CN116981027A - Base station control method, network equipment, base station and storage medium - Google Patents
Base station control method, network equipment, base station and storage medium Download PDFInfo
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Abstract
The embodiment of the invention provides a base station control method, network equipment, a base station and a storage medium, and belongs to the technical field of communication. The method comprises the following steps: acquiring load information and user index parameters of each cell in a target cell group, wherein the target cell group comprises a plurality of frequency layers; predicting the expected overall load of the target cell group according to the load information of each cell; performing iterative optimization processing on the energy efficiency of each frequency layer according to the expected overall load and the user index parameters of each cell to obtain the target expected load of each frequency layer; and generating a load energy saving strategy of the target cell group according to the target expected load of each frequency layer, so that the base station executes the load energy saving strategy on the target cell group. The technical scheme of the embodiment of the invention aims to maximize the overall energy-saving effect of the multilayer network.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a base station control method, a network device, a base station, and a storage medium.
Background
With the large-area spreading of the new air interface (5G New Radio,5G NR) of the fifth-generation wireless communication network, the large-bandwidth, ultra-low delay and mass connection characteristics of the 5G improve the network use experience of users, but meanwhile, the power consumption of the 5G base station is greatly improved, and the number of base stations covering the same area is also more due to higher frequency band (i.e. frequency layer) used by the 5G. This can lead to a surge in energy consumption of the 5G base station, which can lead to an increase in operating costs.
With the evolution of network deployment, operators are increasingly becoming typical application scenes based on the consideration of saving construction investment and improving benefit, such as 2G/3G/4G/5G networks coexist simultaneously to provide services. In the scenario of the multi-layer network (i.e. a plurality of frequency layers), the existing energy-saving strategy generally performs energy saving independently through the cells of each frequency layer, so that the base station can easily perform energy saving only on the cell of one frequency layer, thereby influencing the energy saving effect of the base station on the cells of other frequency layers; or even if a cooperative energy-saving strategy exists among the multi-layer networks, the existing cooperative energy-saving strategy is not considered from the overall energy efficiency, so that the overall energy-saving effect maximization of the multi-layer network cannot be achieved.
Disclosure of Invention
The embodiment of the invention mainly aims to provide a base station control method, terminal equipment and a storage medium, aiming at maximizing the overall energy-saving effect of a multi-layer network.
In a first aspect, an embodiment of the present invention further provides a base station control method, where the control method includes:
acquiring load information and user index parameters of each cell in a target cell group, wherein the target cell group comprises a plurality of frequency layers;
predicting the expected overall load of the target cell group according to the load information of each cell;
performing iterative optimization processing on the comprehensive energy efficiency of each frequency layer according to the expected overall load and the user index parameters of each cell to obtain the target expected load of each frequency layer;
and generating a load energy saving strategy of the target cell group according to the target expected load of each frequency layer, so that the base station executes the load energy saving strategy on the target cell group.
In a second aspect, an embodiment of the present invention further provides a network device, where the network device includes a processor, a memory, a computer program stored on the memory and executable by the processor, and a data bus for implementing a connection communication between the processor and the memory, where the computer program, when executed by the processor, implements the steps of a base station control method as provided in any one of the present specification.
In a third aspect, an embodiment of the present invention further provides a base station, the base station including 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 the base station control method as provided in any one of the present specification.
In a fourth aspect, embodiments of the present invention further provide a storage medium for computer-readable storage, where the storage medium stores one or more programs executable by one or more processors to implement the steps of the base station control method according to any one of the present invention.
The embodiment of the invention provides a base station control method, terminal equipment and storage medium, wherein the method comprises the steps of obtaining load information and user index parameters of each cell in a target cell group, carrying out iterative optimization treatment on comprehensive energy efficiency of each frequency layer according to the load information and the user index parameters of each cell to obtain target expected loads of each frequency layer, thereby generating a load energy-saving strategy of the target cell group, comprehensively considering the influence of energy consumption and user experience from the perspective of the overall energy efficiency of a multi-layer network, maximally reducing the energy consumption of a base station, achieving the purpose of saving energy, and improving the application efficiency of energy saving.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a base station control method according to an embodiment of the present application;
fig. 2 is a schematic diagram of a distribution situation of a target cell group according to an embodiment of the present application;
fig. 3 is a schematic block diagram of a network device according to an embodiment of the present application;
fig. 4 is a schematic block diagram of a base station according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application 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 application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
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 herein 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.
In current operator 4G network operation, the electricity fee is the largest network maintenance cost paid by each large operator. With the spreading of the 5G in a large area, the large-bandwidth, ultra-low delay and mass connection characteristics of the 5G improve the network utilization experience of users, but simultaneously, the power consumption of the 5G base station is also greatly improved. According to the current test results, the electricity consumption of the 5G base station is 2 times to 3 times that of the 4G base station. Meanwhile, as the frequency band used by 5G is higher, the number of base stations covering the same area also needs to be several times greater than 4G. Thus, the preliminary estimated 5G energy consumption can reach more than 10 times of 4G, and the electricity consumption is estimated to account for more than 40% of the operation cost of the 5G base station.
In the current wireless network, the energy consumption of the wireless base station occupies a larger proportion, the indoor baseband processing unit (Building Base band Unit, BBU) and the remote radio unit (Remote Radio Unit, RRU) in the base station occupy about 50% of the energy consumption, wherein the RRU occupies about 80% of the energy consumption of the main equipment, and the power amplifier occupies about 80% of the energy consumption in the RRU. Therefore, how to reduce the energy consumption of the power amplifier module becomes a main direction of energy conservation of the base station.
