CN113766523B - Method and device for predicting network resource utilization rate of serving cell and electronic equipment - Google Patents
Method and device for predicting network resource utilization rate of serving cell and electronic equipment Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/70—Admission control; Resource allocation
- H04L47/83—Admission control; Resource allocation based on usage prediction
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/147—Network analysis or design for predicting network behaviour
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/70—Admission control; Resource allocation
- H04L47/82—Miscellaneous aspects
- H04L47/823—Prediction of resource usage
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The application discloses a method, a device and electronic equipment for predicting network resource utilization rate of a serving cell, which are used for solving the problem that the serving cell cannot effectively implement resource adjustment in time due to the existing mode of predicting network resource utilization rate in a subsequent mode. The method comprises the following steps: determining a network coverage type of a serving cell; acquiring user type distribution information of the serving cell in a target time period and service type distribution information of each user in the target time period, wherein the user types comprise stable users and unstable users; determining the type of a terminal used by each stable user in the service cell; predicting the resource utilization rate of the service cell to a target network in a target time period based on the network coverage type of the service cell, the user type distribution information of the service cell in the target time period, the service type distribution information of each user and the terminal type used by each stable user in the service cell.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method and an apparatus for predicting a network resource utilization rate of a serving cell, and an electronic device.
Background
With the continuous development of mobile network technology, the types of mobile network bearer services are more and more complex. Taking a 5G network as an example, the uncertainty of network services aiming at network resource requirements is more serious in the 5G era along with the access of a large number of internet of things devices. Therefore, the network resource utilization rate of the mobile network service cell is predicted in advance, and then a resource planning strategy is actively adopted, so that the perception that a mobile phone user under the service cell uses a high-quality network is ensured, and the method is very important for 5G network planning construction.
Currently, for predicting the network resource utilization of a serving cell, a subsequent and passive trend prediction method is generally adopted, for example, network performance indexes such as effective RRC (Radio Resource Control ) connection average number of network elements, uplink and downlink PRB (Physical Resource Block ) utilization rate in busy cell, PDCCH (Physical Downlink Control Channel ) utilization rate and the like are monitored in real time to predict the network resource utilization rate; or, based on the service use condition of the user under the service cell and the like, the service growth condition of the service cell in the next hour is estimated, so that the network resource utilization rate is predicted, and the network resource utilization rate is dynamically adjusted in real time through intelligent optimization modes such as load balancing, carrier automatic scheduling, license (License) resource opening and the like in advance through a preset threshold.
However, due to limited resource allocation of the serving cell, the latter network resource utilization trend prediction method can cause that the serving cell cannot effectively implement resource adjustment in time, thereby influencing the use perception of user services.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method, an apparatus, and an electronic device for predicting a network resource utilization rate of a serving cell, so as to solve a problem that a serving cell cannot effectively implement resource adjustment in time due to an existing mode of predicting the network resource utilization rate in a subsequent manner.
In order to solve the technical problems, the following technical solutions are adopted in the embodiments of the present application:
in a first aspect, an embodiment of the present application provides a method for predicting a network resource utilization of a serving cell, including:
determining a network coverage type of a serving cell;
acquiring user type distribution information of the serving cell in a target time period and service type distribution information of each user in the target time period, wherein the user types comprise stable users and unstable users;
determining the type of a terminal used by each stable user in the service cell;
predicting the resource utilization rate of the service cell to a target network in a target time period based on the network coverage type of the service cell, the user type distribution information of the service cell in the target time period, the service type distribution information of each user and the terminal type used by each stable user in the service cell.
Optionally, determining the network coverage type of the serving cell includes:
determining a scene category to which the service cell belongs based on network engineering parameters;
determining a network coverage type of the service cell based on a scene category to which the service cell belongs and planning construction information of the target network, wherein the network coverage type comprises: target network coverage, other network coverage, target network and other network joint coverage, potential target network coverage.
Optionally, the user type distribution information includes a user type to which each user belongs and a proportion of each user type;
the method for obtaining the user type distribution information of the service cell in the target time period comprises the following steps:
acquiring call information of each user in a service cell in a specified historical time period matched with a target time period based on S1-U interface signaling data;
determining, for each user in the serving cell, a user type to which the user belongs based on call information of the user for the specified historical time period;
determining the proportion of each user type of the service cell in the appointed historical time period based on the user type of each user of the service cell;
And predicting the proportion of each user type of the service cell in the target time period based on the ratio of each user type of the service cell in the appointed historical time period.
