device, and the second capability information is used to indicate whether the terminal supports the monitoring based on the terminal suggestion;

[0100] The terminal sends third capability information to the network-side device, and the third capability information is used to indicate the adjustment suggestion that the terminal can make.

[0101] For example, the terminal sends first capability information to the network-side device, and uses the first capability information to indicate the monitoring type of the AI unit supported by the terminal, that is, indicates which type or types of monitoring based on monitoring metrics, monitoring based on raw data, and monitoring based on terminal suggestions the terminal supports, so that the network-side device can clarify the monitoring type of the AI unit supported by the terminal, which helps the network-side device clarify subsequent actions, such as whether to send the configuration information to the terminal, etc.

[0102] For another example, the terminal may send second capability information to the network device, and use the second capability information to indicate whether the terminal supports the monitoring based on the terminal's suggestion, so that the network device can determine whether the terminal supports the monitoring based on the terminal's suggestion, and thus can determine whether the terminal can report adjustment suggestions for the first AI unit, which also helps the network device decide whether to send the configuration information to the terminal.

[0103] For another example, the terminal may also send third capability information to the network device, and use the third capability information to indicate the adjustment suggestions that the terminal can make. Furthermore, this enables the network device to know which adjustment suggestions for the first AI unit can be obtained from the terminal, thus helping the network device make an adjustment decision for the first AI unit.

[0104] Optionally, the first capability information, the second capability information, and the third capability information are sent separately or carried and sent on the same signaling or information.

[0105] In the embodiments of this application, after the terminal sends the first information to the network device, the method further includes:

[0106] The terminal receives third information sent by the network device, and the third information includes an adjustment decision for the first AI unit.

[0107] It can be understood that after the terminal reports the adjustment suggestions for the first AI unit to the network device, the network device can make an adjustment decision on whether to adjust the first AI unit and how to adjust it based on the adjustment suggestions, and send the third information to the terminal. The third information includes an adjustment decision for the first AI unit. Thus, the terminal can adjust the first AI unit based on the adjustment decision, making the adjusted first AI unit more suitable for running on the terminal, which helps to improve the inference performance of the first AI unit.

[0108] Please refer to Figure 3 , Figure 3 which is a flowchart of another transmission method provided by the embodiments of this application. As Figure 3 shown, the method includes the following steps:

[0109] Step 301: The network device receives first information sent by the terminal, and the first information is used to indicate the adjustment suggestions of the terminal for the first AI unit.

[0110] Optionally, before the network device receives the first information sent by the terminal, the method further includes:

[0111] The network-side device sends configuration information to the terminal, and the configuration information is used to instruct the terminal to report adjustment suggestions for the first AI unit.

[0112] Optionally, the first information or the configuration information includes at least one of the following adjustment suggestions:

[0113] Keep the first AI unit running with the current configuration;

[0114] Switch to the second AI unit;

[0115] Fallback to non-AI mode;

[0116] Turn off the function corresponding to the first AI unit;

[0117] Adjust the parameters of the first AI unit;

[0118] Request a new AI unit;

[0119] Deregister the first AI unit;

[0120] Adjust the monitoring configuration of the first AI unit;

[0121] Request training for the first AI unit;

[0122] Request data collection for training the first AI unit;

[0123] Request multiple AI units to run in parallel or monitor multiple AI units;

[0124] Adjust the auxiliary information in the input of the first AI unit.

[0125] Optionally, the first information or the configuration information further includes at least one of the following:

[0126] The reporting conditions for each adjustment suggestion in the first information or the configuration information;

[0127] The reporting priorities for each adjustment suggestion in the first information or the configuration information;

[0128] The reporting order for each adjustment suggestion in the first information or the configuration information;

[0129] The dependency relationships between each adjustment suggestion in the first information or the configuration information.

[0130] Optionally, before the network-side device receives the first information sent by the terminal, the method further includes:

[0131] The network-side device sends second information to the terminal, and the second information is used to instruct the monitoring type of the first AI unit by the terminal, and the monitoring type includes at least one of the following:

[0132] Monitoring based on monitoring metrics;

[0133] Monitoring based on raw data, where the raw data is data used to calculate monitoring metrics;

[0134] Monitoring based on terminal suggestions.

