CN117858169A - Cell flow control method, base station and storage medium - Google Patents
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Abstract
The embodiment of the invention provides a cell flow control method, a base station and a storage medium, and belongs to the field of communication. The method comprises the following steps: obtaining a traffic suppression prediction result of a cell; determining a traffic suppression root cause of the cell under the condition that the cell is determined to have traffic suppression in a preset time period in the future according to the traffic suppression prediction result; and determining a traffic suppression strategy of the cell according to the traffic suppression root cause, and executing the traffic suppression strategy, wherein the traffic suppression strategy is used for adjusting the traffic of the cell. Under the condition that the flow inhibition occurs in a preset time period in the future, the method and the system can accurately determine the flow inhibition root cause of the cell, and determine the flow inhibition strategy of the cell according to the flow inhibition root cause, and the situation that the flow of the cell is inhibited can be solved by executing the flow inhibition strategy, so that the network transmission performance of the cell is greatly improved.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a cell traffic control method, a base station, and a storage medium.
Background
With the development of the internet and communication technologies, wireless networks are indispensable in daily life and work, and bring huge traffic service to operators, but the maximum traffic that can be achieved by each cell is generally limited by the number of users, available resources and software and hardware capabilities, and after the traffic reaches the maximum, the overhead is increased in response to the traffic demand, so that the total traffic of the cell is not increased, and the traffic is possibly reduced, which is called traffic suppression. At present, the problem of traffic suppression is commonly existed in network transmission, resulting in lower performance of network transmission, and no effective solution is available for the problem of traffic suppression, so how to solve the problem of traffic suppression to improve network transmission performance is a problem to be solved at present.
Disclosure of Invention
The embodiment of the invention provides a cell flow control method, a base station and a storage medium, which aim to solve the problem that the cell flow is restrained, thereby improving the network transmission performance of a cell.
In a first aspect, an embodiment of the present invention provides a method for controlling a cell traffic, including:
obtaining a traffic suppression prediction result of a cell;
determining a traffic suppression root cause of the cell under the condition that the cell is determined to have traffic suppression in a preset time period in the future according to the traffic suppression prediction result;
And determining a traffic suppression strategy of the cell according to the traffic suppression root cause, and executing the traffic suppression strategy, wherein the traffic suppression strategy is used for adjusting the traffic of the cell.
In a second aspect, an embodiment of the present invention further provides a base station, the base station comprising a processor, a memory, a computer program stored on the memory and executable by the processor, and a data bus for enabling a connection communication between the processor and the memory, wherein the computer program, when executed by the processor, implements the steps of any of the cell flow control methods as provided in the present specification.
In a third aspect, an embodiment of the present invention further provides a storage medium for computer readable storage, where the storage medium stores one or more programs, where the one or more programs are executable by one or more processors to implement the steps of any of the flow suppression adjustment methods as provided in the present specification.
The embodiment of the invention provides a cell flow control method, a base station and a storage medium; determining a flow suppression root cause of the cell under the condition that the cell is determined to have flow suppression in a preset time period in the future according to a flow suppression prediction result; then, according to the traffic suppression root cause, the traffic suppression policy of the cell can be accurately determined, and the traffic suppression policy is executed to adjust the traffic of the cell. Under the condition that the flow inhibition occurs in a preset time period in the future, the method and the system can accurately determine the flow inhibition root cause of the cell, and determine the flow inhibition strategy of the cell according to the flow inhibition root cause, and the situation that the flow of the cell is inhibited can be solved by executing the flow inhibition strategy, so that the network transmission performance of the cell is greatly improved.
Drawings
Fig. 1 is a flow chart of a cell flow control method according to an embodiment of the present invention;
fig. 2 is a flow chart illustrating sub-steps of the cell traffic control method of fig. 1;
fig. 3 is a flow chart illustrating sub-steps of the cell traffic control method of fig. 2;
FIG. 4 is a flow chart illustrating another sub-step of the cell flow control method of FIG. 1;
FIG. 5 is a flow chart illustrating another sub-step of the cell flow control method of FIG. 1;
fig. 6 is a schematic block diagram of a base station according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
It is to be understood that the terminology used in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The embodiment of the invention provides a cell flow control method, a base station and a storage medium. The cell flow control method can be applied to base stations, and the base stations can be macro base stations, distributed base stations and other types of base stations. For example, the base station is a macro base station, and the macro base station obtains a traffic suppression prediction result of a cell; determining a flow suppression root cause of the cell under the condition that the cell is determined to have flow suppression in a preset time period in the future according to a flow suppression prediction result; and determining a traffic suppression strategy of the cell according to the traffic suppression root cause, and executing the traffic suppression strategy, wherein the traffic suppression strategy is used for adjusting the traffic of the cell.
Some embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a flow chart of a cell flow control method according to an embodiment of the invention.
As shown in fig. 1, the cell flow control method may include steps S101 to S103.
Step S101, obtaining a traffic suppression prediction result of a cell.
