CN116095705A

CN116095705A - Wireless network intelligent communication method, device, electronic equipment and readable medium

Info

Publication number: CN116095705A
Application number: CN202111276042.4A
Authority: CN
Inventors: 刘胜楠; 蒋峥; 佘小明
Original assignee: China Telecom Corp Ltd
Current assignee: China Telecom Corp Ltd
Priority date: 2021-10-29
Filing date: 2021-10-29
Publication date: 2023-05-09

Abstract

The disclosure provides a wireless network intelligent communication method, a wireless network intelligent communication device, electronic equipment and a readable medium, wherein the communication method comprises the following steps: the method comprises the steps of interacting AI use case indication information with OAM side equipment; according to the AI use case indication information, sending measurement configuration signaling to the terminal, receiving a measurement report fed back by the terminal in response to the measurement configuration signaling by the OAM side equipment, and carrying out AI model training based on the measurement report and related data sets; receiving AI model parameters trained by OAM side equipment and carrying out AI model deployment; receiving a real-time measurement report sent by a terminal; carrying out AI model reasoning according to the real-time measurement report and the deployed AI model, and outputting AI model operation information, wherein the information comprises wireless network related prediction information and/or AI model recommendation strategies and/or configuration parameters; and adjusting network configuration information or executing corresponding network actions according to the information. By the embodiment of the disclosure, the reliability and the accuracy of the AI model of the wireless network configuration are improved, and the use experience and the system operation performance of the terminal user are improved.

Description

Wireless network intelligent communication method, device, electronic equipment and readable medium

Technical Field

The disclosure relates to the technical field of communication, in particular to a wireless network intelligent communication method, a wireless network intelligent communication device, electronic equipment and a readable medium.

Background

Currently, wireless networks of 5G (5 th Generation Mobile Communication Technology, fifth generation mobile communication technology), B5G (super 5 generation mobile communication system) and 6G (6 th Generation Mobile Communication Technology, sixth generation mobile communication technology) will introduce artificial intelligence and big data technology to cope with more complex heterogeneous networks and more diverse communication scenarios. The data can be obtained from terminals, network devices, external devices and the like, and the AI algorithm can classify, count and infer based on the data, so as to further give the conclusions of analysis, prediction, recommendation and the like.

In the related art, the 3GPP (Third generation partnership project third generation partnership project) RAN3 (wireless network architecture and interface) working group is currently researching wireless network big data and intellectualization, aiming at researching the intellectualization framework of the RAN by use of examples, analyzing the influence on the existing protocol interface, discussing the collection of wireless network data, discussing the system optimization scheme based on AI technology, and the main application scenarios comprise network energy saving, mobility optimization, load balancing and the like.

However, the process of the AI model requires classifying, cleaning and training a large amount of network data, which requires a strong computing power of the network node, thus causing a large data computing pressure and interaction pressure on the network node, and resulting in low updating efficiency of the AI model, which may be difficult to be suitable for an expected communication scene.

It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

An object of the present disclosure is to provide a wireless network intelligent communication method, apparatus, electronic device, and readable medium for overcoming, at least to some extent, the problem of poor AI model usage in a wireless network due to limitations and disadvantages of the related art.

According to a first aspect of an embodiment of the present disclosure, there is provided a wireless network intelligentized communication method, including: responding to an AI function initialization request, and interacting AI use case indication information with OAM side equipment; according to the AI use case indication information, sending measurement configuration signaling to the terminal, receiving a measurement report fed back by the terminal in response to the measurement configuration signaling by the OAM side equipment, and carrying out AI model training based on the measurement report and related data sets; receiving AI model parameters trained by OAM side equipment and carrying out AI model deployment; receiving a real-time measurement report sent by a terminal; carrying out AI model reasoning according to the real-time measurement report and the deployed AI model, and outputting AI model operation information, wherein the information comprises wireless network related prediction information or AI model recommendation strategies and/or configuration parameters; and adjusting network configuration information or executing corresponding network actions according to the information.

In an exemplary embodiment of the present disclosure, the wireless network-intelligent communication method further includes: determining performance indexes of the system after adjusting network configuration information or executing corresponding network actions based on AI model output information; and feeding back the performance index to the OAM side equipment, and optimizing the AI model by the OAM side equipment according to the performance index.

