CN115580877A - Method and equipment for jointly deploying mobile communication network data and model - Google Patents

Abstract

The application discloses a mobile communication network data and model joint deployment method, which comprises the following steps: the first information of the descending contains the characteristic parameters of an AI model and/or a data set; responding to the first information, and the uplink second information comprises feedback corresponding to the characteristic parameters and is used for confirming the value of at least one of the characteristic parameters; in response to the second information, downstream third information contains an AI model input dataset satisfying the identified characteristic parameters; fourth information upstream contains confirmation of availability of the data set in response to the third information. The application also includes a device applying the method. The method and the device solve the problem that the wireless communication system utilizes the air interface to complete efficient data set transmission between the network and the terminal, and are particularly suitable for 5G and further-evolution mobile communication systems.

Description

Method and equipment for jointly deploying mobile communication network data and model

Technical Field

The present application relates to the field of wireless communication technologies, and in particular, to a method and a device for joint deployment of an AI model and a data set in a mobile communication network.

Background

The mobile communication network contains a large amount of data resources, and the reasonable utilization and the development of 5G data resources by using an Artificial Intelligence (AI) technology can effectively improve the improvement of a mobile communication system. The problems faced in the mobile communication system are complex and various, and a great deal of research shows that the performance of the mobile communication network side and the wireless side can be effectively improved through an AI-based algorithm. Improving mobile system performance using AI techniques has become a major direction in future network design.

The wireless communication system adopts AI technology to enhance the system performance and involves the training and deployment of AI model. The training of the AI model often requires a great deal of computational resources and also requires a great deal of data to train. Model transfer between the network and the terminal is subject to various restrictions, making it difficult for the terminal to directly apply the model transmitted by the network side. For some cases in which some models need to be deployed at the same time on some terminals and network sides, for example, data compression and decompression, the network and the terminals need to perform model deployment respectively, so as to complete the data compression and decompression functions. There are various ways for the model acquisition at the terminal side, e.g. from the network side or by training the data set itself. Direct model transmission over the air interface is subject to various restrictions that the standard has not supported. The network side sends the required data set to the terminal side through an air interface, and the completion of model training of the terminal side becomes another important choice. The scheme of the invention provides a method and a device for acquiring a data set from a network side by a terminal side to finish model training of the terminal side.

Disclosure of Invention

The application provides a method and equipment for jointly deploying mobile communication network data and a model, which solve the problem that a wireless communication system utilizes an air interface to finish efficient data set transmission between a network and a terminal.

In a first aspect, the present application provides a method for jointly deploying mobile communication network data and a model, including the following steps:

the downlink first information comprises characteristic parameters of an AI model and/or a data set;

responding to the first information, and enabling uplink second information to comprise feedback corresponding to the characteristic parameters and used for confirming the value of at least one of the characteristic parameters;

in response to the second information, downstream third information contains an AI model input dataset satisfying the identified characteristic parameters;

in response to the third information, fourth information upstream contains confirmation information of availability of the data set.

The method according to any one of the embodiments of the first aspect of the present application, for a network device, includes the following steps:

sending downlink first information which comprises the characteristic parameters of an AI model and/or a data set;

receiving uplink second information, obtaining feedback of the first information, and confirming the value of at least one of the characteristic parameters;

transmitting downlink third information including the AI model input data set satisfying the confirmed characteristic parameters in response to the second information;

and receiving fourth information of an uplink to obtain confirmation information of the availability of the data set.

The method according to any one of the embodiments of the first aspect of the present application, applied to a terminal device, includes the following steps:

receiving downlink first information, and acquiring characteristic parameters of an AI model and/or a data set;

responding to the first information, sending uplink second information, including feedback corresponding to the characteristic parameters, and confirming the value of at least one of the characteristic parameters;

receiving downlink third information, and obtaining an AI model input data set meeting the confirmed characteristic parameters;

in response to the third information, sending fourth information upstream containing confirmation information of the availability of the data set.

In any one of the embodiments of the first aspect of the present application, preferably, the characteristic parameter includes at least one of: AI model usage AImodel _ case, AI model algorithm type AImodel _ type, DATA set size DATA _ size, DATA set type DATA _ type, DATA segment number DATA _ segment.

In any one of the embodiments of the first aspect of the present application, preferably, the first information includes an indication of a feedback time of the second information.

In any one of embodiments of the first aspect of the present application, preferably, the first information is indicated by DCI information carried by a PDCCH, or the first information is jointly indicated by DCI information carried by the PDCCH and higher layer information carried by a PDSCH.