[0135] Optionally, the sending of the second information and the configuration information satisfies any one of the following:

[0136] The second information and the configuration information are carried in the same information and sent;

[0137] The second information and the configuration information are carried in the same signaling and sent;

[0138] The second information and the configuration information are respectively carried in different signalings and sent.

[0139] Optionally, before the network - side device sends the second information to the terminal, the method further includes at least one of the following:

[0140] The network - side device receives first capability information sent by the terminal, where the first capability information is used to indicate the monitoring types of AI units supported by the terminal;

[0141] The network - side device receives second capability information sent by the terminal, where the second capability information is used to indicate whether the terminal supports the monitoring based on terminal suggestions;

[0142] The network - side device receives third capability information sent by the terminal, where the third capability information is used to indicate the adjustment suggestions that the terminal can make.

[0143] Optionally, the first capability information, the second capability information, and the third capability information are sent separately or carried on the same signaling or information and sent.

[0144] Optionally, after the network - side device receives the first information sent by the terminal, the method further includes:

[0145] The network - side device generates third information according to the first information and sends the third information to the terminal, where the third information includes an adjustment decision for the first AI unit.

[0146] Exemplarily, the network-side device may receive the first information reported by multiple terminals, and generate the third information by combining the adjustment suggestions in the first information reported by the multiple terminals, that is, generate an adjustment decision for the first AI unit. In this way, the network-side device can generate an adjustment decision for the first AI unit by combining the adjustment suggestions reported by multiple terminals, so that the adjustment decision generated by the network-side device incorporates the adjustment suggestions reported by multiple terminals, making the adjustment decision for the first AI unit more comprehensive, more conducive to improving the performance of the first AI unit, and making the adjustment decision applicable to different terminals.

[0147] It should be noted that the transmission method applied to the network-side device provided in the embodiments of the present application corresponds to the above-mentioned transmission method applied to the terminal side. The specific implementation processes and related concepts involved in the embodiments of the present application can refer to the descriptions in the above-mentioned embodiments of the terminal-side method, and will not be specifically elaborated in this embodiment.

[0148] In the embodiments of the present application, the network-side device receives the first information sent by the terminal, and the first information is used to indicate the adjustment suggestions of the terminal for the first AI unit. Furthermore, the terminal can then obtain the adjustment suggestions based on its own operation situation of the first AI unit, which helps the network-side device make decisions on whether to adjust the first AI unit and how to adjust it according to the adjustment suggestions. In this way, it is also ensured that the adjustment of the first AI unit by the network-side device is based on the adjustment suggestions reported by the terminal, making the adjustment of the first AI unit more suitable for the operation scenario of the terminal and effectively improving the applicability of the adjustment of the AI unit.

[0149] Please refer to Figure 4 , Figure 4 which is a flowchart of another transmission method provided in the embodiments of the present application. As Figure 4 shown, the method includes the following steps:

[0150] Step 401, the terminal sends the first capability information, the second capability information, and the third capability information to the network-side device;

[0151] Step 402, the network-side device sends the configuration information and the second information to the terminal;

[0152] Step 403, the terminal generates the first information based on the configuration information and the second information, and the first information includes the adjustment suggestions of the terminal for the first AI unit;

[0153] Step 404, the terminal sends the first information to the network-side device;

[0154] Step 405, the network-side device generates an adjustment decision for the first AI unit according to the first information, and sends the third information to the terminal, and the adjustment decision is included in the third information.

[0155] For the specific implementation process and related concepts involved in the embodiments of this application, reference may be made to the descriptions in the above method embodiments, and details will not be elaborated in this embodiment.

[0156] For the transmission method provided in the embodiments of this application, the execution subject may be a transmission device. In the embodiments of this application, taking the transmission device as the execution subject of the transmission method as an example, the transmission device provided in the embodiments of this application will be described.

[0157] Please refer to Figure 5 , Figure 5 is a structural diagram of a transmission device provided in the embodiments of this application. As Figure 5 shown, the transmission device 500 includes:

[0158] A first sending module 501, configured to send first information to a network-side device, where the first information is used to indicate an adjustment suggestion of the device for a first AI unit.

[0159] Optionally, the device further includes:

[0160] A first receiving module, configured to receive configuration information sent by the network-side device, where the configuration information is used to indicate that the device reports an adjustment suggestion for the first AI unit.