With the evolution of network deployment, operators are increasingly becoming typical application scenes based on the consideration of saving construction investment and improving benefit, such as 2G/3G/4G/5G networks coexist simultaneously to provide services. In the scene, in the current energy-saving strategy, the cells of each frequency layer are independently used for saving energy, and at the moment, the base station only saves energy for the cell of one frequency layer, so that the energy-saving effect of the base station on the cells of other frequency layers is affected, and the energy-saving effect is poor because the cells of other frequency layers cannot save energy; or even if a certain cooperative strategy among the multi-layer networks exists, the overall energy-saving effect of the multi-layer networks cannot be maximized from the aspect of overall energy efficiency.
The embodiment of the invention provides a base station control method, network equipment, a base station and a storage medium. The control method can be applied to a centralized processing unit or a distributed processing unit, wherein the centralized processing unit can be a network device, such as a server, or a data platform of network management, edge computing technology (Mobile Edge Computing, MEC) and the like. The distributed processing units may be base stations or the like. Therefore, from the overall energy efficiency of the multi-layer network, the influence of energy consumption and user experience is comprehensively considered, the energy consumption of the base station is maximally reduced, the purpose of energy conservation is achieved, and the energy-saving application efficiency is improved.
Referring to fig. 1, fig. 1 is a flow chart of a base station control method according to an embodiment of the invention. The base station control method is used for generating the load energy saving strategy of the target cell group so that the base station executes the corresponding load energy saving strategy in the target cell group.
As shown in fig. 1, the base station control method includes steps S101 to S104.
Step S101, load information and user index parameters of each cell in a target cell group are obtained, wherein the target cell group comprises a plurality of frequency layers.
The target cell group includes a plurality of cells corresponding to different frequency layers, for example, may include a cell corresponding to 2.6G NR, a cell corresponding to 2.6G LTE, a cell corresponding to 1.9G LTE, a cell corresponding to 900M LTE, and so on. The target cell group can be obtained by screening the geographical position of the cell, so that user migration is convenient to carry out in the target cell group when the base station executes the energy-saving strategy, and the user can more easily meet the energy-saving strategy to be executed. The load information includes, but is not limited to, the number of users in each cell, the utilization of uplink and downlink physical resource blocks (Physical resource block, PRBs), uplink and downlink throughput, uplink and downlink user rates, and the like. The user index parameters may be key performance indicators (Key Performance Indicator, KPI) and key quality indicators (Key Quality Indicator, KQI). The KPI and KQI may specifically include user perceived information such as user rate, time delay, jitter, voice mos values, etc., where the user perceived information may reflect the user's network experience to a certain extent.
In some embodiments, the target cell group may be obtained by obtaining the geographic locations of all cells in the target area and the geographic locations of the base stations corresponding to each of the cells; determining the signal coverage range of each cell according to the geographic positions of all cells and the geographic positions of the base stations corresponding to each cell; and determining a target cell group according to the signal coverage range of each cell. Therefore, the target cell group can be determined through the geographic position of the cell and the geographic position of the base station corresponding to each cell, so that the target cell group can be accurately constructed, and the follow-up base station can conveniently execute the corresponding energy-saving strategy in the target cell group.
The target area is a preset geographical area, for example, may include a geographical area covered by a base station, or a geographical area defined by a user, which is not limited herein specifically. The signal coverage of the cell is used to indicate the geographical location range covered by the cell signal.
Specifically, the longitude and latitude of the base station and the direction angle of the cell can be obtained in advance, so that the longitude and latitude of the cell can be calculated; the longitude and latitude information of all cells in the target area and the longitude and latitude information of the base station corresponding to each cell can be directly obtained. The distance between the base station and the cell is related to the signal coverage of the cell, wherein the signal coverage of the cell can be obtained through the position relationship between the cell and the neighboring cell in network planning, for example, the signal coverage is obtained by using the average value of the distances between a plurality of cells closest to the cell in the direction angle range of the cell.
For example, if the base station control method is applied to a network device such as a server, that is, the server may obtain latitude and longitude information of all cells in the target area and latitude and longitude information of a base station corresponding to each cell through the base station device, so as to determine a signal coverage area of each cell; and finally, determining a target cell group according to the signal coverage range of each cell.
For example, if the base station control method is applied to a base station, the base station generally only can obtain longitude and latitude information corresponding to all cells included in the base station, and the coverage area of the base station is the target area; meanwhile, longitude and latitude information of the base station is obtained, so that the signal coverage range of each cell is determined; and finally, determining a target cell group according to the signal coverage range of each cell.
In some embodiments, determining inter-zone overlapping coverage of each of the cells from signal coverage of each of the cells; and screening out cells with the interval overlapping coverage exceeding a preset interval overlapping coverage to form the target cell group. Therefore, the cell group with high overlapping coverage can be accurately screened to obtain the target cell group, and the user can be conveniently migrated from one frequency layer to another frequency layer when the subsequent base station executes the energy-saving strategy.
The interval overlapping coverage is used for indicating the overlapping degree of signal coverage of each cell and the adjacent cell; the preset interval overlapping coverage is preset interval overlapping coverage, for example, 80%, and is specifically set by a user.
