Base station energy saving method, energy saving device and readable storage medium of super dense network

Abstract

The application discloses a base station energy-saving method, an energy-saving device and a readable storage medium for a super-dense network, wherein the base station energy-saving method comprises the following steps: clustering each base station in the super-dense network according to a clustering algorithm to obtain a plurality of clustering clusters; for a clustering cluster in which the number of base stations is greater than a preset number, determining a target base station according to the size of the traffic volume of the base stations in the cluster within a preset time period, and performing an energy-saving operation on the base stations other than the target base station in the cluster; and for a clustering cluster in which the number of base stations is less than or equal to the preset number, determining a plurality of dormant base stations according to the similarity of the traffic volume of the base stations in the cluster within the preset time period, and performing an energy-saving operation on the dormant base stations. Through the above method, the coordinated energy saving of the multiple base stations in the super-dense network can be realized, the interference between the dense base stations is reduced, the energy utilization rate is effectively improved on the premise of ensuring the user perception experience, and the base station operation cost of the telecommunication operator is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of base station energy saving, and in particular to a base station energy saving method for an ultra-dense network, an energy saving device and a readable storage medium. BACKGROUND

[0002] With the development of mobile Internet and the rise of the Internet of Things, the explosive growth of services is still continuing. In order to cope with this trend, the fifth generation mobile communication system (5G) has emerged. The 5G system needs higher network capacity and faster transmission rate, and the ultra-dense network (UDN) is an effective way to achieve this goal. However, the densely deployed base stations also bring many problems. First, the service load in the network is dynamically changing, which makes the base station cannot be effectively utilized in many cases, resulting in serious energy waste; second, in the ultra-dense network, the distance between base stations is very close, which will cause serious interference between base stations, and how to coordinate the working state of each base station in the ultra-dense network is imminent. SUMMARY

[0003] The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.

[0004] The embodiments of the present application provide a base station energy saving method for an ultra-dense network, an energy saving device and a readable storage medium, which can save energy according to the service conditions of each base station in the ultra-dense network, reduce the interference between base stations and improve the utilization rate of communication resources.

[0005] In a first aspect, the embodiments of the present application provide a base station energy saving method for an ultra-dense network, comprising:

[0006] According to the clustering algorithm, each base station in the ultra-dense network is clustered to obtain a plurality of clustering clusters;

[0007] For the clustering cluster in which the number of base stations is greater than the preset number, a target base station is determined according to the size of the traffic volume of the base stations in the cluster within a preset time period, and an energy saving operation is performed on the base stations other than the target base station in the cluster;

[0008] For the clustering cluster in which the number of base stations is less than or equal to the preset number, a plurality of dormant base stations are determined according to the similarity of the traffic volume of the base stations in the cluster within a preset time period, and an energy saving operation is performed on the dormant base stations.

[0009] In a second aspect, an embodiment of the present application provides an energy saving device, comprising at least one processor and a memory connected to the at least one processor in communication; the memory stores instructions capable of being executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the base station energy saving method according to the first aspect.

[0010] In a third aspect, an embodiment of the present application provides a computer readable storage medium, which stores computer executable instructions for causing a computer to perform the base station energy saving method according to the first aspect.

[0011] The base station energy saving method for super dense networks provided by the embodiments of the present application has at least the following beneficial effects: a plurality of base stations in the super dense network are clustered by a clustering algorithm, and the number of base stations in each cluster and the traffic of the base stations are subjected to corresponding energy saving operations; for a cluster with a large number of base stations, a base station is selected to be activated based on the maximum interference principle, and the other base stations are subjected to energy saving operations; and for a cluster with a small number of base stations, a plurality of base stations are selected based on the similarity principle to be subjected to energy saving operations. In this way, the energy saving of multiple base stations in the super dense network is realized, the interference between the dense base stations is reduced, the energy utilization rate is effectively improved under the premise of ensuring the user experience, and the base station operation cost of the telecom operator is reduced.

[0012] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be achieved and obtained by the structure particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0013] The accompanying drawings are included to provide a further understanding of the technical solutions of the present application, and constitute a part of the specification, and are used together with the examples of the present application to explain the technical solutions of the present application, and do not constitute a limitation on the technical solutions of the present application.