[0161] Optionally, the first information or the configuration information includes at least one of the following adjustment suggestions:

[0162] Keep the first AI unit running with the current configuration;

[0163] Switch to a second AI unit;

[0164] Fallback to a non-AI mode;

[0165] Turn off the function corresponding to the first AI unit;

[0166] Adjust the parameters of the first AI unit;

[0167] Request a new AI unit;

[0168] Deregister the first AI unit;

[0169] Adjust the monitoring configuration of the first AI unit;

[0170] Request training for the first AI unit;

[0171] Request data collection for training the first AI unit;

[0172] Request multiple AI units to run in parallel or monitor multiple AI units;

[0173] Adjust the auxiliary information in the input of the first AI unit.

[0174] Optionally, the first information or the configuration information further includes at least one of the following:

[0175] The reporting condition of each adjustment suggestion in the first information or the configuration information;

[0176] The reporting priority of each adjustment suggestion in the first information or the configuration information;

[0177] The reporting order of each adjustment suggestion in the first information or the configuration information;

[0178] The dependency relationship between each adjustment suggestion in the first information or the configuration information.

[0179] Optionally, the first receiving module is further configured to:

[0180] Receive second information sent by the network-side device, where the second information is used to indicate the monitoring type of the first AI unit, and the monitoring type includes at least one of the following:

[0181] Monitoring based on monitoring metrics;

[0182] Monitoring based on raw data, where the raw data is data used to calculate monitoring metrics;

[0183] Monitoring based on terminal suggestions.

[0184] Optionally, the first sending module 501 is further configured to:

[0185] Generate first information based on at least one of the second information, the configuration message, and the inference performance of the first AI unit;

[0186] Send the first information to the network-side device.

[0187] Optionally, the sending of the second information and the configuration information satisfies any one of the following:

[0188] The second information and the configuration information are sent in the same information;

[0189] The second information and the configuration information are sent in the same signaling;

[0190] The second information and the configuration information are respectively sent in different signals.

[0191] Optionally, the first sending module 501 is further configured to do any one of the following:

[0192] Send first capability information to the network-side device, where the first capability information is used to indicate the monitoring type of the AI unit supported by the device;

[0193] Send second capability information to the network-side device, where the second capability information is used to indicate whether the device supports the monitoring based on the terminal's suggestion.

[0194] Send third capability information to the network-side device, where the third capability information is used to indicate the adjustment suggestions that the device can make.

[0195] Optionally, the first capability information, the second capability information, and the third capability information are sent separately or carried and sent on the same signaling or information.

[0196] Optionally, the device further includes:

[0197] A third receiving module, configured to receive third information sent by the network-side device, where the third information includes an adjustment decision for the first AI unit.

[0198] In the embodiments of the present application, the device (such as a terminal) can send first information to the network-side device, where the first information is used to indicate the adjustment suggestions of the device for the first AI unit. Furthermore, the device can obtain the adjustment suggestions based on its own operation status of the first AI unit, which helps the network-side device make decisions on whether to adjust the first AI unit and how to adjust it according to the adjustment suggestions. In this way, the adjustment of the first AI unit by the network-side device is based on the adjustment suggestions reported by the terminal, making the adjustment of the first AI unit more suitable for the operation scenario of the terminal and effectively improving the applicability of the adjustment of the AI unit.

[0199] The transmission device in the embodiments of the present application can be an electronic device, such as an electronic device with an operating system, or a component in an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices other than the terminal. Exemplarily, the terminal can include, but is not limited to, the types of the terminal 11 listed above, and other devices can be a server, a Network Attached Storage (NAS), etc., which are not specifically limited in the embodiments of the present application.

[0200] The transmission device provided in the embodiments of the present application can implement Figure 2 Each process implemented by the method embodiments and achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0201] Please refer to Figure 6 , Figure 6 is a structural diagram of another transmission device provided in the embodiments of the present application. As Figure 6 shown, the transmission device 600 includes:

[0202] A second receiving module 601, configured to receive first information sent by a terminal, where the first information is used to indicate an adjustment suggestion of the terminal for a first AI unit.

[0203] Optionally, the apparatus further includes:

[0204] A second sending module, configured to send configuration information to the terminal, where the configuration information is used to instruct the terminal to report an adjustment suggestion for the first AI unit.