The traffic suppression prediction result is used for representing whether the traffic of the cell is suppressed in a preset time period in the future.
In one embodiment, as shown in fig. 2, step S101 may include sub-steps S1011 through S1013.
And S1011, acquiring a network flow model, wherein the network flow model is used for representing a mapping relation table of network state data and flow values.
The network traffic model is a machine learning model, which may be selected according to practical situations, and the embodiment of the present invention is not limited thereto, and for example, the network traffic model may be a neural network model, a machine learning model decision tree, and a regression model.
The network status data is a parameter that can affect the cell traffic, such as a number of radio resource control (Radio Resource Control, RRC) connection users, an activated user number, a traffic channel resource utilization, a control channel resource utilization, a spectrum efficiency (Spectrum Efficiency, SE), a cell traffic, an average modulation and coding scheme (Modulation and Coding scheme, MCS), an average channel quality indication (Channel Quality Indicator, CQI), an average signal-to-noise ratio (Signal to Interference plus Noise Ratio, SINR), and the like.
In an embodiment, a network state sample set is obtained, the network state sample set comprising a sample of network states; and training a network traffic model according to the network state sample set. By acquiring a network state sample set and training the network state sample set, a network traffic model can be accurately obtained.
In an embodiment, a preset acquisition granularity and an acquisition period are acquired, wherein the acquisition granularity characterizes the frequency of acquiring the network state in one acquisition period; collecting samples of network states from historical data according to the collection granularity and the collection period; and carrying out data preprocessing on the acquired network state samples to obtain a network state sample set. The preset collection granularity and the collection period may be set according to practical situations, which is not limited in the embodiment of the present invention, for example, the collection granularity may be set to one minute, and the sampling period may be set to one day. The historical data is the operational data prior to the current timestamp. By collecting samples of network states from historical data and preprocessing the samples, more accurate sample data can be obtained.
It should be noted that, the mode of performing data preprocessing on the samples of the network state may be selected according to the actual situation, which is not limited in the embodiment of the present invention, for example, the data time axis complement, the data time axis de-duplication, and the data complement processing modes of the data preprocessing modes.
Illustratively, the data timeline completion may be: in the data acquisition process, under the condition that acquisition is missed in a certain granularity, if certain data information on a time axis is missing, the data corresponding to the time axis can be complemented to be empty, so that failure in acquiring the data from a data set is avoided, and the follow-up operation is influenced.
Illustratively, the data timeline deduplication may be: the deduplication rule may be to preserve the first occurring data in the dataset and delete the subsequent duplicate data.
Illustratively, the data complement may be: data is missing during the data acquisition process. When the missing data is at the head of the data set, the head null data can be filled into first non-null data from the head, when the missing data is at the tail of the data set, the tail null data is uniformly filled into first non-null data from the tail, when the missing data is at the middle of the data set, the first non-null data can be searched forwards and backwards respectively for linear interpolation filling, and of course, a mean filling mode can be adopted, and the method can be selected according to actual requirements.
In one embodiment, as shown in fig. 3, sub-step S1011 may include sub-steps S1011a through S1011c.
Substep S1011a, obtaining a first training set and a first test set from the network state sample set.
Determining a numerical interval of a network state sample set; dividing the numerical value interval into a plurality of grid intervals according to a preset grid step length; filling each sample of the network state into a grid interval according to a principle of numerical value correspondence to obtain a grid data set; and screening the first training set and the first testing set from the grid data set according to a preset screening strategy. The numerical interval of the network state sample set is determined according to the maximum value of the network state, and if a plurality of types of network states exist, the maximum value of each type of network state is determined respectively.
It should be noted that, the specific value of the grid step size may be determined according to the actual requirement, for example, the network state is the number of users, the value interval is [0, userNumMax ], where UserNumMax is the maximum value of the number of users, and the preset grid step size is usernummeshneshop, the value interval may be divided into UserNumMeshNum grids, where usernummeshnum= (UserNumMax/userneshmeshneshop). If the network state has multiple types, the multiple types may be respectively divided into grid intervals according to the above manner, which will not be described herein.
Since the grid section is actually a value section, the sample of each network state may be filled into the corresponding grid section based on the value of the sample of each network state as a key value.
It should be noted that, the screening policy may be selected according to the actual situation, which is not limited in this embodiment of the present invention, for example, a grid threshold value MeshSampleMin is set, for a grid with data number greater than 2×meshsamplemin in each grid, an average value of 1/2 samples randomly screened in the grid data is a first training set of the grid, and an average value of the remaining 1/2 samples is a first test set of the grid; discarding the raster data for the raster with the raster data number smaller than MeshSampleMin; for grids with raster data between MeshSampleMin and 2 x MeshSampleMin, the mean data of the grids were all taken as the first test set.
Sub-step S1011b, normalizes the first training set and the first test set.
The normalization of the first training set and the first test set may be performed by using a maximum and minimum normalization method or other alternative normalization methods, and those skilled in the art are well aware of how to normalize the sample set by using the above method, which will not be described in detail in this embodiment.