Specifically, determining the inter-cell overlapping coverage of each cell according to the signal coverage of each cell and the signal coverage of the corresponding neighbor cell of each cell; and screening out a plurality of cells of which the interval overlapping coverage exceeds a preset interval overlapping coverage to form the target cell group.
For example, based on the signal coverage of each cell calculated in the above steps, the coverage of the interval between the cells i and j may be calculated according to the following formula:
therein, cov i,j For the inter-zone overlapping coverage of cells i and j, S i For the signal coverage of cell i, S j Signal coverage for cell j; the numerator is the intersection area of the signal coverage of the cell i, and the denominator is the signal coverage area of the cell i, so that the interval overlapping coverage of the cell i and the cell j can be calculated, and the like, the interval overlapping coverage of the cell i and the cell y, the interval overlapping coverage of the cell y and the cell z and the like can be calculated, and finally a plurality of cells with the interval overlapping coverage exceeding the preset interval overlapping coverage are screened out to form the target cell group.
It should be noted that, when the coverage of the overlapping interval of any two cells exceeds the coverage of the overlapping interval of the preset, the two cells can be screened out to form the target cell group.
For example, if the preset inter-zone overlapping coverage is 80%, the inter-zone overlapping coverage of cell i and cell j is 90%, the inter-zone overlapping coverage of cell i and cell y is 85%, and the inter-zone overlapping coverage of cell y and cell j is 70%, then the target cell group is cell i, cell j and cell y.
In some embodiments, the target cell group may further be obtained by obtaining a measurement report in a target area, and sequentially screening the measurement report based on each cell to obtain a target measurement report corresponding to each cell; determining the interval overlapping coverage of each cell according to the target measurement report corresponding to each cell; and screening out cells with the interval overlapping coverage exceeding a preset interval overlapping coverage to form the target cell group. Thus, the target cell group can be accurately obtained through screening by the measurement report.
Wherein the measurement report (Measurement Report, MR) can be used for network evaluation and optimization.
Specifically, for one cell, analyzing the measurement report to obtain the signal quality of the cell; and if the signal quality of the cell meets the preset signal quality requirement, taking the measurement report as a target measurement report.
The target measurement report is a measurement report meeting the preset signal quality requirement in the cell, the MR can be used for network quality analysis, the quality analysis of the uplink and downlink wireless networks can be completed, and the real situation of the call quality of the whole network in the local area can be reflected, so that the signal quality of each cell can be determined through the MR. The preset signal quality requirement can be formed by factors such as signal strength and the like and used for calculating the overlapping coverage of the intervals, and can be specifically set by a user.
For example, if there is a measurement report a, the signal quality of the cell a, the cell b and the cell c may be obtained by analyzing the measurement report a, and in general, the signal quality of the cell a, the cell b and the cell c are different. Namely, for the measurement report A, if the signal quality of the cell a meets the preset signal quality requirement, for the cell a, the measurement report A is a target measurement report; if the signal quality of the cell b meets the preset signal quality requirement, the measurement report A is also a target measurement report for the cell b; if the signal quality of the cell c does not meet the preset signal quality requirement, for the cell c, the measurement report a is not the target measurement report.
Specifically, after the target measurement report corresponding to each cell is obtained, the number of target measurement reports overlapped between cells can be determined, and finally, the coverage of the overlapping between cells is determined according to the number of target measurement reports overlapped between cells and the number of target measurement reports corresponding to each cell.
The formula for calculating the inter-zone overlapping coverage of the cell is as follows:
the numerator is the number of reports of the target measurement report measured in the MR measurement in the cell i and the cell j, and the denominator is the number of reports of the target measurement report measured in the MR measurement in the cell i. And the inter-zone overlapping coverage of the cell i and the cell j can be calculated, and similarly, the inter-zone overlapping coverage of the cell i and the cell y, the inter-zone overlapping coverage of the cell y and the cell z and the like can be calculated, and finally, a plurality of cells with the inter-zone overlapping coverage exceeding the preset inter-zone overlapping coverage are screened out to form the target cell group.
It should be noted that, when the coverage of the overlapping interval of any two cells exceeds the coverage of the overlapping interval of the preset, the two cells can be screened out to form the target cell group.
For example, if the preset inter-zone overlapping coverage is 80%, the inter-zone overlapping coverage of cell i and cell j is 90%, the inter-zone overlapping coverage of cell i and cell y is 85%, and the inter-zone overlapping coverage of cell y and cell j is 70%, then the target cell group is cell i, cell j and cell y.
And step S102, predicting the expected overall load of the target cell group according to the load information of each cell.
The expected overall load is the overall load of the target cell group in a future period, and can be predicted by a load prediction model.
In some embodiments, based on a preset load prediction model, according to the load information of each cell, predicting the load of the target cell group in a target time period to obtain the expected overall load of the target cell group. Therefore, the overall load of the target cell group in the target time period can be accurately predicted through a pre-constructed load prediction model, and the follow-up iterative optimization processing of the energy efficiency of each frequency layer is facilitated.
The load prediction model can be constructed by adopting a prediction modeling method based on time sequences, such as a Long Short-Term Memory (LSTM) and a differential autoregressive moving average (Autoregressive Integrated Moving Average, ARIMA), and the like, and can be specifically constructed by adopting the modeling method aiming at the load condition of a cell group, so that the user load condition of the target cell group in a future period of time is obtained. The target time period is a preset future time period, and a plurality of time granularities can be included in the future time period, for example, the time granularities are related to the effective time granularity required by the finally generated energy-saving strategy in a 1-minute granularity, a 15-minute granularity and the like.
