WO2023184979A1 - Base station energy-saving method for ultra-dense network, energy-saving device, and readable storage medium - Google Patents
Base station energy-saving method for ultra-dense network, energy-saving device, and readable storage medium Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
- H04W52/0203—Power saving arrangements in the radio access network or backbone network of wireless communication networks
- H04W52/0206—Power saving arrangements in the radio access network or backbone network of wireless communication networks in access points, e.g. base stations
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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- 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.
- 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.
- 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.
- 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.
- 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 are used to cause a computer to execute the method described in the first aspect.
- 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.
- ultra-dense networks or ultra-dense networking
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- k-means clustering algorithm mean shift clustering algorithm
- density-based clustering algorithm density-based clustering algorithm
- graph group detection Graph Community Detection
- 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 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.
- N base stations are randomly selected as the initial centers.
- the distances between other base stations except these N base stations are calculated to each initial center.
- 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.
- 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.
- step S200 or step S300 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.
- 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.
- the energy saving operation can be performed according to step S200 or step S300.
- 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.
- the traffic volume of each base station in the set y can be known within the preset time period.
- the base station with the largest traffic volume in the set y is selected as 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.
- 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.
- 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.
- 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.
- 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.
- the business similarity between any two base stations is calculated according to the above-mentioned pairwise combination.
- 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.
- 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.
- the dormant base station calculates the average traffic volume of the base stations in the cluster within the preset time period.
- 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.
- 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.
- shallow sleep includes carrier shutdown, symbol shutdown, time slot shutdown, etc., while deep sleep controls the overall sleep mode of the base station.
- 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.
- the control processor 1001 and the memory 1002 in the energy-saving device 1000 can be connected through a bus.
- the memory 1002 can be configured to store non-transitory software programs and non-transitory computer executable programs.
- 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.
- 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.
- 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 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.
- 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.
- 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.
- 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 .
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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
相关申请的交叉引用Cross-references to related applications
本申请基于申请号为202210316848.X、申请日为2022年03月29日的中国专利申请提出,并要求该中国专利申请的优先权,该中国专利申请的全部内容在此引入本申请作为参考。This application is filed based on the Chinese patent application with application number 202210316848.
本申请涉及基站节能技术领域,尤其涉及一种超密集网络的基站节能方法、节能装置和可读存储介质。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.
随着移动互联网的发展以及物联网的兴起,业务***式的增长仍在继续。为了应对这一发展趋势,第五代移动通信***(5G)应运而生。5G***需要更高的网络容量以及更快的传输速率,而超密集网络(Ultra-Dense Network,UDN)是实现这一目标的有效方式。然而密集部署的基站也带来了很多的问题。首先,网络中的业务负载是动态变化的,这使得基站在很多时候不能得到有效利用,从而导致严重的能量浪费;其次,在超密集网络中,基站间的距离很近,这将使基站之间存在严重的干扰,如何协调超密集网络中各个基站的工作状态迫在眉睫。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.
发明内容Contents of the invention
以下是对本文详细描述的主题的概述。本概述并非是为了限制权利要求的保护范围。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.
本申请实施例提供了一种超密集网络的基站节能方法、节能装置和可读存储介质。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.
第一方面,本申请实施例提供了一种超密集网络的基站节能方法,包括:根据聚类算法将所述超密集网络中的各个基站进行分簇,得到若干个聚类簇;对于簇中基站的数量大于预设数量的所述聚类簇,根据簇中基站在预设时间段内业务量的大小确定出一个目标基站,并对簇中除所述目标基站外的其他基站执行节能操作;对于簇中基站的数量小于等于所述预设数量的所述聚类簇,根据簇中基站在预设时间段内业务量的相似性确定若干个休眠基站,并对所述休眠基站执行节能操作。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.
第二方面,本申请实施例提供了一种节能装置,包括至少一个处理器和被设置为与所述至少一个处理器通信连接的存储器;所述存储器存储有能够被所述至少一个处理器执行的指令,所述指令被所述至少一个处理器执行,以使所述至少一个处理器能够执行如第一方面所述的基站节能方法。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.
第三方面,本申请实施例提供了一种计算机可读存储介质,所述计算机可读存储介质存储有计算机可执行指令,所述计算机可执行指令用于使计算机执行如第一方面所述的基站节能方法。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.
