WO2023082877A1

WO2023082877A1 - Communication method and apparatus

Info

Publication number: WO2023082877A1
Application number: PCT/CN2022/121650
Authority: WO
Inventors: 李卓明; 时书锋
Original assignee: 华为技术有限公司
Priority date: 2021-11-10
Filing date: 2022-09-27
Publication date: 2023-05-19
Also published as: CN116112945A

Abstract

A communication method and apparatus, which are used for enabling an analysis service of a data analysis network element to meet the requirements of an analysis request network element. The method comprises: a data analysis network element receives a request message from an analysis request network element, the request message being used for requesting recommended network parameters, and the request message comprising network parameters required by the analysis request network element and network indexes expected by the analysis request network element. The data analysis network element may also determine the recommended network parameters according to the required network parameters and the expected network indexes. The data analysis network element may also send the recommended network parameters to the analysis request network element. The recommended network parameters correspond to the network parameters required by the analysis request network element and the network indexes expected by the analysis request network element, and thus the analysis result of the data analysis network element meets the requirements of the analysis request network element.

Description

A communication method and device

Cross References to Related Applications

This application claims the priority of a Chinese patent application filed with the Intellectual Property Office of the People's Republic of China on November 10, 2021, with application number 202111325389.3, and application title "A Communication Method and Device", the entire contents of which are incorporated herein by reference. Applying.

technical field

The present application relates to the technical field of communication, and in particular to a communication method and device.

Background technique

The rapid development of artificial intelligence and big data analysis provides the basic technology for network intelligence. In order to realize the intelligentization of 5G mobile network, network data analysis function (network data analytics function, NWDAF) network element and management data analysis system (management data analytics system, MDAS) are defined. NWDAF or MDAS can be used to provide intelligent analysis services for the network, to support network anomaly analysis, optimization adjustment and service level agreement guarantee. In this application, NWDAF and/or MDAS may be referred to as data analysis network elements.

Taking the analysis performed by NWDAF as an example, NWDAF can predict the change trend of network indicators (that is, the indicators that represent the network operation status) through intelligent analysis services, and the service network elements that process services make network adjustments according to the change trends of network indicators. However, the current analysis services of data analysis network elements cannot meet service requirements.

Contents of the invention

Embodiments of the present application provide a communication method and device, so that analysis services of data analysis network elements meet service requirements.

In the first aspect, the embodiment of the present application provides a communication method, and the method may be executed by a data analysis network element or a module (such as a chip) applied to the data analysis network element. Taking the method performed by the data analysis network element as an example, the method includes: the data analysis network element receives a request message from the analysis request network element, the request message is used to request recommended network parameters, and the request message includes the analysis The network parameters required by the requesting network element and the network indicators expected by the analysis requesting network element are requested. The data analysis network element may also determine the recommended network parameters according to the required network parameters and the expected network indicators. The data analysis network element may also send the recommended network parameters to the analysis requesting network element.

According to the above solution, the data analysis network element can determine the recommended network parameters according to the required network parameters and expected network indicators from the analysis requesting network element, and send the recommended network parameters to the analysis requesting network element to realize the recommendation of network parameters, The recommended network parameters correspond to the network parameters and expected network indicators required by the analysis requesting network element, so that the analysis service of the data analysis network element can meet the service requirements of the analysis requesting network element.

In a possible design, the recommended network parameter is within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is not within the range of the expected network index. With this design, the efficiency of recommending network parameters can be improved. For example, within the acceptable range of the network parameters, if there is no predicted network index corresponding to the network parameter within the range of the expected network index, then the recommended network parameter is determined from the range of the required network parameter, and the recommended network parameter corresponds to The value of the predicted network index is within an acceptable range of the network index, and the acceptable range is larger than the expected range of the network index.

In a possible design, the data analysis network element may also send the predicted network index corresponding to the recommended network parameter to the analysis requesting network element. If the predicted network index corresponding to the recommended network parameter is not within the range of the expected network index, the data analysis network element can send the predicted network index corresponding to the recommended network parameter, so that the analysis requesting network element can decide whether to The recommended network parameters are received.

In a possible design, the recommended network parameter is not within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is within the range of the expected network index. With this design, the efficiency of recommending network parameters can be improved. For example, within the range of the required network parameters, if there is no predicted network index corresponding to the network parameter within the expected range of the network index, then the recommended network parameter is determined from the acceptable range of the network parameter, and the recommended network parameter The value of the corresponding predicted network index is within the range of the expected network index.

In a possible design, the data analysis network element may also send indication information to the analysis requesting network element, where the indication information is used by the analysis requesting network element to decide whether to accept the recommended network parameters. With this design, the data analysis network element can trigger the decision of whether the analysis request network element accepts the recommended network parameters, thereby improving system synergy. In a possible implementation manner, if the analysis requesting network element accepts the recommended network parameter, it may also send a response message to the data analysis network element, indicating acceptance of the recommended network parameter.

In a possible design, the recommended network parameter is within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is within the range of the expected network index. With this design, the recommended network parameters meet the requirements of the analysis request network element for network parameters and expectations for network indicators, which can further enable the analysis service of the data analysis network element to meet the requirements of the analysis request network element.

In a possible design, the data analysis network element may also send the tolerance range of the network parameter to the analysis requesting network element, and the recommended network parameter belongs to the tolerance range. With this design, the analysis service of the data analysis network element can further meet the requirements of the analysis request network element. Wherein, the tolerance range is an acceptable value range of the recommended network parameter, and the service requesting network element may adjust the network parameter according to the tolerance range, and the network index corresponding to the adjusted network parameter can meet expectations.

In a possible design, the data analysis network element may also send a guarantee rate to the analysis requesting network element, and the guarantee rate is that the predicted network index corresponding to the network parameter within the tolerance range can satisfy the Probability of predicted network metrics corresponding to recommended network parameters. With this design, the analysis request network element can decide whether to adjust the network parameters within the tolerance range according to the guarantee rate, and further make the analysis service of the data analysis network element meet the requirements of the analysis request network element. For example, if the analysis request network element determines that the guarantee rate is too low, it can refuse to adjust the network parameters within the still range. Adjust the network parameters within the range of network parameters.

In a possible design, the request message further includes requirement information for indicating the recommended network parameters, and the data analysis network element may also select from within the range of the required network parameters and/or within the range of the required network parameters according to the requirement information. The recommended network parameter is determined within the range of the network parameter corresponding to the expected network indicator. With this design, the recommended network parameters are more in line with the requirements of the service request network element for network parameters, and can further enable the analysis service of the data analysis network element to meet the requirements of the analysis request network element.

In a possible design, the requirement information includes a cost function. For example, the cost function can be an objective function for finding an optimal solution using a training model, and the cost function requiring information that can be used to indicate the recommended network parameters is the smallest, and the system overhead corresponding to the recommended network parameters is the smallest at this time, so as to minimize the overhead.

In a possible design, the requirement information indicates that the recommended network parameter is the maximum or minimum value within the range of the required network parameter, or the requirement information indicates that the recommended network parameter is The maximum or minimum value within the range of the network parameter corresponding to the expected network index is satisfied.

In a second aspect, the embodiment of the present application provides a communication method, and the method may be executed by an analysis requesting network element or a module (such as a chip) applied to the analysis request. Taking the analysis request execution method as an example, the method includes: the analysis request network element sends a request message to the data analysis network element, the request message is used to request recommended network parameters, and the request message includes the analysis request network element requirements The network parameters and the expected network indicators of the analysis request network element. The analysis requesting network element may also receive recommended network parameters from the data analysis network element, and the recommended network parameters are determined according to the required network parameters and expected network indicators of the analysis requesting network element. The analysis requesting network element may also adjust network parameters according to the recommended network parameters.

In a possible design, the recommended network parameter is within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is not within the range of the expected network index.

In a possible design, the recommended network parameter is not within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is within the range of the expected network index.

In a possible design, the analysis requesting network element may also receive indication information from the data analysis network element; the analysis requesting network element may also determine whether to accept the recommended network element according to the indication information parameter.

In a possible design, the recommended network parameter is within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is not within the range of the expected network index, the The analysis requesting network element may also receive the predicted network index corresponding to the recommended network parameter from the data analysis network element. The analysis requesting network element may determine whether to accept the recommended network parameter according to the predicted network index corresponding to the recommended network parameter and the indication information.

In a possible design, the recommended network parameter is within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is within the range of the expected network index.

In a possible design, the analysis requesting network element may also receive a tolerance range of the network parameter from the data analysis network element, and the recommended network parameter belongs to the tolerance range. Specifically, the analysis requesting network element may adjust the network parameter within the tolerance range.

In a possible design, the analysis request network element may also receive the tolerance range of the network parameter from the data analysis network element, the recommended network parameter belongs to the tolerance range, and the analysis request The network element may also receive a guarantee rate from the data analysis network element, the guarantee rate is that the predicted network index corresponding to the network parameter within the tolerance range can meet the predicted network index corresponding to the recommended network parameter probability. Specifically, the analysis requesting network element may determine whether to adjust the network parameter within the tolerance range according to the guarantee rate.

In a possible design, the request message may further include requirement information for indicating the recommended network parameters.

In a possible design, the requirement information includes a cost function.

In a third aspect, the embodiment of the present application provides a communication device, which may be a data analysis network element or a module (such as a chip) applied to the data analysis network element. The device has the function of realizing the above-mentioned first aspect and any possible design thereof. This function may be implemented by hardware, or may be implemented by executing corresponding software on the hardware. The hardware or software includes one or more modules corresponding to the above functions.

In a fourth aspect, the embodiment of the present application provides a communication device, which may be an analysis requesting network element or a module (such as a chip) applied to the analysis requesting network element. The device has the function of realizing the above-mentioned second aspect and any possible design thereof. This function may be implemented by hardware, or may be implemented by executing corresponding software on the hardware. The hardware or software includes one or more modules corresponding to the above functions.

In the fifth aspect, the embodiment of the present application provides a communication device, including a processor and a memory; the memory is used to store computer instructions, and when the device is running, the processor executes the computer instructions stored in the memory so that the device executes Any implementation method in the above-mentioned first aspect to the second aspect and any possible design thereof.

