Base station control method, network device, base station and storage medium

Abstract

Embodiments of the present application provide a base station control method, network equipment, a base station and a storage medium, and belong to the technical field of communication. The method comprises: acquiring load information and user index parameters of each cell in a target cell group, the target cell group comprising a plurality of frequency layers; predicting an 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 a 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 in the target cell group. The technical scheme of the embodiments of the present application aims to maximize the overall energy-saving effect of a multi-layer network.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of communication technology, in particular to a base station control method, network equipment, a base station and a storage medium. BACKGROUND

[0002] With the large-scale deployment of the 5G New Radio (5G NR) of the fifth generation wireless communication network, although the large bandwidth, ultra-low latency and massive connection characteristics of 5G improve the user's network experience, at the same time, the power consumption of the 5G base station is also greatly improved. Since the frequency band (i.e. frequency layer) used by 5G is higher, the number of base stations covering the same area is also larger. This will cause the energy consumption of the 5G base station to surge, thereby causing the operating cost to rise.

[0003] With the evolution of network deployment, operators consider saving construction investment and improving efficiency, and multi-mode network co-networking becomes a typical application scenario, such as 2G / 3G / 4G / 5G networks coexisting to provide services. In this multi-layer network (i.e. multiple frequency layers) scenario, the existing energy saving strategy is generally to independently save energy for each frequency layer cell. This can easily cause the base station to only save energy for the cells of one frequency layer, thereby affecting the energy saving effect of the base station on the cells of other frequency layers. Or even if there is a collaborative energy saving strategy between multiple network layers, the existing collaborative energy saving strategy does not consider the overall energy efficiency, so it cannot maximize the overall energy saving effect of the multi-layer network. SUMMARY

[0004] The main purpose of the embodiments of the present application is to provide a base station control method, a terminal device and a storage medium, which aims to maximize the overall energy saving effect of the multi-layer network.

[0005] In a first aspect, the embodiments of the present application also provide a base station control method, which comprises:

[0006] obtaining load information and user index parameters of each cell in a target cell group, the target cell group comprising multiple frequency layers;

[0007] predicting an expected overall load of the target cell group according to the load information of each cell;

[0008] 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 a target expected load of each frequency layer;

[0009] generating a load energy saving strategy for 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.

[0010] In a second aspect, an embodiment of the present application further provides a network device, comprising a processor, a memory, a computer program stored in the memory and executable by the processor, and a data bus for realizing connection communication between the processor and the memory, wherein the computer program, when executed by the processor, realizes the steps of the base station control method according to any one of the embodiments provided in the specification of the present application.

[0011] In a third aspect, an embodiment of the present application further provides a base station, comprising a processor, a memory, a computer program stored in the memory and executable by the processor, and a data bus for realizing connection communication between the processor and the memory, wherein the computer program, when executed by the processor, realizes the steps of the base station control method according to any one of the embodiments provided in the specification of the present application.

[0012] In a fourth aspect, an embodiment of the present application further provides a storage medium for computer readable storage, wherein the storage medium stores one or more programs, and the one or more programs are executable by one or more processors to realize the steps of the base station control method according to any one of the embodiments provided in the specification of the present application.

[0013] The embodiments of the present application provide a base station control method, a terminal device and a storage medium. The embodiments of the present application obtain the load information and user index parameters of each cell in a target cell group, iteratively optimize the comprehensive energy efficiency of each frequency layer according to the load information and user index parameters of each cell, obtain the target expected load of each frequency layer, and generate the load energy-saving strategy of the target cell group. Thus, the energy consumption and user experience can be considered comprehensively from the overall energy efficiency of the multi-layer network, the base station energy consumption is maximized to reduce, and the energy-saving application efficiency is improved. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort.

[0015] Figure 1 A flowchart of a base station control method provided by an embodiment of the present application is shown.

[0016] Figure 2 A distribution diagram of a target cell group provided by an embodiment of the present application is shown.

[0017] Figure 3 A structural schematic block diagram of a network device provided by an embodiment of the present application is shown.

[0018] Figure 4 A structural schematic diagram of a base station is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0020] The flowcharts shown in the drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor are they necessarily executed in the described order. For example, some operations / steps can be further decomposed, combined or partially merged, so that the actual execution order can be changed according to the actual situation.

[0021] It should be understood that the terms used in the present application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0022] In the current operation of the operator 4G network, electricity cost is the largest network maintenance cost of major operators. With the large-scale deployment of 5G, although the characteristics of large bandwidth, ultra-low latency and massive connection of 5G improve the user's network experience, at the same time, the power consumption of 5G base station is also greatly improved. According to the current test results, the power consumption of 5G base station will be 2 to 3 times that of 4G base station. At the same time, since the frequency band used by 5G is higher, the number of base stations covering the same area also needs to be several times more than 4G. Thus, it is preliminarily estimated that the energy consumption of 5G will be more than 10 times that of 4G, and the electricity cost is expected to account for more than 40% of the operation cost of 5G base station.