In any one of the embodiments of the first aspect of the present application, preferably, the second information is carried by a PUCCH or a PUSCH; and/or the fourth information is carried by a PUCCH or PUSCH.

In any embodiment of the first aspect of the present application, preferably, the third information is indicated by DCI information carried by a PDCCH, or the third information is jointly indicated by DCI information carried by the PDCCH and higher layer information carried by a PDSCH.

In any one of the embodiments of the first aspect of the present application, preferably, each of the indication information of the second information is used to confirm that the value of at least one feature parameter is usable or unusable.

In any embodiment of the first aspect of the present application, preferably, the third information includes related information of the data set, where the related information includes at least one of the following information: data format, data quantity, training set and verification set partitioning.

In any embodiment of the first aspect of the present application, preferably, the fourth information includes a usable time of an AI model trained by the data set.

In a second aspect, the present application further provides a network device, configured to implement the method in any one of the first aspects of the present application, where at least one module in the network device is configured to perform at least one of the following functions: sending the first information and the third information; receiving the second information and the fourth information; determining the AI model input dataset in response to the second information; responsive to the fourth information, determining availability of the data set.

In a third aspect, the present application further provides a terminal device, configured to implement the method in any of the first aspects of the present application, where at least one module in the terminal device is configured to: sending the first information and the third information; receiving the second information and the fourth information; determining a value of at least one of the characteristic parameters in response to the first information; responsive to the third information, determining availability of the data set.

In a fourth aspect, the present application further provides a communication device, including: memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the method according to any one of the embodiments of the first aspect of the application.

In a fifth aspect, the present application further proposes a computer-readable medium, on which a computer program is stored, which computer program, when executed by a processor, implements the steps of the method according to any one of the embodiments of the first aspect of the present application.

In a sixth aspect, the present application further proposes a mobile communication system, which includes at least one network device according to any of the embodiments of the present application and/or at least one terminal device according to any of the embodiments of the present application.

The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:

the invention provides a method and a device for supporting a terminal and a network to carry out independent model training. The method and the device provided by the invention can realize that the terminal uses the model provided by the network to carry out corresponding air interface transmission capability enhancement. Particularly, when the network device is unknown in the capability of supporting the AI for the terminal device, the invention provides a method and a device for designing key processes and exchange information related to the interaction and deployment of an AI model. An effective solution is provided for terminal equipment to use the AI model provided by the network equipment.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

FIG. 1 is a flow chart of an embodiment of the method of the present application;

FIG. 2 is a flow chart of an embodiment of the method of the present application for a network device;

FIG. 3 is a flowchart of an embodiment of a method of the present application for a terminal device;

FIG. 4 is a schematic diagram of the first information;

FIG. 5 is a schematic diagram of the second message;

FIG. 6 is a diagram illustrating a third information;

fig. 7 is a schematic view of another embodiment of the first to fourth information configurations;

FIG. 8 is a schematic diagram of an embodiment of a network device;

FIG. 9 is a schematic diagram of an embodiment of a terminal device;

fig. 10 is a schematic structural diagram of a network device according to another embodiment of the present invention;

fig. 11 is a block diagram of a terminal device of another embodiment of the present invention.

Detailed Description

To make the objects, technical solutions and advantages of the present application more clear, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Considering a communication system composed of a network device and a terminal device, one network device can simultaneously transmit and receive data with a plurality of terminal devices. The network data unit and the terminal data unit transmit data through a downlink data shared channel (PDSCH) and an uplink data shared channel (PUSCH). The PDCCH transmits Downlink Control Information (DCI) indicating specific transmission format-related contents of the PDSCH, PUSCH, and PUCCH. The PUCCH transmits Uplink Control Information (UCI) including feedback or acknowledgement information that the terminal device responds to the network device.

The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.

Fig. 1 is a flow chart of an embodiment of the method of the present application. The application provides a method for jointly deploying mobile communication network data and a model, which comprises the following steps of 101-104:

101, downlink first information comprises characteristic parameters of an AI model and/or characteristic parameters of a data set;

the purpose of the first information is to implement training for informing the terminal device that the AI model is ready to run. The characteristic parameter may be a characteristic parameter of an AI model, a characteristic parameter of a data set, or a combination of the two, and includes at least one of the following: AI model usage AImodel _ case, AI model algorithm type AImodel _ type, DATA set size DATA _ size, DATA set type DATA _ type, DATA segment number DATA _ segment.