Specifically, the load information of each cell in the target cell group can be input into a pre-trained load prediction model, so that the load of the target cell group in the target time period is predicted, and finally the load prediction model outputs the expected overall load of the target cell group.
For example, if the base station control method is applied to a base station, the base station has limited computing power, and thus more resources are consumed for constructing a complex prediction model. Thus, a historical load statistical approach can be used to construct future load models. Specifically, the average value of a plurality of data based on the same period of history is regarded as the load at the same time in the future. Wherein, the contemporaneous time may be: the correlation is made in a certain period, and the time of the period with high correlation is taken as the synchronization. Here, correlation calculation can be performed with a day granularity, a week granularity, a month granularity, and the like.
For the expected overall load of the target cell group, the number of users, throughput and the like can be counted and predicted in a summation and aggregation mode, and the PRB utilization rate and the like can be counted and predicted in an average and aggregation mode.
And step 103, performing iterative optimization processing on the comprehensive energy efficiency of each frequency layer according to the expected overall load and the user index parameters of each cell to obtain the target expected load of each frequency layer.
The traditional energy efficiency is defined as an effective output provided by unit energy consumption, and is generally characterized by a data flow energy consumption ratio, namely, the network energy efficiency is evaluated by a ratio of flow and energy consumption:
wherein, data volume is flow, energy Consumption is energy consumption, EE is energy efficiency.
The traffic energy efficiency is actually embodied in the traditional energy efficiency definition, and the energy efficiency is high when the traffic is high and low when the traffic is low. This is because the increasing trend of traffic during high traffic periods is far beyond the increasing trend of power consumption. This may result in a poor actual business experience, although traffic is energy efficient, where even traffic is energy efficient. Therefore, in the embodiment of the application, relevant factors such as user index parameters are added in the definition of the energy efficiency, so that the energy efficiency is redefined.
That is, in the embodiment of the present application, data volume is traffic, energy Consumption is energy consumption, use experientence is a user index parameter, and may include, but is not limited to, related factors such as user Experience rate, user reference signal received power (Reference Signal Receiving Power, RSRP), channel quality indication (Channel Quality Indicator, CQI), and IP delay, EE 1 Is energy-efficient.
In some embodiments, based on a preset optimizing algorithm, performing iterative allocation on the expected load of each frequency layer according to the expected overall load and the user index parameter to obtain a plurality of load allocation combinations; and respectively calculating the energy efficiency of each load distribution combination to obtain comprehensive energy efficiency corresponding to each load distribution combination, and taking the load distribution combination with the highest comprehensive energy efficiency as a target load distribution combination, wherein the target load distribution combination comprises target expected loads of each frequency layer. Therefore, the load distribution combination with highest comprehensive energy efficiency, namely the target expected load of each frequency layer, can be accurately obtained through iterative optimization processing.
The optimizing algorithm is a searching process or rule, and based on a certain thought and mechanism, a solution meeting the problem of the user is obtained through a certain path or rule. The optimizing algorithm can be a hill-climbing algorithm (hill-climbing algorithm), a simulated annealing algorithm (simulated annealing algorithm), a genetic algorithm (genetic algorithm) and the like. The load distribution combinations are expected loads comprising frequency layers, and the expected loads of the frequency layers in each load distribution combination are generally different. The energy efficiency is EE 1 The comprehensive energy efficiency is the overall energy efficiency of the load distribution combination, and the target load distribution combination is the load distribution combination with the highest comprehensive energy efficiency.
The expected load of each frequency layer can be iteratively distributed according to the expected total load obtained through prediction and the user sensing information of user index parameters such as user speed, time delay, jitter, voice mos value and the like through a hill climbing algorithm to obtain a plurality of load distribution combinations, wherein the iterative distribution is a process of an optimizing algorithm and is used for obtaining the optimal result of various iterative distribution, namely a target load distribution combination; and finally, calculating the energy efficiency of each frequency layer in each load distribution combination obtained through iterative distribution, so as to summarize and obtain the comprehensive energy efficiency corresponding to each load distribution combination, and taking the load distribution combination with the highest comprehensive energy efficiency as a target load distribution combination.
In some embodiments, the expected load distribution is performed on each frequency layer according to the expected overall load, so as to obtain the expected load of each frequency layer; based on a preset energy efficiency parameter prediction model, obtaining energy efficiency influence parameters of each frequency layer according to the user index parameters and the expected load of each frequency layer; and generating a load distribution combination according to the expected load of each frequency layer and the expected load influence factor of each frequency layer. Therefore, from the overall energy efficiency of the multi-layer network, the influence of energy consumption and user experience is comprehensively considered, the energy efficiency influence parameters are newly increased, and the influence of the user index parameters on the final energy efficiency is increased, so that the load distribution combination which is more in line with the user experience is generated.
The energy efficiency parameter prediction model can be constructed by adopting a certain method to fit the load, the user experience rate and other user index parameters of each cell, and particularly, the energy efficiency parameter prediction model can be constructed by adopting a linear method, a support vector machine (Support Vector Machine, SVM) method and other methods. The energy efficiency influence parameter is output of the energy efficiency parameter prediction model and is used as an input parameter when the target load distribution combination is determined by using an optimizing algorithm, so that the determination of the target load distribution combination is influenced.
