WO2023184979A1

WO2023184979A1 - Base station energy-saving method for ultra-dense network, energy-saving device, and readable storage medium

Info

Publication number: WO2023184979A1
Application number: PCT/CN2022/130337
Authority: WO
Inventors: 王伟
Original assignee: 中兴通讯股份有限公司
Priority date: 2022-03-29
Filing date: 2022-11-07
Publication date: 2023-10-05
Also published as: CN116939775A

Abstract

Disclosed in the present application are a base station energy-saving method for an ultra-dense network, an energy-saving apparatus, and a readable storage medium. The base station energy-saving method comprises: clustering various base stations in an ultra-dense network by means of a clustering algorithm, and obtaining multiple clusters (S100); for a cluster in which the number of base stations in the cluster is greater than a preset number, determining a target base station according to the traffic of the base stations in the cluster within a preset time period, and executing an energy-saving operation on base stations other than the target base station in the cluster (S200); and for a cluster in which the number of base stations in the cluster is less than or equal to a preset number, determining multiple dormant base stations according to the degree of similarity of the traffic of the base stations in the cluster within a preset time period, and executing an energy-saving operation on the dormant base stations (S300).

Description

Base station energy saving method, energy saving device and readable storage medium for ultra-dense network

Cross-references to related applications

This application is filed based on the Chinese patent application with application number 202210316848.

Technical field

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

Background technique

With the development of mobile Internet and the rise of the Internet of Things, the explosive growth of business continues. In order to cope with this development trend, the fifth generation mobile communication system (5G) came into being. 5G systems require higher network capacity and faster transmission rates, and Ultra-Dense Network (UDN) is an effective way to achieve this goal. However, densely deployed base stations also bring many problems. First, the business load in the network changes dynamically, which makes the base stations unable to be effectively used in many cases, resulting in serious waste of energy; secondly, in ultra-dense networks, the distance between base stations is very close, which makes the base stations There is serious interference between the two base stations, and how to coordinate the working status of various base stations in the ultra-dense network is urgent.

Contents of the invention

The following is an overview of the topics described in detail in this article. This summary is not intended to limit the scope of the claims.

Embodiments of the present application provide an energy-saving method, an energy-saving device, and a readable storage medium for a base station in an ultra-dense network.

In the first aspect, embodiments of the present application provide an energy-saving method for base stations in an ultra-dense network, which includes: clustering each base station in the ultra-dense network according to a clustering algorithm to obtain several clusters; If the number of base stations is greater than the preset number of clusters, a target base station is determined based on the business volume of the base stations in the cluster within the preset time period, and energy-saving operations are performed on other base stations in the cluster except the target base station. ; For the clustering clusters where the number of base stations in the cluster is less than or equal to the preset number, determine several dormant base stations based on the similarity of the traffic volume of the base stations in the cluster within the preset time period, and perform energy saving on the dormant base stations operate.

In a second aspect, embodiments of the present application provide an energy-saving device, including at least one processor and a memory configured to be communicatively connected to the at least one processor; the memory stores information that can be executed by the at least one processor. The instructions are executed by the at least one processor so that the at least one processor can execute the base station energy saving method as described in the first aspect.

In a third aspect, embodiments of the present application provide a computer-readable storage medium that stores computer-executable instructions, and the computer-executable instructions are used to cause a computer to execute the method described in the first aspect. Base station energy saving methods.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the application. The objectives and other advantages of the application may be realized and obtained by the structure particularly pointed out in the specification, claims and appended drawings.

Description of drawings

The drawings are used to provide a further understanding of the technical solution of the present application and constitute a part of the specification. Together with the examples of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation of the technical solution of the present application.

Figure 1 is a structural diagram of a base station transmission link in an ultra-dense network;

Figure 2 is an overall flow chart of a base station energy saving method provided by an embodiment of the present application;

Figure 3 is a flow chart of the application of the k-means clustering algorithm provided by an embodiment of the present application;

Figure 4 is a flow chart for selecting a target base station in a large cluster provided by an embodiment of the present application;

Figure 5 is a flow chart for selecting dormant base stations based on similarity thresholds in a large cluster provided by an embodiment of the present application;

Figure 6 is a flow chart for determining the number of dormant base stations before selecting dormant base stations according to an embodiment of the present application;

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

Figure 8 is a flow chart for determining the sleep mode provided by an embodiment of the present application;

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

Detailed ways

In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application and are not used to limit the present application.