[0023] In the current wireless network, the energy consumption of the wireless base station accounts for a large proportion, and the indoor baseband processing unit (Building Base band Unit, BBU) and the remote radio unit (Remote Radio Unit, RRU) in the base station account for about 50% of the energy consumption, of which the RRU accounts for about 80% of the energy consumption of the main device, and the power amplifier accounts for about 80% of the energy consumption in the RRU. Therefore, how to reduce the energy consumption of the power amplifier module has become the main direction of base station energy saving.

[0024] With the evolution of network deployment, operators consider saving construction investment and improving efficiency, and multi-mode network co-networking becomes a typical application scenario, such as 2G / 3G / 4G / 5G networks coexisting to provide services. In this scenario, in the current energy saving strategy, the cells of each frequency layer independently perform energy saving, at this time the base station only performs energy saving on the cells of one frequency layer, thereby affecting the energy saving effect of the base station on the cells of other frequency layers. Since the cells of other frequency layers cannot save energy, the energy saving effect is not good. Or even if there is a certain coordination strategy between multiple layers of networks, it does not consider the overall energy efficiency, and cannot maximize the overall energy saving effect of the multi-layer network.

[0025] Embodiments of the present application provide 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. The centralized processing unit can be a network equipment such as a server or a data platform such as a network management, mobile edge computing (MEC) and the like. The distributed processing unit can be a base station and the like. Thus, the overall energy efficiency of the multi-layer network can be considered, the influence of energy consumption and user experience can be considered comprehensively, the energy consumption of the base station can be maximized, and the purpose of energy saving can be achieved to improve the application efficiency of energy saving.

[0026] Please refer to Figure 1 , Figure 1 A flowchart of a base station control method provided by embodiments of the present application is shown. The base station control method is used to generate a load energy saving strategy of a target cell group, so that the base station executes the corresponding load energy saving strategy in the target cell group.

[0027] As shown in Figure 1 , the base station control method includes steps S101 to S104.

[0028] Step S101, obtaining load information and user index parameters of each cell in a target cell group, the target cell group including multiple frequency layers.

[0029] The target cell group includes cells corresponding to different frequency layers, such as cells corresponding to 2.6G NR, cells corresponding to 2.6G LTE, cells corresponding to 1.9G LTE, and cells corresponding to 900M LTE. The target cell group can be obtained by filtering the geographical position of the cells, so that user migration can be performed in the target cell group when the base station implements the energy saving strategy, and the user is more likely to meet the energy saving strategy to be implemented. The load information includes, but is not limited to, the number of users in each cell, the utilization rate of uplink and downlink physical resource blocks (PRB), uplink and downlink throughput, uplink and downlink user rate, etc. The user index parameter can be a key performance indicator (KPI) and a key quality indicator (KQI). The KPI and KQI can include user rate, latency, jitter, voice MOS value, and other user perception information. The user perception information can reflect the user's network experience to some extent.

[0030] In some embodiments, the target cell group can be obtained by obtaining the geographical position of all cells in the target area and the geographical position of the base station corresponding to each cell; determining the signal coverage range of each cell according to the geographical position of all cells and the geographical position of the base station corresponding to each cell; and determining the target cell group according to the signal coverage range of each cell. Thus, the target cell group can be determined by the geographical position of the cell and the geographical position of the base station corresponding to each cell, so as to accurately construct the target cell group, which facilitates the base station to implement the corresponding energy saving strategy in the target cell group.

[0031] The target area is a preset geographical area, such as a geographical area covered by a base station or a geographical area defined by a user, which is not limited herein. The signal coverage range of the cell indicates the geographical position range covered by the signal of the cell.

[0032] Specifically, the longitude and latitude of the base station and the direction angle of the cell can be obtained in advance, so as to calculate the longitude and latitude of the cell. 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 also be directly obtained. The distance between the base station and the cell is related to the signal coverage range of the cell. The signal coverage range of the cell can be obtained by the position relationship between the cell and the neighboring cell in network planning, such as the average distance of multiple cells closest to the cell within the direction angle range of the cell.

[0033] For example, if the base station control method is applied to a network device such as a server, the server can obtain the longitude and latitude information of all cells in the target area and the longitude and latitude information of the base stations corresponding to the cells, so as to determine the signal coverage range of each cell; and finally determine the target cell group according to the signal coverage range of each cell.

[0034] For example, if the base station control method is applied to a base station, the base station can generally only obtain the longitude and latitude information of all cells included in the base station, and the coverage range of the base station is the target area; at the same time, the longitude and latitude information of the base station is obtained, so as to determine the signal coverage range of each cell; and finally determine the target cell group according to the signal coverage range of each cell.

[0035] In some embodiments, the interval overlap coverage of each cell is determined according to the signal coverage range of each cell; and the cells with an interval overlap coverage exceeding a preset interval overlap coverage are screened out to form the target cell group. In this way, the target cell group can be accurately screened out, and the user can be conveniently migrated from one frequency layer to another frequency layer when the base station executes the energy saving strategy.

[0036] The interval overlap coverage is used to indicate the overlapping degree of the signal coverage range of each cell and the adjacent cell; and the preset interval overlap coverage is a preset interval overlap coverage, for example, 80%, which is set by the user.