[0014] Figure 1 is a structure diagram of a base station transmission link in a super dense network;

[0015] Figure 2 is a whole flowchart of the base station energy saving method provided by an embodiment of the present application;

[0016] Figure 3 is a flowchart of application of the k-means clustering algorithm provided by an embodiment of the present application;

[0017] Figure 4 is a flowchart of selection of a target base station in a large cluster provided by an embodiment of the present application;

[0018] Figure 5 is a flow chart of selecting dormant base stations according to a similarity threshold in a large cluster provided by an embodiment of the present application;

[0019] Figure 6 is a flow chart of determining the number of dormant base stations before selecting the dormant base stations provided by an embodiment of the present application;

[0020] Figure 7 is a flow chart of selecting dormant base stations according to a target number in a large cluster provided by an embodiment of the present application;

[0021] Figure 8 is a flow chart of judging a dormant mode provided by an embodiment of the present application;

[0022] Figure 9 is a structural schematic diagram of an energy-saving device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the objects, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0024] Referring to Figure 1 , the super dense network, also known as super dense networking, takes a macro base station as a "face" and densely deploys low-power small base stations in indoor and outdoor hotspot areas within the coverage of the macro base station. These small base stations are regarded as "nodes" to break the traditional flat and single-layer macro network coverage mode and form a "macro-micro" dense three-dimensional networking scheme to eliminate signal blind spots and improve network coverage environment.

[0025] Since the service load in the super dense network is dynamically changed, there are some base stations with high service load and some base stations with low service load, which makes the utilization rate of some base stations not high. In addition, due to the close distance between the base stations in the super dense network, there is some interference in signal coverage, which also causes waste of resources.

[0026] Based on this, the embodiments of the present application provide a base station energy-saving method, an energy-saving device and a readable storage medium for a super dense network, which is based on the energy-saving scheme combining base station dormancy and cooperation to selectively perform energy-saving operation on part of the base stations in the super dense network, thereby reducing the interference between the base stations and improving the resource utilization efficiency.

[0027] Referring to Figure 2 , the embodiments of the present application provide a base station energy-saving method, which includes but is not limited to the following steps S100 to S300.

[0028] Step S100, clustering each base station in the ultra-dense network according to a clustering algorithm to obtain a plurality of clustering clusters;

[0029] Step S200, for the clustering cluster in which the number of base stations in the cluster is greater than the preset number, determining a target base station according to the size of the traffic volume of the base stations in the cluster within a preset time period, and performing energy-saving operation on the base stations other than the target base station in the cluster;

[0030] Step S300, for the clustering cluster in which the number of base stations in the cluster is less than or equal to the preset number, determining a plurality of dormant base stations according to the similarity of the traffic volume of the base stations in the cluster within a preset time period, and performing energy-saving operation on the dormant base stations.

[0031] Based on the clustering algorithm, the base stations in the ultra-dense network are clustered, the base stations with great influence on each other are classified into the same cluster, and then two categories are divided according to the number of base stations in the cluster, which are large cluster and small cluster. For the base stations in the large cluster, the algorithm based on the maximum interference ratio is used to determine the state of the base station, and one base station is selected as the target base station. Energy-saving operation is performed on the base stations other than the target base station in the cluster. For the base stations in the small cluster, a traffic comparison algorithm is used to determine which base stations need to perform energy-saving operation, so as to realize dormancy and cooperation according to the distribution of base stations and the traffic situation, and improve the utilization rate of base station resources.

[0032] It can be understood that the length of the preset time period can be set according to actual needs, for example, once every 4 hours, once every 2 hours, etc., which is not limited herein. It can be predicted that the shorter the preset time period is, the higher the control accuracy of the base station energy-saving method is, but the corresponding calculation resource consumption is also larger.

[0033] Specifically, the clustering of the base stations in the ultra-dense network can use different clustering algorithms, such as k-means clustering algorithm, mean shift clustering algorithm, density-based clustering algorithm, graph community detection (Graph Community Detection), etc. Taking the k-means clustering algorithm as an example to explain the clustering method in step S100, referring to Figure 3 The implementation steps of the clustering algorithm in the ultra-dense network specifically include:

[0034] Step S110, randomly selecting N base stations in the ultra-dense network as initial center base stations, and calculating the distances between the remaining base stations in the ultra-dense network and each initial center base station;

[0035] Step S120, dividing N clusters according to the size of the distance between the base stations and the initial center base stations, and recalculating the center base stations in the clusters;

[0036] Step S130, iteratively dividing the cluster according to the distance between the center base station in the cluster and each base station other than the center base station to the center base station until the center base station in the cluster after the division no longer changes, obtaining the N clusters after iteration.