As shown in Figure 1, ultra-dense networks, or ultra-dense networking, use macro base stations as the "face". Within their coverage, low-power small base stations are densely deployed in indoor and outdoor hot spots, and these small base stations are As each "node", it breaks the traditional flat, single-layer macro network coverage model and forms a "macro-micro" dense three-dimensional networking solution to eliminate signal blind spots and improve the network coverage environment.

Since the business load in ultra-dense networks changes dynamically, some base stations have too high business loads while others have low business loads, resulting in low utilization of some base stations. In addition, due to the distance between base stations in ultra-dense networks, Very close, there is some interference in signal coverage, which also causes a waste of resources.

Based on this, embodiments of the present application provide an energy-saving method, an energy-saving device, and a readable storage medium for base stations in an ultra-dense network. Based on an energy-saving solution that combines base station dormancy and collaboration, some base stations in an ultra-dense network are selectively Energy-saving operation, thereby reducing interference between base stations and improving resource utilization efficiency.

Referring to Figure 2, an embodiment of the present application provides a base station energy saving method, including but not limited to the following steps S100 to S300.

Step S100, cluster each base station in the ultra-dense network according to the clustering algorithm to obtain several clusters;

Step S200: For clustering clusters where the number of base stations in the cluster is greater than the preset number, determine a target base station based on the business volume of the base station in the cluster within the preset time period, and perform the operation on other base stations in the cluster except the target base station. Energy-saving operation;

Step S300: For clusters in which the number of base stations in the 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 the preset time period, and energy-saving operations are performed on the dormant base stations.

Based on the clustering algorithm, the base stations in the ultra-dense network are divided into clusters, and the base stations that have a great influence on each other are classified into the same cluster. Then they are divided into two categories according to the number of base stations in the cluster, namely large clusters and small clusters. For base stations in large clusters, an algorithm based on the maximum interference ratio is used to determine the status of the base station, and one base station is selected as the target base station. Energy-saving operations are performed on other base stations in the cluster except the target base station. For base stations in small clusters , a business comparison algorithm is used to determine which base stations need to perform energy-saving operations, so as to achieve dormancy and coordination according to base station distribution and business conditions, and improve the utilization of base station resources.

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 here. It is foreseeable that the shorter the preset time period, the higher the control accuracy of the base station energy saving method of this application, but the corresponding consumption of computing resources also increases.

In one embodiment, different clustering algorithms can be used to cluster base stations in ultra-dense networks, such as k-means clustering algorithm, mean shift clustering algorithm, density-based clustering algorithm, graph group detection (Graph Community Detection), etc., taking k-means clustering algorithm as an example to illustrate the clustering method in step S100. Referring to Figure 3, the implementation steps of the clustering algorithm in ultra-dense networks include:

Step S110, randomly select N base stations as initial central base stations in the ultra-dense network, and calculate the distances between the remaining base stations in the ultra-dense network and each initial central base station;

Step S120, divide N clusters according to the distance between the base station and the initial central base station, and recalculate the central base station in the cluster;

Step S130, iteratively divide the clusters according to the distance between the central base station in the cluster and each base station except the central base station to the central base station, until the central base station in the cluster no longer changes after the division, and the iterated N clusters are obtained. Class cluster.

Assume that there are k base stations in total in the ultra-dense network. First, N base stations are randomly selected as the initial centers. Then, the distances between other base stations except these N base stations are calculated to each initial center. After the distance is obtained, any base station is selected. The base station is divided into the nearest initial center and the base station is divided into the nearest initial center to obtain the first cluster division result. At this time, there are N clusters in total. After dividing the N clusters, recalculate the cluster center of each cluster, and use the newly calculated cluster center to calculate the distance between other base stations and these cluster centers, and then re-divide the clusters. By analogy, it is repeated until the cluster center no longer changes, and the cluster obtained at this time is the above-mentioned cluster cluster.