Optionally, acquiring service type distribution information of each user in the target time period under the serving cell includes:
acquiring service type distribution information of each user in the service cell in a specified historical time period matched with the target time period based on S1-U interface signaling data;
and predicting the service type distribution information of each user in the target time period based on the service type distribution information of each user in the appointed historical time period in the service cell.
Optionally, the predicting the resource utilization rate of the serving cell to the target network in the target time period based on the network coverage type of the serving cell, the user type distribution information of the serving cell in the target time period, the service type distribution information of each user in the target time period, and the terminal type used by each stable user in the serving cell includes:
determining the number of stable users corresponding to different terminal types in the target time period based on the terminal types used by each stable user in the service cell and the user type distribution information of the service cell in the target time;
Predicting the number of users migrating to the target network under the service cell after the target network is opened based on the stable number of users corresponding to different terminal types in the target time period;
determining the total RB resource number occupied by the service cell in the target time period based on the service type distribution information of each user in the target time period and the required RB resource number corresponding to each service type in the service cell;
inputting the network coverage type of the service cell, the user type distribution information of the service cell in a target time period, the number of users migrating to the target network under the service cell after the target network is opened, and the total RB resource number occupied by the service cell in the target time period into a resource utilization rate prediction model so as to obtain the resource utilization rate of the service cell to the target network in the target time period;
the resource utilization rate prediction model is obtained by training a network coverage type of a sample cell, user type distribution information in a specified time period, the number of users migrating to the target network after the target network is opened and the total number of RB resources occupied in the target time period as training samples, and training the resource utilization rate of the target network in the specified time period as a label by the sample cell.
Optionally, the resource utilization prediction model is trained by:
taking the network coverage type of a sample cell, user type distribution information in a specified time period, the number of users migrating to the target network after the target network is opened and the total RB resource number occupied in the target time period as input layers, and taking the resource utilization rate of the sample cell to the target network in the specified time period as output layers;
and optimizing the neural network based on a preset loss function and a back propagation algorithm to obtain the resource utilization rate prediction model.
In a second aspect, an embodiment of the present application further provides a device for predicting a network resource utilization of a serving cell, including:
a first determining unit, configured to determine a network coverage type of a serving cell;
the acquisition unit is used for acquiring user type distribution information of the serving cell in a target time period and service type distribution information of each user in the target time period, wherein the user types comprise stable users and unstable users;
a second determining unit, configured to determine a terminal type used by each stable user in the serving cell;
the prediction unit is used for predicting the resource utilization rate of the service cell to the target network in the target time period based on the network coverage type of the service cell, the user type distribution information of the service cell in the target time period, the service type distribution information of each user and the terminal type used by each stable user in the service cell.
Optionally, the first determining unit is specifically configured to:
determining a scene category to which the service cell belongs based on network engineering parameters;
determining a network coverage type of the service cell based on a scene category to which the service cell belongs and planning construction information of the target network, wherein the network coverage type comprises: target network coverage, other network coverage, target network and other network joint coverage, potential target network coverage.
Optionally, the user type distribution information includes a user type to which each user belongs and a proportion of each user type;
the acquisition unit is specifically configured to:
acquiring call information of each user in a service cell in a specified historical time period matched with a target time period based on S1-U interface signaling data;
determining, for each user in the serving cell, a user type to which the user belongs based on call information of the user for the specified historical time period;
determining the proportion of each user type of the service cell in the appointed historical time period based on the user type of each user of the service cell;
and predicting the proportion of each user type of the service cell in the target time period based on the ratio of each user type of the service cell in the appointed historical time period.
Optionally, the acquiring unit is specifically configured to:
acquiring service type distribution information of each user in the service cell in a specified historical time period matched with the target time period based on S1-U interface signaling data;
and predicting the service type distribution information of each user in the target time period based on the service type distribution information of each user in the appointed historical time period in the service cell.
Optionally, the prediction unit is specifically configured to:
determining the number of stable users corresponding to different terminal types in the target time period based on the terminal types used by each stable user in the service cell and the user type distribution information of the service cell in the target time;
predicting the number of users migrating to the target network under the service cell after the target network is opened based on the stable number of users corresponding to different terminal types in the target time period;
determining the total RB resource number occupied by the service cell in the target time period based on the service type distribution information of each user in the target time period and the required RB resource number corresponding to each service type in the service cell;
inputting the network coverage type of the service cell, the user type distribution information of the service cell in a target time period, the number of users migrating to the target network under the service cell after the target network is opened, and the total RB resource number occupied by the service cell in the target time period into a resource utilization rate prediction model so as to obtain the resource utilization rate of the service cell to the target network in the target time period;
The resource utilization rate prediction model is obtained by training a network coverage type of a sample cell, user type distribution information in a specified time period, the number of users migrating to the target network after the target network is opened and the total number of RB resources occupied in the target time period as training samples, and training the resource utilization rate of the target network in the specified time period as a label by the sample cell.