[0205] Optionally, the first information or the configuration information includes at least one of the following adjustment suggestions:

[0206] Keep the first AI unit running with the current configuration;

[0207] Switch to a second AI unit;

[0208] Fallback to a non-AI mode;

[0209] Turn off the function corresponding to the first AI unit;

[0210] Adjust the parameters of the first AI unit;

[0211] Request a new AI unit;

[0212] Deregister the first AI unit;

[0213] Adjust the monitoring configuration of the first AI unit;

[0214] Request training for the first AI unit;

[0215] Request data collection for training the first AI unit;

[0216] Request multiple AI units to run in parallel or monitor multiple AI units;

[0217] Adjust the auxiliary information in the input of the first AI unit.

[0218] Optionally, the first information or the configuration information further includes at least one of the following:

[0219] Reporting conditions for each adjustment suggestion in the first information or the configuration information;

[0220] Reporting priorities for each adjustment suggestion in the first information or the configuration information;

[0221] Reporting orders for each adjustment suggestion in the first information or the configuration information;

[0222] Dependency relationships between each adjustment suggestion in the first information or the configuration information.

[0223] Optionally, the second sending module is further configured to:

[0224] Send second information to the terminal, where the second information is used to indicate the monitoring type of the first AI unit for the terminal, and the monitoring type includes at least one of the following:

[0225] Monitoring based on monitoring metrics;

[0226] Monitoring based on raw data, where the raw data is data used to calculate monitoring metrics;

[0227] Monitoring based on terminal suggestions.

[0228] Optionally, the sending of the second information and the configuration information satisfies any one of the following:

[0229] The second information and the configuration information are carried in the same information for sending;

[0230] The second information and the configuration information are carried in the same signaling for sending;

[0231] The second information and the configuration information are respectively carried in different signals for sending.

[0232] Optionally, the second receiving module 601 is further configured to perform any one of the following:

[0233] Receive first capability information sent by the terminal, where the first capability information is used to indicate the monitoring type of the AI unit supported by the terminal;

[0234] Receive second capability information sent by the terminal, where the second capability information is used to indicate whether the terminal supports the monitoring based on terminal suggestions;

[0235] Receive third capability information sent by the terminal, where the third capability information is used to indicate the adjustment suggestions that the terminal can make.

[0236] Optionally, the first capability information, the second capability information, and the third capability information are sent separately or carried on the same signaling or information for sending.

[0237] Optionally, the device further includes:

[0238] A third sending module, configured to generate third information according to the first information and send the third information to the terminal, where the third information includes an adjustment decision for the first AI unit.

[0239] In the embodiments of the present application, the device receives the first information sent by the terminal, which helps the device make decisions on whether to adjust the first AI unit and how to adjust it according to the adjustment suggestions in the first information. This also makes the adjustment of the first AI unit by the device based on the adjustment suggestions reported by the terminal, making the adjustment of the first AI unit more suitable for the operating scenario of the terminal and effectively improving the applicability of the adjustment of the AI unit.

[0240] The transmission device provided by the embodiments of the present application can implement Figure 3 each process implemented by the method embodiments and achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0241] As Figure 7 shown, the embodiments of the present application further provide a communication device 700, including a processor 701 and a memory 702. A program or instruction that can run on the processor 701 is stored on the memory 702. For example, when the communication device 700 is a terminal, when the program or instruction is executed by the processor 701, it implements each step of the above transmission method embodiments and can achieve the same technical effects. When the communication device 700 is a network-side device, when the program or instruction is executed by the processor 701, it implements each step of the above transmission method embodiments and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0242] The embodiments of the present application further provide a terminal, including a processor and a communication interface. The communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the steps in the method embodiments as Figure 2 shown. This terminal embodiment corresponds to the above terminal-side method embodiments. Each implementation process and implementation manner of the above method embodiments can be applied to this terminal embodiment and can achieve the same technical effects. Specifically, Figure 8 FIG. is a schematic hardware structure diagram of a terminal for implementing the embodiments of the present application.

[0243] The terminal 800 includes but is not limited to at least some components such as a radio frequency unit 801, a network module 802, an audio output unit 803, an input unit 804, a sensor 805, a display unit 806, a user input unit 807, an interface unit 808, a memory 809, and a processor 810.