And sub-step S1011c, acquiring a preset network flow model, training the preset network flow model according to the first training set, and checking the trained network flow model according to the first test set.
The network traffic model may be a common machine learning model, such as a fully connected neural network model, or a machine learning model decision tree, a regression model, or the like, and a specific model type may be selected according to practical situations, for example, the fully connected neural network model is taken as an example, where the network traffic model includes an input layer, an output layer, and a plurality of hidden layers, the input layer is used for inputting a network state in a network state sample set, the output layer is used for outputting a prediction result, such as SE or cell traffic, and a person skilled in the art is familiar with how to adjust the number of hidden layers and the number of neurons according to practical situations, so that the trained model can meet the needs, and the embodiment is not limited to this.
It can be understood that, because the first training set and the first test set are normalized, after training and testing the network traffic model, the output of the tested network traffic model needs to be inversely normalized, so as to obtain the output of the final network traffic model, and the specific inverse normalization method corresponds to the normalization method, which is not described in detail in this embodiment.
And step S1012, determining a suppression point flow value according to the network flow model, and determining a target network state suppression reference value corresponding to the suppression point flow value, wherein the suppression point flow value is a flow threshold of current communication transmission.
The network traffic model may be a rule obeyed by the network traffic and the network state, and the suppression point characterizes an inflection point where the traffic starts to decrease, so that the traffic threshold may be an average maximum traffic value that can be reached by the transmission network, so that one or more predictions for traffic suppression may be selected from the network states, one network state is selected for traffic prediction, for example, a user number is selected as the network state, a correspondence between the activation user number and the network traffic may be determined according to the network traffic model, and an average maximum traffic value that can be reached by the activation user number under different values is determined, where the average maximum traffic value is the suppression point traffic value; for another example, when a plurality of network states are selected to perform traffic prediction, taking four network states including the number of users, the utilization rate of traffic channel resources, the utilization rate of control channel resources and the channel quality as examples, the average maximum traffic value which can be achieved by the cell under different values of the four network states can be obtained, and then the average maximum traffic value is a suppression point traffic value, and the suppression point traffic value at the moment is associated with the four network states at the same time, so that the maximum traffic which can be achieved by the cell under the influence of the four network states can be determined.
In an embodiment, a preset threshold value is obtained, a first-order difference of a sample in a network state is determined, and the sample in the network state with the first-order difference smaller than the threshold value is determined as a target sample; and determining a target flow value corresponding to the target sample according to the mapping relation, and determining the target flow value as a suppression point flow value. The preset threshold value may be set according to actual situations, which is not specifically limited in the embodiment of the present invention.
It should be noted that, the first-order difference is the difference between two consecutive adjacent terms in the discrete function, and the flow value of the suppression point is the average maximum flow value that can be achieved by the current transmission network, for the transmission network, the change of the flow value starts to increase with the increase of the value of the network state, the speed increases slowly before reaching the suppression point, and stops increasing after reaching the suppression point, and then decreases with the increase of the value of the network state.
It should be noted that, when a plurality of types of network states are involved, for example, the number of users, the traffic channel resource utilization, the control channel resource utilization, and the channel quality in the above examples, a sample of a network state whose first order difference is smaller than a threshold value may be determined as a target sample according to the first order difference of the sample of the network state, respectively, for each network state.
It should be noted that, because the network traffic model can represent the mapping relationship between the network state data and the network traffic, after determining the target sample, the corresponding target traffic value can be determined by the value of the target sample, so that the target traffic value is further determined as the suppression point traffic value, which is not described in detail herein.
In an embodiment, the manner of determining the target network state suppression reference value corresponding to the suppression point traffic value may be: when the target network state is one, taking the number of users as an example, determining a suppression reference value corresponding to the number of users; when the target network state is multiple, taking the number of users, the service channel resource utilization rate, the control channel resource utilization rate and the channel quality as examples, determining that the suppression reference values of the number of users, the service channel resource utilization rate, the control channel resource utilization rate and the channel quality are respectively reached.
Substep S1013, obtaining a parameter prediction value corresponding to the target network state suppression reference value, and determining a traffic suppression prediction result according to the parameter prediction value and the target network state suppression reference value.
The parameter prediction value may be obtained by model prediction, for example, a parameter prediction model is preset, so as to obtain a parameter prediction value representing a future traffic state of the cell. It will be appreciated that when the parameter prediction value is obtained by the parameter prediction model, the input data may be either a time series feature vector at a certain granularity of time or a scalar representing the current state, for example, the parameter value of the future 15 minutes of day particle cell is predicted by taking the data sequence at the granularity of 15 minutes of the past week as the feature vector as the input of the prediction model.