Specifically, to further improve the fitting accuracy, a sectional regression method may be used for modeling, i.e. different modeling modes are used for different data characteristics. Wherein the segmentation principle can be divided based on the user experience rate. The user index parameters can also identify specific application layer services, distinguish different service types such as streaming media and games, and respectively model, so that energy efficiency influence parameters are finally obtained.
In some embodiments, configuration data of a base station is obtained, energy efficiency of each load distribution combination is calculated according to the configuration data, comprehensive energy efficiency corresponding to each load distribution combination is obtained, and a load distribution combination with the highest comprehensive energy efficiency is used as a target load distribution combination. For different station types and RRU models, the consumed power is different under the same load condition due to the differences of components and supported functions, so that the target load distribution combination can be accurately determined according to the configuration data of different base stations.
The configuration data of the base station may include RRU model, station type of the base station, etc., and the configuration data of the base station needs to be considered for calculating the comprehensive energy efficiency.
Specifically, by acquiring configuration data of the base station, for different station types and RRU models, the power consumed under the same load condition is different due to the difference of components and supported functions, so that the relationship between load and power consumption can be constructed based on the data of different station types and RRU models. In this way, in the optimizing iterative algorithm, the power consumption can be calculated according to the target expected load of each frequency layer and the configuration data of the corresponding base station, so that the comprehensive energy efficiency corresponding to each load distribution combination can be accurately obtained, and the load distribution combination with the highest comprehensive energy efficiency is used as the target load distribution combination.
In some embodiments, after the comprehensive energy efficiency corresponding to each load distribution combination is obtained and the load distribution combination with the highest comprehensive energy efficiency is taken as a target load distribution combination, determining whether the target load distribution combination meets a preset load constraint condition; and if the target load distribution combination does not meet the preset load constraint condition, removing the target load distribution combination and redefining the target load distribution combination. Thus, whether the target load distribution combination is the optimal comprehensive energy consumption of the target cell group can be determined by determining whether the target load distribution combination meets the preset load constraint condition.
The preset load constraint condition may be a basic operation condition that needs to be met by load distribution, for example, the preset load constraint condition may be a maximum load that can be distributed by each frequency layer, or may be a reserved frequency layer, where the reserved frequency layer includes two layers of meanings: 1) The planned frequency layer that cannot be shut down or that performs power saving. 2) The method and the device avoid the situation that more different frequency switching is likely to occur due to larger frequency layer difference closed between adjacent stations, and the collaborative constraint is carried out when a plurality of adjacent position areas are calculated, and the method and the device can also be used for limiting the capacity of a frequency band supported by terminal equipment, and the like, and can be specifically set according to actual conditions without specific limitation.
Specifically, determining whether the target load distribution combination meets a preset load constraint condition; if the target load distribution combination meets the preset load constraint condition, generating a load energy-saving strategy of a target cell group according to the target expected load of each frequency layer corresponding to the target load distribution combination; and if the target load distribution combination does not meet the preset load constraint condition, removing the target load distribution combination and redefining the target load distribution combination.
Step S104, generating a load energy saving strategy of the target cell group according to the target expected load of each frequency layer, so that the base station executes the load energy saving strategy in the target cell group.
The load energy saving strategy comprises energy saving strategies such as user migration, carrier turn-off, symbol turn-off and the like, so that the corresponding base station executes the corresponding load energy saving strategy on each frequency layer in the target cell group.
For example, if the base station control method of the embodiment of the present application is applied in a network device, such as a server, the target cell group may be sent to a corresponding base station, so that a corresponding load energy saving policy is executed for each frequency layer in the target cell group.
For example, if the base station control method of the embodiment of the present application is applied to a base station, since the base station can only acquire a cell covered by the base station, the base station can directly execute a corresponding load energy saving policy for each frequency layer in the target cell group.
In some embodiments, configuration data of a base station is obtained; and determining the load energy-saving strategy of each frequency layer in a target time period according to the configuration data and the target expected load of each frequency layer based on a preset energy-saving strategy generation model. Therefore, the load energy-saving strategy conforming to the actual running condition of the base station can be accurately determined according to the configuration data and the target expected load of the base station.
The energy-saving strategy generation model is obtained by training by using configuration data of the base station, target expected loads of all frequency layers and a general load energy-saving strategy, and constructing a relationship among the configuration data of the base station, the target expected loads of all frequency layers and the general load energy-saving strategy, wherein the load energy-saving strategy generally comprises energy-saving strategies such as carrier turn-off, channel turn-off, symbol turn-off and the like.
Specifically, when the 1 RRU includes a plurality of cells, the loads of the plurality of cells may be summarized and modeled with RRU power consumption, so as to obtain the energy saving policy generation model.
Illustratively, if the load of the target cell group in the future 1 hour is predicted, the load energy saving policy of each frequency layer of the target cell group in the future 1 hour is finally obtained, and meanwhile, the target time period (i.e. 1 hour) may include multiple time granularities, such as 1 minute granularity, 15 minute granularity, and the like, where the time granularities are related to the effective granularity required by the final energy saving policy.