Optionally, the apparatus further comprises:
the model training unit is used for taking the network coverage type of the sample cell, the user type distribution information in a specified time period, the number of users migrating to the target network after the target network is opened and the total RB resource number occupied in the target time period as input layers, taking the resource utilization rate of the sample cell to the target network in the specified time period as output layers, and optimizing the neural network based on a preset loss function and a back propagation algorithm to obtain the resource utilization rate prediction model.
In a third aspect, an embodiment of the present application further provides an electronic device, including:
a processor;
a memory for storing the processor-executable instructions;
Wherein the processor is configured to execute the instructions to implement the method of the first aspect.
Fourth aspect embodiments of the present application also provide a computer-readable storage medium, which when executed by a processor of an electronic device, causes the electronic device to perform the method of the first aspect.
The above-mentioned at least one technical scheme that this application embodiment adopted can reach following beneficial effect:
when predicting the resource utilization rate of the serving cell to the target network, the factors of the network coverage type, the user type distribution information, the service characteristic information of each user and the terminal type used by each stable user of the serving cell are considered at the same time, and the factors can accurately reflect the resource utilization trend of the serving cell to the target network, so that the resource utilization rate of the serving cell in the future can be predicted accurately in advance based on the factors, network resources can be planned more effectively, the network quality is ensured, and the network traffic is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
Fig. 1 is a flow chart illustrating a method of predicting network resource utilization of a serving cell in accordance with an exemplary embodiment;
FIG. 2 is a flowchart illustrating a method of determining traffic type distribution information for a user, according to an example embodiment;
fig. 3 is a schematic diagram illustrating a structure of a network resource utilization prediction apparatus of a serving cell according to an exemplary embodiment;
fig. 4 is a schematic diagram illustrating a structure of a network resource utilization prediction apparatus of another serving cell according to an exemplary embodiment;
fig. 5 is a schematic diagram of an electronic device according to an exemplary embodiment.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for predicting network resource utilization of a serving cell according to an exemplary embodiment. As shown in fig. 1, the method comprises the steps of:
s11, determining the network coverage type of the serving cell.
In the embodiment of the application, the service cell refers to a cell accessed by the terminal, provides better coverage and resource guarantee for the terminal user, and can meet the service access and high-experience use of the terminal user.
The network coverage types may include: target network coverage, other network coverage, target network and other network joint coverage, potential target network coverage.
The target network may be, for example, a 5G network, the other network may be a 4G network, or the like.
The target time period may be any time period in the future. In practical application, the time period may be a certain day, such as 12 months, 12 days, friday, etc.; alternatively, the time period may be a certain period of time on a certain day, such as am, 12:00-14:00, etc.; alternatively, the time period may be a certain month, such as 1 month, 2 months, etc.; alternatively, the time period may be a year, such as 2021.
S12, acquiring user type distribution information of the serving cell in a target time period and service type distribution information of each user in the target time period.
The user types include stable users and unstable users, the stable users refer to users with higher frequency of occurrence in the service cell, and the unstable users refer to users with lower frequency of occurrence in the service cell.
The user type distribution information of the serving cell is used for reflecting the distribution situation of different types of users. The user type distribution information of the serving cell may include, but is not limited to, a user type, a total user number, a user number and a proportion corresponding to each user type under the serving cell.
The service type distribution information of the user is used for reflecting the use condition of the user on different types of services. The service type distribution information of the user may include, but is not limited to: the service type to which each service used by the user belongs, the proportion of each service type, and the like.
S13, determining the type of the terminal used by each stable user in the service cell.
By way of example, terminals may be classified into the following types according to the model number and network type of the terminal used by the corresponding stable user: 5G terminals, high-end terminals in 4G after more than one year, high-end terminals in 4G after just replacing, 4G low-end terminals, functional machines and the like.
S14, predicting the resource utilization rate of the service cell to a target network in a target time period based on the network coverage type of the service cell, the user type distribution information and the service type distribution information of each user of the service cell in the target time period, and the terminal type used by each stable user in the service cell.
In the above steps, the order of execution of S11, S12, and S13 is not limited.