It should be noted that the characteristic parameters of the AI model are only index symbols or bytes, and are not all compressed packets of the AI model. According to the scheme of the application, the network does not send the compression model to the terminal, but sends a data set used for training to the terminal, and then the terminal trains the compression model by itself.

Similarly, the characteristic parameter of the data set is only an index symbol or byte, and is not the data set itself. And the terminal equipment confirms whether the terminal equipment has the capability of operating the AI model or the data set specified by the characteristic parameters according to the characteristic parameters contained in the first indication information.

Preferably, the first information comprises an indication of the second information Feedback time, such as timing Feedback _ timing or slot Feedback _ slot.

Preferably, the first information is indicated by DCI information carried by a PDCCH, or the first information is indicated by DCI information carried by a PDCCH and higher layer information carried by a PDSCH in a joint manner. When the joint indication is yes, the DCI first includes signaling information of the first information, indicating a position of the query symbol in the PDSCH.

Table 1 example of the name, indication information and parameter value of the characteristic parameter in the first information (the 1 st row is the indication information of the value of the characteristic parameter, the 1 st column is the kind of the characteristic parameter)

	00	01	10	11
					AImodel_case	CSI feedback	Beam management	Positioning	--
AImodel_type	CNN	DNN	LSTM	Transformer
					Data_size	1M	10M	100M	1G
Data_type	Int 8	float 8	Float 16	Float 32
					Data_segement	2	3	4	5
Feedback_timing(slot)	5	10	15	20

Step 102, responding to the first information, wherein the uplink second information comprises feedback corresponding to the characteristic parameters and is used for confirming the value of at least one of the characteristic parameters;

the second information is carried by a PUCCH or PUSCH.

And the terminal equipment feeds back the second information to confirm the capability of the terminal equipment capable of supporting AI model training. For example, each of the indication information of the second information is used to confirm the value of at least one characteristic parameter (as in table 1).

It should be noted that, the confirmation in the present application, including the availability or non-availability, for example, one indication information in the second information confirms that the value of at least one characteristic parameter is available for the terminal device with a preset combination, that is, the terminal device can support the corresponding AI model to be trained according to the indicated data set. For another example, one of the indication information in the second information confirms that the value of at least one feature parameter is unavailable to the terminal device with a preset combination, that is, the terminal device cannot run corresponding AI model training.

103, responding to the second information, and the downlink third information comprises an AI model input data set meeting the confirmed characteristic parameters;

the third information is used for realizing the data set issuing of the AI model.

The third information includes, but is not limited to, the data set and data set related information. The data set can be sent in a single time or sent in multiple times, and the size, the number and the like of the data set do not exceed the relevant limit fed back in the second information.

The third information is indicated by DCI information carried by a PDCCH, or the third information is indicated by a combination of DCI information carried by the PDCCH and high-layer information carried by a PDSCH. When the joint indication is performed, signaling information of third information is first included in the DCI information, and an AI model indicating the third information indicates a position where data is transmitted in the PDSCH.

Preferably, the third information comprises related information of the data set, the related information comprises at least one of the following information: data format, data quantity, training set and verification set division.

And step 104, responding to the third information, and the upstream fourth information comprises confirmation information of the availability of the data set.

And the fourth information realizes the confirmation of the deployment result of the AI model.

The fourth information is carried by a PUCCH or PUSCH.

Preferably, the fourth information further includes a usable time of the AI model trained by the data set.

It should be noted that, in the present application, the availability of the data set means that the data set can be used for AI model training, and run under the support of the capability of the terminal device, for example, software and hardware of the AI model loaded with the data set can work and output a result according to a set function.

Fig. 2 is a flowchart of an embodiment of the method of the present application for a network device. The method according to any one of the embodiments of the first aspect of the present application, for a network device, includes the following steps 201 to 204:

step 201, the network device sends downlink first information, which includes characteristic parameters of an AI model and/or a data set; and the network equipment informs the terminal equipment of AI model training preparation through the first information.

Step 202, the network device receives uplink second information, obtains feedback of the first information, and confirms a value of at least one of the characteristic parameters;

step 203, responding to the second information, the network device sends downlink third information, which includes an AI model input data set meeting the confirmed characteristic parameters;

and step 204, the network device receives the fourth uplink information to obtain the confirmation information of the availability of the data set.