Specifically, according to the target expected load of each frequency layer, generating a load energy-saving strategy of the multi-layer network based on load conditions of different time points: the method comprises the steps of carrying out load energy saving strategies such as a frequency layer to be turned off, a frequency layer to be migrated by a user and the like. And finally, the load energy-saving strategy is issued to a corresponding base station, and after the base station receives the corresponding load energy-saving strategy, a user migration strategy is executed at a corresponding time point, so that the user can more easily meet the energy-saving strategy to be executed, and further the subsequent energy-saving shutdown strategy is executed.
Referring to fig. 2, a method for controlling a base station according to an embodiment of the present application is described below with reference to a specific example. The base station control method can be applied to a server, a network management device, a base station, or the like.
If the base station control method can be applied to a server, the load energy-saving strategy can be generated through the server, the network management equipment can receive the data modification request through the interface, so that the generated load energy-saving strategy is saved and updated, and finally the network management equipment sends the load energy-saving strategy to the corresponding base station through the interface, so that the base station executes the load energy-saving strategy in the target cell group. If the base station control method can be applied to network management equipment or a base station, the implementation steps are similar.
As shown in fig. 2, an area 4 layer network is illustrated as comprising 4 frequency layers: NR2.6G, LTE2.6G, LTE1.9G and LTE900M, where NR2.6G and LTE2.6G share an active antenna unit (Active Antenna Unit, AAU).
First, a target cell group may be constructed by a method based on a cell geographical location or MR measurement, etc., and exemplary constructed cells are shown in fig. 2, including cells corresponding to NR2.6G, LTE2.6G, LTE1.9G and LTE900M frequency layers.
And acquiring load information and user index parameters of each cell in the target cell group, inputting the load information of each cell in the target cell group into a pre-trained load prediction model, so as to predict the load of the target cell group in a target time period, and finally outputting the expected overall load of the target cell group by the load prediction model.
And then, based on an optimizing algorithm, carrying out iterative optimizing processing on the energy efficiency of each frequency layer according to the expected overall load and the user index parameters of each cell to obtain the target expected load of the frequency layer.
For a certain time granularity in a target time period, a specific solving algorithm of the optimizing algorithm is as follows:
the layerNum refers to the number of frequency layers or the number of cells in the target cell group, and since there are 4 frequency layers in this embodiment, the value is 5.w (w) k The weight factor is related to the carrier coverage characteristics of each cell. In general, the lower the frequency band, the greater the coverage and the greater the weight. n is n k The expected load allocated for frequency layer k, EE 1 Power is used for generating a model for the constructed energy-saving strategy for the comprehensive energy efficiency model. esDur is when the frequency layer is allocated n k And comparing the energy-saving time with an energy-saving strategy threshold, and judging the obtained energy-saving time. Exp is an energy efficiency parameter prediction model constructed, x k And the energy efficiency influence parameter corresponding to the frequency layer k. The solution algorithm also includes a load constraint, where validList refers to a retainable frequency layer that contains two layers of meaning: 1) The planned frequency layer that cannot be turned off or performs energy saving, such as the 900M frequency layer in this example, cannot perform the turning off due to the wide coverage. 2) And the situation that more different frequency switching is likely to occur due to larger frequency layer difference of closing between adjacent stations is avoided, and collaborative constraint is carried out when a plurality of adjacent position areas are calculated. Terminal equipment support for Ue capability fingerThe capacity constraint of the frequency band.
Finally, solving by an optimizing algorithm to obtain the optimal comprehensive energy consumption of the target cell group under the time granularity and the target expected load of each frequency layer under the optimal comprehensive energy consumption,
illustratively, table 1 is the optimal integrated energy consumption of the target cell group at this time granularity and the target expected load for each frequency layer.
| 2.6G NR | 2.6G LTE | 1.9G LTE | 900M LTE | |
| User load | 0 | load2 | load3 | load4 |
| Energy saving strategy | Carrier shutdown | Symbol off | Energy-saving-free strategy | Energy-saving-free strategy |
And respectively calculating the optimal comprehensive energy consumption of the target cell group under different time granularities and the target expected load of each frequency layer according to the expected overall load in the target time period. And finally, the load energy-saving strategy is issued to a corresponding base station, and after the base station receives the corresponding load energy-saving strategy, a user migration strategy is executed at a corresponding time point, so that the user can more easily meet the energy-saving strategy to be executed, and further the subsequent energy-saving shutdown strategy is executed.
Illustratively, table 2 shows the optimal integrated energy consumption of the target cell group and the target expected load of each frequency layer within the target time period (the target time period is one day, and the time granularity length is 15 minutes as an example).
The frequency layers 2.6G NR and 2.6GLTE have the same AAU, so that the energy saving benefit is high only when the energy saving benefit is closed at the same time; when the load is lowest, the load of the target cell group is mainly borne by 1.9G LTE and 900M LTE, and 2.6G NR and 2.6G LTE can execute a power saving strategy; when the load is general, 2.6G NR and 2.6G LTE are taken as capacity layers to absorb most of business, and 1.9G LTE can execute a certain energy-saving strategy; when the load is higher, the service is commonly absorbed by the 1.9G LTE, the 2.6G NR and the 2.6G LTE, the energy saving is not executed, and the 900M LTE does not implement the energy saving strategy due to the wide coverage range. At this time, the cell without energy saving policy in the target cell group can be used as the target cell for user migration. And finally, the base station actively executes user migration according to the received energy-saving strategy of the target cell group, so that the user can more easily meet the energy-saving strategy to be executed, and further execute the subsequent energy-saving shutdown strategy.