The above are only examples of applying the k-means clustering algorithm. Other clustering algorithms can be applied according to the actual situation of ultra-dense networking, and are not listed here.

It is understandable that in ultra-dense networks, due to the different distribution density of base stations, the number of base stations in clusters is often also different. How to determine whether to adopt the energy-saving operation of step S200 or step S300 depends on the preset Determined by the size of the quantity. For example, the preset number is set artificially, or it can be determined based on the number of base stations in an ultra-dense network. In a possible embodiment, the preset number is determined based on the median number of base stations in several clusters. : Suppose there are k base stations in the ultra-dense network, and N clusters are obtained based on the above clustering algorithm. The number of base stations in each cluster is known, then arrange it according to the number of base stations, and take the arranged The median serves as the preset quantity. If N is an odd number, the number of base stations in the middle cluster after the arrangement is the preset number. If N is an even number, the average number of base stations in the two middle clusters after the arrangement is the preset number.

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

Referring to Figure 4, for large clusters, that is, clusters in which the number of base stations in the cluster is greater than the preset number, selecting a target base station based on the business conditions of each base station includes the following steps:

Step S210: Obtain the traffic volume of each base station in the cluster within a preset time period;

Step S220: Select the base station with the largest traffic volume within the preset time period as the target base station.

Assume that the set of base stations in a large cluster is y, then the total traffic volume of y can be expressed as y=(y1, y2, y3,...,yn), each element represents a business in a preset time period, At the same time, the traffic volume of each base station in the set y can be known within the preset time period. According to the maximum interference ratio principle, the base station with the largest traffic volume in the set y is selected as the target base station. In addition to the target base station, other base stations in the cluster are configured with energy-saving operations. , at this time the target base station carries the services of other base stations.

For small clusters, that is, clusters in which the number of base stations in the cluster is less than or equal to the preset number, in one embodiment, referring to Figure 5, determining the base station that needs to sleep based on the similarity of the traffic volume of each base station includes the following steps:

Step S310: Combine base stations in the cluster in pairs, and calculate the similarity of the traffic volume of the two base stations in the combination;

Step S320: When the similarity exceeds the preset similarity threshold, one of the base stations in the combination is selected as a dormant base station.

Assume that any two base stations in the small cluster are represented by a and b respectively. The traffic volume of base station a within a preset time period can be expressed as a matrix a = (a1, a2, a3,...,an), and base station b is in the same The traffic volume within the preset time period can also be expressed as a matrix b = (b1, b2, b3,..., bn), and the similarity between matrices a and b is calculated. If the traffic between base station a and base station b If the similarity is high (the similarity exceeds the preset similarity threshold), you need to select one of the base stations between a and b to perform the sleep operation. If the service similarity between base station a and base station b is low (the similarity does not exceed the preset similarity threshold), there is no need to perform sleep operations on a and b.

It can be understood that combining base stations in small clusters in pairs has

There are several combinations, m is the total number of base stations in the small cluster, and the similarity is calculated for each combination and judged whether it exceeds the preset similarity threshold; of course, the base stations in the small cluster can be combined in pairs, or m base stations can be combined Line up a row and compare only the similarity of two adjacent base stations.

For small clusters, that is, clusters in which the number of base stations in the cluster is less than or equal to the preset number, in another embodiment, referring to Figure 6, before determining the base station that needs to sleep based on the similarity of the traffic volume of each base station, include:

Step S330: Determine the target number of dormant base stations based on the total traffic volume of the cluster within the preset time period and the traffic volume of each base station in the cluster cluster within the preset time period.

The purpose of the above steps is to consider whether the remaining base stations can still meet the business needs of the terminal equipment in the small cluster after putting some base stations in the small cluster to sleep. This is equivalent to transferring the business volume of the dormant base stations to the normal working base stations, and will not This causes the normally working base stations to be overloaded; based on this, it can be determined how many base stations are retained in the small cluster without performing energy-saving operations.

Based on the above step S330, dormant base stations in the small cluster can be selected. Referring to Figure 7, the following steps are included:

Step S340: Combine base stations in the cluster in pairs, and calculate the similarity of the traffic volume of the two base stations in the combination;

Step S350: Select one of the base stations in the combination as a dormant base station in descending order of similarity until the number of selected dormant base stations is the same as the target number.