[0037] Assuming that there are k base stations in the super dense network, first randomly select N base stations as the initial center, then calculate the distance between each base station other than the n base stations and each initial center, obtain the distance, then select the nearest initial center for any base station, and divide the base station into the initial center with the nearest distance, thereby obtaining the first clustering division result, at this time there are N clusters. After the division of the N clusters, the cluster center of each cluster is recalculated, and the distance between other base stations and the newly calculated cluster center is calculated, and then the cluster is re-divided, and so on until the cluster center no longer changes, at this time the obtained cluster is the above-mentioned cluster.

[0038] The above is only an embodiment of applying the k-means clustering algorithm, and other clustering algorithms can be applied according to the actual situation of the super dense networking, which is not listed one by one.

[0039] It can be understood that the number of base stations in the cluster obtained by dividing the super dense network is different due to the different distribution of base stations, and how to determine whether to use step S200 or step S300 for energy saving operation depends on the size of the preset number. For example, the preset number is artificially set, or it can be determined according to the number of base stations in the super dense network. Specifically, in a possible embodiment, the preset number is determined according to the median of the number of base stations in a plurality of cluster clusters: assuming that there are k base stations in the super dense network, and N cluster clusters are obtained by dividing based on the above clustering algorithm, the number of base stations in each cluster is known, then arrange the number of base stations according to the number, and take the median of the arrangement as the preset number. If N is odd, the number of base stations in the middle cluster after arrangement is the preset number, and if N is even, the average of the number of base stations in the middle two clusters after arrangement is the preset number.

[0040] After determining whether the cluster is a large cluster or a small cluster by the above method, the energy saving operation can be performed according to step S200 or step S300.

[0041] Reference Figure 4 For large clusters, that is, the number of base stations in the cluster is greater than the preset number, a target base station is selected according to the business situation of each base station, which includes the following steps:

[0042] Step S210, obtaining the traffic of each base station in the cluster within a preset time period;

[0043] Step S220, selecting the base station with the largest amount of traffic in the preset time period as the target base station.

[0044] Suppose the set of base stations in the large cluster is y, the total amount of traffic of y can be expressed as y=(y1, y2, y3, …, yn), each element represents the traffic in a preset time period, and the amount of traffic of each base station in the set y in the preset time period can be known. According to the maximum interference ratio principle, the base station with the largest amount of traffic in the set y is selected as the target base station, and the base stations in the cluster other than the target base station are configured to perform energy-saving operation. At this time, the target base station carries the traffic of other base stations.

[0045] For small clusters, i.e., clustering clusters with the number of base stations in the cluster less than or equal to a preset number, in one embodiment, referring to Figure 5 , the base stations that need to sleep are determined according to the similarity of the traffic of each base station, which specifically includes the following steps:

[0046] Step S310, combining the base stations in the cluster two by two, and calculating the similarity of the traffic of the two base stations in the combination;

[0047] Step S320, when the similarity exceeds a preset similarity threshold, selecting one of the base stations in the combination as a sleep base station.

[0048] Suppose any two base stations in the small cluster are represented by a and b, the amount of traffic of base station a in the preset time period can be expressed as a matrix a=(a1, a2, a3, …, an), and the amount of traffic of base station b in the same preset time period can also be expressed as a matrix b=(b1, b2, b3, …, bn). The similarity between the matrices a and b is calculated. If the traffic similarity between base station a and base station b is high (the similarity exceeds a preset similarity threshold), one of a and b needs to be selected to perform sleep operation. If the traffic similarity between base station a and base station b is low (the similarity does not exceed the preset similarity threshold), a and b do not need to perform sleep operation.

[0049] It can be understood that the base stations in the small cluster are combined two by two, which has combinations, m is the total number of base stations in the small cluster, and the similarity of each combination is calculated to determine whether it exceeds the preset similarity threshold. Of course, the base stations in the small cluster can also be combined two by two by arranging the m base stations in a row and only comparing the similarity of the adjacent two base stations.

[0050] For small clusters, i.e., clustering clusters with the number of base stations in the cluster less than or equal to a preset number, in another embodiment, referring to Figure 6 , before determining the base stations that need to sleep according to the similarity of the traffic of each base station, it further includes:

[0051] Step S330, determining the target number of dormant base stations according to the total traffic of the cluster in the preset time period and the traffic of each base station in the cluster in the preset time period.

[0052] The purpose of the above step is to consider whether the remaining base stations in the small cluster can still meet the service demand of the terminal devices in the small cluster after part of the base stations in the small cluster are put into dormancy, which is equivalent to transferring the traffic of the dormant base stations to the normally working base stations and will not cause the normally working base stations to be overloaded; based on this, it can be determined how many base stations in the small cluster need not to perform the energy saving operation.