[0244] Those skilled in the art can understand that the terminal 800 may further include a power source (such as a battery) for supplying power to each component. The power source can be logically connected to the processor 810 through a power management system, so as to implement functions such as management of charging, discharging, and power consumption management through the power management system. Figure 8The terminal structure shown does not constitute a limitation on the terminal. The terminal may include more or fewer components than shown, or combine certain components, or have a different component arrangement, which will not be elaborated here.

[0245] It should be understood that in the embodiments of the present application, the input unit 804 may include a Graphics Processing Unit (GPU) 8041 and a microphone 8042. The graphics processor 8041 processes the image data of static pictures or videos obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 806 may include a display panel 8061, and the display panel 8061 may be configured in the form of a liquid crystal display, an organic light-emitting diode, etc. The user input unit 807 includes at least one of a touch panel 8071 and other input devices 8072. The touch panel 8071 is also called a touch screen. The touch panel 8071 may include two parts: a touch detection device and a touch controller. The other input devices 8072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, which will not be elaborated here.

[0246] In the embodiments of the present application, after the radio frequency unit 801 receives downlink data from a network-side device, it can be transmitted to the processor 810 for processing; in addition, the radio frequency unit 801 can send uplink data to the network-side device. Generally, the radio frequency unit 801 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, etc.

[0247] The memory 809 can be used to store software programs or instructions as well as various data. The memory 809 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data. Among them, the first storage area may store an operating system, application programs or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory 809 may include volatile memory or non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDR SDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM), and a direct rambus random access memory (DRRAM). The memory 809 in the embodiments of the present application includes but is not limited to these and any other suitable types of memory.

[0248] The processor 810 may include one or more processing units; optionally, the processor 810 integrates an application processor and a modem processor. Among them, the application processor mainly processes operations related to the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communication signals, such as a baseband processor. It can be understood that the above-mentioned modem processor may not be integrated into the processor 810 either.

[0249] Among them, the radio frequency unit 801 is used to send a first piece of information to a network-side device, and the first piece of information is used to indicate an adjustment suggestion of the terminal for the first AI unit.

[0250] In the embodiments of the present application, the terminal sends first information to the network-side device, and the first information is used to indicate the adjustment suggestion of the terminal for the first AI unit. Furthermore, the terminal can then obtain the adjustment suggestion based on its own operation status of the first AI unit, thereby helping the network-side device to make decisions on whether to adjust the first AI unit and how to adjust it according to the adjustment suggestion. In this way, the adjustment of the first AI unit by the network-side device is based on the adjustment suggestion reported by the terminal, making the adjustment of the first AI unit more suitable for the operation scenario of the terminal and effectively improving the applicability of the adjustment of the AI unit.

[0251] It can be understood that the implementation processes of the various implementation manners mentioned in this embodiment can refer to the relevant descriptions of the above-mentioned terminal-side method embodiments and achieve the same or corresponding technical effects. To avoid repetition, they will not be elaborated here.

[0252] The embodiments of the present application further provide a network-side device, including a processor and a communication interface. The communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the steps of the method embodiment as Figure 3 shown. This network-side device embodiment corresponds to the above-mentioned network-side device method embodiment. The various implementation processes and implementation manners of the above method embodiment can all be applied to this network-side device embodiment and can achieve the same technical effects.

[0253] Specifically, the embodiments of the present application further provide a network-side device. As Figure 9 shown, this network-side device 900 includes: an antenna 91, a radio frequency device 92, a baseband device 93, a processor 94, and a memory 95. The antenna 91 is connected to the radio frequency device 92. In the uplink direction, the radio frequency device 92 receives information through the antenna 91 and sends the received information to the baseband device 93 for processing. In the downlink direction, the baseband device 93 processes the information to be sent and sends it to the radio frequency device 92. After the radio frequency device 92 processes the received information, it is sent out through the antenna 91.

[0254] The method executed by the network-side device in the above embodiments can be implemented in the baseband device 93, and the baseband device 93 includes a baseband processor.

[0255] The baseband device 93 may include, for example, at least one baseband board, and multiple chips are provided on this baseband board. As Figure 9 shown, one of the chips is, for example, a baseband processor, which is connected to the memory 95 through a bus interface to call the program in the memory 95 and execute the operations of the network device shown in the above method embodiments.