本申请的其它特征和优点将在随后的说明书中阐述,并且,部分地从说明书中变得显而易见,或者通过实施本申请而了解。本申请的目的和其他优点可通过在说明书、权利要求书 以及附图中所特别指出的结构来实现和获得。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.
附图用来提供对本申请技术方案的进一步理解,并且构成说明书的一部分,与本申请的示例一起用于解释本申请的技术方案,并不构成对本申请技术方案的限制。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.
图1是超密集网络中基站传输链路的结构图;Figure 1 is a structural diagram of a base station transmission link in an ultra-dense network;
图2是本申请一个实施例提供的基站节能方法的整体流程图;Figure 2 is an overall flow chart of a base station energy saving method provided by an embodiment of the present application;
图3是本申请一个实施例提供的k-means聚类算法应用的流程图;Figure 3 is a flow chart of the application of the k-means clustering algorithm provided by an embodiment of the present application;
图4是本申请一个实施例提供的大簇中选取目标基站的流程图;Figure 4 is a flow chart for selecting a target base station in a large cluster provided by an embodiment of the present application;
图5是本申请一个实施例提供的大簇中根据相似度阈值选取休眠基站的流程图;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;
图6是本申请一个实施例提供的选取休眠基站之前确定休眠基站数量的流程图;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;
图7是本申请一个实施例提供的大簇中根据目标数量选取休眠基站的流程图;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;
图8是本申请一个实施例提供的判断休眠方式的流程图;Figure 8 is a flow chart for determining the sleep mode provided by an embodiment of the present application;
图9是本申请一个实施例提供的节能装置的结构示意图。Figure 9 is a schematic structural diagram of an energy-saving device provided by an embodiment of the present application.
为了使本申请的目的、技术方案及优点更加清楚明白,以下结合附图及实施例,对本申请进行进一步详细说明。应当理解,此处所描述的具体实施例仅用以解释本申请,并不用于限定本申请。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.
参照图1所示,超密集网络,或称超密集组网,是以宏基站为“面”,在其覆盖范围内,在室内外热点区域,密集部署低功率的小基站,将这些小基站作为一个个“节点”,打破传统的扁平、单层宏网络覆盖模式,形成“宏-微”密集立体化组网方案,以消除信号盲点、改善网络覆盖环境。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.
由于超密集网络中业务负载是动态变化的,因此存在一部分基站业务负载过高而另一部分基站业务负载较低,使得部分基站的利用率不高,另外,由于超密集网络中基站之间的距离很近,信号覆盖方面多少存在干扰,同样造成了资源的浪费。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.
基于此,本申请实施例提供了一种超密集网络的基站节能方法、节能装置和可读存储介质,基于基站休眠与协作结合的节能方案,有选择性地将超密集网络中的部分基站进行节能操作,从而降低基站之间的干扰并提高资源利用效率。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.
参照图2,本申请实施例提供了一种基站节能方法,包括但不限于以下步骤S100至步骤S300。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.
步骤S100,根据聚类算法将超密集网络中的各个基站进行分簇,得到若干个聚类簇;Step S100, cluster each base station in the ultra-dense network according to the clustering algorithm to obtain several clusters;
步骤S200,对于簇中基站的数量大于预设数量的聚类簇,根据簇中基站在预设时间段内业务量的大小确定出一个目标基站,并对簇中除目标基站外的其他基站执行节能操作;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;
步骤S300,对于簇中基站的数量小于等于预设数量的聚类簇,根据簇中基站在预设时间段内业务量的相似度确定若干个休眠基站,并对休眠基站执行节能操作。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.
基于聚类算法将超密集网络中的基站进行分簇,将相互之间影响极大的基站划入同一个簇中,然后根据簇中基站的数量划分两类,分别是大簇和小簇,对于大簇中的基站,采用基于最大干扰比的算法来确定基站的状态,并选取一个基站作为目标基站,对于簇中除目标基 站外的其他基站都进行节能操作,而对于小簇中的基站,则采用业务比较算法来确定哪些基站需要进行节能操作,从而根据基站分布和业务情况实现休眠和协同,提高基站资源的利用率。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.