Referring to fig. 3, fig. 3 is a schematic block diagram of a network device according to an embodiment of the present invention.
As shown in fig. 3, the network device 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 overall network device. 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. 3 is merely a block diagram of a portion of the structure related to the embodiment of the present invention, and does not constitute a limitation of the terminal device to which the embodiment of the present invention is applied, and that a specific server may include more or less components than those shown in the drawings, or may combine some components, or have a different arrangement of components.
Referring to fig. 4, fig. 4 is a schematic block diagram of a base station according to an embodiment of the present invention.
As shown in fig. 4, the base station 300 includes a processor 301 and a memory 302, the processor 301 and the memory 302 being connected by a bus 303, such as an I2C (Inter-integrated Circuit) bus.
In particular, the processor 301 is configured to provide computing and control capabilities to support the operation of the entire base station. The processor 301 may be a central processing unit (Central Processing Unit, CPU), the processor 301 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 302 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. 4 is merely a block diagram of a portion of the structure related to the embodiment of the present invention, and does not constitute a limitation of the terminal device to which the embodiment of the present invention is applied, and that a specific server may include more or less components than those shown in the drawings, or may combine some components, or have a different arrangement of components.
The processor 201 or the processor 301 is configured to execute a computer program stored in a memory, and implement any one of the base station control methods provided by the embodiments of the present invention when the computer program is executed.
In an embodiment, the processor is configured to run a computer program stored in a memory and to implement the following steps when executing the computer program:
acquiring load information and user index parameters of each cell in a target cell group, wherein the target cell group comprises a plurality of frequency layers; predicting the expected overall load of the target cell group according to the load information of each cell; performing iterative optimization processing on the comprehensive energy efficiency of each frequency layer according to the expected overall load and the user index parameters of each cell to obtain the target expected load of each frequency layer; and generating a load energy saving strategy of the target cell group according to the target expected load of each frequency layer, so that the base station executes the load energy saving strategy on the target cell group.
In an embodiment, when implementing the predicting the expected total load of the target cell group according to the load information of each cell, the processor is configured to implement: and predicting the load of the target cell group in a target time period according to the load information of each cell based on a preset load prediction model to obtain the expected overall load of the target cell group.
In an embodiment, when implementing the iterative optimization process for the energy efficiency of each frequency layer according to the expected overall load and the user index parameters of each cell, the processor is configured to implement: based on a preset optimizing algorithm, carrying out iterative allocation on the expected load of each frequency layer according to the expected overall load and the user index parameter to obtain a plurality of load allocation combinations; and respectively calculating the energy efficiency of each load distribution combination to obtain comprehensive energy efficiency corresponding to each load distribution combination, and taking the load distribution combination with the highest comprehensive energy efficiency as a target load distribution combination, wherein the target load distribution combination comprises target expected loads of each frequency layer.
In an embodiment, when implementing the optimizing algorithm based on the preset, the processor is configured to perform iterative allocation on the expected load of each frequency layer according to the expected overall load and the user index parameter to obtain a plurality of load allocation combinations, where the method is used to implement: carrying out expected load distribution on each frequency layer according to the expected overall load to obtain the expected load of each frequency layer; based on a preset energy efficiency parameter prediction model, obtaining energy efficiency influence parameters of each frequency layer according to the user index parameters and the expected load of each frequency layer; and generating a load distribution combination according to the expected load of each frequency layer and the expected load influence factor of each frequency layer.
In an embodiment, after implementing the obtained comprehensive energy efficiency corresponding to each load distribution combination and taking the load distribution combination with the highest comprehensive energy efficiency as the target load distribution combination, the processor is configured to implement: determining whether the target load distribution combination meets a preset load constraint condition; and if the target load distribution combination does not meet the preset load constraint condition, removing the target load distribution combination and redefining the target load distribution combination.
In an embodiment, when implementing the load power saving strategy for generating the target cell group according to the target expected load of each frequency layer, the processor is configured to implement: acquiring configuration data of a base station; and determining the load energy-saving strategy of each frequency layer in a target time period according to the configuration data and the target expected load of each frequency layer based on a preset energy-saving strategy generation model.
In an embodiment, when implementing the acquisition target cell group, the processor is configured to: obtaining the geographic positions of all cells in a target area and the geographic positions of base stations corresponding to the cells; determining the signal coverage range of each cell according to the geographic positions of all cells and the geographic positions of the base stations corresponding to each cell; and determining a target cell group according to the signal coverage range of each cell.
In an embodiment, when implementing the determining a target cell group according to the signal coverage of each cell, the processor is configured to implement: determining the inter-zone overlapping coverage of each cell according to the signal coverage of each cell; and screening out cells with the interval overlapping coverage exceeding a preset interval overlapping coverage to form the target cell group.
In an embodiment, when implementing the acquisition target cell group, the processor is configured to: acquiring a measurement report in a target area, and sequentially screening the measurement report based on each cell to obtain a target measurement report corresponding to each cell; determining the interval overlapping coverage of each cell according to the target measurement report corresponding to each cell; and screening out cells with the interval overlapping coverage exceeding a preset interval overlapping coverage to form the target cell group.
In an embodiment, when the processor performs the sequentially screening on the measurement reports based on each cell to obtain a target measurement report corresponding to each cell, the processor is configured to perform: analyzing the measurement report aiming at one cell to obtain the signal quality of the cell; and if the signal quality of the cell meets the preset signal quality requirement, taking the measurement report as a target measurement report.