[0037] Specifically, the interval overlap coverage of each cell is determined according to the signal coverage range of each cell and the signal coverage range of the adjacent cell corresponding to each cell; and the target cell group is formed by screening out a plurality of cells with an interval overlap coverage exceeding a preset interval overlap coverage.

[0038] For example, based on the above steps, the signal coverage range of each cell is calculated, and the interval overlap coverage of cells i and j can be calculated according to the following formula:

[0039]

[0040] cov i,j is the interval overlap coverage of cells i and j, S i is the signal coverage range of cell i, and S jis the signal coverage range of cell j; the numerator is the intersection area of the signal coverage ranges of cell i and cell j, and the denominator is the area of the signal coverage range of cell i, so that the interval overlap coverage of cell i and cell j can be calculated, and in the same way, the interval overlap coverage of cell i and cell y, the interval overlap coverage of cell y and cell z, etc. can be calculated, and finally, the multiple cells whose interval overlap coverage exceeds the preset interval overlap coverage are screened out to form the target cell group.

[0041] It should be noted that the interval overlap coverage of any two cells exceeding the preset interval overlap coverage can be screened out to form the target cell group.

[0042] For example, if the preset interval overlap coverage is 80%, the interval overlap coverage of cell i and cell j is 90%, the interval overlap coverage of cell i and cell y is 85%, and the interval overlap coverage of cell y and cell j is 70%, the target cell group is cell i, cell j and cell y.

[0043] In some embodiments, the target cell group can also be obtained by the following method: 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 overlap coverage of each cell according to the target measurement report corresponding to each cell; and screening out the cells whose interval overlap coverage exceeds the preset interval overlap coverage to form the target cell group. Thus, the target cell group can be accurately screened out through the measurement report.

[0044] The measurement report (MR) can be used for network evaluation and optimization.

[0045] Specifically, for one of the cells, the measurement report is analyzed to obtain the signal quality of the cell; if the signal quality of the cell meets the preset signal quality requirement, the measurement report is taken as a target measurement report.

[0046] The target measurement report is a measurement report in the cell that meets the preset signal quality requirement, and the MR can be used for network quality analysis, can complete the quality analysis of the uplink and downlink wireless network, and can reflect the real situation of the local network call quality, so that the signal quality of each cell can be determined through the MR. The preset signal quality requirement can be composed of factors such as signal strength, which is used to calculate the interval overlap coverage, and can be set by the user.

[0047] For example, a signal quality of the cell a, the cell b and the cell c can be obtained respectively by analyzing the measurement report A. Generally, the signal quality of the cell a, the cell b and the cell c is different. That is, for the measurement report A, if the signal quality of the cell a meets the preset signal quality requirement, the measurement report A is a target measurement report for the cell a; 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; and if the signal quality of the cell c does not meet the preset signal quality requirement, the measurement report A is not a target measurement report for the cell c.

[0048] Specifically, after obtaining the target measurement report corresponding to each cell, the number of target measurement reports overlapping between cells can be determined, and finally the inter-cell overlapping coverage of each cell can be determined according to the number of target measurement reports overlapping between cells and the number of target measurement reports corresponding to each cell.

[0049] The formula for calculating the inter-cell overlapping coverage of a cell is as follows:

[0050]

[0051] The numerator is the number of reports that are target measurement reports in both the cell i and the cell j in the MR measurement, and the denominator is the number of reports that are target measurement reports in the cell i in the MR measurement. Thus, the inter-cell overlapping coverage of the cell i and the cell j can be calculated, and the inter-cell overlapping coverage of the cell i and the cell y, the inter-cell overlapping coverage of the cell y and the cell z, etc. can be calculated in the same way. Finally, the multiple cells whose inter-cell overlapping coverage exceeds the preset inter-cell overlapping coverage are screened out to form the target cell group.

[0052] It should be noted that if the inter-cell overlapping coverage of any two cells exceeds the preset inter-cell overlapping coverage, the two cells can be screened out to form the target cell group.

[0053] For example, if the preset inter-cell overlapping coverage is 80%, the inter-cell overlapping coverage of the cell i and the cell j is 90%, the inter-cell overlapping coverage of the cell i and the cell y is 85%, and the inter-cell overlapping coverage of the cell y and the cell j is 70%, the target cell group is the cell i, the cell j and the cell y.

[0054] In step S102, the expected overall load of the target cell group is predicted according to the load information of each cell.

[0055] The expected overall load is the overall load of the target cell group in a future time period, which can be predicted by a load prediction model.

[0056] In some embodiments, based on the preset load prediction model, the load of the target cell group in a target time period is predicted according to the load information of each cell, and the expected overall load of the target cell group is obtained. Thus, the overall load of the target cell group in the target time period can be accurately predicted by the pre-constructed load prediction model, and the energy efficiency of each frequency layer can be iteratively optimized subsequently.

[0057] The load prediction model can be constructed by using a time series-based prediction modeling method such as a long short-term memory (LSTM) network, an autoregressive integrated moving average (ARIMA) model, etc. Specifically, the load prediction model can be constructed by using the above modeling method according to the load of the cell group, so as to obtain the user load of the target cell group in a future time period. The target time period is a preset future time period, which can include multiple time granularities, such as 1-minute granularity, 15-minute granularity, etc. The time granularity is related to the effective time granularity required by the energy-saving strategy finally generated.