In a sixth aspect, the embodiment of the present application provides a communication device, including units or means for performing the steps in the above-mentioned first aspect to the second aspect and any possible designs thereof.

In the seventh aspect, the embodiment of the present application provides a communication device, including a processor and an interface circuit, the processor is used to communicate with other devices through the interface circuit, and implement the above-mentioned first to second aspects and any possible designs thereof method in . The processor includes one or more.

In an eighth aspect, the embodiment of the present application provides a communication device, including a processor coupled to a memory, and the processor is used to call a program stored in the memory to execute the above-mentioned first to second aspects and any possible method in design. The memory may be located within the device or external to the device. And there may be one or more processors.

In the ninth aspect, the embodiment of the present application also provides a computer-readable storage medium, the computer-readable storage medium stores instructions, and when it is run on a communication device, the above-mentioned first aspect to the second aspect and its Methods in any possible design are implemented.

In the tenth aspect, the embodiment of the present application also provides a computer program product, the computer program product includes a computer program or instruction, when the computer program or instruction is run by the communication device, the above-mentioned first aspect to the second aspect and any possibility thereof The method in the design is executed.

In an eleventh aspect, the embodiment of the present application further provides a chip system, including: a processor, configured to execute the methods in the first aspect to the second aspect and any possible designs thereof.

In the twelfth aspect, the embodiment of the present application also provides a communication system, including a data analysis network element for performing the method in the above first aspect and any possible design thereof, and a network element for performing the above second aspect and any possible design thereof An analysis of possible design methods requests network elements.

For the beneficial effects of the above second to twelfth aspects and any possible designs thereof, reference may be made to the description of the beneficial effects of the first aspect and any possible designs thereof, and no further details are given.

Description of drawings

FIG. 1 is a schematic structural diagram of a communication system provided by an embodiment of the present application;

FIG. 2 is a schematic diagram of a machine learning model provided in an embodiment of the present application;

FIG. 3 is a schematic structural diagram of another communication system provided by an embodiment of the present application;

FIG. 4 is a flow diagram of a communication method provided by an embodiment of the present application;

FIG. 5 is a flow diagram of another communication method provided by the embodiment of the present application;

FIG. 6 is a flow diagram of another communication method provided by the embodiment of the present application;

FIG. 7 is a flowchart of another communication method provided by the embodiment of the present application;

FIG. 8 is a schematic structural diagram of a communication device provided by an embodiment of the present application;

FIG. 9 is a schematic structural diagram of another communication device provided by an embodiment of the present application.

Detailed ways

In order to make the purpose, technical solution and advantages of the application clearer, the application will be further described in detail below in conjunction with the accompanying drawings.

Figure 1 is a schematic diagram of a 5G network architecture based on a service-based architecture. The 5G network architecture shown in FIG. 1 may include terminal equipment, access network (access network, AN) equipment, and core network equipment. The terminal device accesses the data network (data network, DN) through the access network device and the core network device. Among them, the core network equipment includes some or all of the network functions (network function, NF) in the following network elements: unified data management (unified data management, UDM) network elements, network exposure function (network exposure function, NEF) network elements (Fig. not shown), application function (application function, AF) network element, policy control function (policy control function, PCF) network element, access and mobility management function (access and mobility management function, AMF) network element, network Slice selection function (network slice selection function, NSSF) network element, session management function (session management function, SMF) network element, user plane function (user plane function, UPF) network element, network data analysis function (network data analytics function, NWDAF) network element and network repository function (network repository function, NRF) network element (not shown in the figure), etc.

The access network device may be a radio access network (radio access network, RAN) device. For example: base station (base station), evolved base station (evolved NodeB, eNodeB), transmission reception point (transmission reception point, TRP), next generation base station (next generation NodeB, gNB) in the 5G mobile communication system, the sixth generation ( The next-generation base station in the 6th generation (6G) mobile communication system, the base station in the future mobile communication system, or the access node in the wireless fidelity (Wireless Fidelity, WiFi) system, etc.; it can also be a module that completes some functions of the base station or A unit, for example, can be a centralized unit (central unit, CU) or a distributed unit (distributed unit, DU). The radio access network equipment may be a macro base station, a micro base station or an indoor station, or a relay node or a donor node. The embodiment of the present application does not limit the specific technology and specific equipment form adopted by the radio access network equipment.

The terminal device may be a user equipment (user equipment, UE), a mobile station, a mobile terminal, and the like. Terminal devices can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (internet of things, IOT), virtual reality, augmented reality, industrial control, automatic driving, telemedicine, smart grid, smart furniture, smart office, smart wear, smart transportation, smart city, etc. Terminal devices can be mobile phones, tablet computers, computers with wireless transceiver functions, wearable devices, vehicles, urban air vehicles (such as drones, helicopters, etc.), ships, robots, robotic arms, smart home devices, etc.

Access network equipment and terminal equipment can be fixed or mobile. Access network equipment and terminal equipment can be deployed on land, including indoor or outdoor, handheld or vehicle-mounted; they can also be deployed on water; they can also be deployed on aircraft, balloons and artificial satellites in the air. The embodiments of the present application do not limit the application scenarios of the access network device and the terminal device.

The access management network element includes the functions of mobility management, access authentication/authorization, etc. It is mainly used for the attachment, mobility management, and tracking area update process of terminals in the mobile network. Incoming layer (non access stratum, NAS) message, complete registration management, connection management and reachability management, allocation tracking area list (track area list, TA list) and mobility management, etc., and transparent routing session management (session management, SM) message to the session management network element. In the 5th generation (5th generation, 5G) communication system, the access management network element may be an AMF network element (hereinafter referred to as AMF). In addition, the access management network element is also responsible for transferring user policies between the terminal equipment and the PCF.

The session management network element is mainly used for session management in the mobile network, such as session establishment, modification, and release. Specific functions include assigning an Internet protocol (internet protocol, IP) address to the terminal, selecting a user plane network element that provides a packet forwarding function, and the like. In a 5G communication system, the session management network element may be an SMF network element (hereinafter referred to as SMF).

The selection of network elements for network slicing is mainly used to select a suitable network slice for terminal services. In a 5G communication system, the network element selected for network slicing may be an NSSF network element.

The user plane network element is mainly responsible for processing user packets, such as forwarding, charging, and lawful interception. The user plane network element can serve as a protocol data unit (protocol data unit, PDU) session anchor (PDU session anchor, PSA). In a 5G communication system, the user plane network element may be a UPF network element (hereinafter referred to as UPF). UPF can directly communicate with NWDAF through a service-like interface, or communicate with NWDAF through other channels, such as SMF or a private interface or internal interface with NWDAF.

Unified data management network element: responsible for managing terminal subscription information. In a 5G communication system, the unified data management network element may be a UDM network element (hereinafter referred to as UDM).

Network capability opening network element is used to support the opening of capabilities and events. In the 5G communication system, the network element of network capability exposure may be a NEF network element (hereinafter referred to as NEF).

The application function network element is used to transmit the requirements from the application side to the network side, for example, QoS requirements or user status event subscription. The application function network element can be a third-party functional entity, or an application server deployed by an operator. In a 5G communication system, the application function network element may be an AF network element (hereinafter referred to as AF).

Policy control network element, including user subscription data management function, policy control function, charging policy control function, quality of service (quality of service, QoS) control, etc. In a 5G communication system, the policy control network element may be a PCF network element (hereinafter referred to as PCF). It should be pointed out that the PCF in the actual network may also be divided into multiple entities according to the level or function, such as the global PCF and the PCF in the slice, or the session management PCF (session management PCF, SM-PCF) and the access management PCF (access management PCF). , AM-PCF).

The network element in the network warehouse can be used to provide the network element discovery function, and provide the network element information corresponding to the network element type based on the request of other network elements. The network element of the network warehouse also provides network element management services, such as network element registration, update, de-registration, and network element status subscription and push. In the 5G communication system, the network element of the network warehouse may be an NRF network element (hereinafter referred to as NRF).

The data analysis network element can be used to collect data and perform analysis and prediction. Among them, collecting data includes but is not limited to: collecting data from other NFs, such as collecting data through AMF, SMF, PCF, collecting data through NEF or directly from AF, or from operation, administration, and maintenance (OAM) systems At least one of the data is collected. Among them, these data can be the data of terminal equipment, access network equipment, core network elements or third-party application equipment, or the data of terminal equipment on the access network equipment, the core network network elements or the third-party application equipment. data, and then perform intelligent analysis based on the collected data, and output the analysis results. In a 5G communication system, the data analysis network element may be a NWDAF network element (hereinafter referred to as NWDAF). Among them, intelligent analysis refers to the analysis of collected data with the help of artificial intelligence (AI) and other intelligent technologies. In this application, intelligent analysis includes but is not limited to predicting network indicators and recommending network parameters.

In this application, NWDAF can use machine learning models for intelligent analysis. The NWDAF can also output recommended values to the above-mentioned NFs, AFs or OAMs, for use by the NFs, AFs or OAMs in implementing policy decisions. In the 3rd generation partnership project (3GPP) version (release) 17, the training function and inference function of NWDAF are split. One NWDAF can only support model training function, or only support data inference function. , or support both model training and data inference functions. Wherein, the NWDAF that supports the model training function may also be called the training NWDAF, or the NWDAF that supports the model training logical function (model training logical function, MTLF) (NWDAF (MTLF) for short). Training NWDAF can perform model training based on the acquired data to obtain the trained model. The NWDAF that supports the data reasoning function may also be called the reasoning NWDAF, or the NWDAF that supports the analysis logic function (analytics logical function, AnLF) (referred to as NWDAF (AnLF) for short). Inference NWDAF can input the input data into the trained model to get analysis results or inference data. In this embodiment of the present application, the training NWDAF refers to an NWDAF that supports at least a model training function. As a possible implementation method, training NWDAF can also support data reasoning functions. An inference NWDAF refers to an NWDAF that supports at least a data inference function. As a possible implementation method, inference NWDAF can also support the model training function. If an NWDAF supports both the model training function and the data reasoning function, the NWDAF may be called a training NWDAF, an inference NWDAF, or a training and reasoning NWDAF or NWDAF. In the embodiment of the present application, a NWDAF can be a single network element, or can be set up together with other network elements, for example, the NWDAF is set in a PCF network element or an AMF network element.