可以理解的是,预设时间段的长短可以根据实际需要设置,例如4小时一次,2小时一次等等,在此不作限定。可以预见的是,预设时间段越短,本申请基站节能方法的控制精度越高,但是相应的计算资源消耗也变大。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.
在一实施例中,对超密集网络中基站进行分簇,可以采用不同的聚类算法,例如k-means聚类算法、均值漂移聚类算法、基于密度的聚类算法、图团体检测(Graph Community Detection)等等,以k-means聚类算法为例对步骤S100中的分簇方式进行说明,参照图3,聚类算法在超密集网络中的实施步骤包括: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:
步骤S110,在超密集网络中随机选择N个基站作为初始中心基站,并计算超密集网络中余下基站到各个初始中心基站之间的距离;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;
步骤S120,根据基站到初始中心基站的距离的大小划分N个聚类,并重新计算聚类内的中心基站;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;
步骤S130,根据聚类内的中心基站和除中心基站外的各个基站到中心基站之间的距离迭代划分聚类,直到划分后聚类内的中心基站不再变化,得到迭代后的N个聚类簇。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.
假设超密集网络中共有k个基站,首先随机选择N个基站作为初始中心,然后计算除这N个基站外的其他基站到各个初始中心之间的距离,得到距离之后对于任一个基站都选出与其最近的一个初始中心,并将该基站划分到这个距离最近的初始中心,从而得到第一次聚类划分结果,此时共有N个聚类。划分完成后的N个聚类,重新计算每个聚类的聚类中心,并以新计算出来的聚类中心,计算其他基站到这些聚类中心之间的距离,之后又重新划分聚类,以此类推,不断重复直到聚类中心不再变化,此时得到的聚类即为上述聚类簇。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.
上述只是应用k-means聚类算法的实施例,其他聚类算法则可以根据超密集组网的实际情况套用算法内容,在此不一一列举。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.
可以理解的是,超密集网络中由于基站的分布疏密不同,因此划分得到聚类簇内的基站的数量往往也不相同,如何判断采用步骤S200还是采用步骤S300的节能操作,则依靠预设数量的大小来确定。例如,预设数量是人为设定的,也可以是根据超密集组网内的基站数量确定,在一个可能的实施例中,预设数量根据若干个聚类簇中基站数量的中位数确定:假设超密集网络中有k个基站,并基于上述聚类算法划分得到N个聚类簇,每个聚类簇中的基站数量已知,则按照基站数量的多少排列,并取排列后的中位数作为预设数量。如果N为奇数,则排列后中间的聚类簇的基站数量就是预设数量,如果N为偶数,则排列后中间的两个聚类簇的基站数量的平均数就是预设数量。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.
通过上述方法判断出聚类簇是大簇还是小簇后,则可以按照步骤S200或者步骤S300执行节能操作。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.
参照图4,对于大簇而言,即簇中基站数量大于预设数量的聚类簇,根据各个基站的业务情况选出一个目标基站,包括以下步骤: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:
步骤S210,获取聚类簇中各个基站在预设时间段内的业务量;Step S210: Obtain the traffic volume of each base station in the cluster within a preset time period;
步骤S220,选出在预设时间段内业务量最大的基站作为目标基站。Step S220: Select the base station with the largest traffic volume within the preset time period as the target base station.
假设大簇的簇中基站组成的集合为y,则y的总业务量可以表示成y=(y1,y2,y3,…,yn), 每个元素项表示在预设时间段的一个业务,同时可以知道集合y中各个基站在预设时间段内的业务量,根据最大干扰比原则,取集合y中业务量最大的基站作为目标基站,除目标基站外的其他簇中基站则配置节能操作,此时目标基站承载其他基站的业务。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.
对于小簇而言,即簇中基站数量小于等于预设数量的聚类簇,在一个实施例中,参照图5,根据各个基站的业务量的相似度确定需要休眠的基站,包括以下步骤: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:
步骤S310,对簇中基站进行两两组合,并计算组合中两个基站的业务量的相似度;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;
步骤S320,当相似度超过预设相似度阈值,将组合中的其中一个基站选为休眠基站。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.