In one embodiment, the acquisition time of each acquisition cycle is determined according to the acquisition granularity; acquiring a prediction sample set from a network state sample set, wherein sample data of the prediction sample set comprises acquisition time and network state data corresponding to the acquisition time; and training a parameter prediction model according to the prediction sample set, acquiring a prediction feature sample corresponding to the target network state, and inputting the prediction feature sample into the parameter prediction model to obtain a parameter prediction value. By training the parameter prediction model and accurately outputting the parameter prediction value corresponding to the target network state inhibition reference value through the parameter prediction model, the accuracy and the efficiency of parameter prediction value determination are greatly improved. Wherein the collection granularity generally characterizes the collection frequency, and the collection time of the collection period is determined according to the collection frequency, for example, the collection of the obtained prediction sample set can refer to the form of table 1. Wherein, table 1 is sample data corresponding to one network state, and the data corresponding to the acquisition time may be increased for multiple network states, which is not repeated herein. In table 1, N is the data length of a network state sample set, each row in the table is a sample of a network state, data_i represents the parameter value of the ith sample, and time_i is the acquisition time of data_i.
TABLE 1
The sample data of the prediction sample set may be k+l+m+1 dimensions, where K is a parameter value of the same collection time in the previous K period, L is a parameter value of the previous L time, and M is time information of the current data, such as week, hour, minute, whether holiday, etc.; meanwhile, the front K+L+M of each sample is the characteristic of the sample, and the last dimension is the state value at the current moment and is used for representing the label of the sample, so that different samples can be distinguished conveniently. It is understood that the first k+l dimension of each sample may include all types of network states, and will not be described in detail herein.
It should be noted that, the prediction feature samples may be obtained by selecting a time dimension, for example, parameter predicted values of K periods and L moments in the future need to be predicted, and then data in k+l+m dimensions may be obtained from the prediction sample set as the prediction feature samples, so that a person skilled in the art is motivated to adjust the dimensions of the prediction feature samples according to the actual prediction requirement, which is not limited herein.
In one embodiment, the method for training the parameter prediction model according to the prediction sample set may be: normalizing the data of the prediction sample set; dividing the normalized prediction sample set into a second training set and a second test set according to a preset dividing proportion; acquiring an initial parameter prediction model, and determining training parameters of the initial parameter prediction model; training an initial parameter prediction model according to the second training set, and checking the trained initial parameter prediction model according to the second testing set; and determining the initial parameter prediction model after the inspection as a parameter prediction model. The initial parameter prediction model may be selected according to actual situations, which is not limited in the embodiment of the present invention, for example, the initial parameter prediction model may be a Long Short-Term Memory (LSTM) neural network, or may be other alternative machine learning model decision trees, regression models, or the like.
In an embodiment, after obtaining the parameter predicted value and the target network state suppression reference value, comparing the parameter predicted value and the target network state suppression reference value to obtain a flow suppression predicted result. For example, if the target network state is only one, when the parameter predicted value is greater than the suppression reference value, it can be determined that the network flow is limited and smaller than the suppression point flow value as the value of the target network state increases, and flow suppression occurs; for another example, the target network state is the four types exemplified above, and the relationship model between the number of users and the network traffic is obtained through the section by quantifying three parameters, namely the utilization rate of the traffic channel resources, the utilization rate of the control channel resources and the channel quality; and then determining a currently used inflection point model according to the state of the traffic channel resource utilization rate, the control channel resource utilization rate and the channel quality of the current network, and judging whether suppression occurs according to a single-parameter method.
And step S102, determining the flow inhibition root cause of the cell under the condition that the cell is determined to have flow inhibition in a preset time period in the future according to the flow inhibition prediction result.
The traffic suppression root causes comprise that the communication channel resource utilization rate of the cell is too high and the communication channel available resource is too low, wherein the communication channel resource utilization rate is that the communication channel resource utilization rate of the cell is larger than or equal to the preset utilization rate, and the communication channel available resource is that the communication channel available resource of the cell is too low and is smaller than or equal to the preset available resource. The preset utilization rate and the preset available resources may be set according to actual situations, which is not limited in the embodiment of the present invention, for example, the preset utilization rate may be 90%, and the preset available resources may be 100RB.
In one embodiment, network state data of a cell is obtained; and determining the traffic suppression root cause of the cell according to the network state data. The network status data includes a traffic channel resource utilization rate and a control channel resource utilization rate, where the traffic channel resource utilization rate and the control channel resource utilization rate may be determined according to practical situations, and the embodiment of the present invention is not limited thereto specifically, for example, the traffic channel resource utilization rate may include a physical resource block (Physical Resource Block, PRB) utilization rate, and the control channel resource utilization rate may include a physical downlink control channel (Physical Downlink Control Channel, PDCCH) resource utilization rate. The flow inhibition root cause of the cell can be accurately determined according to the network state data, and the cell flow inhibition efficiency and accuracy can be improved.