Similarly, the business similarity between any two base stations is calculated according to the above-mentioned pairwise combination. In this embodiment, the relationship between similarity and threshold is not judged, but is arranged from large to small according to the similarity, from similarity to Starting from the maximum, one dormant base station is selected for each combination. 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 previously selected dormant base stations will perform energy-saving operations.

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

There are two types of energy-saving operations. The sleep mode is determined based on the business volume of the base station that needs to sleep. The sleep mode includes shallow sleep and deep sleep.

The business volume of the base station is determined by the average traffic volume of the cluster. In one embodiment, referring to Figure 8, the following steps are included:

Step S410, determine the cluster cluster where the base station that needs to sleep is located, and determine the average traffic volume of all base stations in the cluster cluster;

Step S420: When the traffic volume of the base station that needs to sleep is greater than the average traffic volume, control the base station that needs to sleep to perform shallow sleep;

Step S430: When the traffic volume of the base station that needs to sleep is less than or equal to the average traffic volume, control the base station that needs to sleep to perform deep sleep.

Calculate the average traffic volume of the base stations in the cluster within the preset time period. When the business volume of the dormant base station during the preset time period is greater than the average traffic volume, the dormant base station will be put into shallow sleep. When the dormant base station is in the preset time period, If the traffic volume in the segment is less than or equal to the average traffic volume, the dormant base station will be put into deep sleep. Among them, shallow sleep includes carrier shutdown, symbol shutdown, time slot shutdown, etc., while deep sleep controls the overall sleep mode of the base station.

It is understandable that the above comparison of business volume is also done in the form of a matrix.

Through the above steps, a base station energy-saving solution based on dormancy and collaboration in ultra-dense networks can be realized. Through precise energy-saving strategies, resource utilization efficiency can be effectively improved while ensuring user experience, reducing telecom operators' OPEX costs, and effectively enhancing wireless base station products. market competitiveness.

The embodiment of the present application also provides an energy-saving device, including at least one processor and a memory configured to be communicatively connected with the at least one processor; the memory stores instructions that can be executed by at least one processor, and the instructions are processed by at least one processor. The processor is executed, so that at least one processor can execute the aforementioned base station energy saving method.

Referring to FIG. 9 , it is taken as an example that the control processor 1001 and the memory 1002 in the energy-saving device 1000 can be connected through a bus. As a non-transitory computer-readable storage medium, the memory 1002 can be configured to store non-transitory software programs and non-transitory computer executable programs. In addition, memory 1002 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk memory, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 1002 may include memory located remotely relative to the control processor 1001, and these remote memories may be connected to the energy saving device 1000 through a network. Examples of the above-mentioned networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks and combinations thereof.

Those skilled in the art can understand that the device structure shown in FIG. 9 does not limit the energy-saving device 1000, and may include more or fewer components than shown, or combine certain components, or arrange different components.

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

The device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separate, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

The energy-saving method for base stations in ultra-dense networks provided by the embodiments of the present application has at least the following beneficial effects: clustering numerous base stations in ultra-dense networks through a clustering algorithm, and based on the number of base stations in each cluster cluster and the services of the base stations. amount, perform corresponding energy-saving operations. For clusters with a large number of base stations, select one base station to activate based on the maximum interference principle and perform energy-saving operations on other base stations. For clusters with a small number of base stations, based on the similarity principle, Select several base stations for energy-saving operations based on similar services. Through the above methods, collaborative energy saving of multiple base stations in ultra-dense networks is achieved, interference between dense base stations is reduced, energy utilization is effectively improved while ensuring user perception experience, and base station operating costs of telecom operators are reduced.

Those of ordinary skill in the art can understand that all or some steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all of the physical components may 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 may be distributed on computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). As is known to those of ordinary skill in the art, the term computer storage media includes volatile and non-volatile media implemented in any method or technology configured for storage of information, such as computer readable instructions, data structures, program modules or other data. lossless, removable and non-removable media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disk (DVD) or other optical disk storage, magnetic cassettes, tapes, disk storage or other magnetic storage devices, or may Any other medium configured to store the desired information and accessible to the computer. Additionally, it is known to those of ordinary skill in the art that 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 may include any information delivery media .