假设小簇中的任意两个基站分别以a和b表示,基站a在预设时间段内的业务量可以表示成一个矩阵a=(a1,a2,a3,…,an),基站b在同样的预设时间段内的业务量也可以表示成一个矩阵b=(b1,b2,b3,…,bn),计算矩阵a和b之间的相似度,如果基站a和基站b之间的业务相似度较高(相似度超过预设相似度阈值),则需要在a和b之中选择其中一个基站执行休眠操作。如果基站a和基站b之间的业务相似度较低(相似度不超过预设相似度阈值),则不需要对a和b执行休眠操作。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.
可以理解的是,对小簇中的基站进行两两组合,具有
种组合,m为小簇中基站的总数,每个组合都计算出相似度并判断是否超过预设相似度阈值;当然,对小簇中的基站进行两两组合,也可以是将m个基站排成一列并仅对比相邻两个基站的相似度。
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.
对于小簇而言,即簇中基站数量小于等于预设数量的聚类簇,在另一个实施例中,参照图6,在根据各个基站的业务量的相似度确定需要休眠的基站之前,还包括: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:
步骤S330,根据聚类簇在预设时间段内的总业务量和聚类簇中各个基站在预设时间段内的业务量,确定休眠基站的目标数量。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.
上述步骤的目的在于考虑将小簇中部分基站休眠后,剩下基站是否还能够满足小簇内终端设备的业务需求,相当于将休眠基站的业务量转移到正常工作的基站中,并且不会使得正常工作的基站出现超负载的情况;基于此可以确定在小簇中保留多少个基站不需要执行节能操作。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.
基于上述步骤S330可以选出小簇内的休眠基站,参照图7,包括以下步骤: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:
步骤S340,对簇中基站进行两两组合,并计算组合中两个基站的业务量的相似度;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;
步骤S350,从相似度由高到低,将组合中的其中一个基站选为休眠基站,直到所选出的休眠基站的数量与目标数量相同。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.
同样地,按照上述两两组合的方式计算任意两个基站之间的业务相似度,本实施例中不判断相似度与阈值之间关系,而是根据相似度从大到小排列,从相似度最大的开始,将每个组合选出一个休眠基站,当选出的休眠基站的数量等于目标数量,则停止选取休眠基站。此时将之前选出来的所有休眠基站都执行节能操作。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.
可以理解的是,上述相似度判断可以通过多种算法实现,例如皮尔逊相关算法、欧几里得距离算法、余弦相似度算法等等。以余弦相似度为例,对于基站a和基站b的两个矩阵,有相似度计算公式: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:
节能操作分为两种,即根据需要休眠的基站的业务量的大小确定休眠方式,休眠方式包括浅度休眠和深度休眠。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.
基站的业务量大小的判断通过聚类簇的平均业务量来确定,在一实施例中,参照图8,包括以下步骤: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:
步骤S410,确定需要休眠的基站所在的聚类簇,并确定聚类簇中所有基站的平均业务量;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;
步骤S420,在需要休眠的基站的业务量大于平均业务量的情况下,控制需要休眠的基站进行浅度休眠;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;
步骤S430,在需要休眠的基站的业务量小于等于平均业务量的情况下,控制需要休眠的基站进行深度休眠。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.
计算聚类簇中基站在预设时间段内的平均业务量,当休眠基站在预设时间段内的业务量大于平均业务量,则对休眠基站进行浅度休眠,当休眠基站在预设时间段内的业务量小于等于平均业务量,则对休眠基站进行深度休眠。其中,浅度休眠包括载波关断、符号关断、时隙关断等,深度休眠则控制基站整体进行睡眠模式。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.
可以理解的是,上述业务量的比较也是通过矩阵的形式进行比较的。It is understandable that the above comparison of business volume is also done in the form of a matrix.
通过上述各个步骤可以实现超密集网络中基于休眠与协同的基站节能方案,通过精确的节能策略在确保用户感知体验的前提下有效提升资源利用效率,降低电信运营商OPEX费用,有效增强无线基站产品的市场竞争力。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.
本申请实施例的还提供了一种节能装置,包括至少一个处理器和被设置为与至少一个处理器通信连接的存储器;存储器存储有能够被至少一个处理器执行的指令,指令被至少一个处理器执行,以使至少一个处理器能够执行前述的基站节能方法。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.