By adopting the technical scheme of the embodiment of the application, when the resource utilization rate of the serving cell to the target network is predicted, the factors of the network coverage type, the user type distribution information, the service characteristic information of each user and the terminal type used by each stable user of the serving cell are considered, and the factors can accurately reflect the resource utilization trend of the serving cell to the target network, so that the resource utilization rate of the serving cell in the future can be predicted in advance and accurately based on the factors, network resources can be planned and optimized allocation in a more effective way, network quality is ensured, network traffic is improved, and the method has higher practical value.
Implementation of the embodiments of the present application will be described below with reference to specific examples.
In one embodiment, for the above step S11, the network coverage type of the serving cell may be obtained by:
and step A1, determining the scene category to which the serving cell belongs based on the network engineering parameters.
In particular, the scenario category to which the serving cell belongs may be determined based on a scenario field in the network infrastructure parameters. Among other things, scene categories may include, for example, but are not limited to, the following analogy: urban roads, rural areas, traffic trunks, villages and towns, industrial parks, scenic spots, institutions, residential areas, suburban areas, transfer institutions, middle and primary schools, universities, transportation hubs, high-speed rail private networks, subway private networks and the like, and the embodiment of the application is not limited to this.
And step A2, determining the network coverage type of the service cell based on the scene category of the service cell and the planning construction information of the target network.
For example, after determining the scene category to which the serving cell belongs, whether the serving cell is an anchor station of the target network may be determined based on the target network cell engineering parameter, and if yes, the network coverage type of the serving cell may be determined to be the target network coverage; otherwise, based on the scene category to which the service cell belongs and the planning construction information of the target network, whether the common base station address of the service cell has the planning of the construction target network base station (such as the non-independent networking NSA base station or the independent networking SA base station) in the target time period is further judged. If yes, determining that the network coverage type of the service cell is a potential target network coverage; otherwise, further judging whether the planning of the construction target network base station exists in the target time period within the preset range (such as within 1 km) around the service cell. If the planning of the base station of the construction target network exists in the target time period within the preset range around the service cell, the network coverage type of the service cell can be determined to be the potential target network coverage.
In one embodiment, in the step S12, the user type distribution information of the serving cell may include the user types to which each user belongs and the proportion of each user type under the serving cell. Accordingly, the user type distribution information of the serving cell in the target period may be obtained by:
and B1, acquiring call information of each user in the service cell in a specified historical time period matched with the target time period based on the S1-U interface signaling data.
The call information may include the number of calls, the time of the call, the residence time, etc.
And B2, determining the user type of each user in the service cell based on the call information of the user in the appointed historical time period.
Wherein the specified history period that matches the target period may be a history period contemporaneous with the target period, e.g., if the target period is 3 months of the year, then the specified history period that matches the target period may be 3 months of the year; alternatively, the specified history period that matches the target period may also be a history period that is adjacent to the target period, for example, if the target period is the next week, the specified history period that matches the target period may be the present week, and so on.
Specifically, the call information of all users in the service cell in the specified historical time period can be counted, and the users with the call information meeting the preset conditions are screened out to be stable users in a PN judgment mode, while other users are unstable users. For example, the preset condition may be that more than D days occur within a specified period of time and the average residence time per day exceeds H hours.
And B3, determining the proportion of each user type of the service cell in the appointed historical time period based on the user type of each user in the service cell.
The proportion of the user types in the specified historical time period specification of the service cell can be obtained by counting the users of each user type in the service cell. Of course, the ring ratio variation of the total number of users, the stable number of users, and the stable user ratio can also be obtained. For example, table 1 shows statistics for user types in cell a.
TABLE 1
And B4, predicting the proportion of the user types of the serving cell in the target time period based on the proportion of the user types of the serving cell in the appointed historical time period.
Specifically, the number of stable users in a specified historical time period can be used as a sample data set, the sample data set is divided into a training data set and a test data set according to a preset division rule, the periodicity and the trending of the training data set are analyzed based on a time sequence analysis algorithm, the time sequence analysis algorithm is corrected based on the test data set, and finally the proportion of different user types of a serving cell in the target time period is predicted based on the corrected time sequence analysis algorithm.
For example, a training data set may be fitted using different time series fitting models such as the Seasonal ARIMA model, the Holt-Winter model, and the STL Decomposition model to obtain periodic and trending prediction results. And then, verifying the prediction results of each time sequence fitting model based on the test data set, and selecting the model with the minimum mean square error MSE as the final time sequence fitting model. Further, the proportion of different user types of the serving cell in the specified historical time period is input into the time sequence fitting model, so that the proportion of different user types of the serving cell in the target time period is obtained.