DN is a network outside the operator's network. The operator's network can access multiple DNs, and various services can be deployed on the DN, which can provide data and/or voice services for terminal equipment. For example, DN is a private network of a smart factory. The sensors installed in the workshop of the smart factory can be terminal devices. The control server of the sensor is deployed in the DN, and the control server can provide services for the sensor. The sensor can communicate with the control server, obtain instructions from the control server, and transmit the collected sensor data to the control server according to the instructions. For another example, DN is a company's internal office network, and the mobile phone or computer of the company's employees can be a terminal device, and the employee's mobile phone or computer can access information and data resources on the company's internal office network.

In Figure 1, Npcf, Nnef, Namf, Nudm, Nsmf, Naf, Nnssf, and Nnwdaf are the service interfaces provided by the above-mentioned PCF, NEF, AMF, UDM, SMF, AF, NSSF, and NWDAF, respectively, and are used to call corresponding service operations . N1, N2, N3, N4, and N6 are interface serial numbers. The meanings of these interface serial numbers are as follows:

1), N1: the interface between the AMF and the terminal device, which can be used to transmit non-access stratum (non access stratum, NAS) signaling (such as including QoS rules from the AMF) to the terminal device.

2), N2: the interface between the AMF and the access network device, which can be used to transfer radio bearer control information from the core network side to the access network device.

3), N3: the interface between the access network device and the UPF, mainly used to transfer the uplink and downlink user plane data between the access network device and the UPF.

4), N4: The interface between SMF and UPF, which can be used to transfer information between the control plane and the user plane, including controlling the delivery of forwarding rules, QoS rules, traffic statistics rules, etc. for the user plane, as well as user plane information report.

5), N6: interface between UPF and DN, used to transmit uplink and downlink user data flow between UPF and DN.

The service-oriented architecture shown in Figure 1 enables the 5G core network to form a flat architecture. Through the signaling bus of the control plane, the network function entities of the control plane of the same network slice can discover each other through NRF and obtain each other’s information. access address information and can then directly communicate with each other over the control plane signaling bus.

It can be understood that the above-mentioned network element or function may be a network element in a hardware device, or a software function running on dedicated hardware, or a virtualization function instantiated on a platform (for example, a cloud platform). As a possible implementation method, the foregoing network element or function may be implemented by one device, or jointly implemented by multiple devices, or may be a functional module in one device, which is not specifically limited in this embodiment of the present application.

As an implementation method, the data analysis network element in the embodiment of the present application may be the above-mentioned NWDAF, or may be a network element having the above-mentioned NWDAF function in a future communication such as a 6G network. The data analysis network element may also be an MDAS. MDAS is a data analysis system deployed on the network management plane. It can be used to collect management data such as performance statistics, alarms, and operation configurations for analysis and prediction, and can also output suggestions for resource allocation or configuration optimization. MDAS also has training and reasoning functions. Compared with NWDAF, MDAS is a part of the network management system, which often runs offline and in non-real time. It provides operators with resource and deployment adjustment and optimization suggestions, and trends analysis and optimization suggestions for a longer period. For the convenience of description, the data analysis network element is NWDAF as an example for description below, and the actions performed by NWDAF in this application can also be performed by MDAS.

The following describes the process of intelligent analysis by NWDAF. NWDAF can collect data of multiple dimensions from multiple sources, perform correlation analysis, output historical statistics, or train and fit a model, and output the predicted value of network indicators according to the model to guide service network elements to adjust network parameters to Optimize network indicators. It should be understood that different network indicators correspond to different network parameters, and network indicators are related to network operation status. Among them, network indicators such as network service evaluation value (hereinafter referred to as service experience), network key performance indicator value or network overhead indicator, etc., network parameters may include time, UE location, application location, bit rate of service flow, packet time factors such as delay, number of transmitted and retransmitted packets, etc.

Here, the intelligent analysis (hereinafter referred to as service experience analysis) process in which the network index is the service experience is taken as an example. The service experience refers to the user's evaluation of the user's experience in accessing the service process through the network. At this time, the network index can be quantified by the user. assessment, the process may include the following steps:

Step 1, NWDAF first collects the following data:

(1) Collect the service experience score from the AF, the percentage of UEs that achieve the experience score (for example, the proportion of excellent service quality of experience is no less than 90%), the IP address of the UE, and the location information of the application (such as data network access Identification (data network access identify, DNAI). Wherein, the experience score is, for example, an average subjective evaluation (mean opinion score, MOS).

(2) Collect UE's subscription permanent identifier (SUPI) and UE location information (such as global cell identifier (GCI)) through AMF;

(3) Collect UE's SUPI, network slice identifier of PDU session (such as single-network slice selection assistance information (single-network slice selection assistance information, S-NSSAI)), UPF information (such as UPF identifier (identifier, ID)) from SMF )), IP filtering information and service flow identifier (QoS flow identifier, QFI);

(4) Collect parameters such as the bit rate of the service flow, the end-to-end delay (or packet delay), the number of transmitted and retransmitted packets, etc. from the UPF.

Step 2: NWDAF associates the data collected from AF of a UE with the data collected from SMF of the same UE by using the IP filtering information and the IP address of the UE, and then associates the location data collected from AMF of the same UE with the data collected from AMF according to SUPI Associated with session data from SMF. The data collected from the UPF of the same UE is further associated with the above data through QFI. Similarly, the NWDAF further performs association analysis on the data of a large number of UEs.

Step 3, NWDAF trains and fits the model according to the above data. For example, train a deep learning network using the above data. The deep learning network is shown in Figure 2, for example.

Training process For example, NWDAF uses the training function to take network parameters such as UE location, application location, time, QoS Flow bit rate, packet delay, number of transmitted and retransmitted packets as independent variables (independent variables), and Network indicators such as the experience and the proportion of UEs that achieve the corresponding service experience are used as dependent variables. The data after the above-mentioned correlation processing is used to train the deep learning network to obtain a deep learning model. That is, during training, the independent variable is the network parameter and the dependent variable is the network index.

Step 4, NWDAF sets the trained deep learning model to inference mode (that is, uses inference function), predicts the most likely value range of each independent variable in the future according to the historical statistical change trend of each independent variable, and then According to the deep learning model obtained through training, the predicted results of the output dependent variable in the future are calculated through the predicted values of each independent variable.

Accordingly, through the service experience analysis process, the NWDAF can predict the predicted value of the network index corresponding to the network parameter. The NWDAF can also send the predicted value to the service network element (or called the service processing network element), so that the service network element can adjust the network parameters according to the predicted value, so that the network index after adjusting the network parameters can be optimized.

Specifically, in the service experience analysis process, the service network element may include the SMF, the network parameters may include the QoS parameters, and the network indicators may include the experience score. The NWDAF can output the predicted value of the experience score to the SMF. The SMF may determine an adjusted QoS parameter according to the predicted value of the experience score, and the adjusted QoS parameter may specifically include an adjusted bit rate and/or an adjusted packet delay. The SMF can also implement the adjusted QoS parameters through the UPF, so as to improve the service score through the optimization of the QoS parameters.

However, in the above service experience analysis process, NWDAF outputs predicted network indicators, and service network elements adjust network parameters with reference to the predicted values. However, in some cases, the network parameters determined by service network elements based on the predicted values The method may not meet the requirements of service network elements for network parameter adjustment. Still taking the service experience analysis process as an example, if the network parameter corresponding to the predicted value output by NWDAF is adjusted by adjusting the end-to-end delay of the network slice, but SMF may not support this adjustment or does not want to make this adjustment, or wants to Adjust other network parameters such as bit rate, then SMF may not be able to adjust the network parameters according to the predicted value at this time, so the predicted value has no practical value. Therefore, NWDAF needs to perform analysis according to the requirements of service network elements, so that the analysis service of data analysis network elements can meet the requirements of analysis request network elements.

In order to enable the analysis service of the data analysis network element to meet the requirements of the analysis requesting network element, an embodiment of the present application provides a communication method. The communication method can be executed by the data analysis network element and the analysis request network element. As shown in Figure 3, the data analysis network element can be used to perform intelligent analysis on the network according to the request message (or analysis request) from the analysis request network element, and send the analysis result (or call the request message corresponding to the analysis request network element) to the analysis request network element. response message, referred to as a response message), for example, the data analysis network element includes NWDAF or MDAS. The analysis requesting network element may be a network element in the network to be analyzed, or a network element outside the network. The analysis request network element may include a service network element for adjusting the network parameters of the network according to the analysis result, or may include other network elements other than the service network element, for example, the analysis request network element may be, for example, an AMF or an SMF, etc., Not specifically limited. The network may include at least one network element, for example, at least one NF in the architecture shown in FIG. 1 .

A communication method provided by an embodiment of the present application will be described below in conjunction with FIG. 4. The communication method may include the following steps:

S101: The analysis request network element sends a request message to the data analysis network element, where the request message can be used to request recommended network parameters. Wherein, the request message may include at least one of network parameters required by the analysis requesting network element and network indicators expected by the analysis requesting network element.

In this application, at least one of the network parameters required by the analysis requesting network element and the network index expected by the analysis requesting network element can be used to determine the analysis results, so that the number of analysis results can meet the requirements of the analysis requesting network element. Wherein, the analysis result may include recommended network parameters, so that the analysis requesting network element can adjust the network parameters according to the recommended network parameters, so as to obtain a better network optimization effect.

Specifically, analyzing the network parameter required by the requesting network element may include analyzing the type of the network parameter required by the requesting network element, or including the type and value of the required network parameter. The network parameters required by the analysis requesting network element may be an acceptable adjustment range of the network parameters for the analysis requesting network element, so that the data analysis network element determines the recommended network parameters according to the acceptable adjustment range of the network parameters, avoiding data analysis network The recommended network parameters determined by the element are outside the acceptable range of the analysis requesting network element. For example, taking network parameters as two parameters of bit rate and end-to-end delay as an example, the required network parameters may indicate an acceptable bit rate less than or equal to 20 megabits per second (Mbps), and an acceptable end-to-end delay. The end-to-end delay is greater than or equal to 20 milliseconds (ms). It should be understood that the request message may carry a network parameter list including at least one required network parameter.

