WO2023184979A1 - Procédé d'économie d'énergie de station de base pour un réseau ultra-dense, dispositif d'économie d'énergie et support d'enregistrement lisible - Google Patents

Procédé d'économie d'énergie de station de base pour un réseau ultra-dense, dispositif d'économie d'énergie et support d'enregistrement lisible Download PDF

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Publication number
WO2023184979A1
WO2023184979A1 PCT/CN2022/130337 CN2022130337W WO2023184979A1 WO 2023184979 A1 WO2023184979 A1 WO 2023184979A1 CN 2022130337 W CN2022130337 W CN 2022130337W WO 2023184979 A1 WO2023184979 A1 WO 2023184979A1
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Prior art keywords
base station
base stations
cluster
traffic volume
energy
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PCT/CN2022/130337
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English (en)
Chinese (zh)
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王伟
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中兴通讯股份有限公司
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Publication of WO2023184979A1 publication Critical patent/WO2023184979A1/fr

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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
    • 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
    • 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

Definitions

  • 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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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
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Abstract

Un procédé d'économie d'énergie de station de base pour un réseau ultra-dense, un appareil d'économie d'énergie et un support d'enregistrement lisible sont divulgués dans la présente demande. Le procédé d'économie d'énergie de station de base consiste à : regrouper diverses stations de base dans un réseau ultra-dense au moyen d'un algorithme de regroupement, et obtenir de multiples grappes (S100) ; pour une grappe dans laquelle le nombre de stations de base dans la grappe est supérieur à un nombre prédéfini, déterminer une station de base cible selon le trafic des stations de base dans la grappe dans une période de temps prédéfinie, et exécuter une opération d'économie d'énergie sur des stations de base autres que la station de base cible dans la grappe (S200) ; et, pour une grappe dans laquelle le nombre de stations de base dans la grappe est inférieur ou égal à un nombre prédéfini, déterminer de multiples stations de base dormantes selon le degré de similarité du trafic des stations de base dans la grappe dans une période de temps prédéfinie, et exécuter une opération d'économie d'énergie sur les stations de base dormantes (S300).
PCT/CN2022/130337 2022-03-29 2022-11-07 Procédé d'économie d'énergie de station de base pour un réseau ultra-dense, dispositif d'économie d'énergie et support d'enregistrement lisible WO2023184979A1 (fr)

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CN202210316848.X 2022-03-29

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103269511A (zh) * 2013-04-23 2013-08-28 北京邮电大学 无线接入网络节能方法
US20140219150A1 (en) * 2013-02-01 2014-08-07 Hitachi, Ltd. Base station in cellular network system and sleep control method for base station
CN111050387A (zh) * 2019-11-21 2020-04-21 北京邮电大学 基于能效估计的基站休眠方法、装置、电子设备及介质
CN112235852A (zh) * 2020-10-12 2021-01-15 江苏亨鑫众联通信技术有限公司 一种封闭覆盖区基站簇的节能方法和系统

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140219150A1 (en) * 2013-02-01 2014-08-07 Hitachi, Ltd. Base station in cellular network system and sleep control method for base station
CN103269511A (zh) * 2013-04-23 2013-08-28 北京邮电大学 无线接入网络节能方法
CN111050387A (zh) * 2019-11-21 2020-04-21 北京邮电大学 基于能效估计的基站休眠方法、装置、电子设备及介质
CN112235852A (zh) * 2020-10-12 2021-01-15 江苏亨鑫众联通信技术有限公司 一种封闭覆盖区基站簇的节能方法和系统

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