It should be noted that, since the total number of users and the stable number of users in the serving cell generally have a relatively obvious trend and/or periodicity, the selected time series fitting model may be a time series fitting model that effectively processes the periodicity and the trend.
The method for predicting the proportion of each user type of the service cell in the target time period based on the S1-U interface signaling data provided in the embodiment has the basis of universal application of the whole network because the S1-U interface signaling data is independent of the manufacturer and the scene category to which the service cell belongs.
In one embodiment, for the step S12, the service type distribution information of each user in the serving cell in the target time period may be obtained by the following steps:
and C1, acquiring service type distribution information of each user in the service cell in a specified historical time period matched with the target time period based on the S1-U interface signaling data.
Specifically, as shown in fig. 2, the use condition of the APP by each user in a specified historical time period can be identified based on an application APP field in the S1-U interface signaling data, and the proportion of different service types of each user in the specified historical time period is further determined based on the service type of each type of APP used by each user.
The service types may include a big packet service, a middle packet service, and a small packet service, among others. The large packet service refers to a service occupying more RB resources, for example, including but not limited to: tencel video, aiqi art, etc.; packet traffic refers to traffic that occupies fewer RB resources, including, for example, but not limited to: weChat, QQ, etc.; RB resources occupied by the middle packet traffic are then located between the large packet traffic and the small packet traffic, including, for example and without limitation: new wave microblog, search for fox news, etc.
And C2, predicting the service type distribution information of each user in the target time period based on the service type distribution information of each user in the appointed historical time period in the service cell.
Specifically, for each user, fitting analysis can be performed based on the proportion of the different service types of the user in the specified historical time period, so as to obtain the proportion of the different service types of the user in the target time period.
The method for predicting the service type distribution information of each user in the target time period based on the S1-U interface signaling data provided by the embodiment has the basis of universal application of the whole network because the S1-U interface signaling data does not depend on scene categories of manufacturers and service cells.
In one embodiment, for the above step S13, the type of terminal used by each stable user in the serving cell may be determined by the model number indicated by the first eight digits (TAC) of the IMEI (International Mobile Equipment Identity ) of the terminal used by the user. For example, the terminal model may include, for example, but not limited to: 5G terminals, high-end terminals in the newly replaced 4G, high-end terminals in the 4G after more than one year, low-end terminals in the 4G, functional machines (such as POS machine) and the like.
In one embodiment, for the step S14, the resource utilization of the serving cell for the target network in the target period may be predicted by:
and D1, determining the stable user numbers corresponding to different terminal types in the target time period based on the terminal types used by each stable user in the service cell and the user type distribution information of the service cell in the target time.
For example, the user type distribution information of the serving cell at the target time may include the total number of users and the stable number of users of the serving cell at the target time, and the stable users of the serving cell are classified and integrated according to the used terminal types, so that the stable number of users corresponding to different terminal types in the target time period can be further counted.
And D2, predicting the number of users migrating to the target network under the service cell after the target network is opened based on the stable number of users corresponding to different terminal types in the target time period.
Specifically, the stable user numbers corresponding to different terminal types in the target time period can be fitted to obtain the correspondence of different terminal types after the target network is openedThe number f of users migrated to the target network i (t) determining the number of users migrating to the target network after the target network is turned on, based on the number of users migrating to the target network corresponding to different terminal types, namely, F (t) = Σ i f i And (t), wherein t is the opening days of the target network base station.
It should be noted that, considering that the migration volume of the user is larger in the initial stage of opening the target network, the subsequent accounts are stable, so that the Logistic curve and the Gompertz curve can be adopted for fitting.
In addition, when curve fitting is performed, the data to be fitted can be divided into training data set test data sets according to a preset division rule, the training data sets are fitted based on a preset fitting curve, the fitting curve is corrected based on the test data sets and a fitting result, and finally the number of users which migrate to the target network and correspond to different terminal types after the target network is opened is obtained.
And D3, determining the total RB resource number occupied by the service cell in the target time period based on the service type distribution information of each user in the target time period and the required RB resource number corresponding to each service type in the service cell.
For example, table 2 shows a correspondence between traffic types and required RB resources, where X < Y < Z.