In an embodiment, according to the network status data, the manner of determining the traffic suppression root cause of the cell may be: under the condition that the PRB utilization rate is larger than or equal to the preset PRB utilization rate, determining that the traffic suppression root cause of the cell is that the communication channel resource utilization rate is too high or the available resource of the communication channel is too low; under the condition that the PDCCH resource utilization rate is greater than or equal to the preset PDCCH resource utilization rate, determining that the traffic suppression root cause of the cell is that the available resources of the communication channel are too low; under the condition that the PRB utilization rate is larger than or equal to the preset PRB utilization rate and the PDCCH resource utilization rate is smaller than the preset PDCCH resource utilization rate, determining that the traffic suppression root cause of the cell is the too high communication channel resource utilization rate; and under the condition that the PRB utilization rate is smaller than the preset PRB utilization rate and the PDCCH resource utilization rate is larger than or equal to the preset PDCCH resource utilization rate, determining that the traffic suppression root cause of the cell is that the available resources of the communication channel are too low. The preset PRB utilization and the preset PDCCH resource utilization may be set according to actual situations, which is not limited in the embodiment of the present invention, for example, the preset PRB utilization may be set to 90%, and the preset PDCCH resource utilization may be set to 90%. The traffic suppression root cause of the cell can be accurately known according to the PRB utilization rate and the PDCCH resource utilization rate, and the cell traffic suppression efficiency and accuracy can be improved.
Step S103, determining a traffic suppression strategy of the cell according to the traffic suppression root cause, and executing the traffic suppression strategy, wherein the traffic suppression strategy is used for adjusting the traffic of the cell.
The traffic suppression strategy is used for adjusting the traffic of the cell so as to improve the network transmission performance of the cell, and comprises a scheduling priority adjustment strategy and a user migration strategy.
In one embodiment, as shown in fig. 4, step S103 includes sub-step S1031.
And step S1031, when the traffic suppression root is larger than or equal to a preset utilization rate because of the communication channel resource utilization rate of the cell, determining a preset scheduling priority adjustment strategy as the traffic suppression strategy.
The scheduling priority adjustment strategy is used for adjusting the scheduling priority of each user terminal in a cell, and the higher the scheduling priority is, the higher the communication transmission priority is. For example, the cell includes the user terminal 1, the user terminal 2, the user terminal 3, the user terminal 4 and the user terminal 5, and the priority of each user terminal scheduling is sequentially from high to low in order of the user terminal 2, the user terminal 3, the user terminal 1, the user terminal 5 and the user terminal 4, and then the priority of each user terminal performing communication transmission is sequentially from high to low in order of the user terminal 2, the user terminal 3, the user terminal 1, the user terminal 5 and the user terminal 4.
For example, if the communication channel resource utilization rate of the cell is 98%, the preset utilization rate is 90%, and the communication channel resource utilization rate of the cell is 98% greater than the preset utilization rate 90%, the preset scheduling priority adjustment policy is determined as the traffic suppression policy. In an embodiment, under the condition that the traffic suppression policy is a scheduling priority adjustment policy, acquiring channel quality of a plurality of user terminals in a cell; and adjusting the scheduling priority of the user terminals in the cell according to the channel quality of the plurality of user terminals, and scheduling the plurality of user terminals in the cell according to the adjusted scheduling priority. The scheduling priority of each user terminal is adjusted according to the channel quality of each user terminal, so that the communication transmission order of each user terminal can be accurately obtained.
The exemplary range of channel quality is 0 to 31, where the signal quality is the worst when the channel quality is 0, the signal quality is the best when the channel quality is 31, the channel quality for user terminal 1 is 20, the channel quality for user terminal 2 is 15, the channel quality for user terminal 3 is 31, the channel quality for user terminal 4 is 10, and the channel quality for user terminal 5 is 2. The scheduling priorities of the user terminals are ordered according to the channel quality of the user terminals, and the order of the scheduling priorities from high to low is sequentially user terminal 3, user terminal 1, user terminal 2, user terminal 4 and user terminal 5.
In an embodiment, under the condition that the traffic suppression policy is a scheduling priority adjustment policy, channel quality of a plurality of user terminals in a cell is acquired, a preset channel quality threshold is acquired, user terminals with channel quality greater than or equal to the preset channel quality threshold are collected to a first user terminal set, users with channel quality smaller than the preset channel quality threshold are collected to a second user terminal set, wherein each user terminal in the first user terminal set is a user terminal with a high scheduling priority, and each user terminal in the second user terminal set is a user terminal with a low scheduling priority. And adjusting the scheduling priority according to whether each user terminal is in the first user terminal set or the second user terminal set. The preset channel quality threshold may be set according to practical situations, which is not specifically limited in the embodiment of the present invention. And comparing the channel quality of the user terminal with a preset channel quality threshold, so that the efficiency of adjusting the scheduling priority of the user terminal can be improved.