参照图9,以节能装置1000中的控制处理器1001和存储器1002可以通过总线连接为例。存储器1002作为一种非暂态计算机可读存储介质,可被设置为存储非暂态软件程序以及非暂态性计算机可执行程序。此外,存储器1002可以包括高速随机存取存储器,还可以包括非暂态存储器,例如至少一个磁盘存储器、闪存器件、或其他非暂态固态存储器件。在一些实施方式中,存储器1002可包括相对于控制处理器1001远程设置的存储器,这些远程存储器可以通过网络连接至节能装置1000。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。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.
本领域技术人员可以理解,图9中示出的装置结构并不构成对节能装置1000的限定,可以包括比图示更多或更少的部件,或者组合某些部件,或者不同的部件布置。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.
本申请实施例的还提供了一种计算机可读存储介质,该计算机可读存储介质存储有计算机可执行指令,该计算机可执行指令被一个或多个控制处理器执行,例如,被图9中的一个控制处理器1001执行,可使得上述一个或多个控制处理器执行上述方法实施例中的基站节能方法,例如,执行以上描述的图2中的方法步骤S100至步骤S300、图3中的方法步骤S110至步骤S130、图4中的方法步骤S210至步骤S220、图5中的方法步骤S310至步骤S320、图6中的方法步骤S330、图7中的方法步骤S340至步骤S350以及图8中的方法步骤S410至步骤S430。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 .
以上所描述的装置实施例仅仅是示意性的,其中作为分离部件说明的单元可以是或者也可以不是物理上分开的,即可以位于一个地方,或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。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.
本申请实施例提供的超密集网络的基站节能方法,至少具有如下有益效果:通过聚类算法将超密集网络中的众多基站进行分簇,并根据每个聚类簇中基站数量和基站的业务量,执 行相应的节能操作,对于基站数量较多的聚类簇,基于最大干扰原则选定一个基站激活并对其他基站进行节能操作,对于基站数量较少的聚类簇,基于相似性原则根据业务相似选出若干个基站进行节能操作。通过上述方式,实现超密集网络中多个基站的协同节能,降低密集基站之间的干扰,在确保用户感知体验的前提下有效提升能源利用率,降低电信运营商的基站运营成本。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.
本领域普通技术人员可以理解,上文中所公开方法中的全部或某些步骤、***可以被实施为软件、固件、硬件及其适当的组合。某些物理组件或所有物理组件可以被实施为由处理器,如中央处理器、数字信号处理器或微处理器执行的软件,或者被实施为硬件,或者被实施为集成电路,如专用集成电路。这样的软件可以分布在计算机可读介质上,计算机可读介质可以包括计算机存储介质(或非暂时性介质)和通信介质(或暂时性介质)。如本领域普通技术人员公知的,术语计算机存储介质包括在被设置为存储信息(诸如计算机可读指令、数据结构、程序模块或其他数据)的任何方法或技术中实施的易失性和非易失性、可移除和不可移除介质。计算机存储介质包括但不限于RAM、ROM、EEPROM、闪存或其他存储器技术、CD-ROM、数字多功能盘(DVD)或其他光盘存储、磁盒、磁带、磁盘存储或其他磁存储装置、或者可以被设置为存储期望的信息并且可以被计算机访问的任何其他的介质。此外,本领域普通技术人员公知的是,通信介质通常包含计算机可读指令、数据结构、程序模块或者诸如载波或其他传输机制之类的调制数据信号中的其他数据,并且可包括任何信息递送介质。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 .
以上是对本申请的若干实施进行了具体说明,但本申请并不局限于上述实施方式,熟悉本领域的技术人员在不违背本申请本质的前提下还可作出种种的等同变形或替换,这些等同的变形或替换均包含在本申请权利要求所限定的范围内。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 (13)
- 一种超密集网络的基站节能方法,包括:An energy-saving method for base stations in ultra-dense networks, including:根据聚类算法将所述超密集网络中的各个基站进行分簇,得到若干个聚类簇;Cluster each base station in the ultra-dense network according to a clustering algorithm to obtain several clusters;对于簇中基站的数量大于预设数量的所述聚类簇,根据簇中基站在预设时间段内业务量的大小确定出一个目标基站,并对簇中除所述目标基站外的其他基站执行节能操作;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;对于簇中基站的数量小于等于所述预设数量的所述聚类簇,根据簇中基站在预设时间段内业务量的相似度确定若干个休眠基站,并对所述休眠基站执行节能操作。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. .