[0256] The network-side device may further include a network interface 96, such as a Common Public Radio Interface (CPRI).

[0257] Specifically, the network-side device 900 according to the embodiment of the present invention further includes: instructions or programs stored in the memory 95 and executable on the processor 94. The processor 94 calls the instructions or programs in the memory 95 to execute Figure 6 the methods executed by the modules shown, and achieves the same technical effects. To avoid repetition, they will not be elaborated here.

[0258] The embodiment of the present application further provides a readable storage medium, on which a program or instructions are stored. When the program or instructions are executed by a processor, each process of the above-mentioned transmission method embodiment is implemented, and the same technical effects can be achieved. To avoid repetition, they will not be elaborated here.

[0259] Wherein, the processor is the processor in the terminal described in the above embodiment. The readable storage medium includes computer-readable storage media, such as computer read-only memory ROM, random access memory RAM, magnetic disks or optical discs, etc. In some examples, the readable storage medium may be a non-transitory readable storage medium.

[0260] The embodiment of the present application further 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 each process of the above-mentioned transmission method embodiment, and the same technical effects can be achieved. To avoid repetition, they will not be elaborated here.

[0261] It should be understood that the chip mentioned in the embodiment of the present application may also be referred to as a system-on-chip, system chip, chip system or system-on-chip, etc.

[0262] The embodiment of the present application further provides a computer program / program product, which is stored in a storage medium. The computer program / program product is executed by at least one processor to implement each process of the above-mentioned transmission method embodiment, and the same technical effects can be achieved. To avoid repetition, they will not be elaborated here.

[0263] The embodiment of the present application further provides a wireless communication system, including: a terminal and a network-side device. The terminal can be used to execute the steps of the above-mentioned transmission method, and the network-side device can be used to execute the steps of the above-mentioned transmission method.

[0264] It should be noted that in this article, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such a process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including that element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the reverse order according to the functions involved. For example, the described methods may be performed in 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.

[0265] From the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of a computer software product plus a necessary general hardware platform, and of course, they can also be implemented by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disc, etc.) and includes several instructions for causing a terminal or a network-side device to execute the methods described in the various embodiments of the present application.

[0266] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms of embodiments without departing from the purpose of the present application and the scope protected by the claims. These embodiments are all within the protection scope of the present application.

Claims

1. A transmission method, characterized in that Including: The terminal sends first information to the network - side device, and the first information is used to indicate the adjustment suggestion of the terminal for the first artificial intelligence (AI) unit.

2. The method according to claim 1, wherein Before the terminal sends the first information to the network - side device, the method further includes: The terminal receives configuration information sent by the network - side device, and the configuration information is used to indicate that the terminal reports the adjustment suggestion for the first AI unit.

3. The method according to claim 2, wherein The first information or the configuration information includes at least one of the following adjustment suggestions: Keep the first AI unit running with the current configuration; Switch to the second AI unit; Fallback to non - AI mode; Turn off the function corresponding to the first AI unit; Adjust the parameters of the first AI unit; Request a new AI unit; Deregister the first AI unit; Adjust the monitoring configuration of the first AI unit; Request training for the first AI unit; Request data collection for training the first AI unit; Request multiple AI units to run in parallel or monitor multiple AI units; Adjust the auxiliary information in the input of the first AI unit.

4. The method according to claim 3, characterized in that, The first information or the configuration information further includes at least one of the following: The reporting conditions of each adjustment suggestion in the first information or the configuration information; The reporting priorities of each adjustment suggestion in the first information or the configuration information; The reporting order of each adjustment suggestion in the first information or the configuration information; The dependency relationships between each adjustment suggestion in the first information or the configuration information.

5. The method according to claim 2, wherein Before the terminal sends the first information to the network - side device, the method further includes: The terminal receives second information sent by the network - side device, and the second information is used to indicate the monitoring type of the terminal for the first AI unit, and the monitoring type includes at least one of the following: Monitoring based on monitoring metrics; Monitoring based on raw data, where the raw data is the data used to calculate the monitoring metrics; Monitoring based on terminal suggestions.

6. The method according to claim 5, characterized in that, The terminal sending the first information to the network - side device includes: The terminal generates the first information based on at least one of the second information, the configuration message, and the inference performance of the first AI unit; The terminal sends the first information to the network - side device.