In an exemplary embodiment of the present disclosure, the wireless network-intelligent communication method further includes: judging whether the received AI model can be operated locally; if the received AI model which can be operated locally is determined, the AI model deployment success information is fed back to the OAM side equipment; and if the received AI model can not be operated locally, feeding back AI model deployment failure signaling to the OAM side equipment.

In one exemplary embodiment of the present disclosure, if it is determined that the received AI model cannot be locally run, feeding back AI model deployment failure signaling to the OAM side device includes: if the received AI model cannot be identified or the received model parameters are not matched with the configuration of the AI model, determining that the AI model to be deployed cannot be operated locally; and feeding back AI model deployment failure signaling to the OAM side equipment.

In an exemplary embodiment of the present disclosure, the wireless network-intelligent communication method further includes: sending an AI function request to OAM side equipment; receiving AI function response information fed back by OAM side equipment in response to the AI function request; and receiving AI function initialization success information or AI function initialization failure information fed back by the OAM side device, wherein the AI function initialization success information is used for indicating the OAM side device to complete the initialization of the AI function, and the AI function initialization failure signaling is used for indicating the initialization failure of the OAM side device to the AI function.

In an exemplary embodiment of the present disclosure, the wireless network-intelligent communication method further includes: receiving AI function request information sent by OAM side equipment; the method comprises the steps of sending AI function response information fed back in response to an AI function request to OAM side equipment; and the AI function initialization success information or AI function initialization failure information is fed back to the OAM side equipment, the AI function initialization success information is used for indicating that the initialization of the AI function is completed, and the AI function initialization failure signaling is used for indicating the initialization failure of the AI function.

In one exemplary embodiment of the present disclosure, the prediction information output by the AI model includes a moving direction and speed of the terminal, and/or a position and trajectory prediction of the terminal, and/or predicted operational load information of the accessed base station, and/or base station energy consumption, and/or energy consumption of the terminal.

In one exemplary embodiment of the present disclosure, the recommended policies output by the AI model include a base station identity suggesting access, and/or a handover pattern suggesting configuration, and/or a threshold condition for handing over a base station, and/or a base station power saving policy.

In one exemplary embodiment of the present disclosure, the request for the AI function includes one or more requested AI use cases.

In one exemplary embodiment of the present disclosure, the AI use cases are configured as use cases that implement one or more of mobility optimization, network power saving, and load balancing.

According to a second aspect of embodiments of the present disclosure, there is provided a wireless network-intelligent communication apparatus, including: the interaction module is used for responding to the AI function initialization request and interacting AI use case indication information with the OAM side equipment; the training module is arranged to send measurement configuration signaling to the terminal according to the AI use case indication information, and the OAM side equipment receives a measurement report fed back by the terminal in response to the measurement configuration signaling and carries out AI model training based on the measurement report and a related data set; the receiving module is arranged for receiving the AI model parameters trained by the OAM side equipment and carrying out AI model deployment; the receiving module is used for receiving a real-time measurement report sent by the terminal; the reasoning module is used for carrying out AI model reasoning according to the real-time measurement report and the deployed AI model, outputting information of AI model operation, wherein the information comprises wireless network related prediction information or AI model recommendation strategies and/or configuration parameters; and the adjusting module is used for adjusting the network configuration information or executing corresponding network actions according to the information.

According to a third aspect of the present disclosure, there is provided an electronic device comprising: a memory; and a processor coupled to the memory, the processor configured to perform the method of any of the above based on instructions stored in the memory.

According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a program which, when executed by a processor, implements a wireless network-intelligentized communication method as in any one of the above.

According to the embodiment of the disclosure, under the architecture that a Model training (Model training) functional entity is deployed on OAM and a Model reasoning (Model information) functional entity is deployed on RAN nodes, the wireless network is supported by designing a network signaling flow related to AI management, so that the wireless network can improve operation performance by means of the advantages of AI algorithm in the aspects of prediction, recommendation and the like, the configuration efficiency of the wireless network is optimized, the interaction pressure and operation pressure of the wireless network are reduced, and the system performance of the wireless network is improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.

