• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
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    • 专利摘要:
    A computer implemented method of monitoring a cell of a communication network. The method comprises receiving (301), from the cell, performance data comprising samples classified into a plurality of performance classes; comparing (302) number of samples in a first performance class with number of samples in a second performance class representing better performance than the first performance class; and detecting (303) increased number of samples in the first performance class compared to the second performance class and responsively performing (304)at least one action.
    公开号:FI20205254A1
    申请号:FI20205254
    申请日:2020-03-11
    公开日:2021-09-12
    发明作者:Henri Karikallio；Karri Sunila
    申请人:Elisa Oyj；
    IPC主号:
    专利说明:

    [0001] [0001] The present application generally relates to automated communication network monitoring.BACKGROUND
    [0002] [0002] This section illustrates useful background information without admission of any technique described herein representative of the state of the art.
    [0003] [0003] Cellular communication networks are complex systems comprising a plurality of cells serving users of the network. When users of the communication network move in the area of the network, connections of the users are seamlessly handed over between cells of the network. There are various factors that affect operation of individual cells and co-operation between the cells. In order for the communication network to operate as intended and to provide planned quality of service, cells of the communication network need to operate as planned. For example, the cells need to provide sufficient coverage without too much interfering with operation of neighboring cells.
    [0004] [0004] There are various automated measures that monitor operation of the communication networks in order to detect any problems in operation of the network as soon as possible so that corrective actions can be taken. The challenge is that there are problem situations that are not detected by current automated monitoring arrangements and therefore there is room for further development of the automated monitoring arrangements.N
    [0007] [0007] In an example embodiment, the at least one action is selected from a group consisting of resetting the cell, restarting the cell, issuing an alert, logging the detected increase.
    [0008] [0008] In an example embodiment, the method further comprises performing the comparison for selected one or more first performance classes representing worst performance in the cell.
    [0009] [0009] In an example embodiment, the selected one or more first performance classes are performance classes configured to trigger handover to a neighboring cell.
    [0010] [0010] In an example embodiment, the communication network is 2G network and the performance classes are quality classes of 2G network technology.
    [0011] [0011] In an example embodiment, the selected one or more first performance classes are quality classes 5-7 of 2G network technology.
    [0012] [0012] In an example embodiment, the second performance class represents one step better performance than the first performance class.
    [0013] [0013] In an example embodiment, detecting increased number of samples in the first performance class requires that ratio of the number of samples in the first performance class to the number of samples in the second performance class exceeds a S threshold.
    [0017] [0017] The computer program of the third aspect may be a computer program product stored on a non-transitory memory medium.
    [0018] [0018] Different non-binding example aspects and embodiments of the present invention have been illustrated in the foregoing. The embodiments in the foregoing are used merely to explain selected aspects or steps that may be utilized in implementations of the present invention. Some embodiments may be presented only with reference to certain example aspects of the invention. It should be appreciated that corresponding embodiments may apply to other example aspects as well.BRIEF DESCRIPTION OF THE DRAWINGS
    [0019] [0019] For a more complete understanding of example embodiments of the present invention, reference is now made to the following descriptions taken in connection with the accompanying drawings in which:
    [0020] [0020] Figs. 1A-1C are graphs showing distribution of samples into different performance classes in certain examples;
    [0021] [0021] Fig. 2A shows an example scenario according to an embodiment;
    [0022] [0022] Fig. 2B shows an apparatus according to an embodiment; and
    [0023] [0023] Figs. 3-4 show flow diagrams illustrating example methods according to certain embodiments.DETAILED DESCRIPTION OF THE DRAWINGS
    [0024] [0024] Example embodiments of the present invention and its potential N advantages are understood by referring to Figs. 1 through 4 of the drawings. In this N document, like reference signs denote like parts or steps. 7 [0025] Example embodiments of the invention provide new mechanisms to z monitor operation of cellular communication networks. Certain example embodiments + of the invention are based on monitoring number of samples in different performance E classes and detecting problems in operation responsive to detecting certain distribution ä of samples.
    [0026] [0026] The performance class may be for example guality class of 2G network technology or some other classification related to signal guality or guality of service in the cell. For example, the performance class may depend on a counter related to error correction.
    [0027] [0027] Itis to be noted that in the following, mainly monitoring of a single cell is discussed, but clearly plurality of cells may be monitored correspondingly in parallel or sequentially one after another.
    [0028] [0028] Figs. 1A-1C are graphs showing distribution of samples (users of one cell) into different performance classes in certain examples. The graphs show number of samples in performance classes 1-7. The performance class 1 is the class with the best quality of service and the performance class 7 is the class with worse quality of service. In an example, the performance classes 4-7 or 5-7 relate to performance that triggers handover to a neighboring cell to obtain better quality of service.
    [0029] [0029] The graph of Fig. 1A shows a distribution of samples in a usual case. Most of the samples are in performance classes 1-2 or 1-3. That is, most of the users in the cell that is monitored receive good quality of service. Only few samples exist in performance classes 5-7 or 6-7. These are likely samples of users that are located in the border are of the cell and moving to coverage area of a neighboring cell. In an example embodiment, network is designed so that minimal number of samples should exist in performance classes 5-7. Also some other kind of design rules may be used resulting in different distribution. In general, the design rules aim at having small number of samples in the worst performance classes.