[0058] Specifically, the load information of each cell in the target cell group can be input into the pre-trained load prediction model, so as to predict the load of the target cell group in the target time period, and finally the expected overall load of the target cell group is output by the load prediction model.

[0059] For example, if the base station control method is applied to a base station, the construction of a complex prediction model will consume a large amount of resources due to the limited computing capacity of the base station. Therefore, a historical load statistical method can be used to construct the future load model. Specifically, the average value of multiple data in the same period is considered as the load in the same future time period. The same period can be a period with high correlation, and the time of the period with high correlation is taken as the same period. Here, the correlation can be calculated in terms of day granularity, week granularity, month granularity, etc.

[0060] It should be noted that the expected overall load of the target cell group can be calculated and predicted by using a summation method for the number of users and throughput, and an average method for the PRB utilization rate.

[0061] In step S103, the comprehensive energy efficiency of each frequency layer is iteratively optimized according to the expected overall load and the user index parameter of each cell, and the target expected load of each frequency layer is obtained.

[0062] In the traditional definition of energy efficiency, the energy efficiency is defined as the effective output provided by unit energy consumption, which is usually represented by the data flow energy consumption ratio, i.e., the energy efficiency of the network is evaluated by the ratio of flow to energy consumption:

[0063]

[0064] In the traditional definition of energy efficiency, the energy efficiency is defined as the effective output provided by unit energy consumption, which is usually represented by the data flow energy consumption ratio, i.e., the energy efficiency of the network is evaluated by the ratio of flow to energy consumption:

[0065] In the traditional definition of energy efficiency, the energy efficiency is defined as the effective output provided by unit energy consumption, which is usually represented by the data flow energy consumption ratio, i.e., the energy efficiency of the network is evaluated by the ratio of flow to energy consumption:

[0066]

[0067] In the traditional definition of energy efficiency, the energy efficiency is defined as the effective output provided by unit energy consumption, which is usually represented by the data flow energy consumption ratio, i.e., the energy efficiency of the network is evaluated by the ratio of flow to energy consumption:

[0068] In some embodiments, based on a preset optimization algorithm, the expected load of each frequency layer is iteratively allocated according to the expected overall load and the user index parameter, to obtain a plurality of load allocation combinations; the energy efficiency of each load allocation combination is calculated respectively to obtain the comprehensive energy efficiency corresponding to each load allocation combination, and the load allocation combination with the highest comprehensive energy efficiency is taken as the target load allocation combination, which includes the target expected load of each frequency layer. Thus, through iterative optimization processing, the load allocation combination with the highest comprehensive energy efficiency, i.e., the target expected load of each frequency layer, can be accurately obtained.

[0069] The optimization algorithm is a search process or rule based on certain ideas and mechanisms to obtain a solution to the user's problem through certain ways or rules. The optimization algorithm can be hill-climbing, simulated annealing algorithm, genetic algorithm, etc. The load distribution combination is the expected load of each frequency layer, and the expected load of each frequency layer in each load distribution combination is generally different. The energy efficiency is EE1, 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.

[0070] For example, the expected overall load and user perception information such as user rate, delay, jitter, voice MOS value, etc. can be obtained by hill-climbing algorithm, and the expected load of each frequency layer is iteratively distributed according to the expected overall load and user perception information such as user rate, delay, jitter, voice MOS value, etc. to obtain a plurality of load distribution combinations, wherein the iterative distribution is the process of the optimization algorithm, and is used to obtain the optimal result of each iteration distribution, i.e. the target load distribution combination; finally, the energy efficiency of each frequency layer in each load distribution combination obtained by iterative distribution is calculated, thereby 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 the target load distribution combination.

[0071] In some embodiments, the expected load of each frequency layer is distributed according to the expected overall load to obtain the expected load of each frequency layer; based on a preset energy efficiency parameter prediction model, the energy efficiency influence parameter of each frequency layer is obtained according to the user index parameter and the expected load of each frequency layer; and a load distribution combination is generated according to the expected load of each frequency layer and the expected load influence factor of each frequency layer. Thus, the overall energy efficiency of the multi-layer network can be considered, the influence of energy consumption and user experience is considered comprehensively, the energy efficiency influence parameter is added, the influence of the user index parameter on the final energy efficiency is increased, and a load distribution combination more consistent with the user experience is generated.

[0072] The load and user experience rate of each cell and other user index parameters can be fitted by a certain method to construct the energy efficiency parameter prediction model. Specifically, the energy efficiency parameter prediction model can be constructed by linear, support vector machine (SVM) and other methods. The energy efficiency influence parameter is the output of the energy efficiency parameter prediction model, which is used as an input parameter when the optimization algorithm is used to determine the target load distribution combination, thereby affecting the determination of the target load distribution combination.

[0073] Specifically, to further improve the accuracy of fitting, a segmented regression method can be used for modeling, i.e., different modeling methods are used for different data characteristics. The segmentation principle can be based on the high and low of user experience rate. For example, the user index parameter can also identify specific application layer services, and different business types such as streaming media and games are distinguished for modeling, so as to finally obtain the energy efficiency impact parameter.