FIG. 1 is a flow chart of a wireless network intelligent communication method in an exemplary embodiment of the present disclosure;

FIG. 2 is a flow chart of another wireless network intelligent communication method in an exemplary embodiment of the present disclosure;

FIG. 3 is a flow chart of another wireless network intelligent communication method in an exemplary embodiment of the present disclosure;

FIG. 4 is a flow chart of another wireless network intelligent communication method in an exemplary embodiment of the present disclosure;

FIG. 5 is a flow chart of another wireless network intelligent communication method in an exemplary embodiment of the present disclosure;

fig. 6 is a schematic diagram of an NG-RAN initiated AI function of a wireless network intelligent communication method in an exemplary embodiment of the present disclosure;

FIG. 7 is a schematic diagram of an interaction process of a wireless network intelligent communication scheme in an exemplary embodiment of the present disclosure;

fig. 8 is a schematic diagram of an OAM initializing AI function of another wireless network-intelligent communication method in an exemplary embodiment of the present disclosure;

fig. 9 is a schematic architecture diagram of a wireless network intelligent communication platform in an exemplary embodiment of the present disclosure;

FIG. 10 is an interactive schematic diagram of a wireless network intelligent communication platform in an exemplary embodiment of the present disclosure;

FIG. 11 is a block diagram of a wireless network intelligent communications apparatus in an exemplary embodiment of the present disclosure;

fig. 12 is a block diagram of an electronic device in an exemplary embodiment of the present disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present disclosure. However, those skilled in the art will recognize that the aspects of the present disclosure may be practiced with one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.

Furthermore, the drawings are only schematic illustrations of the present disclosure, in which the same reference numerals denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.

The following describes example embodiments of the present disclosure in detail with reference to the accompanying drawings.

Fig. 1 is a flow chart of a communication method of wireless network intelligence in an exemplary embodiment of the present disclosure.

Referring to fig. 1, a wireless network-intelligent communication method may include:

step S102, responding to an AI function initialization request, and interacting AI use case indication information with OAM side equipment;

step S104, sending measurement configuration signaling to the terminal according to the AI use case indication information, receiving a measurement report fed back by the terminal in response to the measurement configuration signaling by the OAM side equipment, and carrying out AI model training based on the measurement report and related data sets;

step S106, receiving the AI model parameters trained by the OAM side equipment and deploying the AI model;

step S108, receiving a real-time measurement report sent by a terminal;

step S110, AI model reasoning is carried out according to the real-time measurement report and the deployed AI model, and information of AI model operation is output, wherein the information comprises wireless network related prediction information or AI model recommendation strategies and/or configuration parameters;

step S112, the network configuration information is adjusted or corresponding network actions are executed according to the information.

According to the embodiment of the disclosure, under the architecture that a Model training (Model training) functional entity is deployed on OAM and a Model reasoning (Model information) functional entity is deployed on RAN nodes, the intelligent of the wireless network is supported by designing a network signaling flow related to AI management, so that the wireless network can improve the operation performance by means of the advantages of AI algorithm in the aspects of prediction, recommendation and the like, the configuration efficiency of the wireless network is optimized, the interaction pressure and the operation pressure of the wireless network are reduced, and the system performance of the wireless network is improved.

In one exemplary embodiment of the present disclosure, NG-RAN node refers to a node device of a 5G access network.

In an exemplary embodiment of the present disclosure, OAM (Operation Administration and Maintenance, operation and Maintenance management), management work of a network is generally divided into 3 major categories, namely Operation (Operation), administration (Maintenance), and Maintenance (OAM) according to actual needs of an operator's network Operation. The operation mainly completes analysis, prediction, planning and configuration work of daily network and business; maintenance is mainly a daily operation activity performed on testing and fault management of a network and services thereof.

In one exemplary embodiment of the present disclosure, RRC (Radio Resource Control) refers to radio resource control. RRC processes layer three information of a control plane between UE (User Equipment) and an eNodeB (Evolved Node-B). The RRC allocates radio resources and sends related signaling, and the main part of the control signaling between the UE and the UTRAN is the RRC message, which carries all the parameters required to establish, modify and release layer 2 and physical layer protocol entities.