Analyzing the expected network index of the requesting network element may include analyzing the type of the expected network index of the requesting network element, or including the type and value (or value range) of the expected network index. The network index expected by the analysis requesting network element may be a value that the analysis requesting network element expects the network index to achieve. For example, if the analysis request network element hopes to adjust the network parameters so that the network index can reach a certain value, it can send this value to the data analysis network element, so that the data analysis network element can predict the recommendation that makes the network index reach the value. Network parameters. Therefore, the data analysis network element can determine the network parameters that make the network index reach the expected network index, and use it to determine the recommended network parameters. Taking the network index as MOS as an example, if the expected MOS of the analysis request network element is not lower than 4.5, the data analysis network element can analyze the network parameters that make the MOS not lower than 4.5, and determine the recommended network parameters based on these network parameters, where , 0≤MOS≤5.

In a possible implementation manner, the request message may further include requirement information of recommended network parameters. Wherein, the requirement information is used to indicate that the recommended network parameters are determined within the range of network parameters required by the analysis requesting network element and/or within the range of network parameters corresponding to network indicators expected by the analysis requesting network element. Specifically, the requirement information may be used to indicate the maximum or minimum network parameter within the range of the required network parameter and/or within the range of the network parameter corresponding to the expected network index of the analysis requesting network element as the recommended network parameter, or, The requirement information may be used to indicate that the network parameter corresponding to the predicted maximum or minimum value of the network index is used as the recommended network parameter. In addition, the required information can also be used in the cost function. Wherein, the cost function is an objective function for finding the optimal solution by using the training model, and is used to determine the optimal network parameter as a recommended value from a plurality of network parameters corresponding to the network index satisfying the network element expectation of the analysis request. Specifically, the requirement information can be used to indicate that the cost function of the recommended network parameters is the smallest, and at this time the system overhead corresponding to the recommended network parameters is the smallest.

In a possible implementation manner, if the request message includes the network index expected by the analysis requesting network element, the request message may further include an expected ratio of the network index reaching the expected network index. Taking the network index as MOS as an example, the expected ratio may indicate the expected ratio of the user's MOS expected by the analysis requesting network element to reach the expected MOS after the network parameters are adjusted according to the analysis result corresponding to the data request message. For example, the expected ratio is not less than 90%.

In a possible implementation manner, the request message may also include the analysis type requested by the analysis requesting network element, for example, carrying an analysis type identifier such as service experience analysis.

In this application, the request message shown in S101 may be a request for requesting the data analysis network element to provide an intelligent analysis service, or may be a subscription request for requesting a subscription to the analysis service. If it is a request to provide an intelligent analysis service, the data analysis network element outputs the analysis result to the analysis requesting network element at one time according to the request. If it is a subscription request for an analysis service, the data analysis network element outputs analysis results to the analysis requesting network element multiple times according to the request, timing or event trigger, until the analysis requesting network element cancels the subscription. If the request message is a subscription request, the request message may also include a subscription identifier for identifying this subscription, and the analysis requesting network element may distinguish different analysis subscriptions through the subscription identifier.

Correspondingly, the data analysis network element receives the request message from the analysis requesting network element.

S102: After receiving the request message, the data analysis network element may determine recommended network parameters according to network parameters required by the analysis requesting network element and/or network indicators expected by the analysis requesting network element. In a possible implementation manner, the recommended network parameters determined in S102 are within the range of the network parameters required by the analysis requesting network element, and/or, the predicted network indicators corresponding to the recommended network parameters are within the range of the expected network indicators within range.

In S102, the data analysis network element may determine recommended network parameters through the trained model. Among them, if there is no trained model, the data analysis network element needs to enter the model training phase first, and obtain the trained model in the training phase. In the model training phase, the input data is the data collected by the data analysis network element, including the independent variables of the model (ie, network parameters) and the corresponding dependent variables (ie, network indicators), and the structure and internal parameters of the output network model, that is, the trained model, at this time, the data analysis network element obtains the trained model. If the data analysis network element already has a trained model, for example, the model is obtained through the previous training stage, or the model is received from other network elements or devices, the data analysis network element can use the trained model for inference, prediction or recommendation. When the model is used for inference and prediction, the input data of the model may include independent variables, and the output of the model may include dependent variables. When the trained model is used to determine the recommended independent variable, the input data of the model may include the dependent variable of the predicted model (that is, one or more predicted network indicators to be achieved, specifically, in S102, it may be the analysis request network element expected network index), the output result of the model may include at least one type of recommended independent variable (ie, one or more network parameters).

Further, the data analysis network element can determine the type of network parameter required by the analysis request network element according to the request message, and determine one of the multiple types of independent variables of the trained model that needs to output a recommended value according to the type of the required network parameter or multiple types of independent variables, as recommended network parameters. In addition, the data analysis network element can also determine other types of recommended network parameters (hereinafter referred to as unrequired network parameters) other than the type of required network parameters. These unrequired network parameters can take the current value of this type of network parameter Value or historical average value, or the predicted value of the maximum probability of future occurrence of this type of network parameter. Then the data analysis network element analyzes and outputs the recommended value of one or more corresponding network parameters according to the model and the one or more predicted network indicators to be achieved, where the recommended value of the network parameter includes the required network parameter Recommended values may also include recommended values for network parameters that are not required. For example, the type of required network parameter is bit rate, the data analysis network element can determine the recommended bit rate, and can also determine the recommended value of the recommended end-to-end delay, and send the recommended bit rate and the recommended bit rate to the analysis requesting network element Recommended end-to-end latency.

In a possible implementation, the output result of the data analysis network element can also include the predicted ratio of the network index reaching the predicted network index, and the predicted ratio can indicate that after a certain network parameter is adjusted, the actual network index reaches the network parameter The corresponding predicted ratio of the network index, the data analysis network element may also send the predicted ratio corresponding to the recommended network parameter to the analysis requesting network element. Wherein, the predicted ratio may be equal to or greater than the expected ratio stated in the request message, and may also be smaller than the expected ratio stated in the request message. The prediction ratio can help the analysis requesting network element to determine whether to accept and adjust the recommended network parameters.

It should be understood that the training process of the model described here may be performed in the data analysis network element, or the trained model may be obtained by other network elements through training and then sent to the data analysis network element. If the model is determined by the data analysis network element, the data analysis network element can collect data and train the model according to a certain period, so it is not necessary to retrain the model every time the intelligent network analysis is performed.

In the implementation of S102, the data analysis network element can determine the network parameter and the predicted value of the predicted network index corresponding to the network parameter through the model, and determine the network parameter with a better predicted value of the corresponding predicted network index (hereinafter referred to as the backup Optional network parameters), the candidate network parameters can be used to determine the recommended network parameters. The predicted network index corresponding to the network parameter refers to the network index as the output result of the model when the network parameter is used as the input data of the model (or as a part of the input data). In a possible implementation manner, the candidate network parameters are within the range of the network parameters required by the analysis requesting network element, and/or the predicted network index corresponding to the candidate network parameter is within the range of the expected network index.

Taking service experience analysis as an example, if the network indicator is MOS, the data analysis network element can determine the network parameters that make the MOS not lower than the threshold (for example, 4.5) as the candidate network parameters, and further determine the recommended network parameters based on the candidate network parameters. In a possible implementation manner, the threshold is to analyze the expected network index of the requesting network element.

In a possible implementation manner, in the process of determining the recommended network parameters, if the request message includes the network parameters required by the analysis requesting network element, then in S102, the recommended network parameters determined by the data analysis network element are within the required within the range of network parameters. For example, the data analysis network element uses the required network parameters as the given input data to determine the output result, and determines the output result with a higher value within the range of the obtained output result, and the network parameter corresponding to the output result with a higher value can be used as Alternative network parameters. Taking service experience analysis as an example, the network parameters required by the analysis request network element are, for example, the bit rate is less than or equal to 20Mbps, and the end-to-end delay is greater than or equal to 20ms, then the recommended network parameters determined by the data analysis network element include the bit rate and End-to-end delay, and the bit rate is not higher than 20Mbps, and the end-to-end delay is not lower than 20ms.

In addition, if the request message includes the expected network index of the analysis requesting network element, in the process of determining the candidate network parameters, the data analysis network element can determine the range of the network parameter corresponding to the expected network index according to the model, for example, the data analysis network The element determines the input data with the expected network index as a given output result. The range of the obtained input data is the range of the expected network index corresponding to the network parameter, and then the data analysis network element can correspond to the range of the network parameter from the expected network index The candidate network parameters are determined within, and then the recommended network parameters are determined according to the candidate network parameters. Still taking the service experience analysis as an example, if the network index expected by the analysis requesting network element is, for example, MOS not lower than 4.5, then the MOS corresponding to the recommended network parameter determined by the data analysis network element is not lower than 4.5.

If the request message includes the expected ratio of the network index reaching the desired network index, the output result of the model may also include the predicted ratio of the network index reaching the predicted network index. In the process of determining the candidate network parameters, the data analysis network element A network parameter such that the predicted proportion included in the output result is not lower than the expected proportion may be determined as an alternative network parameter. For example, if the expected ratio is not lower than 95%, the data analysis network element may determine the network parameters that make the predicted ratio not lower than 95% as candidate network parameters, and further determine recommended network parameters from the candidate network parameters.

In a possible implementation, if the output result of the model includes the predicted ratio of the network index reaching the predicted network index, the data analysis network element can also determine the predicted ratio corresponding to the recommended network parameter. At this time, the input data of the model includes Recommended network parameters, the output of the model includes the predicted proportion corresponding to the recommended network parameters. The data analysis network element may also send the predicted ratio corresponding to the recommended network parameter to the analysis requesting network element, which is used to indicate the ratio of the actual network parameter to the predicted network parameter after the recommended network parameter is adjusted.