[0074] In some embodiments, configuration data of the base station is obtained, the energy efficiency of each load distribution combination is calculated according to the configuration data, the corresponding comprehensive energy efficiency of each load distribution combination is obtained, and the load distribution combination with the highest comprehensive energy efficiency is taken as the target load distribution combination. For different station types and RRU models, due to the differences in components and supported functions, the power consumed under the same load is different, so the target load distribution combination can be accurately determined according to the configuration data of different base stations.

[0075] The configuration data of the base station can include RRU model, station type of the base station, etc., and the calculation of the comprehensive energy efficiency also needs to consider the configuration data of the base station.

[0076] Specifically, by obtaining the configuration data of the base station, for different station types and RRU models, due to the differences in components and supported functions, the power consumed under the same load is different, so 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 optimization iteration 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 as to accurately obtain the comprehensive energy efficiency corresponding to each load distribution combination, and the load distribution combination with the highest comprehensive energy efficiency is taken as the target load distribution combination.

[0077] In some embodiments, after obtaining the 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, it is determined whether the target load distribution combination meets the preset load constraint condition; if the target load distribution combination does not meet the preset load constraint condition, the target load distribution combination is removed and the target load distribution combination is re-determined. In this way, it can be determined whether the target load distribution combination is the optimal comprehensive energy consumption of the target cell group by determining whether the target load distribution combination meets the preset load constraint condition.

[0078] The preset load constraint condition can be a basic operation condition that needs to be met by load distribution, such as a maximum load that can be distributed to each frequency layer, or a specified reserved frequency layer, the reserved frequency layer including two meanings: 1) a frequency layer that cannot be closed or performs energy saving in planning; and 2) a frequency layer that avoids large differences in closing between adjacent stations, which can cause more cross-frequency switching. The supported frequency band capability constraint range of the terminal device can also be used, and the specific setting can be made according to the actual situation, which is not limited here.

[0079] Specifically, it is determined whether the target load distribution combination meets the preset load constraint condition; if the target load distribution combination meets the preset load constraint condition, a load energy saving strategy of the target cell group is generated according to the target expected load of each frequency layer corresponding to the target load distribution combination; if the target load distribution combination does not meet the preset load constraint condition, the target load distribution combination is removed, and a target load distribution combination is re-determined.

[0080] In step S104, a load energy saving strategy of the target cell group is generated 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.

[0081] The load energy saving strategy includes user migration, carrier shutdown, symbol shutdown and other energy saving strategies, so that the corresponding base station executes the corresponding load energy saving strategy for each frequency layer in the target cell group.

[0082] For example, if the base station control method of the embodiment of the application is applied in a network device such as a server, the target cell group can be sent to the corresponding base station, so that the corresponding load energy saving strategy is executed for each frequency layer in the target cell group.

[0083] For example, if the base station control method of the embodiment of the application is applied in a base station, since the base station can only obtain the cells covered by the base station, the base station can directly execute the corresponding load energy saving strategy for each frequency layer in the target cell group.

[0084] In some embodiments, configuration data of the base station is obtained; based on a preset energy saving strategy generation model, the load energy saving strategy of each frequency layer in a target time period is determined according to the configuration data and the target expected load of each frequency layer. Thus, the load energy saving strategy that meets the actual operation of the base station can be accurately determined according to the configuration data of the base station and the target expected load.

[0085] The relationship between the configuration data of the base station, the target expected load of each frequency layer, and the general load energy saving strategy can be constructed by training the configuration data of the base station, the target expected load of each frequency layer, and the general load energy saving strategy, so as to obtain the energy saving strategy generation model. The load energy saving strategy generally includes energy saving strategies such as carrier shutdown, channel shutdown, and symbol shutdown.

[0086] Specifically, when multiple cells are included in one RRU, the loads of the multiple cells can be aggregated and modeled with the RRU power consumption, so as to obtain the energy saving strategy generation model.

[0087] For example, if the load of the target cell group in the next one hour is predicted, the load energy saving strategy of each frequency layer of the target cell group in the next one hour will be finally obtained. Meanwhile, the target time period (i.e., one hour) can include multiple time granularities, such as one minute granularity, 15-minute granularity, etc. The time granularity is related to the effective granularity required by the final energy saving strategy.

[0088] Specifically, according to the target expected load of each frequency layer, the load energy saving strategy of the multi-layer network is generated based on the load at different time points, including the load energy saving strategy of the frequency layer to be shut down, the frequency layer for user migration, etc. Finally, the load energy saving strategy is sent to the corresponding base station. After receiving the corresponding load energy saving strategy, the base station executes the user migration strategy at the corresponding time point, so that the user is more likely to meet the energy saving strategy to be executed, thereby further executing the subsequent energy saving shutdown strategy.

[0089] Please refer to Figure 2 The base station control method of the embodiments of the present application will be described below in combination with specific examples. The base station control method can be applied to a server, a network management device, or a base station, etc.

[0090] For example, if the base station control method can be applied to a server, the load energy saving strategy can be generated by the server. The network management device can receive a modification data request through an interface, so as to save and update the generated load energy saving strategy. Finally, the network management device 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 a network management device or a base station, the implementation steps are similar.