The steps of the wireless network intelligent communication method will be described in detail with reference to fig. 2 to 8.

In an exemplary embodiment of the present disclosure, as shown in fig. 2, the wireless network intelligent communication method further includes:

step S202, determining to adjust network configuration information or performance index of the system after performing corresponding network actions based on the AI model output information.

Step S204, the performance index is fed back to the OAM side equipment, and the OAM side equipment optimizes the AI model according to the performance index.

In an exemplary embodiment of the present disclosure, as shown in fig. 3, the wireless network intelligent communication method further includes:

step S302, judging whether the received AI model can be operated locally, if so, executing step S304, otherwise, executing step S306.

In step S304, if it is determined that the received AI model can be locally operated, AI model deployment success information is fed back to the OAM side device.

In step S306, if it is determined that the received AI model cannot be locally operated, an AI model deployment failure signaling is fed back to the OAM side device.

In an exemplary embodiment of the present disclosure, as shown in fig. 4, if it is determined that the received AI model cannot be locally operated, feeding back AI model deployment failure signaling to the OAM side device includes:

in step S402, if it is determined that the received AI model cannot be identified, or that the received model parameters do not match the configuration of the AI model, it is determined that the AI model to be deployed cannot be locally run.

Step S404, feeding back AI model deployment failure signaling to the OAM side device.

In an exemplary embodiment of the present disclosure, as shown in fig. 5, the wireless network intelligent communication method further includes:

step S502, an AI function request is sent to an OAM side device.

Step S504, receives AI function response information fed back by the OAM side device in response to the AI function request.

In step S506, the AI function initialization success information or the AI function initialization failure information fed back by the OAM side device is received, where the AI function initialization success information is used to instruct the OAM side device to complete initialization of the AI function, and the AI function initialization failure signaling is used to instruct the OAM side device to fail initialization of the AI function.

As shown in fig. 6, the AI function initialization may be triggered by the NG-RAN node 604, and for the NG-RAN-initiated AI function initialization, the NG-RAN node 604 sends an AI function request signaling to the OAM 606, where the signaling carries a use case of the present application, and the use case includes, but is not limited to: mobility enhancement, load balancing and network energy saving, one use case can be applied for one request, and a plurality of use cases can be applied according to requirements.

After the OAM 606 receives the request, if the request of the current AI function can be received, the response signaling of the AI function is fed back, and the signaling carries the use case of the request.

If the OAM 606 does not support AI function or if the OAM 606 determines that the current AI request cannot be accepted according to the resource situation, an AI function failure signaling is fed back to the NG-RAN node 604.

In an exemplary embodiment of the present disclosure, as shown in fig. 7, the wireless network intelligent communication method further includes:

step S702, the receiving OAM side device sends AI function request information.

Step S704, AI function response information fed back in response to the AI function request is sent to the OAM side device.

In step S707, AI function initialization success information or AI function initialization failure information fed back to the OAM side device is used to indicate that the initialization of the AI function is completed, and AI function initialization failure signaling is used to indicate that the initialization of the AI function fails.

As shown in fig. 8, the AI function initialization may be triggered by the NG-RAN node 804, and for the NG-RAN-initiated AI function initialization, the NG-RAN node 804 sends an AI function request signaling to the OAM 806, where the signaling carries a use case of the present application, and the use case includes, but is not limited to: mobility enhancement, load balancing and network energy saving, one use case can be applied for one request, and a plurality of use cases can be applied according to requirements.

After the OAM 806 receives the request, if the current AI function requirement can be received, the AI function command signaling is fed back, and the signaling carries the use case of the request.

If the OAM 806 does not support AI function or if the OAM 806 determines that the current AI request cannot be accepted according to the resource situation, an AI function failure signaling is fed back to the NG-RAN node 804.

Corresponding to the above method embodiment, the present disclosure further provides a wireless network-intelligent communication device, which may be used to perform the above method embodiment.