In a possible implementation manner, the data analysis network element may also determine a tolerance range of network parameters, and the recommended network parameters belong to the tolerance range. The data analysis network element may also send the tolerance range to the analysis requesting network element. The tolerance range may be a numerical range including recommended network parameters, and the numerical range represents acceptable values of actual network parameters. It should be understood that after the analysis request network element or other service network elements adjust the network parameters according to the analysis results, there is a certain deviation between the network indicators corresponding to the actual network parameters and the predicted network indicators corresponding to the recommended network parameters, and the data analysis network element can determine An acceptable range of actual network parameter values, that is, a tolerance range, is indicated to the analysis requesting network element, so that the analysis requesting network element adjusts the network parameter within the range. For example, analyzing the requesting network element can ensure that the actual value of the adjusted network parameter does not exceed the tolerance range. In the present application, the tolerance range may be represented by the distance between the central value of the tolerance range and the boundary value of the tolerance range. Wherein, the central value of the tolerance range may be a recommended network parameter. Take the network parameter bit rate as an example, if the recommended bit rate is 20Mbps and the tolerance range is 19Mbps to 21Mbps, the tolerance range can be represented by 20±1Mbps, and 1Mbps is the radius of the tolerance range. Alternatively, the recommended bit rate may be a value range spanning 2 Mbps. In this case, there is a certain deviation between the network index corresponding to the actual bit rate and the predicted network index corresponding to the recommended network parameter.

In a possible implementation, the data analysis network element can determine the tolerance range of network parameters according to the expected ratio of network indicators. For example, if the expected ratio in the request message is not less than 95%, the data analysis network element can determine some values of network parameters, and these values greater than or smaller than the cell can meet the following conditions: the prediction corresponding to the recommended network parameters Compared with the value of the network index, the predicted value of the corresponding network index is controlled within a deviation range of no more than 5%. The data analysis network element takes this value as the boundary value of the tolerance range of the network parameter.

When the expected ratio of the network index is not specified in the request message, the data analysis network element can also deduce the tolerance range of the network parameter according to the pre-configured guarantee rate or the guarantee rate indicated by other network elements or devices. For example, when the pre-configured guarantee rate is not lower than 90%, the data analysis network element can determine the network parameters whose value range of the corresponding predicted network index and the value range of the network index of the recommended network parameter do not exceed 10%. The value of is used as the boundary value of the tolerance range of the network parameter.

In addition, the data analysis network element can also determine the guarantee rate, which is the probability that the actual network index (or predicted network index) corresponding to the network parameter within the tolerance range can meet the predicted network index corresponding to the recommended network parameter. For example, the data analysis network element can determine that the guarantee rate is not lower than 90% according to the pre-configuration, and then determine the corresponding network parameter tolerance range according to the guarantee rate. That is, according to the model, when the network parameters are within the tolerance range, the predicted network index can be achieved with a probability of 90%. For example, when the actual bit rate is the recommended bit rate of 20 Mbps, the predicted MOS value is greater than or equal to 4.5, and when the network parameters are within the tolerance range of 20 ± 1 Mbps, the prediction can have a 90% probability that the MOS value is greater than or equal to 4.5. The analysis request network element can decide whether to adjust strictly according to the recommended network parameters according to the guaranteed rate, or relatively loosely control the value of the network parameters within the tolerance range.

It should be understood that the above tolerance range and guarantee rate are determined for one type of network parameter. For example, the recommended network parameters include recommended bit rate and recommended end-to-end delay, then the tolerance range and guarantee rate can be determined for the recommended bit rate, and the tolerance range and guarantee rate can be determined for the recommended end-to-end delay .

In a possible implementation manner, if there are multiple candidate network parameters, and the request message also includes requirement information, the data analysis network element may determine recommended network parameters from the plurality of candidate network parameters according to the requirement information. For the required information, please refer to the description in S101. For example, the data analysis network element can determine the recommended network parameters from the candidate network parameters according to the required information. For example, the data analysis network element can determine the largest or smallest network parameter from the candidate network parameters as the recommended network parameter according to the requirement information (for example, the recommended network parameter is the network parameter with the lowest requirement), or, from the candidate network parameters The network parameter with the largest or smallest predicted network index is determined among the network parameters as the recommended network parameter (for example, the recommended network parameter is the network parameter with the best predicted network index). As another example, when the required information includes a cost function, the recommended network parameters are determined according to the cost function, wherein, if the required information requires that the cost function of the recommended network parameters be the smallest, the data analysis network element can determine the cost from the alternative network parameters The network parameter with the smallest function is the recommended network parameter.

Specifically, the cost function may be an expression for calculating the network overhead according to the recommended network parameters, and the data analysis network element may determine from multiple alternative network parameters based on the expression that the network overhead expression can be minimized The network parameters of are used as recommended network parameters. The cost function can also be an expression for calculating the service rate according to the required network parameters, and the data analysis network element can determine the network that can minimize the value of the charging rate from multiple alternative network parameters according to this expression parameters as recommended network parameters.

It should be understood that the alternative network parameters described here may also be replaced by network parameters within the range of network parameters required by the analysis requesting network element and/or within the range of network parameters corresponding to the network indicators expected by the analysis requesting network element. That is to say, the data analysis network element can determine recommended network parameters within the range of network parameters required by the analysis requesting network element and/or within the range of network parameters corresponding to network indicators expected by the analysis requesting network element according to the required information, The specific method of determining the recommended network parameters based on the required information will not be expanded any further. For details, please refer to the description of determining the recommended network parameters from the alternative network parameters based on the required information.

S103: The data analysis network element sends recommended network parameters to the analysis requesting network element.

In a possible implementation manner, the data analysis network element may send an analysis result to the analysis requesting network element, and the analysis result may include recommended network parameters. In addition, the analysis results may also include network parameters required by the analysis requesting network element, network indicators expected by the analysis requesting network element, predicted network indicators corresponding to recommended network parameters, predicted ratios corresponding to recommended network parameters, network parameters Tolerance range, or at least one of the guaranteed rates corresponding to the tolerance range of network parameters.

Correspondingly, the analysis requesting network element receives recommended network parameters (or analysis results including recommended network parameters) from the data analysis network element. If the analysis requesting network element is a service network element, the analysis requesting network element can adjust the network parameters according to the recommended network parameters, so as to optimize the network and improve the network index. If the analysis request network element does not belong to the service network element, the analysis request network element can send the recommended network parameters to the service network element, or send the network adjustment strategy determined according to the recommended network parameters, so that the service network element can adjust the network parameters Make adjustments. Taking service experience analysis as an example, the recommended network parameters include the recommended bit rate and/or the recommended end-to-end delay, the analysis request network element can be SMF, the service network element can be UPF, and the SMF can receive the analysis result , determine the adjusted QoS parameters according to the recommended bit rate and/or the recommended end-to-end delay, the adjusted QoS parameters may include the adjusted bit rate and/or the adjusted end-to-end delay, and the SMF also The adjusted QoS parameters may be sent to the UPF so that the UPF enforces the adjusted QoS parameters.

It can be seen that in the communication method provided by the embodiment of the present application, the analysis request network element can send the required network parameters and expected network indicators to the data analysis network element, so that the analysis process of the data analysis network element can be based on the network parameters and expectations required by the analysis request network element. The network index is carried out, and the analysis result is obtained, and the analysis result may include recommended network parameters. Wherein, the analysis result conforms to the requirements of the analysis requesting network element for network parameters and expectations for network indicators, so the analysis service of the data analysis network element can meet the requirements of the analysis requesting network element.

Several implementations of S103 will be described below based on the relationship between the recommended network parameters and the range of the required network parameters, and the relationship between the predicted network index corresponding to the recommended network parameter and the expected range of the network index.

way 1

The recommended network parameters are within the range of network parameters required by the analysis requesting network element, and the predicted network indicators corresponding to the recommended network parameters are within the range of expected network indicators. In manner 1, the data analysis network element may send recommended network parameters to the analysis requesting network element.

Taking the service experience analysis process with NWDAF as the data analysis network element and SMF as the analysis request network element as an example, a communication method implemented according to mode 1 provided by the embodiment of the present application includes the following steps as shown in FIG. 5:

S201: The SMF sends a request message to the NWDAF.

Wherein, the request message includes the type identification of the service experience analysis, the network parameters required by the SMF, the network index expected by the SMF, and the expected ratio of the network index reaching the expected network index. Among them, the network parameters required by SMF are used to indicate that the required bit rate is less than or equal to 20Mbps and the required end-to-end delay is greater than or equal to 20ms, and the network indicators expected by SMF are used to indicate that the MOS is not lower than 4.5.

In a possible implementation manner, the request message may further include requirement information, and for the requirement information, refer to the description in S101.

Accordingly, NWDAF receives the request message.

S202: After identifying the type identifier carried in the request message, the NWDAF can determine an analysis result according to the request message, where the analysis result includes recommended network parameters. For example, the recommended network parameters include recommended bit rate and recommended end-to-end delay, for example, the recommended bit rate is 15Mbps, and the recommended end-to-end delay is 30ms.

In implementation, NWDAF can take the network index expected by SMF as the dependent variable of the model, determine the independent variable of the model according to the dependent variable, and use it as the network parameter satisfying the network index expected by SMF. NWDAF can take the intersection of the network parameters that meet the network indicators expected by the SMF and the network parameters required by the SMF to obtain alternative network parameters. Further, when there are multiple candidate network parameters and the request message includes the required information, NWDAF can also determine the recommended network parameters from the candidate network parameters according to the required information. Description of network parameters.

In addition, the analysis result may also include the tolerance range of the recommended network parameters, or include the tolerance range and guarantee rate of the recommended network parameters. For the meaning and determination method of tolerance range and guarantee rate, please refer to the description in S102. For example, the analysis result may indicate that the radius of the tolerance range corresponding to the recommended bit rate is 1 Mbps and the corresponding guarantee rate is 90%, and indicate that the radius of the tolerance range corresponding to the recommended end-to-end delay is 5 ms and the corresponding guarantee rate is 95%. The central value of the tolerance range corresponding to the recommended bit rate may be the recommended bit rate by default, and the central value of the tolerance range corresponding to the recommended end-to-end delay is the recommended end-to-end delay.