[0091] As shown in Figure 2 , a 4-layer network in a region is taken as an example for illustration, including 4 frequency layers: NR 2.6G, LTE 2.6G, LTE 1.9G, and LTE 900M. NR 2.6G and LTE 2.6G share an active antenna unit (AAU).

[0092] Firstly, the target cell group can be constructed by methods based on cell geographic location or MR measurement, and the constructed cells are exemplarily as shown in FIG. 2, including cells corresponding to NR 2.6G, LTE 2.6G, LTE 1.9G and LTE 900M frequency layers. Figure 2

[0093] The load information and user index parameters of each cell in the target cell group are acquired, and the load information of each cell in the target cell group is input into the pre-trained load prediction model, so as to predict the load of the target cell group in the target time period. Finally, the load prediction model outputs the expected overall load of the target cell group.

[0094] Then, based on the optimization algorithm, the energy efficiency of each frequency layer is iteratively optimized according to the expected overall load and the user index parameters of each cell, and the target expected load of each frequency layer is obtained.

[0095] For a certain time granularity in the target time period, the specific solving algorithm of the optimization algorithm is as follows:

[0096]

[0097] layerNum indicates the number of frequency layers or the number of cells in the target cell group. Since there are four frequency layers in the embodiment, the value is 5. k is a weight factor, which is related to the carrier coverage characteristics of each cell. Generally, the lower the frequency band, the greater the coverage, and the greater the weight. k is the expected load allocated to the frequency layer k, EE1 is a comprehensive energy efficiency model, Power is a constructed energy saving strategy generation model, and esDur is the energy saving duration determined after comparing the energy saving strategy threshold. k is the energy saving duration determined after comparing the energy saving strategy threshold. Exp is a constructed energy efficiency parameter prediction model, and x k is the energy efficiency impact parameter corresponding to the frequency layer k. The solving algorithm also includes a load constraint condition, wherein validList indicates the retainable frequency layer, which contains two layers of meaning: 1) the frequency layer that cannot be closed or execute energy saving in planning, such as the 900M frequency layer in this example, which cannot execute closing due to wide coverage range; and 2) the frequency layer that avoids large differences in closing between adjacent stations, which can cause more inter-frequency handovers. Ue capability indicates the capability constraint of the terminal device supporting the frequency band.

[0098] Finally, 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 are obtained by solving the optimization algorithm,

[0099] ​Exemplarily, Table 1 is the optimal comprehensive energy consumption of the target cell group and the target expected load of each frequency layer under the time granularity.

[0100] 2.6G NR 2.6G LTE 1.9G LTE 900M LTE User load 0 load2 load3 load4 Energy saving policy Carrier off Symbol off No energy saving policy No energy saving policy

[0101] According to the expected overall load in the target time period, the optimal comprehensive energy consumption of the target cell group and the target expected load of each frequency layer under different time granularities are calculated respectively. Finally, the corresponding base station is issued with the load energy-saving strategy, and after the base station receives the corresponding load energy-saving strategy, the user migration strategy is executed at the corresponding time point, so that the user is more likely to meet the energy-saving strategy to be executed, thereby further executing the subsequent energy-saving shutdown strategy.

[0102] Exemplarily, Table 2 is the optimal comprehensive energy consumption of the target cell group and the target expected load of each frequency layer in the target time period (the target time period is one day, and the time granularity length is 15 minutes for example).

[0103]

[0104]

[0105] Among them, the frequency layer 2.6G NR and 2.6G LTE need to be closed at the same time due to the same AAU, so that the energy-saving benefit is high; at the lowest load, 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 the energy-saving strategy; at the general load, 2.6G NR and 2.6G LTE absorb most of the business as the capacity layer, and 1.9G LTE can execute certain energy-saving strategy; at the higher load, 1.9G LTE, 2.6G NR and 2.6G LTE jointly absorb the business, none of them executes energy-saving, and 900M LTE does not implement the energy-saving strategy due to wide coverage. At this time, the cell without energy-saving strategy in the target cell group can be used as the target cell for user migration. Finally, the base station actively executes user migration according to the received energy-saving strategy of the target cell group, so that the user is more likely to meet the energy-saving strategy to be executed, thereby further executing the subsequent energy-saving shutdown strategy.

[0106] Please refer to Figure 3 , Figure 3 A structural schematic block diagram of a network device provided by an embodiment of the present application is shown.

[0107] As Figure 3 shown, the network device 200 includes a processor 201 and a memory 202, and the processor 201 and the memory 202 are connected through a bus 203, such as an I2C (Inter-integrated Circuit) bus.

[0108] Specifically, processor 201 provides computing and control capabilities to support the operation of the entire network device. Processor 201 can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0109] Specifically, the memory 202 can be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a portable hard drive, etc.

[0110] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the embodiments of the present invention, and does not constitute a limitation on the terminal device to which the embodiments of the present invention are applied. A specific server may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0111] Please see Figure 4 , Figure 4 This is a schematic block diagram of a base station structure provided in an embodiment of the present invention.