Fig. 3 is a flowchart of an embodiment of the method of the present application, applied to a terminal device. The method according to any one of the embodiments of the first aspect of the present application, applied to a terminal device, includes the following steps:

301, the terminal device receives downlink first information to obtain characteristic parameters of an AI model and/or a data set;

step 302, in response to the first information, the terminal device sends uplink second information, which includes feedback corresponding to the characteristic parameters and is used for confirming a value of at least one of the characteristic parameters;

step 303, the terminal device receives downlink third information to obtain an AI model input data set meeting the confirmed characteristic parameters;

step 304, in response to the third information, the terminal device sends fourth information of uplink, which includes confirmation information of availability of the data set.

In step 304, the terminal device first parses the third information, the terminal device performs model training on the third information, completes model training according to a data set, and feeds back the fourth information to the network device, where the fourth information includes information such as whether the third information transmission data set is available or not, and an AI model trained according to the third information transmission data set is available at a time point.

According to the embodiments of steps 101 to 104, 201 to 204, and 301 to 304, before performing AI model training using the data set provided by the network device, the terminal device performs a series of information interactions with the network device to ensure that the terminal obtains the required data set, and the specific process of information interaction is shown in fig. 4 to 5. The network device first needs to initiate AI model training preparation. After obtaining the feedback of the terminal device, the network device performs data set and related information transmission required for AI model training, as shown in fig. 6. After the terminal device obtains the data set and the relevant information, training an AI model, deploying, and informing the network device that the AI model can be used. The solution provided by the present invention is further illustrated by a number of examples.

Fig. 4 is a schematic diagram of the first information. In this embodiment, the network device sends the first information through the PDCCH. The first information (AI _ tracking _ prepare) includes a plurality of fields respectively representing the use (AImodel _ case) of supporting an AI model, the type (AImodel _ type) of the AI model, the Data set size (Data _ size), the Data set Data format (Data _ type), and the Data set segmentation (Data _ segment). Each field is represented by a certain bit, informing the terminal of the data set and usage model information. The AI _ tracking _ preamble further includes a Feedback time point (Feedback _ timing) for the first information-related information, which may be composed of a plurality of bits. Fig. 4 shows a schematic diagram of the first information, in which 12 bits and 2 bits in each field represent corresponding information.

Fig. 5 is a schematic diagram of the second information. And after receiving the first information, the terminal equipment feeds back the second information (AI _ tracking _ feedback) on the PUCCH. And feeding back the content of the second information according to the content corresponding to the first information. The second information may feed back a part of the first information. As shown in fig. 5, if the terminal device confirms that model training is to be performed using the Data set provided by the network device, AI _ training _ confirm is set to 1, and meanwhile, training of the Data set format and the segmentation mode adopted by the network device is also received, and Data _ type _ f and Data _ segment _ f both repeat the indication information sent by the network device.

Fig. 6 is a schematic diagram of the third information. And after receiving the second information, the network equipment feeds back a data set and related information, namely the third information. The specific information (Data _ inf) of the Data set includes a Data set format, a Data set size, a training set, a Data set identification, and the like. The Data set (Data _ inf) of the third information is transmitted by higher layer signaling or Data plane information carried by the PDSCH. Data set related information (Data _ rel _ inf) is carried by the PDCCH, and DCI information carrying the Data set related information also indicates a Data set transmission position and indicates the fourth information feedback point. The figure shows, by way of example, a signaling information portion in the third information carried by the DCI, including Data set related information (Data _ rel _ inf), where the DCI information carrying the AI model related information is carried by the PDCCH, and also indicates the location of the Data set (at "1" in the figure, for example, the training Data set and the verification Data set may be further distinguished), and indicates a fourth information feedback point (at "2" in the figure).

And after receiving the third information, the terminal device finishes interpretation of the third information and training of an AI model, and sends the fourth information to the network device at the position indicated by the third information. And the fourth information completes the confirmation of the training of the AI model, and further confirms the usable time of the AI model.

Fig. 7 is a schematic diagram of another embodiment of the first to fourth information configurations. And the network equipment sends the first information through the PDCCH. The first information (AI _ capacity _ report) is the same as the above embodiment, and triggers the terminal device to perform an AI model training query whether to use a network device to provide a data set. And after receiving the first information, the terminal equipment completes 1-bit report of the second information according to the first information, and respectively represents whether to adopt the data set adopted by the first information for training. And when the terminal can not carry out AI model training, reporting by utilizing the second information specific combination, such as all 0 s. The combination of the plurality of characteristic parameter values is indicated by one indication information. After receiving the second information, the network device sends a data set and related information, that is, sends the third information, where the third information is the same as the above embodiment. And after receiving the third information, the terminal device finishes interpretation of the third information and training of an AI model, and sends the fourth information to the network device at the position indicated by the third information.