As shown in fig. 9, the wireless network-intelligent communication framework includes data collection 902, model training 904, model reasoning 906, and executor 908, and the specific communication interaction process is as follows:

(1) Data Collection 902 is a functional module that provides input Data for Model Training (Model Training) 904 and Model reasoning (Model reference) 906. The possibilities of the input data include tests from the UE or different network entities, performance feedback, AI/ML model output. The data types include training data (information required for model training) and inferred data (input information required for model reference)

(2) Model training (model training) 904 is a functional module that performs ML model training. Model translation may also perform data preparation (e.g., data preprocessing and cleansing, formatting, and raw data conversion) work on an as-needed basis.

(3) Model Inference (model information) 906 is a functional module that provides AI/ML model Inference output (e.g., predictions or decisions). The Model reference function may also perform data preparation (e.g., data preprocessing and cleansing, formatting, and conversion of raw data) as needed.

(4) An executor (Actor) 908 is a functional module that receives output from the Model inference function and triggers or performs corresponding operations, which may trigger actions of other entities or itself.

(5) Feedback (Feedback): information that may be needed to acquire training or extrapolated data or performance feedback.

The Model training process needs to classify, wash and train a large amount of network data, so that the corresponding nodes need to have strong calculation power, and the Model training can be deployed with OAM in consideration of the universality of data collection. The Model reference is mainly based on real-time data of a network and a trained AI Model, so that corresponding output (such as prediction or decision making and the like) is deduced, and a Model information function is generally deployed at a RAN node in consideration of timeliness of data collection and decision making implementation.

As shown in fig. 10, in the wireless network intelligent communication scheme, a terminal 1002, an NG-RAN node 1004, and an OAM 1006 are included, which includes the following interaction steps:

step 1: the AI function initialization, which may be triggered by the NG-RAN node 1004 or by the OAM 1006, is mainly used for the NG-RAN node 1004 and the OAM 1006 to request or confirm to start the AI function, and in this step, use cases (Use cases) are interactively supported between the NG-RAN node 1004 and the OAM 1006.

Step 2: the NG-RAN node 1004 issues test configurations to the terminal 1002, and related test configurations may be configured differently according to use cases, which may be combined with an MDT mechanism.

Step 3: the test result is reported, and the terminal 1002 counts and reports relevant test data/information of the OAM 1006 according to the test information indicated.

Step 4: AI model training, OAM 1006 performs AI model training according to information required for different scenarios.

Step 5: AI model deployment, OAM 1006 issues trained models to NG-RAN node 1004.

Step 5a: if NG-RAN node 1004 successfully receives the trained AI model and can correctly understand and use the model, NG-RAN node 1004 feeds back AI model deployment success signaling to OAM 1006.

Step 5b: if NG-RAN node 1004 cannot identify the received AI model, or there is a mismatch in parameter configuration, or other situations occur that cause NG-RAN node 1004 to fail to perform the model reference step, NG-RAN node 1004 feeds back an AI model deployment failure signaling to OAM 1006.

Step 6: terminal 1002 reports network real-time test reports to NG-RAN node 1004.

Step 7: AI model reasoning, NG-RAN node 1004 performs reasoning based on the required real-time information and the trained AI model, and outputs information such as predictions or recommendations (e.g., recommended target base station, or load conditions of the base station in a future period of time, etc.).

Step 8: based on the output of AI reasoning, NG-RAN node 1004 may adjust its own configuration (e.g., handover threshold)/policy (base station power saving policy, e.g., whether Duan Jizhan is off) or instruct terminal 1002 to perform a related action (e.g., instruct terminal 1002 to perform a handover) via RRC signaling.

Step 9: the NG-RAN node 1004 feeds back the performance of the current AI model to the OAM 1006, such as accuracy of prediction, execution time of algorithm, etc., so that the OAM 1006 optimizes and updates the AI model in time.

Figure device figure numbers are block diagrams of a wireless network-intelligent communication device in an exemplary embodiment of the present disclosure.

Referring to the figure device diagram number, the wireless network-intelligent communication device diagram number 00 may include:

the interaction module 1102 is configured to interact the AI use case indication information with the OAM side device in response to the AI function initialization request.

The training module 1104 is configured to send measurement configuration signaling to the terminal according to the AI use case indication information, where the OAM side device receives a measurement report fed back by the terminal in response to the measurement configuration signaling, and performs AI model training based on the measurement report and the related data set.