[0112] like Figure 4 As shown, the base station 300 includes a processor 301 and a memory 302, which are connected by a bus 303, such as an I2C (Inter-integrated Circuit) bus.

[0113] Specifically, the processor 301 is configured to provide computing and control capabilities to support the operation of the entire base station. The processor 301 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0114] Specifically, the memory 302 can be a Flash chip, a read-only memory (ROM) disk, an optical disk, a U disk or a mobile hard disk, etc.

[0115] Those skilled in the art can understand that, Figure 4 The structure shown in the figure is only a block diagram of part of the structure related to the embodiment of the present application, and does not constitute a limitation on the terminal device to which the embodiment of the present application is applied. Specifically, the server can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0116] The processor 201 or the processor 301 is configured to run a computer program stored in the memory and implement any one of the base station control methods provided by the embodiments of the present application when the computer program is executed.

[0117] In an embodiment, the processor is configured to run a computer program stored in the memory and implement the following steps when the computer program is executed:

[0118] Obtain load information and user index parameters of each cell in a target cell group, the target cell group including a plurality of frequency layers; predict an expected overall load of the target cell group according to the load information of each cell; perform 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 a target expected load of each frequency layer; and generate a load energy-saving strategy for 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.

[0119] In an embodiment, the processor, when implementing the step of predicting the expected overall load of the target cell group according to the load information of each cell, is configured to: predict 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.

[0120] In an embodiment, the processor, when implementing the step of iteratively optimizing the energy efficiency of each frequency layer according to the expected overall load and the user index parameter of each cell to obtain the target expected load of each frequency layer, is configured to: iteratively allocate the expected load of each frequency layer according to the expected overall load and the user index parameter based on a preset optimization algorithm to obtain a plurality of load allocation combinations; calculate the energy efficiency of each load allocation combination respectively to obtain the comprehensive energy efficiency corresponding to each load allocation combination, and take the load allocation combination with the highest comprehensive energy efficiency as the target load allocation combination, wherein the target load allocation combination comprises the target expected load of each frequency layer.

[0121] In an embodiment, the processor, when implementing the step of iteratively allocating the expected load of each frequency layer according to the expected overall load and the user index parameter based on a preset optimization algorithm to obtain a plurality of load allocation combinations, is configured to: allocate the expected load of each frequency layer according to the expected overall load to obtain the expected load of each frequency layer; obtain the energy efficiency impact parameter of each frequency layer according to the user index parameter and the expected load of each frequency layer based on a preset energy efficiency parameter prediction model; and generate a load allocation combination according to the expected load of each frequency layer and the expected load impact factor of each frequency layer.

[0122] In an embodiment, after the processor implements the steps of obtaining the comprehensive energy efficiency corresponding to each load allocation combination and taking the load allocation combination with the highest comprehensive energy efficiency as the target load allocation combination, the processor is configured to: determine whether the target load allocation combination meets a preset load constraint condition; and if the target load allocation combination does not meet the preset load constraint condition, remove the target load allocation combination and re-determine the target load allocation combination.

[0123] In an embodiment, the processor, when implementing the step of generating the load energy-saving strategy of the target cell group according to the target expected load of each frequency layer, is configured to: obtain configuration data of a base station; and determine 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.

[0124] In an embodiment, the processor, when implementing the acquiring the target cell group, is configured to: acquire geographic positions of all cells in a target area and geographic positions of base stations corresponding to the cells; determine signal coverage ranges of the cells according to the geographic positions of the cells and the geographic positions of the base stations corresponding to the cells; and determine the target cell group according to the signal coverage ranges of the cells.

[0125] In an embodiment, the processor, when implementing the determining the target cell group according to the signal coverage ranges of the cells, is configured to: determine interval overlapping coverage degrees of the cells according to the signal coverage ranges of the cells; and screen out the cells with the interval overlapping coverage degrees exceeding a preset interval overlapping coverage degree to form the target cell group.

[0126] In an embodiment, the processor, when implementing the acquiring the target cell group, is configured to: acquire a measurement report in a target area, and sequentially screen the measurement report based on the cells to obtain target measurement reports corresponding to the cells; determine interval overlapping coverage degrees of the cells according to the target measurement reports corresponding to the cells; and screen out the cells with the interval overlapping coverage degrees exceeding a preset interval overlapping coverage degree to form the target cell group.

[0127] In an embodiment, the processor, when implementing the sequentially screening the measurement report based on the cells to obtain the target measurement reports corresponding to the cells, is configured to: analyze the measurement report to obtain a signal quality of one of the cells; and if the signal quality of the cell meets a preset signal quality requirement, take the measurement report as a target measurement report.

[0128] It should be noted that, for the convenience and brevity of description, the specific working process of the terminal device described above can refer to the corresponding process in the foregoing base station control method embodiments, and will not be described here.

[0129] The embodiment of the present application further provides a storage medium for computer readable storage, the storage medium storing one or more programs, the one or more programs being executable by one or more processors to implement the steps of any base station control method provided in the specification of the embodiment of the present application.

[0130] The storage medium can be an internal storage unit of the terminal device, such as a hard disk or a memory of the terminal device. The storage medium can 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 card, and the like.