Table 2:
| APP application | Service type | Number of occupied RB resources |
| Small Bao YeBusiness service | X | |
| Aiqi art | Big packet service | Z |
| Hundred degrees of mobile phone | Packet service | X |
| Tremble sound | Middle packet service | Y |
| Payment device | Packet service | X |
And D4, inputting the network coverage type of the service cell, the user type distribution information of the service cell in a target time period, the number of users migrating to the target network under the service cell after the target network is opened, and the total RB resource number occupied by the service cell in the target time period into a resource utilization rate prediction model so as to obtain the resource utilization rate of the service cell to the target network in the target time period.
The resource utilization rate prediction model is obtained by training a network coverage type of a sample cell, user type distribution information in a specified time period, the number of users migrating to the target network after the target network is opened and the total number of RB resources occupied in the target time period as training samples, and training the resource utilization rate of the target network in the specified time period as a label by the sample cell.
In this embodiment, by taking the factors of the four dimensions as input, the resource utilization rate prediction model established based on the machine learning algorithm predicts based on the input, so that the resource utilization rate of the serving cell to the target network can be rapidly and accurately obtained.
In one embodiment, the resource utilization prediction model may be trained by:
and E1, taking the network coverage type of a sample cell, user type distribution information in a specified time period, the number of users migrating to the target network after the target network is opened and the total RB resource number occupied in the target time period as input layers, and taking the resource utilization rate of the sample cell to the target network in the specified time period as output layers.
And E2, optimizing the neural network based on a preset loss function and a back propagation algorithm to obtain the resource utilization rate prediction model.
In consideration of the complex association relationship among the four-dimensional factors, the neural network can be utilized to perform analysis fitting by infinitely approximating the characteristics of any function.
In particular, the predetermined loss function may be a cross-entopy function.
In order to continuously optimize the resource utilization rate prediction model and improve the accuracy of the output result of the resource utilization rate prediction model, the model may be modified based on the obtained resource utilization rate of the serving cell to the target network in the target time period and the actual resource utilization rate of the target network.
In the embodiment, the neural network can be utilized to perform analysis fitting on the corresponding data of the sample cell by infinitely approaching the characteristics of any function, so that the accuracy of a resource utilization rate prediction model can be improved, further, the prediction result of the resource utilization rate of the service cell in the future can be more accurate, network resources can be planned and optimized and adjusted more effectively, the network quality is ensured, and the network traffic is improved.
Referring to fig. 3, fig. 3 is a schematic diagram illustrating a structure of a serving cell network resource utilization prediction apparatus 300 according to an exemplary embodiment. As shown in fig. 3, the apparatus 300 includes:
a first determining unit 301, configured to determine a network coverage type of a serving cell.
An obtaining unit 302, configured to obtain user type distribution information of the serving cell in a target time period and service type distribution information of each user in the target time period, where the user types include a stable user and an unstable user.
A second determining unit 303, configured to determine a type of terminal used by each stable user in the serving cell.
And a prediction unit 304, configured to predict a resource utilization ratio of the serving cell to a target network in the target time period based on a network coverage type of the serving cell, user type distribution information and service type distribution information of each user in the target time period, and a terminal type used by each stable user in the serving cell.
Alternatively, the first determining unit 301 is specifically configured to:
determining a scene category to which the service cell belongs based on network engineering parameters;
determining a network coverage type of the service cell based on a scene category to which the service cell belongs and planning construction information of the target network, wherein the network coverage type comprises: target network coverage, other network coverage, target network and other network joint coverage, potential target network coverage.
Optionally, the user type distribution information includes a user type to which each user belongs and a proportion of each user type;
the acquiring unit 302 is specifically configured to:
acquiring call information of each user in a service cell in a specified historical time period matched with a target time period based on S1-U interface signaling data;
determining, for each user in the serving cell, a user type to which the user belongs based on call information of the user for the specified historical time period;
determining the proportion of each user type of the service cell in the appointed historical time period based on the user type of each user of the service cell;
and predicting the proportion of each user type of the service cell in the target time period based on the ratio of each user type of the service cell in the appointed historical time period.
Optionally, the acquiring unit 302 is specifically configured to:
acquiring service type distribution information of each user in the service cell in a specified historical time period matched with the target time period based on S1-U interface signaling data;
and predicting the service type distribution information of each user in the target time period based on the service type distribution information of each user in the appointed historical time period in the service cell.