Illustratively, if the channel quality of the ue 6 is 25, the channel quality of the ue 7 is 18, the channel quality of the ue 8 is 30, the channel quality of the ue 9 is 10, and the channel quality of the ue 10 is 15, and the preset channel quality threshold is 20, the ue whose channel quality is greater than or equal to the preset channel quality threshold 20 is grouped into a first ue set, and the ue whose channel quality is less than the preset channel quality threshold 20 is grouped into a second ue set, where the first ue set includes the ue 6 and the ue 8, and the second ue set includes the ue 7, the ue 9, and the ue 10.
In one embodiment, as shown in fig. 5, step S103 includes sub-step S1032.
And a substep S1032, wherein when the traffic suppression root is that the available resources of the communication channel of the cell are smaller than or equal to the preset available resources, the preset user terminal migration strategy is determined as the traffic suppression strategy.
The user terminal migration strategy is to migrate the user terminal in the cell to the neighboring cell, so as to alleviate the problem of insufficient available resources of the communication channel in the cell.
For example, if the available resources of the communication channel of the cell are 50RB, the preset available resources are 100RB, and the available resources of the communication channel of the cell are 5RB less than the preset available resources 100RB, the preset user terminal migration policy is determined as the traffic suppression policy.
In an embodiment, under the condition that the traffic suppression policy is a user terminal migration policy, acquiring traffic suppression information of each neighboring cell in a plurality of neighboring cells of a cell; selecting a target neighbor cell from a plurality of neighbor cells according to the flow suppression information of each neighbor cell; and selecting user terminals meeting preset conditions from the cells to obtain a plurality of target user terminals, and transferring each target user terminal to a target neighbor cell. Wherein, the neighboring cell is the neighboring cell of the cell. The problem of insufficient available resources of the communication channel of the cell is relieved by transferring a plurality of target terminals to the target neighbor cell, so that the communication channel of the cell is free from communication blockage, and the flow value of the cell is greatly improved.
It should be noted that, the preset condition may be set according to the actual situation, which is not specifically limited in the embodiment of the present invention, the preset condition may be that the occupied communication channel resource is greater than or equal to a preset threshold, the preset threshold may be set according to the actual situation, and the preset threshold may be a communication channel resource of 10 RB.
For example, the cell is attached with a user terminal 1, a user terminal 2, a user terminal 3, a user terminal 4, a user terminal 5, a user terminal 6, a user terminal 7, a user terminal 8, a user terminal 9 and a user terminal 10, where the preset condition is that the occupied communication channel resource is greater than or equal to 10RB, where the occupied communication channel resource of the user terminal 1 is 2RB, the occupied communication channel resource of the user terminal 2 is 5RB, the occupied communication channel resource of the user terminal 3 is 20RB, the occupied communication channel resource of the user terminal 4 is 10RB, the occupied communication channel resource of the user terminal 5 is 4RB, the occupied communication channel resource of the user terminal 6 is 100RB, the occupied communication channel resource of the user terminal 7 is 6RB, the occupied communication channel resource of the user terminal 8 is 20RB, the occupied communication channel resource of the user terminal 9 is 7RB and the occupied communication channel resource of the user terminal 10 is 60RB. The user terminals occupying communication channel resources greater than or equal to 10RB include user terminal 3, user terminal 4, user terminal 6, user terminal 8 and user terminal 10, i.e. the screened target user terminals include user terminal 3, user terminal 4, user terminal 6, user terminal 8 and user terminal 10.
In an embodiment, according to the traffic suppression information of each neighboring cell, the method for selecting the target neighboring cell from the plurality of neighboring cells may be: sequencing the flow inhibition degree of each adjacent cell according to the flow inhibition information of each adjacent cell to obtain a flow inhibition degree queue of each adjacent cell; and selecting a neighboring cell with the flow inhibition degree meeting the preset requirement from the flow inhibition degree queue, and determining the neighboring cell as a target neighboring cell. And screening the target neighbor cells according to the traffic suppression information of each neighbor cell, so that the target neighbor cells with better network transmission performance can be obtained.
It should be noted that the preset requirement may be set according to an actual situation, and the embodiment of the present invention is not limited to this specifically, for example, the preset requirement may have a minimum traffic suppression degree, or the traffic suppression degree is smaller than the traffic suppression degree of the cell.
In an embodiment, after the traffic suppression policy of the cell is determined, the traffic suppression policy is executed, so that the problem of cell traffic suppression is solved, and the network transmission performance of the cell is greatly improved.
The cell flow control method in the above embodiment obtains the flow suppression prediction result of the cell; determining a flow suppression root cause of the cell under the condition that the cell is determined to have flow suppression in a preset time period in the future according to a flow suppression prediction result; then, according to the traffic suppression root cause, the traffic suppression policy of the cell can be accurately determined, and the traffic suppression policy is executed to adjust the traffic of the cell. Under the condition that the flow inhibition occurs in a preset time period in the future, the method and the system can accurately determine the flow inhibition root cause of the cell, and determine the flow inhibition strategy of the cell according to the flow inhibition root cause, and the situation that the flow of the cell is inhibited can be solved by executing the flow inhibition strategy, so that the network transmission performance of the cell is greatly improved.