S203: The NWDAF sends the analysis result to the SMF.

In a possible implementation manner, the analysis result may also include network parameters required by the SMF and/or network indicators expected by the SMF.

Accordingly, the SMF receives the analysis results.

S204: The SMF determines the adjusted QoS parameter according to the analysis result.

S205: The SMF sends the adjusted QoS parameters to the UPF.

Correspondingly, the UPF receives and executes the adjusted QoS parameters.

way 2

The recommended network parameters are within the range of network parameters required by the analysis requesting network element, and the predicted network indicators corresponding to the recommended network parameters are not within the range of expected network indicators. In mode 2, if the data analysis network element determines that it is within the range of the required network parameters, there is no network parameter that can make the predicted network index within the range of the expected network index, but within the range of the required network parameters there is at least A network parameter makes the range of the corresponding predicted network index and the expected network index not much different, or the network parameter makes the corresponding predicted network index close to the range of the expected network index, then the data analysis network element can set at least A network parameter is used as a recommended network parameter, and the data analysis network element may send the recommended network parameter to the analysis requesting network element in S103. In addition, the data analysis network element may also send the predicted network index corresponding to the recommended network parameter to the analysis requesting network element, and is used for the analysis requesting network element to decide whether to accept the recommended network parameter according to the predicted network index corresponding to the recommended network parameter. For example, if the analysis request network element believes that the predicted network index corresponding to the recommended network parameter does not meet the requirements, for example, it is too far from the expected network index, it decides not to accept the recommended network parameter; if the analysis request network element believes that the recommended network parameter If the corresponding predicted network index is acceptable, it may be decided to accept the recommended network parameter.

The predicted network index corresponding to the network parameter is not much different from the expected network index range, which means that the distance between the predicted network index corresponding to the network parameter and the expected network index range is within the first threshold. In a possible implementation manner, the first threshold may be determined according to the range of the expected network index, for example, determined according to a certain ratio or size according to the range of the expected network index. Alternatively, the first threshold may be preconfigured in the data analysis network element, or indicated by the analysis requesting network element, other network elements or devices.

In a possible implementation manner, in manner 2, the data analysis network element may also send indication information to the analysis requesting network element, and the first indication information may be used to indicate whether the analysis requesting network element decides whether to accept the recommended network parameter. Specifically, the indication information may be used to indicate that the recommended network parameters cannot meet the range of expected network indicators, or it may be used to indicate that the service requesting network element decides whether to accept the recommended network parameters. For example, the indication information may be an identifier carried in a specific bit.

If the analysis requesting network element accepts the recommended network parameters, that is, the network parameters are adjusted using the recommended network parameters, the analysis requesting network element may send the response information corresponding to the indication information to the data analysis network element to instruct the analysis requesting network Meta accepts the recommended network parameters.

Taking the service experience analysis process in which NWDAF is used as the data analysis network element and SMF is used as the analysis requesting network element as an example, a communication method implemented according to mode 2 provided by the embodiment of the present application includes the following steps as shown in FIG. 6:

S301: The SMF sends a request message to the NWDAF, where the request message is a subscription request. The subscription identifier can be carried in the request message.

Wherein, the request message includes the type identification of the service experience analysis, the network parameters required by the SMF, the network index expected by the SMF, and the expected ratio of the network index reaching the expected network index. Among them, the network parameters required by the SMF are used to indicate that the bit rate is less than or equal to 10 Mbps, and the network indicators expected by the SMF are used to indicate that the MOS is not lower than 4.5.

Accordingly, NWDAF receives the request message.

S302: The NWDAF sends a response message of the subscription request to the SMF.

Wherein, the response message may be used to indicate that the subscription is successful. The subscription identifier in S301 may be included in the response message of the subscription request.

Correspondingly, the SMF receives the response message of the subscription request.

S303: After identifying the type identifier carried in the request message, the NWDAF can determine the analysis result according to the request message, where the analysis result includes recommended network parameters and predicted network indicators corresponding to the recommended network parameters. Among them, the recommended network parameters cannot meet the expected range of network indicators. NWDAF takes the network parameter value that can make the predicted network index closest to the expected network index range within the required network parameter range as the recommended network parameter.

In implementation, NWDAF can use the network parameters required by SMF as the independent variables of the model, and the network indicators expected by SMF as the dependent variables of the model, and determine that within the range of the required network parameters, no network parameters can make the predicted dependent variable (that is, the network index) is within the range of the expected network index. For example, the recommended network parameters include the recommended bit rate, for example, the recommended bit rate is 10Mbps, and the recommended value is that within the range of less than or equal to 10Mbps required by SMF, there is no bit rate that can satisfy the expected MOS value greater than or equal to 4.5 , but the MOS value is the best when the bit rate is equal to 10Mbps (e.g. equal to 4.3). At this time, NWDAF will use the bit rate of 10Mbps as the recommended network parameter. At the same time, MOS is equal to 4.3 as the predicted network index corresponding to the recommended network parameters.

Wherein, the manner in which the NWDAF determines the recommended network parameters from the candidate network parameters can refer to the description in this application, and will not be expanded here.

In addition, the analysis result may also include the tolerance range of the recommended network parameters, or include the tolerance range and guarantee rate of the recommended network parameters. For the meaning and determination method of tolerance range and guarantee rate, please refer to the explanation in S102. For example, the analysis result may indicate that the radius of the tolerance range corresponding to the recommended bit rate is 1 Mbps and the corresponding guarantee rate is 90%. Wherein, the central value of the tolerance range corresponding to the recommended bit rate may be the recommended bit rate by default.

In a possible implementation manner, the analysis result includes indication information, which is used to indicate that the SMF decides whether to accept the recommended network parameters.

S304: The NWDAF sends the analysis result to the SMF. The analysis result may include the subscription identifier in S301.

Accordingly, the SMF receives the analysis results.

If the SMF accepts the recommended network parameters, execute S305; otherwise, if the SMF does not accept the recommended network parameters, end this process, or send a response message to NWDAF indicating that it refuses to accept the recommended network parameters, and then NWDAF can re-determine Recommended network parameters.

S305: The SMF sends a response message to the NWDAF, indicating acceptance of the recommended network parameters. The response information may include the subscription identifier in S301. The response information may include the subscription identifier in S301.

After that, S306-S307 is executed, and S306-S307 can be referred to as shown in S204-S205.

Correspondingly, NWDAF receives the response information.

S308: The NWDAF stores the recommendation result this time. Wherein, the recommendation result includes, but is not limited to, the analysis result, and may also include a result indicating whether the SMF accepts or does not accept the recommended network parameters indicated by the response information in S305. In the subsequent time, NWDAF can use these stored recommendation results to determine whether the actual network indicators are consistent with the predicted network indicators, further train the model, and enhance the accuracy of prediction and recommendation. It should be understood that the present application does not limit the execution sequence of S308 and S306.

The difference between the process shown in Figure 6 and the process shown in Figure 5 is mainly that: the recommended network parameters determined by NWDAF are within the range of network parameters required by the analysis request network element, and the predicted network indicators corresponding to the recommended network parameters are not within the expected range. Therefore, the analysis requesting network element may decide whether to accept the recommended network parameters according to the predicted network indicators corresponding to the recommended network parameters. If the recommended network parameters are accepted, the steps shown in S305 to S308 may be continued to adjust the network parameters according to the recommended network parameters.

way 3

The recommended network parameters are not within the range of network parameters required by the analysis requesting network element, and the predicted network indicators corresponding to the recommended network parameters are within the range of expected network indicators. In mode 3, if the data analysis network element determines that there is no network parameter that can make the predicted network index within the range of the expected network index, but at least one network parameter that is outside the range of the required network parameter can make the corresponding prediction The network index is within the range of the expected network index, if the at least one network parameter is not much different from the required network parameter range, the data analysis network element can use the at least one network parameter as a recommended network parameter, and the data analysis The network element may send the recommended network parameter to the analysis requesting network element in S103, and send the predicted network index corresponding to the recommended network parameter. Wherein, the predicted network index corresponding to the recommended network parameter is not within the range of the expected network index.

Wherein, the network parameter is not much different from the required range of the network parameter, which means that the distance between the network parameter and the required range of the network parameter is within the second threshold. In a possible implementation manner, the second threshold may be determined according to the required range of the network parameter, for example, determined according to a certain ratio or size according to the required range of the network parameter. Alternatively, the second threshold may be preconfigured in the data analysis network element, or indicated by the analysis requesting network element, other network elements or devices.

In a possible implementation manner, in manner 3, the data analysis network element may also send indication information to the analysis requesting network element, and the first indication information may be used to indicate whether the analysis requesting network element decides whether to accept the recommended network parameter. Specifically, the indication information may be used to indicate that the recommended network parameters cannot meet the required range of network parameters, or it may be used to indicate that the service requesting network element decides whether to accept the recommended network parameters. For example, the indication information may be an identifier carried in a specific bit.

Taking the service experience analysis process with NWDAF as the data analysis network element and SMF as the analysis request network element as an example, a communication method implemented according to mode 3 provided by the embodiment of the present application includes the following steps shown in FIG. 7:

S401: The SMF sends a request message to the NWDAF, where the request message is a subscription request. The subscription identifier can be carried in the request message.

Accordingly, NWDAF receives the request message.

S402: The NWDAF sends a response message of the subscription request to the SMF.

Wherein, the response message may be used to indicate that the subscription is successful. The subscription identifier in S401 may be included in the response message of the subscription request.

S403: After identifying the type identifier carried in the request message, the NWDAF can determine the analysis result according to the request message, wherein the analysis result includes recommended network parameters, and the recommended network parameters are not within the range of required network parameters. NWDAF can determine the network parameters corresponding to the predicted network indicators within the range of the expected network indicators according to the expected network indicators, and use the network parameters that are not much different from the range of the required network parameters as recommended network parameters.