Optionally, the prediction unit 304 is specifically configured to:
determining the number of stable users corresponding to different terminal types in the target time period based on the terminal types used by each stable user in the service cell and the user type distribution information of the service cell in the target time;
predicting the number of users migrating to the target network under the service cell after the target network is opened based on the stable number of users corresponding to different terminal types in the target time period;
determining the total RB resource number occupied by the service cell in the target time period based on the service type distribution information of each user in the target time period and the required RB resource number corresponding to each service type in the service cell;
inputting the network coverage type of the service cell, the user type distribution information of the service cell in a target time period, the number of users migrating to the target network under the service cell after the target network is opened, and the total RB resource number occupied by the service cell in the target time period into a resource utilization rate prediction model so as to obtain the resource utilization rate of the service cell to the target network in the target time period;
The resource utilization rate prediction model is obtained by training a network coverage type of a sample cell, user type distribution information in a specified time period, the number of users migrating to the target network after the target network is opened and the total number of RB resources occupied in the target time period as training samples, and training the resource utilization rate of the target network in the specified time period as a label by the sample cell.
Optionally, as shown in fig. 4, the apparatus 300 further includes:
the model training unit 305 is configured to take a network coverage type of a sample cell, user type distribution information in a specified time period, a number of users migrating to the target network after the target network is turned on, and a total RB resource number occupied in the target time period as input layers, take a resource utilization rate of the sample cell to the target network in the specified time period as output layers, and tune the neural network based on a preset loss function and a back propagation algorithm, so as to obtain the resource utilization rate prediction model.
The specific manner in which the individual units perform the operations in relation to the apparatus of the above embodiments has been described in detail in relation to the embodiments of the method and will not be described in detail here.
Fig. 5 is a block diagram of an electronic device 500, according to an example embodiment. For example, electronic device 500 may be provided as a server. Referring to fig. 5, the electronic device 500 includes a processor 522, which may be one or more in number, and a memory 532 for storing computer programs executable by the processor 522. The computer program stored in memory 532 may include one or more modules each corresponding to a set of instructions. Further, the processor 522 may be configured to execute the computer program to perform the network resource utilization prediction method of the serving cell described above.
In addition, the electronic device 500 may further include a power component 526 and a communication component 550, the power component 526 may be configured to perform power management of the electronic device 500, and the communication component 550 may be configured to enable communication of the electronic device 500, such as wired or wireless communication. In addition, the electronic device 500 may also include an input/output (I/O) interface 558. The electronic device 500 may operate based on an operating system stored in the memory 532, such as Windows Server, mac OS XTM, unixTM, linuxTM, and the like.
In another exemplary embodiment, a computer readable storage medium is also provided, comprising program instructions which, when executed by a processor, implement the steps of the network resource utilization prediction method of a serving cell described above. For example, the computer readable storage medium may be the memory 532 comprising program instructions described above, which are executable by the processor 522 of the electronic device 500 to perform the network resource utilization prediction method of a serving cell described above.
Of course, other implementations, such as a logic device or a combination of hardware and software, are not excluded from the electronic device of the present application, that is, the execution subject of the following processing flow is not limited to each logic unit, but may be hardware or a logic device.
The present embodiments also provide a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a portable electronic device comprising a plurality of application programs, enable the portable electronic device to perform the method of the embodiment of fig. 1, and in particular to:
determining a network coverage type of a serving cell;
acquiring user type distribution information of the serving cell in a target time period and service type distribution information of each user in the target time period, wherein the user types comprise stable users and unstable users;
determining the type of a terminal used by each stable user in the service cell;
predicting the resource utilization rate of the service cell to a target network in a target time period based on the network coverage type of the service cell, the user type distribution information of the service cell in the target time period, the service type distribution information of each user and the terminal type used by each stable user in the service cell.
In summary, the foregoing description is only a preferred embodiment of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
Claims (8)
1. A method for predicting network resource utilization of a serving cell, comprising:
determining a network coverage type of a serving cell;
Acquiring user type distribution information of the serving cell in a target time period and service type distribution information of each user in the target time period, wherein the user types comprise stable users and unstable users;
determining the type of a terminal used by each stable user in the service cell;
predicting the resource utilization rate of the serving cell to a target network in a target time period based on the network coverage type of the serving cell, the user type distribution information of the serving cell in the target time period, the service type distribution information of each user and the terminal type used by each stable user in the serving cell;
the predicting the resource utilization rate of the serving cell to the target network in the target time period based on the network coverage type of the serving cell, the user type distribution information of the serving cell in the target time period, the service type distribution information of each user in the target time period, and the terminal type used by each stable user in the serving cell includes:
determining the number of stable users corresponding to different terminal types in the target time period based on the terminal types used by each stable user in the service cell and the user type distribution information of the service cell in the target time;
Predicting the number of users migrating to the target network under the service cell after the target network is opened based on the stable number of users corresponding to different terminal types in the target time period;
determining the total RB resource number occupied by the service cell in the target time period based on the service type distribution information of each user in the target time period and the required RB resource number corresponding to each service type in the service cell;
inputting the network coverage type of the service cell, the user type distribution information of the service cell in a target time period, the number of users migrating to the target network under the service cell after the target network is opened, and the total RB resource number occupied by the service cell in the target time period into a resource utilization rate prediction model so as to obtain the resource utilization rate of the service cell to the target network in the target time period;
the resource utilization rate prediction model is obtained by training a network coverage type of a sample cell, user type distribution information in a specified time period, the number of users migrating to the target network after the target network is opened and the total number of RB resources occupied in the target time period as training samples, and training the resource utilization rate of the target network in the specified time period as a label by the sample cell.