[0053] Based on the above step S330, the dormant base stations in the small cluster can be selected, referring to Figure 7 , specifically including the following steps:

[0054] Step S340, combining the base stations in the cluster two by two and calculating the similarity of the traffic of the two base stations in the combination;

[0055] Step S350, selecting one of the base stations in the combination as a dormant base station from high to low similarity, until the number of selected dormant base stations is the same as the target number.

[0056] Similarly, the traffic similarity between any two base stations is calculated in the above two-by-two combination manner, and in the embodiment, the relationship between the similarity and the threshold is not judged, but the similarity is arranged from large to small, and one dormant base station is selected from each combination starting from the largest similarity, and when the number of selected dormant base stations is equal to the target number, the selection of dormant base stations is stopped. At this time, all the previously selected dormant base stations perform energy saving operation.

[0057] It can be understood that the above similarity judgment can be realized by various algorithms, such as Pearson correlation algorithm, Euclidean distance algorithm, cosine similarity algorithm, etc. Taking the cosine similarity as an example, for two matrices of base station a and base station b, there is a similarity calculation formula:

[0058]

[0059] The energy saving operation is divided into two kinds, i.e. determining the dormancy mode according to the size of the traffic of the base station to be put into dormancy, and the dormancy mode includes shallow dormancy and deep dormancy.

[0060] The judgment of the size of the traffic of the base station is determined by the average traffic of the cluster, specifically referring to Figure 8 , including the following steps:

[0061] Step S410, determining the cluster in which the base station to be put into dormancy is located, and determining the average traffic of all base stations in the cluster;

[0062] Step S420, in the case that the traffic volume of the base station requiring dormancy is greater than the average traffic volume, controlling the base station requiring dormancy to perform light dormancy;

[0063] Step S430, in the case that the traffic volume of the base station requiring dormancy is less than or equal to the average traffic volume, controlling the base station requiring dormancy to perform deep dormancy.

[0064] The average traffic volume of the base station in the cluster in a preset time period is calculated, when the traffic volume of the base station requiring dormancy in the preset time period is greater than the average traffic volume, the base station requiring dormancy is controlled to perform light dormancy, and when the traffic volume of the base station requiring dormancy in the preset time period is less than or equal to the average traffic volume, the base station requiring dormancy is controlled to perform deep dormancy. The light dormancy includes carrier shutdown, symbol shutdown, time slot shutdown, etc., and the deep dormancy controls the whole base station to perform sleep mode.

[0065] It can be understood that the comparison of the traffic volumes is also compared in the form of a matrix.

[0066] The base station energy saving scheme based on dormancy and cooperation in the super dense network can be realized through the above steps, the resource utilization efficiency is effectively improved under the premise of ensuring the user perception experience through the accurate energy saving strategy, the OPEX cost of the telecommunication operator is reduced, and the market competitiveness of the wireless base station product is effectively enhanced.

[0067] The energy saving device provided by the embodiment of the present application comprises at least one processor and a memory connected in communication with the at least one processor; the memory stores instructions capable of being executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the base station energy saving method.

[0068] Reference Figure 9 For example, the control processor 1001 and the memory 1002 in the energy saving device 1000 can be connected through a bus. The memory 1002 is a kind of non-transient computer readable storage medium, and can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory 1002 can include a high-speed random access memory, and can also include a non-transient memory, such as at least one disk memory, a flash memory device, or other non-transient solid state memory device. In some embodiments, the memory 1002 can optionally include a memory remotely arranged relative to the control processor 1001, and these remote memories can be connected to the energy saving device 1000 through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0069] Those skilled in the art can understand that, Figure 9The device structure shown in the figure does not constitute a limitation on the energy-saving device 1000, and can include more or fewer components than shown, or combine certain components, or different component arrangements.

[0070] The embodiment of the present application also provides a computer readable storage medium, which stores computer executable instructions, wherein the computer executable instructions are executed by one or more control processors, for example, are executed by a control processor 1001 in the computer readable storage medium, so that the one or more control processors execute the base station energy-saving method in the method embodiment. Figure 9 Figure 2 The method steps S100 to S300 in the method embodiment, Figure 3 The method steps S110 to S130 in the method embodiment, Figure 4 The method steps S210 to S220 in the method embodiment, Figure 5 The method steps S310 to S320 in the method embodiment, Figure 6 The method step S330 in the method embodiment, Figure 7 The method steps S340 to S350 in the method embodiment, and Figure 8 The method steps S410 to S430 in the method embodiment.