In implementation, NWDAF can use the network index expected by SMF as the dependent variable of the model, and the network parameter required by SMF as the independent variable of the model, and determine that within the range of the required network parameter, no network parameter can make the predicted dependent variable (that is, the network index) is within the range of the expected network index. For example, the recommended network parameters include the recommended bit rate, for example, the recommended bit rate is 10Mbps, and the recommended value is that within the range of less than or equal to 10Mbps required by SMF, there is no bit rate that can satisfy the expected MOS value greater than or equal to 4.5 , but the MOS is 4.5 when the bit rate is 12 Mbps, so the MOS value is greater than or equal to 4.5 when the bit rate is 12 Mbps. At this time, NWDAF will use the bit rate of 12Mbps as the recommended network parameter.

S404: The NWDAF sends the analysis result to the SMF. The analysis result may include the subscription identifier in S401.

Accordingly, the SMF receives the analysis results.

If the SMF accepts the recommended network parameters, execute S405; otherwise, if the SMF does not accept the recommended network parameters, end this process, or send a response message to NWDAF indicating that it refuses to accept the recommended network parameters, and then NWDAF can re-determine Recommended network parameters.

S405: The SMF may send a response message to the NWDAF, indicating acceptance of the recommended network parameters. The response information may include the subscription identifier in S401. The response information may include the subscription identifier in S401. After that, S406-S407 is executed, and S406 to S407 can be referred to as shown in S204-S205.

Accordingly, NWDAF receives the response message.

S408: The NWDAF stores the recommendation result this time.

For the implementation manner of S408, refer to the description of S306.

The main difference between the flow shown in Figure 7 and the flow shown in Figure 6 is that the recommended network parameters determined by NWDAF are not within the range of network parameters required by SMF, and the predicted network indicators corresponding to the recommended network parameters are within the range of expected network indicators. In this case, the analysis result carries the recommended network parameters, and the analysis requesting network element decides whether to accept the recommended network parameters. If the recommended network parameters are accepted, the steps shown in S405 to S408 may be continued to adjust the network parameters according to the recommended network parameters.

FIG. 8 and FIG. 9 are schematic structural diagrams of possible communication devices provided by the embodiments of the present application. These communication devices can be used to implement the functions of the data analysis network element or the analysis request network element in the above method embodiments, and thus can also realize the beneficial effects of the above method embodiments. In the embodiment of the present application, the communication device may be a data analysis network element or an analysis request network element, or a module (such as a chip) applied to a data analysis network element or an analysis request network element.

As shown in FIG. 8 , a communication device 800 includes a processing unit 810 and a transceiver unit 820 . The communication device 800 is configured to implement the functions of the data analysis network element or the analysis request network element in the foregoing method embodiments.

In the first embodiment, the communication device is used to realize the function of the data analysis network element in the above method embodiment, and the transceiver unit 820 can be used to receive a request message from the analysis request network element, and the request message is used to request a recommendation The request message includes the network parameters required by the analysis requesting network element and the network indicators expected by the analysis requesting network element. The processing unit 810 may be configured to determine the recommended network parameter according to the required network parameter and the expected network index. The transceiving unit 820 is further configured to send the recommended network parameters to the analysis requesting network element.

As a possible implementation method, the recommended network parameter is within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is not within the range of the expected network index.

As a possible implementation method, the transceiving unit 820 is further configured to send the predicted network index corresponding to the recommended network parameter to the analysis requesting network element.

As a possible implementation method, the recommended network parameter is not within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is within the range of the expected network index.

As a possible implementation method, the transceiving unit 820 may also be configured to send indication information to the analysis requesting network element, where the indication information is used for the analysis requesting network element to decide whether to accept the recommended network parameters.

As a possible implementation method, the recommended network parameter is within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is within the range of the expected network index.

As a possible implementation method, the transceiving unit 820 is further configured to send the tolerance range of the network parameter to the analysis requesting network element, and the recommended network parameter belongs to the tolerance range.

As a possible implementation method, the transceiver unit 820 can also be configured to send a guarantee rate to the analysis requesting network element, the guarantee rate is that the predicted network index corresponding to the network parameter within the tolerance range can meet the recommended Probability of predicted network metrics corresponding to network parameters.

As a possible implementation method, the request message further includes requirement information for indicating the recommended network parameters, and the data analysis network element may also select from within the range of the required network parameters and/or within the range of the required network parameters according to the requirement information. The recommended network parameter is determined within the range of the network parameter corresponding to the expected network indicator. For example, the requirement information includes a cost function. In another example, the requirement information indicates that the recommended network parameter is the maximum or minimum value within the range of the required network parameter, or the requirement information indicates that the recommended network parameter meets the expectation The maximum or minimum value within the range of the network parameter corresponding to the network index.

In the second embodiment, the communication device is used to implement the function of the analysis request network element in the above method embodiment, and the transceiver unit 820 can be used to send a request message to the data analysis network element, and the request message is used to request a recommended Network parameters, where the request message includes network parameters required by the analysis requesting network element and network indicators expected by the analysis requesting network element. The transceiving unit 820 is further configured to receive recommended network parameters from the data analysis network element, where the recommended network parameters are determined according to the required network parameters and the expected network index of the analysis requesting network element. The processing unit 810 may be configured to adjust network parameters according to the recommended network parameters.

In a possible design, the transceiver unit 820 is further configured to receive indication information from the data analysis network element; the processing unit 810 is further configured to determine whether to accept the recommended network parameters according to the indication information.

In a possible design, the recommended network parameter is within the range of the required network parameter, the predicted network index corresponding to the recommended network parameter is not within the range of the expected network index, and the transceiver unit 820 is further configured to receive a predicted network index corresponding to the recommended network parameter from the data analysis network element. The processing unit 810 may determine whether to accept the recommended network parameter according to the predicted network index corresponding to the recommended network parameter and the indication information.

In a possible design, the transceiver unit 820 is further configured to receive the tolerance range of the network parameter from the data analysis network element, and the recommended network parameter belongs to the tolerance range. The processing unit 810 is specifically configured to adjust the network parameter within the tolerance range.

In a possible design, the transceiving unit 820 is further configured to receive the tolerance range of the network parameter from the data analysis network element, and the recommended network parameter belongs to the tolerance range. The transceiver unit 820 is also configured to receive a guarantee rate from the data analysis network element, the guarantee rate is that the predicted network index corresponding to the network parameter within the tolerance range can satisfy the predicted network corresponding to the recommended network parameter Probability of the indicator. The processing unit 810 is specifically configured to determine whether to adjust the network parameter within the tolerance range according to the guarantee rate.

In a possible design, the request message may further include requirement information for indicating the recommended network parameters. For example, the requirement information includes a cost function. In another example, the requirement information indicates that the recommended network parameter is the maximum or minimum value within the range of the required network parameter, or the requirement information indicates that the recommended network parameter meets the expectation The maximum or minimum value within the range of the network parameter corresponding to the network indicator.

More detailed descriptions about the processing unit 810 and the transceiver unit 820 can be directly obtained by referring to related descriptions in the above method embodiments, and details are not repeated here.

As shown in FIG. 9 , the communication device 900 includes a processor 910 . As an implementation method, the communication device 900 further includes an interface circuit 920, and the processor 910 and the interface circuit 920 are coupled to each other. It can be understood that the interface circuit 920 may be a transceiver or an input/output interface. As an implementation method, the communication device 900 may further include a memory 930 for storing instructions executed by the processor 910 or storing input data required by the processor 910 to execute the instructions or storing data generated after the processor 910 executes the instructions.

When the communication device 900 is used to implement the above method embodiments, the processor 910 is used to implement the functions of the processing unit 810 , and the interface circuit 920 is used to implement the functions of the transceiver unit 820 .

It can be understood that the processor in the embodiments of the present application may be a central processing unit (central processing unit, CPU), and may also be other general processors, digital signal processors (digital signal processor, DSP), application specific integrated circuits (application specific integrated circuit, ASIC), field programmable gate array (field programmable gate array, FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. A general-purpose processor can be a microprocessor, or any conventional processor.

The method steps in the embodiments of the present application may be implemented by means of hardware, or may be implemented by means of a processor executing software instructions. Software instructions can be composed of corresponding software modules, and software modules can be stored in random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only Memory, registers, hard disk, removable hard disk, CD-ROM or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be a component of the processor. The processor and storage medium can be located in the ASIC. In addition, the ASIC can be located in the base station or the terminal. Certainly, the processor and the storage medium may also exist in the base station or the terminal as discrete components.

In the above embodiments, all or part of them may be implemented by software, hardware, firmware or any combination thereof. When implemented using software, it may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer programs or instructions. When the computer program or instructions are loaded and executed on the computer, the processes or functions described in the embodiments of the present application are executed in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, a base station, user equipment or other programmable devices. The computer program or instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer program or instructions may be downloaded from a website, computer, A server or data center transmits to another website site, computer, server or data center by wired or wireless means. The computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server or a data center integrating one or more available media. The available medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; it may also be an optical medium, such as a digital video disk; or it may be a semiconductor medium, such as a solid state disk. The computer readable storage medium may be a volatile or a nonvolatile storage medium, or may include both volatile and nonvolatile types of storage media.

In each embodiment of the present application, if there is no special explanation and logical conflict, the terms and/or descriptions between different embodiments are consistent and can be referred to each other, and the technical features in different embodiments are based on their inherent Logical relationships can be combined to form new embodiments.

In this application, "at least one" means one or more, and "multiple" means two or more. "And/or" describes the association relationship of associated objects, indicating that there may be three types of relationships, for example, A and/or B, which can mean: A exists alone, A and B exist simultaneously, and B exists alone, where A, B can be singular or plural. In the text description of this application, the character "/" generally indicates that the contextual objects are an "or" relationship; in the formulas of this application, the character "/" indicates that the contextual objects are a "division" Relationship.

It can be understood that the various numbers involved in the embodiments of the present application are only for convenience of description, and are not used to limit the scope of the embodiments of the present application. The size of the serial numbers of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its functions and internal logic.