The receiving module 1106 is configured to receive the trained AI model parameters of the OAM side device and perform AI model deployment.

The receiving module 1108 is configured to receive a real-time measurement report sent by the terminal.

The reasoning module 1110 is configured to perform AI model reasoning according to the real-time measurement report and the deployed AI model, and output information of AI model operation, where the information includes wireless network related prediction information or AI model recommendation policies and/or configuration parameters.

An adjustment module 1112 is configured to adjust the network configuration information or perform a corresponding network action based on the information.

In an exemplary embodiment of the present disclosure, the wireless network-intelligent communication method further includes:

determining performance indexes of the system after adjusting network configuration information or executing corresponding network actions based on AI model output information; and feeding back the performance index to the OAM side equipment, and optimizing the AI model by the OAM side equipment according to the performance index.

judging whether the received AI model can be operated locally;

if the received AI model which can be operated locally is determined, the AI model deployment success information is fed back to the OAM side equipment;

and if the received AI model can not be operated locally, feeding back AI model deployment failure signaling to the OAM side equipment.

In one exemplary embodiment of the present disclosure, if it is determined that the received AI model cannot be locally run, feeding back AI model deployment failure signaling to the OAM side device includes:

if the received AI model cannot be identified or the received model parameters are not matched with the configuration of the AI model, determining that the AI model to be deployed cannot be operated locally;

and feeding back AI model deployment failure signaling to the OAM side equipment.

sending an AI function request to OAM side equipment;

receiving AI function response information fed back by OAM side equipment in response to the AI function request;

and receiving AI function initialization success information or AI function initialization failure information fed back by the OAM side device, wherein the AI function initialization success information is used for indicating the OAM side device to complete the initialization of the AI function, and the AI function initialization failure signaling is used for indicating the initialization failure of the OAM side device to the AI function.

receiving AI function request information sent by OAM side equipment;

the method comprises the steps of sending AI function response information fed back in response to an AI function request to OAM side equipment;

and the AI function initialization success information or AI function initialization failure information is fed back to the OAM side equipment, the AI function initialization success information is used for indicating that the initialization of the AI function is completed, and the AI function initialization failure signaling is used for indicating the initialization failure of the AI function.

Since the functions of the apparatus 1100 are described in detail in the corresponding method embodiments, the disclosure is not repeated herein.

It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.

In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

Those skilled in the art will appreciate that the various aspects of the invention may be implemented as a system, method, or program product. Accordingly, aspects of the invention may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.

An electronic device 1200 according to this embodiment of the present invention is described below with reference to fig. 12. The electronic device 1200 shown in fig. 12 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.

As shown in fig. 12, the electronic device 1200 is in the form of a general purpose computing device. Components of electronic device 1200 may include, but are not limited to: the at least one processing unit 1210, the at least one memory unit 1220, and a bus 1230 connecting the different system components (including the memory unit 1220 and the processing unit 1210).

Wherein the storage unit stores program code that is executable by the processing unit 1210 such that the processing unit 1210 performs steps according to various exemplary embodiments of the present invention described in the above-described "exemplary methods" section of the present specification. For example, the processing unit 1210 may perform the methods as shown in the embodiments of the present disclosure.

The storage unit 1220 may include a readable medium in the form of a volatile storage unit, such as a Random Access Memory (RAM) 12201 and/or a cache memory 12202, and may further include a Read Only Memory (ROM) 12203.

Storage unit 1220 may also include a program/utility 12204 having a set (at least one) of program modules 12205, such program modules 12205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.

Bus 1230 may be a local bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or using any of a variety of bus architectures.

The electronic device 1200 may also communicate with one or more external devices 1240 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 1200, and/or any devices (e.g., routers, modems, etc.) that enable the electronic device 1200 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 1250. Also, the electronic device 1200 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet through the network adapter 1260. As shown, the network adapter 1260 communicates with other modules of the electronic device 1200 over bus 1230. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 1200, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.

From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.

In an exemplary embodiment of the present disclosure, a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification is also provided. In some possible embodiments, the various aspects of the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the invention as described in the "exemplary methods" section of this specification, when said program product is run on the terminal device.