7. The method according to claim 5, characterized in that, The sending of the second information and the configuration information satisfies any one of the following: The second information and the configuration information are carried in the same information for sending; The second information and the configuration information are carried in the same signaling for sending; The second information and the configuration information are respectively carried in different signals for sending.

8. The method according to claim 5, characterized in that, Before the terminal receives the second information sent by the network - side device, the method further includes at least one of the following: The terminal sends first capability information to the network - side device, and the first capability information is used to indicate the monitoring types of AI units supported by the terminal; The terminal sends second capability information to the network - side device, and the second capability information is used to indicate whether the terminal supports the monitoring based on terminal suggestions; The terminal sends third capability information to the network - side device, and the third capability information is used to indicate the adjustment suggestions that the terminal can make.

9. The method according to claim 8, characterized in that The first capability information, the second capability information, and the third capability information are sent separately or sent on the same signaling or information.

10. The method according to any one of claims 1-9, characterized in that, After the terminal sends the first information to the network device, the method further includes: The terminal receives third information sent by the network device, where the third information includes an adjustment decision for the first AI unit.

11. A transmission method, characterized in that, including: The network device receives first information sent by the terminal, where the first information is used to indicate an adjustment suggestion of the terminal for the first AI unit.

12. The method according to claim 11, wherein Before the network device receives the first information sent by the terminal, the method further includes: The network device sends configuration information to the terminal, where the configuration information is used to instruct the terminal to report an adjustment suggestion for the first AI unit.

13. The method according to claim 12, wherein The first information or the configuration information includes at least one of the following adjustment suggestions: Keep the first AI unit running with the current configuration; Switch to the second AI unit; Fallback to non-AI mode; Turn off the function corresponding to the first AI unit; Adjust the parameters of the first AI unit; Request a new AI unit; Deregister the first AI unit; Adjust the monitoring configuration of the first AI unit; Request training for the first AI unit; Request data collection for training the first AI unit; Request multiple AI units to run in parallel or monitor multiple AI units; Adjust the auxiliary information in the input of the first AI unit.

14. The method according to claim 13, wherein The first information or the configuration information further includes at least one of the following: Reporting conditions for each adjustment suggestion in the first information or the configuration information; Reporting priorities for each adjustment suggestion in the first information or the configuration information; Reporting orders for each adjustment suggestion in the first information or the configuration information; Dependency relationships between each adjustment suggestion in the first information or the configuration information.

15. The method according to claim 12, characterized in that Before the network device receives the first information sent by the terminal, the method further includes: The network device sends second information to the terminal, where the second information is used to indicate the monitoring type of the first AI unit by the terminal, and the monitoring type includes at least one of the following: Monitoring based on monitoring metrics; Monitoring based on raw data, where the raw data is data used to calculate monitoring metrics; Monitoring based on terminal suggestions.

16. The method according to claim 15, characterized in that, The sending of the second information and the configuration information satisfies any one of the following: The second information and the configuration information are carried in the same information and sent; The second information and the configuration information are carried in the same signaling and sent; The second information and the configuration information are respectively carried in different signaling and sent.

17. The method according to claim 15, wherein Before the network device sends the second information to the terminal, the method further includes at least one of the following: The network device receives first capability information sent by the terminal, where the first capability information is used to indicate the monitoring type of the AI unit supported by the terminal; The network device receives second capability information sent by the terminal, where the second capability information is used to indicate whether the terminal supports the monitoring based on terminal suggestions; The network device receives third capability information sent by the terminal, where the third capability information is used to indicate the adjustment suggestions that the terminal can make.

18. The method according to claim 17, characterized in that, The first capability information, the second capability information, and the third capability information are sent separately or carried and sent on the same signaling or information.

19. The method according to any one of claims 11 - 18, characterized in that, After the network-side device receives the first information sent by the terminal, the method further includes: The network-side device generates third information according to the first information, and sends the third information to the terminal, where the third information includes an adjustment decision for the first AI unit.

20. A transmission device, characterized in that, The device includes: A first sending module, configured to send first information to a network-side device, where the first information is used to indicate an adjustment recommendation of the device for a first AI unit.

21. The device according to claim 20, characterized in that, The device further includes: A first receiving module, configured to receive configuration information sent by the network-side device, where the configuration information is used to indicate that the device reports an adjustment recommendation for the first AI unit.