The above is a detailed description of several implementations of the present application, but the present application is not limited to the above-mentioned implementations. Those skilled in the art can also make various equivalent modifications or substitutions without violating the essence of the present application. These equivalents All modifications or substitutions are included in the scope defined by the claims of this application.

Claims

An energy-saving method for base stations in ultra-dense networks, including:

Cluster each base station in the ultra-dense network according to a clustering algorithm to obtain several clusters;

For the clustering clusters in which the number of base stations in the cluster is greater than the preset number, a target base station is determined based on the traffic volume of the base stations in the cluster within the preset time period, and other base stations in the cluster except the target base station are perform energy-saving operations;

For the clustering clusters where the number of base stations in the 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 the preset time period, and energy-saving operations are performed on the dormant base stations. .
The base station energy saving method according to claim 1, wherein each base station in the ultra-dense network is clustered according to a clustering algorithm to obtain several clusters, including:

Randomly select N base stations in the ultra-dense network as initial central base stations, and calculate the distance between the remaining base stations in the ultra-dense network and each of the initial central base stations;

Divide N clusters according to the distance between the base station and the initial central base station, and recalculate the central base station within the cluster;

Clusters are iteratively divided according to the distance between the central base station in the cluster and each base station except the central base station to the central base station, until the central base station in the cluster no longer changes after the division, and the iterative N clusters.
The base station energy saving method according to claim 1, wherein determining a target base station according to the traffic volume of the base stations in the cluster within a preset time period includes:

Obtain the traffic volume of each base station in the cluster cluster within a preset time period;

The base station with the largest traffic volume within the preset time period is selected as the target base station.
The base station energy saving method according to claim 1, wherein the determining several dormant base stations according to the similarity of the traffic volume of the base stations in the cluster within a preset time period includes:

Perform pairwise combinations of base stations in the cluster, and calculate the similarity of the traffic volume of the two base stations in the combination;

When the similarity exceeds the preset similarity threshold, one of the base stations in the combination is selected as a dormant base station.
The base station energy saving method according to claim 1, wherein before determining several dormant base stations based on the similarity of the traffic volume of the 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 according to the total traffic volume of the cluster within the preset time period and the traffic volume of each base station in the cluster cluster within the preset time period.
The base station energy saving method according to claim 5, wherein the determining several dormant base stations according to the similarity of the traffic volume of the base stations in the cluster within a preset time period includes:

Perform pairwise combinations of base stations in the cluster, and calculate the similarity of the traffic volume of the two base stations in the combination;

From high to low similarity, 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.
The base station energy saving method according to claim 1, wherein the service similarity is calculated according to a cosine similarity algorithm.
The base station energy saving method according to claim 1, wherein the energy saving operation is: determining a sleep mode according to the traffic volume of the base station that needs to sleep, and the sleep mode includes shallow sleep and deep sleep.
The base station energy saving method according to claim 8, wherein the determining the sleep mode according to the traffic volume of the base station that needs to sleep includes:

Determine the cluster cluster where the base station that needs to sleep is located, and determine the average traffic volume of all base stations in the cluster cluster;

When the traffic volume of the base station that needs to sleep is greater than the average traffic volume, control the base station that needs to sleep to perform shallow sleep;

When the traffic volume of the base station that needs to sleep is less than or equal to the average traffic volume, the base station that needs to sleep is controlled to perform deep sleep.
The base station energy saving method according to any one of claims 1 to 9, wherein the business volume is expressed in a matrix, and each element in the matrix represents a business of the base station corresponding to the matrix within a preset time period.
The base station energy saving method according to any one of claims 1 to 9, wherein the preset number is determined based on the median number of base stations in the several clusters.
An energy-saving device includes at least one processor and a memory configured to be communicatively connected to the at least one processor; the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor. One processor executes, so that the at least one processor can execute the base station energy saving method according to any one of claims 1 to 11.
A computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to cause a computer to execute the base station energy-saving method according to any one of claims 1 to 11.