Referring to fig. 6, fig. 6 is a schematic block diagram of a base station according to an embodiment of the present invention.
As shown in fig. 6, the base station 200 includes a processor 201 and a memory 202, and the processor 201 and the memory 202 are connected by a bus 203, such as an I2C (Inter-integrated Circuit) bus.
In particular, the processor 201 is configured to provide computing and control capabilities to support the operation of the entire base station. The processor 201 may be a central processing unit (Central Processing Unit, CPU), and the processor 201 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Specifically, the Memory 202 may be a Flash chip, a Read-Only Memory (ROM) disk, an optical disk, a U-disk, a removable hard disk, or the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 6 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the base station to which the present inventive arrangements are applied, and that a particular base station may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
The processor is configured to run a computer program stored in the memory, and implement any one of the cell flow control methods provided by the embodiments of the present invention when the computer program is executed.
In an embodiment, the processor 201 is configured to execute a computer program stored in a memory, and when executing the computer program, implement the following steps:
obtaining a traffic suppression prediction result of a cell;
determining a traffic suppression root cause of the cell under the condition that the cell is determined to have traffic suppression in a preset time period in the future according to the traffic suppression prediction result;
and determining a traffic suppression strategy of the cell according to the traffic suppression root cause, and executing the traffic suppression strategy, wherein the traffic suppression strategy is used for adjusting the traffic of the cell.
In an embodiment, when implementing the determining the traffic suppression root cause of the cell, the processor 201 is configured to implement:
acquiring network state data of the cell;
and determining the flow inhibition root cause of the cell according to the network state data.
In an embodiment, when implementing the determining, by the processor 201, the traffic suppression policy of the cell according to the traffic suppression root cause, the method is configured to implement:
And under the condition that the traffic suppression root is larger than or equal to a preset utilization rate because of the communication channel resource utilization rate of the cell, determining a preset scheduling priority adjustment strategy as the traffic suppression strategy.
In an embodiment, when implementing the determining, by the processor 201, the traffic suppression policy of the cell according to the traffic suppression root cause, the method is configured to implement:
and under the condition that the traffic suppression root is smaller than or equal to the preset available resources because of the communication channel of the cell, determining a preset user terminal migration strategy as the traffic suppression strategy.
In an embodiment, when implementing the executing the traffic suppression policy, the processor 201 is configured to implement:
acquiring channel quality of a plurality of user terminals in the cell under the condition that the flow suppression strategy is the scheduling priority adjustment strategy;
and adjusting the scheduling priority of the user terminals in the cell according to the channel quality of the user terminals, and scheduling the user terminals in the cell according to the adjusted scheduling priority.
In an embodiment, when implementing the executing the traffic suppression policy, the processor 201 is configured to implement:
Acquiring traffic suppression information of each of a plurality of neighbor cells of the cell under the condition that the traffic suppression policy is the user terminal migration policy;
selecting a target neighbor cell from a plurality of neighbor cells according to the flow suppression information of each neighbor cell;
and selecting user terminals meeting preset conditions from the cells to obtain a plurality of target user terminals, and transferring each target user terminal to the target neighbor cell.
In an embodiment, when implementing selecting a target neighbor cell from the neighbor cells according to the traffic suppression information of each neighbor cell, the processor 201 is configured to implement:
according to the flow suppression information of each adjacent cell, sequencing the flow suppression degree of each adjacent cell to obtain a flow suppression degree queue of each adjacent cell;
and selecting a neighboring cell with the flow inhibition degree meeting the preset requirement from the flow inhibition degree queue to determine the neighboring cell as a target neighboring cell.
In an embodiment, when implementing the traffic suppression prediction result of the acquired cell, the processor 201 is configured to implement:
acquiring a network flow model, wherein the network flow model is used for representing a mapping relation table of network state data and flow values;
Determining a suppression point flow value according to the network flow model, and determining a target network state suppression reference value corresponding to the suppression point flow value, wherein the suppression point flow value is a flow threshold value of current communication transmission;
and acquiring a parameter predicted value corresponding to the target network state suppression reference value, and determining a flow suppression predicted result according to the parameter predicted value and the target network state suppression reference value.
In an embodiment, when implementing the acquiring network traffic model, the processor 201 is configured to implement:
acquiring a network state sample set, wherein the network state sample set comprises a sample of network state;
and training the network traffic model according to the network state sample set.
It should be noted that, for convenience and brevity of description, a specific operation process of the base station described above may refer to a corresponding process in the foregoing embodiment of the cell flow control method, which is not described herein again.
Embodiments of the present invention also provide a storage medium for computer readable storage storing one or more programs executable by one or more processors to implement the steps of any of the methods of flow suppression adjustment as provided in the present specification.