22. The device according to claim 21, wherein, The first information or the configuration information includes at least one of the following adjustment recommendations: Keep the first AI unit running with the current configuration; Switch to the second AI unit; Fallback to non-AI mode; Turn off the function corresponding to the first AI unit; Adjust the parameters of the first AI unit; Request a new AI unit; Deregister the first AI unit; Adjust the monitoring configuration of the first AI unit; Request training for the first AI unit; Request data collection for training the first AI unit; Request multiple AI units to run in parallel or monitor multiple AI units; Adjust the auxiliary information in the input of the first AI unit.

23. The device according to claim 21, wherein, The first receiving module is further configured to: Receive second information sent by the network-side device, where the second information is used to indicate a monitoring type of the device for the first AI unit, and the monitoring type includes at least one of the following: Monitoring based on monitoring metrics; Monitoring based on raw data, where the raw data is data used to calculate monitoring metrics; Monitoring based on terminal recommendations.

24. The device according to claim 23, characterized in that, The first sending module is further configured to: Generate first information based on at least one of the second information, the configuration message, and the inference performance of the first AI unit; Send the first information to the network-side device.

25. The device according to claim 23, characterized in that, The first sending module is further configured to perform any one of the following: Send first capability information to the network-side device, where the first capability information is used to indicate a monitoring type of the AI unit supported by the device; Send second capability information to the network-side device, where the second capability information is used to indicate whether the device supports the monitoring based on terminal recommendations; Send third capability information to the network-side device, where the third capability information is used to indicate the adjustment recommendations that the device can make.

26. A transmission device, characterized in that, The device includes: A second receiving module, configured to receive first information sent by a terminal, where the first information is used to indicate an adjustment recommendation of the terminal for a first AI unit.

27. The device according to claim 26, characterized in that, The device further includes: A second sending module, configured to send configuration information to the terminal, where the configuration information is used to indicate that the terminal reports an adjustment recommendation for the first AI unit.

28. The device according to claim 27, characterized in that, The first information or the configuration information includes at least one of the following adjustment recommendations: Keep the first AI unit running with the current configuration; Switch to the second AI unit; Fallback to non-AI mode; Turn off the function corresponding to the first AI unit; Adjust the parameters of the first AI unit; Request a new AI unit; Deactivate the first AI unit; Adjust the monitoring configuration of the first AI unit; Request the training of the first AI unit; Request the data collection for training the first AI unit; Request multiple AI units to run in parallel or monitor multiple AI units; Adjust the auxiliary information in the input of the first AI unit.

29. The device according to claim 27, characterized in that, The second sending module is further configured to: Send a second message to the terminal, where the second message is used to indicate the monitoring type of the first AI unit by the terminal, and the monitoring type includes at least one of the following: Monitoring based on monitoring metrics; Monitoring based on raw data, where the raw data is the data used to calculate the monitoring metrics; Monitoring based on terminal suggestions.

30. The device according to claim 29, characterized in that, The second receiving module is further configured to perform any one of the following: Receive the first capability information sent by the terminal, where the first capability information is used to indicate the monitoring type of the AI unit supported by the terminal; Receive the second capability information sent by the terminal, where the second capability information is used to indicate whether the terminal supports the monitoring based on terminal suggestions; Receive the third capability information sent by the terminal, where the third capability information is used to indicate the adjustment suggestions that the terminal can make.

31. The device according to any one of claims 26 - 30, characterized in that, The device further includes: A third sending module, configured to generate a third message according to the first message and send the third message to the terminal, where the third message includes an adjustment decision for the first AI unit.

32. A terminal, characterized in that, Comprising a processor and a memory, where the memory stores a program or instruction that can run on the processor, and when the program or instruction is executed by the processor, the steps of the transmission method according to any one of claims 1-10 are implemented.

33. A network-side device, characterized in that, Comprising a processor and a memory, where the memory stores a program or instruction that can run on the processor, and when the program or instruction is executed by the processor, the steps of the transmission method according to any one of claims 11-19 are implemented.

34. A readable storage medium, characterized in that, The program or instruction is stored on the readable storage medium, and when the program or instruction is executed by the processor, the steps of the transmission method according to any one of claims 1-10 are implemented, or the steps of the transmission method according to any one of claims 11-19 are implemented.

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