- 根据权利要求1所述的基站节能方法,其中,所述根据聚类算法将所述超密集网络中的各个基站进行分簇,得到若干个聚类簇,包括: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:在所述超密集网络中随机选择N个基站作为初始中心基站,并计算所述超密集网络中余下基站到各个所述初始中心基站之间的距离;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;根据基站到所述初始中心基站的距离的大小划分N个聚类,并重新计算聚类内的中心基站;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;根据聚类内的所述中心基站和除所述中心基站外的各个基站到所述中心基站之间的距离迭代划分聚类,直到划分后聚类内的中心基站不再变化,得到迭代后的N个聚类簇。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.
- 根据权利要求1所述的基站节能方法,其中,所述根据簇中基站在预设时间段内业务量的大小确定出一个目标基站,包括: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:获取所述聚类簇中各个基站在预设时间段内的业务量;Obtain the traffic volume of each base station in the cluster cluster within a preset time period;选出在所述预设时间段内业务量最大的基站作为目标基站。The base station with the largest traffic volume within the preset time period is selected as the target base station.
- 根据权利要求1所述的基站节能方法,其中,所述根据簇中基站在预设时间段内业务量的相似度确定若干个休眠基站,包括: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:对簇中基站进行两两组合,并计算所述组合中两个基站的业务量的相似度;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;当所述相似度超过预设相似度阈值,将所述组合中的其中一个基站选为休眠基站。When the similarity exceeds the preset similarity threshold, one of the base stations in the combination is selected as a dormant base station.
- 根据权利要求1所述的基站节能方法,其中,在根据簇中基站在预设时间段内业务量的相似度确定若干个休眠基站之前,所述基站节能方法还包括: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:根据所述聚类簇在预设时间段内的总业务量和所述聚类簇中各个基站在所述预设时间段内的业务量,确定休眠基站的目标数量。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.
- 根据权利要求5所述的基站节能方法,其中,所述根据簇中基站在预设时间段内业务量的相似度确定若干个休眠基站,包括: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:对簇中基站进行两两组合,并计算所述组合中两个基站的业务量的相似度;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;从所述相似度由高到低,将所述组合中的其中一个基站选为休眠基站,直到所选出的休眠基站的数量与所述目标数量相同。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.
- 根据权利要求1所述的基站节能方法,其中,所述业务相似度根据余弦相似度算法计算得到。The base station energy saving method according to claim 1, wherein the service similarity is calculated according to a cosine similarity algorithm.
- 根据权利要求1所述的基站节能方法,其中,所述节能操作是:根据需要休眠的基站的业务量的大小确定休眠方式,所述休眠方式包括浅度休眠和深度休眠。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.
- 根据权利要求8所述的基站节能方法,其中,所述根据需要休眠的基站的业务量的大小确定休眠方式,包括: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:确定需要休眠的基站所在的所述聚类簇,并确定所述聚类簇中所有基站的平均业务量;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;在需要休眠的基站的业务量大于所述平均业务量的情况下,控制需要休眠的基站进行浅度休眠;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;在需要休眠的基站的业务量小于等于所述平均业务量的情况下,控制需要休眠的基站进行深度休眠。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.
- 根据权利要求1至9任一所述的基站节能方法,其中,所述业务量以矩阵的方式表示,矩阵内的每个元素项表示矩阵对应的基站在预设时间段内的一个业务。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.
- 根据权利要求1至9任一所述的基站节能方法,其中,所述预设数量根据所述若干个聚类簇中基站数量的中位数确定。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.
- 一种节能装置,包括至少一个处理器和被设置为与所述至少一个处理器通信连接的存储器;所述存储器存储有能够被所述至少一个处理器执行的指令,所述指令被所述至少一个处理器执行,以使所述至少一个处理器能够执行如权利要求1至11中任意一项所述的基站节能方法。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.
- 一种计算机可读存储介质,存储有计算机可执行指令,所述计算机可执行指令用于使计算机执行如权利要求1至11中任意一项所述的基站节能方法。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.
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