The storage medium may be an internal storage unit of the base station according to the foregoing embodiment, for example, a hard disk or a memory of the base station. The storage medium may also be an external storage device of the base station, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the base station.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, functional modules/units in the apparatus, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between the functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed cooperatively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
It should be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system 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 system. 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 system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments. While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (11)
1. A method for controlling cell traffic, comprising:
obtaining a traffic suppression prediction result of a cell;
determining a traffic suppression root cause of the cell under the condition that the cell is determined to have traffic suppression in a preset time period in the future according to the traffic suppression prediction result;
and determining a traffic suppression strategy of the cell according to the traffic suppression root cause, and executing the traffic suppression strategy, wherein the traffic suppression strategy is used for adjusting the traffic of the cell.
2. The method for controlling traffic of a cell according to claim 1, wherein the determining a traffic suppression root cause of the cell comprises:
acquiring network state data of the cell;
and determining the flow inhibition root cause of the cell according to the network state data.
3. The method for controlling traffic of a cell according to claim 1, wherein determining a traffic suppression policy of the cell according to the traffic suppression root cause comprises:
and under the condition that the traffic suppression root is larger than or equal to a preset utilization rate because of the communication channel resource utilization rate of the cell, determining a preset scheduling priority adjustment strategy as the traffic suppression strategy.
4. The method for controlling traffic of a cell according to claim 1, wherein determining a traffic suppression policy of the cell according to the traffic suppression root cause comprises:
and under the condition that the traffic suppression root is smaller than or equal to the preset available resources because of the communication channel of the cell, determining a preset user terminal migration strategy as the traffic suppression strategy.
5. The method of cell traffic control according to claim 3, wherein said executing said traffic suppression policy comprises:
acquiring channel quality of a plurality of user terminals in the cell under the condition that the flow suppression strategy is the scheduling priority adjustment strategy;
and adjusting the scheduling priority of the user terminals in the cell according to the channel quality of the user terminals, and scheduling the user terminals in the cell according to the adjusted scheduling priority.
6. The method of cell traffic control according to claim 4, wherein said executing the traffic suppression policy comprises:
acquiring traffic suppression information of each of a plurality of neighbor cells of the cell under the condition that the traffic suppression policy is the user terminal migration policy;
Selecting a target neighbor cell from a plurality of neighbor cells according to the flow suppression information of each neighbor cell;
and selecting user terminals meeting preset conditions from the cells to obtain a plurality of target user terminals, and transferring each target user terminal to the target neighbor cell.
7. The cell traffic control method according to claim 6, wherein selecting a target neighbor cell from among the neighbor cells based on traffic suppression information of each of the neighbor cells, comprises:
according to the flow suppression information of each adjacent cell, sequencing the flow suppression degree of each adjacent cell to obtain a flow suppression degree queue of each adjacent cell;
and selecting a neighboring cell with the flow inhibition degree meeting the preset requirement from the flow inhibition degree queue to determine the neighboring cell as a target neighboring cell.
8. The method for cell traffic control according to any one of claims 1 to 7, wherein the obtaining the traffic suppression prediction result of the cell includes:
acquiring a network flow model, wherein the network flow model is used for representing a mapping relation table of network state data and flow values;
determining a suppression point flow value according to the network flow model, and determining a target network state suppression reference value corresponding to the suppression point flow value, wherein the suppression point flow value is a flow threshold value of current communication transmission;
And acquiring a parameter predicted value corresponding to the target network state suppression reference value, and determining a flow suppression predicted result according to the parameter predicted value and the target network state suppression reference value.
9. The method for cell traffic control according to claim 8, wherein the acquiring a network traffic model comprises:
acquiring a network state sample set, wherein the network state sample set comprises a sample of network state;
and training the network traffic model according to the network state sample set.
10. A base station, characterized in that the base station comprises a processor, a memory, a computer program stored on the memory and executable by the processor, and a data bus for enabling a connection communication between the processor and the memory, wherein the computer program, when being executed by the processor, implements the steps of the cell flow control method according to any of claims 1 to 9.
11. A storage medium for computer readable storage, wherein the storage medium stores one or more programs executable by one or more processors to implement the steps of the cell flow control method of any of claims 1 to 9.
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| CN202211212558.7A CN117858169A (en) | 2022-09-29 | 2022-09-29 | Cell flow control method, base station and storage medium |
| PCT/CN2023/118535 WO2024067095A1 (en) | 2022-09-29 | 2023-09-13 | Cell traffic control method, base station and storage medium |
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| CN105634787B (en) * | 2014-11-26 | 2018-12-07 | 华为技术有限公司 | Appraisal procedure, prediction technique and the device and system of network key index |
| US9756518B1 (en) * | 2016-05-05 | 2017-09-05 | Futurewei Technologies, Inc. | Method and apparatus for detecting a traffic suppression turning point in a cellular network |
| US11496353B2 (en) * | 2019-05-30 | 2022-11-08 | Samsung Electronics Co., Ltd. | Root cause analysis and automation using machine learning |
| CN115550195A (en) * | 2021-06-11 | 2022-12-30 | 中兴通讯股份有限公司 | Traffic suppression prediction method, electronic device, and storage medium |
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