Claims

A communication method, characterized in that, comprising:

The data analysis network element receives a request message from the analysis requesting network element, the request message is used to request recommended network parameters, and the request message includes the network parameters required by the analysis requesting network element and the analysis requesting network element's expected network indicators;

The data analysis network element determines the recommended network parameters according to the required network parameters and the expected network indicators;

The data analysis network element sends the recommended network parameters to the analysis requesting network element.
The method according to claim 1, wherein the recommended network parameter is within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is not within the range of the expected network index within range.
The method of claim 2, further comprising:

The data analysis network element sends the predicted network index corresponding to the recommended network parameter to the analysis requesting network element.
The method according to claim 1, wherein the recommended network parameters are not within the range of the required network parameters, and the predicted network index corresponding to the recommended network parameter is within the range of the expected network index within range.
The method according to any one of claims 2-4, further comprising:

The data analysis network element sends indication information to the analysis requesting network element, where the indication information is used by the analysis requesting network element to decide whether to accept the recommended network parameters.
The method according to claim 1, wherein the recommended network parameter is within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is within the expected network index In the range.
The method of claim 6, further comprising:

The data analysis network element sends the tolerance range of the network parameter to the analysis requesting network element, and the recommended network parameter belongs to the tolerance range.
The method of claim 7, further comprising:

The data analysis network element sends a guarantee rate to the analysis requesting network element, and the guarantee rate is that the predicted network index corresponding to the network parameter within the tolerance range can meet the predicted network index corresponding to the recommended network parameter The probability.
The method according to any one of claims 1 to 8, wherein the request message further includes requirement information for indicating the recommended network parameters, and the data analysis network element Determining the recommended network parameters with the expected network indicators, including:

The data analyzing network element determines the recommended network parameter from within the range of the required network parameter and/or within the range of the network parameter corresponding to the expected network indicator according to the requirement information.
The method of claim 9, wherein said requirement information includes a cost function.
The method according to claim 9, wherein the requirement information indicates that the recommended network parameter is the maximum or minimum value within the range of the required network parameters, or the requirement information indicates that the The recommended network parameter is the maximum or minimum value within the range of network parameters corresponding to the desired network index.
A communication method, characterized in that, comprising:

The analysis requesting network element sends a request message to the data analysis network element, the request message is used to request recommended network parameters, and the request message includes the network parameters required by the analysis requesting network element and the network expected by the analysis requesting network element index;

The analysis requesting network element receives recommended network parameters from the data analysis network element, and the recommended network parameters are determined according to the required network parameters and expected network indicators of the analysis requesting network element;

The analysis requests the network element to adjust network parameters according to the recommended network parameters.
The method according to claim 12, wherein the recommended network parameter is within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is not within the range of the expected network index within range.
The method according to claim 12, wherein the recommended network parameter is not within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is within the range of the expected network index within range.
The method according to claim 13 or 14, further comprising:

The analysis requesting network element receives instruction information from the data analysis network element;

The analysis requesting network element determines whether to accept the recommended network parameters according to the indication information.
The method according to claim 15, wherein the recommended network parameter is within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is not within the range of the expected network index Within the scope, the method also includes:

The analysis requesting network element receives the predicted network index corresponding to the recommended network parameter from the data analysis network element;

The analysis requesting network element determines whether to accept the recommended network parameters according to the indication information, including:

The analysis requesting network element determines whether to accept the recommended network parameter according to the predicted network index corresponding to the recommended network parameter and the indication information.
The method according to claim 12, wherein the recommended network parameter is within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is within the expected network index In the range.
The method of claim 17, further comprising:

The analysis requesting network element receives the tolerance range of the network parameter from the data analysis network element, and the recommended network parameter belongs to the tolerance range;

The analysis requesting network element to adjust network parameters according to the recommended network parameters includes:

The analysis requests the network element to adjust the network parameters within the tolerance range.
The method of claim 17, further comprising:

The analysis requesting network element receives the tolerance range of the network parameter from the data analysis network element, and the recommended network parameter belongs to the tolerance range;

The analysis requesting network element receives a guarantee rate from the data analysis network element, and the guarantee rate is that the predicted network index corresponding to the network parameter within the tolerance range can meet the predicted network corresponding to the recommended network parameter the probability of the indicator;

The analysis requesting network element to adjust network parameters according to the recommended network parameters includes:

The analysis requests the network element to determine whether to adjust the network parameter within the tolerance range according to the guarantee rate.
The method according to any one of claims 12-19, wherein the request message further includes requirement information for indicating the recommended network parameters.
The method of claim 20, wherein said requirement information includes a cost function.
The method according to claim 20, wherein the requirement information indicates that the recommended network parameter is the maximum or minimum value within the range of the required network parameters, or the requirement information indicates that the The recommended network parameter is the maximum or minimum value within the range of network parameters corresponding to the desired network index.
A communication device, characterized by comprising:

A transceiver unit, configured to receive a request message from an analysis requesting network element, the request message is used to request recommended network parameters, and the request message includes the network parameters required by the analysis requesting network element and the analysis requesting network element desired network metrics;

a processing unit, configured to determine the recommended network parameters according to the required network parameters and the expected network indicators;

The transceiving unit is further configured to send the recommended network parameters to the analysis requesting network element.
The device according to claim 23, wherein the recommended network parameter is within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is not within the range of the expected network index. within range.
The device according to claim 24, wherein the transceiver unit is also used for:

Sending the predicted network index corresponding to the recommended network parameter to the analysis requesting network element.
The device according to claim 23, wherein the recommended network parameter is not within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is within the range of the expected network index within range.
The device according to any one of claims 24-26, wherein the transceiver unit is also used for:

sending indication information to the analysis requesting network element, where the indication information is used by the analysis requesting network element to decide whether to accept the recommended network parameters.
The device according to claim 23, wherein the recommended network parameter is within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is within the expected network index In the range.
The device according to claim 28, wherein the transceiver unit is also used for:

Sending the tolerance range of the network parameter to the analysis requesting network element, and the recommended network parameter belongs to the tolerance range.
The device according to claim 29, wherein the transceiver unit is also used for:

Sending a guarantee rate to the analysis requesting network element, where the guarantee rate is a probability that the predicted network index corresponding to the network parameter within the tolerance range can satisfy the predicted network index corresponding to the recommended network parameter.
The device according to any one of claims 23 to 30, wherein the request message further includes requirement information for indicating the recommended network parameters, and the processing unit is specifically configured to:

The recommended network parameter is determined from within the range of the required network parameter and/or within the range of the network parameter corresponding to the expected network indicator according to the requirement information.
The apparatus of claim 31, wherein said requirement information includes a cost function.
The apparatus according to claim 31, wherein the requirement information indicates that the recommended network parameter is the maximum or minimum value within the range of the required network parameters, or the requirement information indicates that the The recommended network parameter is the maximum or minimum value within the range of network parameters corresponding to the desired network index.
A communication device, characterized by comprising:

A transceiver unit, configured to send a request message to the data analysis network element, the request message is used to request recommended network parameters, and the request message includes the network parameters required by the analysis requesting network element and the expected network indicators of the analysis requesting network element and, receiving recommended network parameters from the data analysis network element, where the recommended network parameters are determined according to the required network parameters and expected network indicators of the analysis requesting network element;

A processing unit, configured to adjust network parameters according to the recommended network parameters.
The device according to claim 34, wherein the recommended network parameters are within the range of the required network parameters, and the predicted network indicators corresponding to the recommended network parameters are not within the range of the expected network indicators. within range.
The device according to claim 34, wherein the recommended network parameter is not within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is within the range of the expected network index within range.
The device according to claim 35 or 36, wherein the transceiver unit is also used for:

receiving instruction information from the data analysis network element;

The processing unit is also used for:

Determine whether to accept the recommended network parameters according to the indication information.
The device according to claim 37, wherein the recommended network parameter is within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is not within the range of the expected network index Within the scope, the transceiver unit is also used for:

receiving predicted network indicators corresponding to the recommended network parameters from the data analysis network element;

The processing unit is specifically used for:

Determine whether to accept the recommended network parameters according to the predicted network index corresponding to the recommended network parameters and the indication information.
The device according to claim 34, wherein the recommended network parameter is within the range of the required network parameter, and the predicted network index corresponding to the recommended network parameter is within the expected network index In the range.
The device according to claim 39, wherein the transceiver unit is also used for:

receiving the tolerance range of the network parameter from the data analysis network element, the recommended network parameter belongs to the tolerance range;

The processing unit is specifically used for:

Adjusting the network parameters within the tolerance range.
The device according to claim 39, wherein the transceiver unit is also used for:

receiving the tolerance range of the network parameter from the data analysis network element, the recommended network parameter belongs to the tolerance range;

receiving a guarantee rate from the data analysis network element, where the guarantee rate is a probability that the predicted network index corresponding to the network parameter within the tolerance range can satisfy the predicted network index corresponding to the recommended network parameter;

The processing unit is specifically used for:

Determine whether to adjust the network parameter within the tolerance range according to the guarantee rate.
The device according to any one of claims 34-41, wherein the request message further includes requirement information for indicating the recommended network parameters.
The apparatus of claim 42, wherein said requirement information includes a cost function.
The apparatus according to claim 42, wherein the requirement information indicates that the recommended network parameter is the maximum or minimum value within the range of the required network parameters, or the requirement information indicates that the The recommended network parameter is the maximum or minimum value within the range of network parameters corresponding to the desired network index.
A communication device, characterized in that it includes a processor and a memory; the memory is used to store computer instructions, and the processor executes the computer instructions stored in the memory, so that the device performs the above-mentioned claims 1 to 1. The method described in any one of 22.
A communication system, characterized in that it includes:

A data analysis network element, configured to perform the method described in any one of claims 1 to 11 above; and

The analysis request network element is configured to receive recommended network parameters from the data analysis network element, and adjust network parameters according to the recommended network parameters.
A computer-readable storage medium, characterized in that computer programs or instructions are stored in the storage medium, and when the computer programs or instructions are executed by a communication device, the implementation of any one of claims 1 to 22 Methods.
A computer program product, characterized in that computer-readable instructions are stored in the computer program product, and when the computer-readable instructions are run, the method according to any one of claims 1-11 is executed, Or the method as described in any one of claims 12-22 is carried out.
A chip, characterized in that the chip includes at least one processor, and the processor is used to execute the method according to any one of claims 1-11, or to execute the method according to claim 12- The method described in any one of 22.