The program product for implementing the above-described method according to an embodiment of the present invention may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

Furthermore, the above-described drawings are only schematic illustrations of processes included in the method according to the exemplary embodiment of the present invention, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. A method of wireless network intelligent communication, comprising:

responding to an AI function initialization request, and interacting AI use case indication information with OAM side equipment;

sending measurement configuration signaling to a terminal according to the AI use case indication information, receiving a measurement report fed back by the terminal in response to the measurement configuration signaling by the OAM side equipment, and performing AI model training based on the measurement report and a related data set;

receiving the AI model parameters trained by the OAM side equipment and deploying the AI model;

receiving a real-time measurement report sent by the terminal;

carrying out AI model reasoning according to the real-time measurement report and the deployed AI model, and outputting information operated by the AI model, wherein the information comprises wireless network related prediction information or AI model recommendation strategies and/or configuration parameters;

and adjusting network configuration information or executing corresponding network actions according to the information.

2. The wireless network-intelligent communication method according to claim 1, further comprising:

determining performance indexes of the system after adjusting network configuration information or executing corresponding network actions based on the AI model output information;

and feeding the performance index back to the OAM side equipment, and optimizing an AI model by the OAM side equipment according to the performance index.

3. The wireless network-intelligent communication method according to claim 1, further comprising:

judging whether the received AI model can be operated locally;

4. The wireless network-intelligent communication method according to claim 1, wherein if it is determined that the received AI model cannot be locally operated, feeding back AI model deployment failure signaling to the OAM side device includes:

5. The wireless network-intelligent communication method according to claim 1, further comprising:

sending an AI function request to the OAM side equipment;

receiving AI function response information fed back by the OAM side equipment in response to the AI function request;

and receiving AI function initialization success information or AI function initialization failure information fed back by the OAM side device, wherein the AI function initialization success information is used for indicating the OAM side device to finish the initialization of the AI function, and the AI function initialization failure signaling is used for indicating the initialization failure of the OAM side device to the AI function.

6. The wireless network-intelligent communication method according to claim 1, further comprising:

receiving the AI function request information sent by OAM side equipment;

sending AI function response information fed back in response to the AI function request to the OAM side equipment;

7. The wireless network-intelligent communication method according to any of claims 1-6, wherein the prediction information output by the AI model comprises a movement direction and speed of the terminal, and/or a position and trajectory prediction of the terminal, and/or a predicted operational load information of an accessed base station, and/or an energy consumption of the terminal.

8. The wireless network intelligent communication method according to any of claims 1-6, wherein the recommended policy output by the AI model comprises a base station identity recommended to access, and/or a handover pattern recommended to be configured, and/or a threshold condition for handing over a base station, and/or a base station power saving policy.

9. The wireless network-intelligent communication method according to any one of claims 1-6, wherein the request for AI functionality includes one or more requested AI use cases.

10. The wireless network-intelligent communication method of any of claims 1-6, wherein the AI use-cases are configured as use-cases that implement one or more of mobility optimization, network power saving, and load balancing.

11. A wireless network-intelligent communication device, comprising:

the interaction module is used for responding to the AI function initialization request and interacting AI use case indication information with the OAM side equipment;

the training module is configured to send measurement configuration signaling to a terminal according to the AI use case indication information, and the OAM side equipment receives a measurement report fed back by the terminal in response to the measurement configuration signaling and carries out AI model training based on the measurement report and a related data set;

the receiving module is arranged to receive the AI model parameters trained by the OAM side equipment and to deploy the AI model;

the receiving module is used for receiving a real-time measurement report sent by the terminal;

the reasoning module is used for carrying out AI model reasoning according to the real-time measurement report and the deployed AI model, outputting the information operated by the AI model, wherein the information comprises wireless network related prediction information or AI model recommendation strategies and/or configuration parameters;

and the adjusting module is used for adjusting the network configuration information or executing corresponding network actions according to the information.

12. An electronic device, comprising:

a memory; and

a processor coupled to the memory, the processor configured to perform the wireless network-intelligent communication method of any of claims 1-10 based on instructions stored in the memory.

13. A computer readable storage medium having stored thereon a program which when executed by a processor implements a wireless network intelligentized communication method according to any one of claims 1-10.