2. The method of claim 1, wherein determining the network coverage type of the serving cell comprises:
determining a scene category to which the service cell belongs based on network engineering parameters;
determining a network coverage type of the service cell based on a scene category to which the service cell belongs and planning construction information of the target network, wherein the network coverage type comprises: target network coverage, other network coverage, target network and other network joint coverage, potential target network coverage.
3. The method according to claim 1, wherein the user type distribution information includes a user type to which each user belongs and a ratio of each user type;
the method for obtaining the user type distribution information of the service cell in the target time period comprises the following steps:
acquiring call information of each user in a service cell in a specified historical time period matched with a target time period based on S1-U interface signaling data;
determining, for each user in the serving cell, a user type to which the user belongs based on call information of the user for the specified historical time period;
determining the proportion of each user type of the service cell in the appointed historical time period based on the user type of each user of the service cell;
And predicting the proportion of each user type of the service cell in the target time period based on the ratio of each user type of the service cell in the appointed historical time period.
4. The method according to claim 1, wherein obtaining service type distribution information of each user in the serving cell in the target time period includes:
acquiring service type distribution information of each user in the service cell in a specified historical time period matched with the target time period based on S1-U interface signaling data;
and predicting the service type distribution information of each user in the target time period based on the service type distribution information of each user in the appointed historical time period in the service cell.
5. The method of claim 1, wherein the resource utilization prediction model is trained by:
taking the network coverage type of a sample cell, user type distribution information in a specified time period, the number of users migrating to the target network after the target network is opened and the total RB resource number occupied in the target time period as input layers, and taking the resource utilization rate of the sample cell to the target network in the specified time period as output layers;
And optimizing the neural network based on a preset loss function and a back propagation algorithm to obtain the resource utilization rate prediction model.
6. A network resource utilization prediction apparatus for a serving cell, comprising:
a first determining unit, configured to determine a network coverage type of a serving cell;
the acquisition unit is used for acquiring user type distribution information of the serving cell in a target time period and service type distribution information of each user in the target time period, wherein the user types comprise stable users and unstable users;
a second determining unit, configured to determine a terminal type used by each stable user in the serving cell;
a prediction unit, configured to predict a resource utilization ratio of the serving cell to a target network in a target time period based on a network coverage type of the serving cell, user type distribution information of the serving cell in the target time period, service type distribution information of each user, and a terminal type used by each stable user in the serving cell;
the prediction unit is specifically configured to:
determining the number of stable users corresponding to different terminal types in the target time period based on the terminal types used by each stable user in the service cell and the user type distribution information of the service cell in the target time;
Predicting the number of users migrating to the target network under the service cell after the target network is opened based on the stable number of users corresponding to different terminal types in the target time period;
determining the total RB resource number occupied by the service cell in the target time period based on the service type distribution information of each user in the target time period and the required RB resource number corresponding to each service type in the service cell;
inputting the network coverage type of the service cell, the user type distribution information of the service cell in a target time period, the number of users migrating to the target network under the service cell after the target network is opened, and the total RB resource number occupied by the service cell in the target time period into a resource utilization rate prediction model so as to obtain the resource utilization rate of the service cell to the target network in the target time period;
the resource utilization rate prediction model is obtained by training a network coverage type of a sample cell, user type distribution information in a specified time period, the number of users migrating to the target network after the target network is opened and the total number of RB resources occupied in the target time period as training samples, and training the resource utilization rate of the target network in the specified time period as a label by the sample cell.
7. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the network resource utilization prediction method of a serving cell as claimed in any one of claims 1 to 5.
8. A computer readable storage medium, which when executed by a processor of an electronic device, causes the electronic device to perform the network resource utilization prediction method of a serving cell of any of claims 1 to 5.
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