[0131] Those of ordinary skill in the art understand that all or some of the steps in the methods disclosed above and the functional modules / units in the systems and devices can be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware embodiments, 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 can have multiple functions, or one function or step can be performed by several physical components in cooperation. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transitory media). As is well known to those of ordinary skill in the art, the term computer storage media includes volatile and non-volatile, 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. Computer storage media include, but are 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 tapes, magnetic disk storage, or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by a computer. Furthermore, it is well known to those of ordinary skill in the art that communication media typically include 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 can include any information delivery medium.

[0132] It should be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items, and that the term "comprises" or "comprising" or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0133] The above-mentioned embodiment numbers of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments. The above description is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A base station control method characterized by comprising: The control method comprises: obtaining load information and user index parameters of each cell in a target cell group, the target cell group comprising a plurality of frequency layers; predicting an 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 target expected loads of each frequency layer; generating a load energy-saving strategy of the target cell group according to the target expected loads of each frequency layer, so that the base station executes the load energy-saving strategy in the target cell group.

2. The method of claim 1, wherein, The expected overall load of the target cell group is predicted according to the load information of each cell, which comprises: based on a preset load prediction model, predicting the load of the target cell group in a target time period according to the load information of each cell to obtain the expected overall load of the target cell group.

3. The method of claim 1, wherein, The 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 loads of each frequency layer comprises: based on a preset optimization algorithm, iteratively distributing the expected loads of each frequency layer according to the expected overall load and the user index parameters to obtain a plurality of load distribution combinations; calculating the energy efficiency of each load distribution combination to obtain the corresponding comprehensive energy efficiency of each load distribution combination, and taking the load distribution combination with the highest comprehensive energy efficiency as the target load distribution combination, the target load distribution combination comprising the target expected loads of each frequency layer.

4. The method of claim 3, wherein, The iterative distribution of the expected loads of each frequency layer according to the expected overall load and the user index parameters based on the preset optimization algorithm to obtain a plurality of load distribution combinations comprises: distributing the expected loads of each frequency layer according to the expected overall load to obtain the expected loads of each frequency layer; based on a preset energy efficiency parameter prediction model, obtaining the energy efficiency influence parameters of each frequency layer according to the user index parameters and the expected loads of each frequency layer; generating a load distribution combination according to the expected loads of each frequency layer and the expected load influence factors of each frequency layer.

5. The method of claim 3, wherein, After obtaining the corresponding comprehensive energy efficiency of each load distribution combination and taking the load distribution combination with the highest comprehensive energy efficiency as the target load distribution combination, it further comprises: determining whether the target load distribution combination meets a preset load constraint condition; if the target load distribution combination does not meet the preset load constraint condition, removing the target load distribution combination and re-determining the target load distribution combination.

6. The method of claim 1, wherein, The generation of the load energy-saving strategy of the target cell group according to the target expected loads of each frequency layer comprises: obtaining configuration data of the base station; based on a preset energy-saving strategy generation model, determining the load energy-saving strategy of each frequency layer in a target time period according to the configuration data and the target expected loads of each frequency layer.

7. The method of claim 1, wherein, The target cell group is obtained by the following method, which comprises: obtaining the geographical positions of all cells in a target area and the geographical positions of the corresponding base stations of each cell; Determine a signal coverage range of each of the cells according to the geographical position of each of the cells and the geographical position of a base station corresponding to each of the cells; Determine a target cell group according to the signal coverage range of each of the cells.

8. The method of claim 7, wherein, The step of determining the target cell group according to the signal coverage range of each of the cells comprises: Determine an interval overlapping coverage degree of each of the cells according to the signal coverage range of each of the cells; Screen out a cell whose interval overlapping coverage degree exceeds a preset interval overlapping coverage degree to form the target cell group.

9. The method of claim 1, wherein, The target cell group is obtained by the following steps, comprising: Obtain a measurement report in a target area, and sequentially screen the measurement report based on each of the cells to obtain a target measurement report corresponding to each of the cells; Determine an interval overlapping coverage degree of each of the cells according to the target measurement report corresponding to each of the cells; Screen out a cell whose interval overlapping coverage degree exceeds a preset interval overlapping coverage degree to form the target cell group.

10. The method of claim 9, wherein, The step of sequentially screening the measurement report based on each of the cells to obtain a target measurement report corresponding to each of the cells comprises: Analyze the measurement report to obtain a signal quality of one of the cells; If the signal quality of the cell meets a preset signal quality requirement, the measurement report is taken as a target measurement report.

11. A network device, comprising: The network device comprises: A processor, a memory, a computer program stored on the memory and executable by the processor, and a data bus for realizing connection communication between the processor and the memory, wherein the computer program is executed by the processor to realize the steps of the base station control method according to any one of claims 1 to 10.

12. A base station, characterized by 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 realizing connection communication between the processor and the memory, wherein the computer program is executed by the processor to realize the steps of the base station control method according to any one of claims 1 to 10.

13. A storage medium for computer-readable storage, characterized in that, The storage medium stores one or more programs executable by one or more processors to realize the steps of the base station control method according to any one of claims 1 to 10.

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