It should be noted that, for convenience and brevity of description, specific working procedures of the above-described terminal device may refer to corresponding procedures in the foregoing base station control method embodiment, and are not described herein again.
The embodiment of the invention also provides a storage medium for computer readable storage, where one or more programs are stored, and the one or more programs can be executed by one or more processors, so as to implement the steps of any base station control method provided in the embodiment specification of the invention.
The storage medium may be an internal storage unit of the terminal device according to the foregoing embodiment, for example, a hard disk or a memory of the terminal device. The storage medium may also be an external storage device of the terminal device, 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 terminal device.
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 embodiment, 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 may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (13)
1. A base station control method, characterized in that the control method comprises:
acquiring load information and user index parameters of each cell in a target cell group, wherein the target cell group comprises a plurality of frequency layers;
predicting the expected overall load of the target cell group according to the load information of each cell;
performing iterative optimization processing on the comprehensive energy efficiency of each frequency layer according to the expected overall load and the user index parameters of each cell to obtain the target expected load of each frequency layer;
and generating a load energy saving strategy of the target cell group according to the target expected load of each frequency layer, so that the base station executes the load energy saving strategy on the target cell group.
2. The method of claim 1, wherein predicting an expected overall load of the target cell group based on the load information of each cell comprises:
and predicting the load of the target cell group in a target time period according to the load information of each cell based on a preset load prediction model to obtain the expected overall load of the target cell group.
3. The method according to claim 1, wherein said iteratively optimizing the integrated energy efficiency of each of said frequency layers based on said expected overall load and said user indicator parameters of each cell to obtain a target expected load for each of said frequency layers comprises:
Based on a preset optimizing algorithm, carrying out iterative allocation on the expected load of each frequency layer according to the expected overall load and the user index parameter to obtain a plurality of load allocation combinations;
and respectively calculating the energy efficiency of each load distribution combination to obtain comprehensive energy efficiency corresponding to each load distribution combination, and taking the load distribution combination with the highest comprehensive energy efficiency as a target load distribution combination, wherein the target load distribution combination comprises target expected loads of each frequency layer.
4. A method according to claim 3, wherein the iteratively distributing the expected load of each frequency layer according to the expected overall load and the user index parameter based on the preset optimizing algorithm to obtain a plurality of load distribution combinations, includes:
carrying out expected load distribution on each frequency layer according to the expected overall load to obtain the expected load of each frequency layer;
based on a preset energy efficiency parameter prediction model, obtaining energy efficiency influence parameters of each frequency layer according to the user index parameters and the expected load of each frequency layer;
and generating a load distribution combination according to the expected load of each frequency layer and the expected load influence factor of each frequency layer.
5. The method according to claim 3, further comprising, after said obtaining the integrated energy efficiency corresponding to each of said load distribution combinations and taking the load distribution combination with the highest integrated energy efficiency as the target load distribution combination:
determining whether the target load distribution combination meets a preset load constraint condition;
and if the target load distribution combination does not meet the preset load constraint condition, removing the target load distribution combination and redefining the target load distribution combination.
6. The method of claim 1, wherein the generating a load power saving strategy for the target cell group based on the target expected load for each of the frequency layers comprises:
acquiring configuration data of a base station;
and determining the load energy-saving strategy of each frequency layer in a target time period according to the configuration data and the target expected load of each frequency layer based on a preset energy-saving strategy generation model.
7. The method of claim 1, wherein the target cell group is obtained by:
obtaining the geographic positions of all cells in a target area and the geographic positions of base stations corresponding to the cells;
Determining the signal coverage range of each cell according to the geographic positions of all cells and the geographic positions of the base stations corresponding to each cell;
and determining a target cell group according to the signal coverage range of each cell.
8. The method of claim 7, wherein said determining a target cell group based on signal coverage of each of said cells comprises:
determining the inter-zone overlapping coverage of each cell according to the signal coverage of each cell;
and screening out cells with the interval overlapping coverage exceeding a preset interval overlapping coverage to form the target cell group.
9. The method of claim 1, wherein the target cell group is obtained by:
acquiring a measurement report in a target area, and sequentially screening the measurement report based on each cell to obtain a target measurement report corresponding to each cell;
determining the interval overlapping coverage of each cell according to the target measurement report corresponding to each cell;
and screening out cells with the interval overlapping coverage exceeding a preset interval overlapping coverage to form the target cell group.
10. The method of claim 9, wherein the sequentially screening the measurement reports based on each cell to obtain a target measurement report corresponding to each cell comprises:
analyzing the measurement report aiming at one cell to obtain the signal quality of the cell;
and if the signal quality of the cell meets the preset signal quality requirement, taking the measurement report as a target measurement report.
11. A network device, the network device comprising:
a processor, a memory, a computer program stored on the memory and executable by the processor, and a data bus for enabling a connected communication between the processor and the memory, wherein the computer program, when executed by the processor, implements the steps of the base station control method according to any of claims 1 to 10.
12. 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 connected communication between the processor and the memory, wherein the computer program, when executed by the processor, implements the steps of the base station control method according to any of claims 1 to 10.
13. 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 base station control method of any of claims 1 to 10.
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| CN105472660B (en) * | 2014-09-11 | 2019-10-15 | 中国移动通信集团公司 | A kind of load-balancing method, the network equipment and system |
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