Fig. 8 is a schematic diagram of an embodiment of a network device. An embodiment of the present application further provides a network device, and using the method according to any of the embodiments of the present application, at least one module in the network device is configured to perform at least one of the following functions: sending the first information and the third information; receiving the second information and the fourth information; determining the AI model input dataset in response to the second information; in response to the fourth information, determining availability of the data set.

In order to implement the foregoing technical solution, a network device 400 provided in the present application includes a network sending module 401, a network determining module 402, and a network receiving module 403 that are connected to each other.

And the network sending module is used for sending the first information and the second information.

The network determination module is configured to determine the AI model and/or the data set according to the value of the characteristic parameter determined in the second information; and the fourth information is used for determining the availability and the running time of the data set according to the fourth information, and further determining that AI deployment is successful.

And the network receiving module is used for receiving the third information and the fourth information.

The specific method for implementing the functions of the network sending module, the network determining module, and the network receiving module is described in the embodiments of the methods of the present application, and is not described herein again.

Fig. 9 is a schematic diagram of an embodiment of a terminal device. The present application further provides a terminal device, and using the method of any embodiment of the present application, at least one module in the terminal device is configured to perform at least one of the following functions: sending the first information and the third information; receiving the second information and the fourth information; determining a value of at least one of the characteristic parameters in response to the first information; responsive to the third information, determining availability of the data set.

In order to implement the foregoing technical solution, the terminal device 500 provided in this application includes a terminal sending module 501, a terminal determining module 502, and a terminal receiving module 503 that are connected to each other.

And the terminal sending module is used for sending the second information and the fourth information.

The terminal determining module is configured to determine a value of the at least one characteristic parameter according to indication information of the characteristic parameter in the first information and the operation capability of the terminal device; and according to the indication of the third information, the data set is tried to be run, and the availability and the running time of the data set are determined.

And the terminal receiving module is used for receiving the first information and the third information.

The specific method for implementing the functions of the terminal sending module, the terminal determining module and the terminal receiving module is as described in the method embodiments of the present application, and is not described herein again.

The terminal equipment can be mobile terminal equipment.

Fig. 10 is a schematic structural diagram of a network device according to another embodiment of the present invention. As shown, the network device 600 includes a processor 601, a wireless interface 602, and a memory 603. Wherein the wireless interface may be a plurality of components, i.e. including a transmitter and a receiver, providing means for communicating with various other apparatus over a transmission medium. The wireless interface implements a communication function with the terminal device, and processes wireless signals through the receiving and transmitting devices, and data carried by the signals are communicated with the memory or the processor through the internal bus structure. The memory 603 contains a computer program that executes any of the embodiments of the present application, running or changed on the processor 601. When the memory, processor, wireless interface circuit are connected through a bus system. The bus system includes a data bus, a power bus, a control bus, and a status signal bus, which are not described in detail herein.

Fig. 11 is a block diagram of a terminal device of another embodiment of the present invention. The terminal device 700 comprises at least one processor 701, a memory 702, a user interface 703 and at least one network interface 704. The various components in the terminal device 700 are coupled together by a bus system. A bus system is used to enable the communication of the connections between these components. The bus system includes a data bus, a power bus, a control bus, and a status signal bus.

The user interface 703 may include a display, a keyboard, or a pointing device, such as a mouse, a trackball, a touch pad, or a touch screen.

The memory 702 stores executable modules or data structures. The memory may store an operating system and application programs. The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, and is used for implementing various basic services and processing hardware-based tasks. The application programs include various application programs such as a media player, a browser, and the like for implementing various application services.

In the embodiment of the present invention, the memory 702 contains a computer program for executing any of the embodiments of the present application, and the computer program runs or changes on the processor 701.

The memory 702 contains a computer readable storage medium, and the processor 701 reads the information in the memory 702 and combines the hardware to complete the steps of the above method. In particular, the computer-readable storage medium has stored thereon a computer program which, when being executed by the processor 701, carries out the steps of the method embodiments as described above with reference to any of the embodiments.

The processor 701 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the method of the present application may be implemented by hardware integrated logic circuits in the processor 701 or by instructions in the form of software. The processor 701 may be a general purpose processor, a digital signal processor, an application specific integrated circuit, an off-the-shelf programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. In a typical configuration, the device of the present application includes one or more processors (CPUs), an input/output user interface, a network interface, and a memory.

Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application therefore also proposes a computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of the embodiments of the present application. For example, the memory 603, 702 of the present invention may comprise volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM).

Computer-readable media, including both permanent and non-permanent, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or apparatus comprising the element.

The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.

Claims (16)

1. A method for jointly deploying data and a model of a mobile communication network is characterized by comprising the following steps:

the downlink first information comprises characteristic parameters of an AI model and/or a data set;

responding to the first information, and the uplink second information comprises feedback corresponding to the characteristic parameters and is used for confirming the value of at least one of the characteristic parameters;

in response to the second information, descending third information contains AI model input data sets satisfying the identified characteristic parameters;

fourth information upstream contains confirmation of availability of the data set in response to the third information.

2. A method for jointly deploying mobile communication network data and a model is used for network equipment, and is characterized by comprising the following steps:

sending downlink first information which comprises characteristic parameters of an AI model and/or a data set;

receiving uplink second information, obtaining feedback of the first information, and confirming the value of at least one of the characteristic parameters;

in response to the second information, sending downstream third information containing AI model input datasets satisfying the identified characteristic parameters;

and receiving fourth information of an uplink to obtain confirmation information of the availability of the data set.

3. A method for jointly deploying mobile communication network data and a model is used for terminal equipment, and is characterized by comprising the following steps:

receiving downlink first information, and obtaining characteristic parameters of an AI model and/or a data set;

responding to the first information, sending uplink second information, including feedback corresponding to the characteristic parameters, and confirming the value of at least one of the characteristic parameters;

receiving downlink third information, and obtaining an AI model input data set meeting the confirmed characteristic parameters;

in response to the third information, sending fourth information upstream, including confirmation information of availability of the data set.

4. The method for jointly deploying data and models of mobile communication network according to any one of claims 1 to 3,

the characteristic parameters comprise at least one of the following parameters: AI model usage AImodel _ case, AI model algorithm type AImodel _ type, DATA set size DATA _ size, DATA set type DATA _ type, DATA segment number DATA _ segment.

5. The method for jointly deploying data and models in a mobile communication network according to any one of claims 1 to 3,

the first information includes an indication of a feedback time for the second information.

6. The method for jointly deploying data and models of mobile communication network according to any one of claims 1 to 3,

the first information is indicated by DCI information carried on a PDCCH, or,

the first information is jointly indicated through DCI information carried by the PDCCH and high-level information carried by the PDSCH.

7. The method for jointly deploying data and models of mobile communication network according to any one of claims 1 to 3,

the second information is carried by PUCCH or PUSCH;

the fourth information is carried by a PUCCH or PUSCH.

8. The method for jointly deploying data and models in a mobile communication network according to any one of claims 1 to 3,

the third information is indicated by DCI information carried on a PDCCH, or,

and the third information is jointly indicated through DCI information carried by the PDCCH and high-layer information carried by the PDSCH.

9. The method for jointly deploying data and models in a mobile communication network according to any one of claims 1 to 3,

each indication information of the second information is used for confirming that the value of at least one characteristic parameter is usable or unusable.

10. The method for jointly deploying data and models of mobile communication network according to any one of claims 1 to 3,

third information comprising information related to the data set, the related information comprising at least one of: data format, data quantity, training set and verification set division.

11. The method for jointly deploying data and models of mobile communication network according to any one of claims 1 to 3,

the fourth information includes a usable time of the AI model trained by the data set.

12. A network device for implementing the method for jointly deploying data and model of mobile communication network according to any one of claims 1-2 and 4-11,

at least one module in the network device for at least one of the following functions: sending the first information and the third information; receiving the second information and the fourth information; determining the AI model input dataset in response to the second information; responsive to the fourth information, determining availability of the data set.

13. A terminal device for implementing the method for jointly deploying data and models in a mobile communication network according to any one of claims 1 and 3 to 11,

at least one module in the terminal device, configured to perform at least one of the following functions: sending the first information and the third information; receiving the second information and the fourth information; determining a value of at least one of the characteristic parameters in response to the first information; responsive to the third information, determining availability of the data set.

14. A communication device, comprising: memory, processor and computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of the method for jointly deploying mobile communication network data and models according to any one of claims 1 to 11.

15. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for joint deployment of mobile communication network data and models according to any one of claims 1 to 11.

16. A mobile communication system comprising at least 1 network device according to claim 12 and/or at least 1 terminal device according to claim 13.

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