[0071] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, that is, can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0072] ​As will be appreciated by one of ordinary skill in the art, all or some of the steps, systems, etc. in the methods disclosed above can be embodied in software, firmware, hardware, and / or suitable combinations thereof. Some or all of the physical components can be implemented with software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or can be implemented as hardware, or as an integrated circuit, such as an application- specific integrated circuit. Such software can be distributed on computer readable media, which can comprise 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 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. Computer storage media include, 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. Further, 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.

[0073] The above description is that of the preferred embodiments of the present application. Various equivalents substitutions of the techniques described herein can be implemented, both currently known or later developed, without departing from the spirit and scope of the application. Such equivalents substitutions are also intended to be encompassed by the claims of the present application.

Claims

1. A method for energy saving of base stations in an ultra-dense network, comprising: The base stations in the ultra-dense network are clustered according to the clustering algorithm to obtain several clusters; For clusters where the number of base stations in a cluster is greater than a preset number, a target base station is determined based on the traffic volume of the base stations in the cluster within a preset time period, and energy-saving operations are performed on the other base stations in the cluster except for the target base station. For clusters where the number of base stations in a cluster is less than or equal to the preset number, several dormant base stations are determined based on the similarity of the traffic volume of the base stations in the cluster within a preset time period, and energy-saving operations are performed on the dormant base stations. The step of determining several dormant base stations based on the similarity of traffic volume among base stations in the cluster within a preset time period includes: The base stations in the cluster are paired, and the similarity of traffic volume between the two base stations in the pair is calculated. When the similarity exceeds a preset similarity threshold, one of the base stations in the combination will be selected as a dormant base station.

2. The base station energy-saving method according to claim 1, characterized in that, The ultra-dense network is divided into clusters based on a clustering algorithm, resulting in several clusters, including: In the ultra-dense network, N base stations are randomly selected as initial central base stations, and the distances between the remaining base stations in the ultra-dense network and each of the initial central base stations are calculated. The system is divided into N clusters based on the distance from each base station to the initial central base station, and the central base station within each cluster is recalculated. The clusters are iteratively divided based on the distances between the central base station within each cluster and each base station other than the central base station to the central base station, until the central base station within each cluster no longer changes after the division, resulting in N clusters after iteration.

3. The base station energy-saving method according to claim 1, characterized in that, The step of determining a target base station based on the traffic volume of base stations in the cluster within a preset time period includes: Obtain the traffic volume of each base station in the cluster within a preset time period; The base station with the highest traffic volume within the preset time period is selected as the target base station.

4. The base station energy-saving method according to claim 1, characterized in that, Before determining several dormant base stations based on the similarity of traffic volume among base stations in the cluster within a preset time period, the base station energy-saving method further includes: The target number of dormant base stations is determined based on the total traffic volume of the cluster within a preset time period and the traffic volume of each base station in the cluster within the preset time period.

5. The base station energy-saving method according to claim 4, characterized in that, The step of determining several dormant base stations based on the similarity of traffic volume among base stations in the cluster within a preset time period includes: The base stations in the cluster are paired, and the similarity of traffic volume between the two base stations in the pair is calculated. From the similarity score from high to low, one of the base stations in the combination is selected as a dormant base station until the number of selected dormant base stations is the same as the target number.

6. The base station energy-saving method according to claim 1, characterized in that, The similarity of the business volume is calculated using the cosine similarity algorithm.

7. The base station energy-saving method according to claim 1, characterized in that, The energy-saving operation is as follows: determine the hibernation mode according to the amount of traffic of the base station to be hibernated, and the hibernation mode includes shallow hibernation and deep hibernation.

8. The base station energy-saving method according to claim 7, characterized in that, The process of determining the hibernation mode based on the traffic volume of the base station requiring hibernation includes: Determine the cluster in which the base stations that need to go into hibernation belong, and determine the average traffic volume of all base stations in the cluster; If the traffic volume of a base station that needs to go into hibernation exceeds the average traffic volume, the base station that needs to go into hibernation will be controlled to enter shallow hibernation. If the traffic volume of a base station that needs to hibernate is less than or equal to the average traffic volume, the base station that needs to hibernate will be controlled to enter deep hibernation.

9. The base station energy-saving method according to any one of claims 1 to 8, characterized in that, The traffic volume is represented in a matrix, where each element in the matrix represents a service provided by the base station corresponding to the matrix within a preset time period.

10. The base station energy-saving method according to any one of claims 1 to 8, characterized in that, The preset number is determined based on the median number of base stations in the plurality of clusters.

11. An energy-saving device, characterized in that, It includes at least one processor and a memory for communicatively connecting to the at least one processor; the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the base station power saving method as described in any one of claims 1 to 10.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform the base station energy-saving method as described in any one of claims 1 to 10.

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