    [0030] [0030] Now, certain experiments have shown that in some cases there exists an altered distribution of samples in different performance classes. Such altered distribution is substantially different from distribution of samples in different performance classes the usual case shown in Fig. 1A. There are differences especially in N performance classes associated with worse guality of service. In such situations the N network may not operate as intended and the users may experience degraded guality of service. = [0031] The graphs of Figs. 1B and 1C show examples of such altered + distribution of samples. The graph of Fig. 1B shows mediocre number of samples in E performance classes 1-3 and large number of samples in the worst performance class 7. N The graph of Fig. 1C shows largest number of samples in performance class 1 but also N performance classes 5-6 comprise significant number of samples.
    [0032] [0032] It has been noted that in the example cases of Figs. 1B and 1C, the devices /users associated with the samples in performance classes 5-7 in a source cell are not necessarily located in the border area of the source cell and may not receive sufficiently high signal level from any neighboring cell to be handed over. Instead at least some of the devices /users associated with the samples in performance classes 5-7 are likely located within normal coverage area of the source cell and receive relatively high signal level from the source cell. For some reason relatively high error correction rates are experienced at the same time and therefore the performance class is degraded. For this reason, there are devices/users that on one hand require handover to a neighboring cell due to bad performance class, but that cannot be handed over due to not receiving sufficient signal level from the neighboring cell. This causes degraded user experience. It has been noted that for example resetting or restarting the cell may resolve the situation and return operation of the cell to normal. Such corrective action may be automatically performed without needing to involve human actions.
    [0033] [0033] Various example embodiments of present disclosure provide measures to automatically detect such altered distribution of samples to enable corrective actions. In this way, it may be possible to avoid degraded user experience.
    [0034] [0034] Fig. 2A shows an example scenario according to an embodiment. The scenario shows a communication network 101 comprising a plurality of cells and base stations and other network devices, and an automation system 111 configured to implement automatic monitoring according to example embodiments.
    [0035] [0035] In an embodiment of the invention the scenario of Fig. 1 operates as follows: In phase 11, the automation system 111 obtains data from a cell of the network. The data comprises at least information about performance classes in the cell. The data may be obtained directly from the cell or through some intermediate system. Also other N data may be obtained from the cell.
    [0039] [0039] Fig. 2B shows an apparatus 20 according to an embodiment. The apparatus 20 is for example a general-purpose computer or server or some other electronic data processing apparatus. The apparatus 20 can be used for implementing embodiments of the invention. That is, with suitable configuration the apparatus 20 is suited for operating for example as the automation system 111 of foregoing disclosure.
    [0040] [0040] The general structure of the apparatus 20 comprises a processor 21, and a memory 22 coupled to the processor 21. The apparatus 20 further comprises software 23 stored in the memory 22 and operable to be loaded into and executed in the processor 21. The software 23 may comprise one or more software modules and can be in the form of a computer program product. Further, the apparatus 20 comprises a communication interface 25 coupled to the processor 21.
    [0041] [0041] The processor 21 may comprise, e.g., a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a graphics processing unit, or the like. Fig. 2 shows one processor 21, but the apparatus 20 may comprise a plurality of processors.
    [0042] [0042] The memory 22 may be for example a non-volatile or a volatile memory, such as a read-only memory (ROM), a programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), a random-access memory (RAM), a flash memory, a data disk, an optical storage, a magnetic storage, a smart card, or the S like. The apparatus 20 may comprise a plurality of memories. 0 [0043] The communication interface 25 may comprise communication modules 7 that implement data transmission to and from the apparatus 20. The communication z modules may comprise, e.g, a wireless or a wired interface module. The wireless + interface may comprise such as a WLAN, Bluetooth, infrared (IR), radio freguency E identification (RF ID), GSM/GPRS, CDMA, WCDMA, LTE (Long Term Evolution) or 5G N radio module. The wired interface may comprise such as Ethernet or universal serial N bus (USB), for example. Further the apparatus 20 may comprise a user interface (not shown) for providing interaction with a user of the apparatus. The user interface may comprise a display and a keyboard, for example. The user interaction may be implemented through the communication interface 25, too.
    [0044] [0044] A skilled person appreciates that in addition to the elements shown in Fig. 2, the apparatus 20 may comprise other elements, such as displays, as well as additional circuitry such as memory chips, application-specific integrated circuits (ASIC), other processing circuitry for specific purposes and the like. Further, it is noted that only one apparatus is shown in Fig. 2, but the embodiments of the invention may equally be implemented in a cluster of shown apparatuses.
    [0045] [0045] Figs. 3-4 show flow diagrams illustrating example methods according to certain embodiments. The methods may be implemented in the automation system 111 of Fig. 1 and/or in the apparatus 20 of Fig. 2. The methods are implemented in a computer and do not require human interaction unless otherwise expressly stated. It is to be noted that the methods may however provide output that may be further processed by humans and/or the methods may require user input to start. Different phases shown in Figs. 3-4 may be combined with each other and the order of phases may be changed except where otherwise explicitly defined. Furthermore, it is to be noted that performing all phases of the flow charts is not mandatory.
    [0046] [0046] The method of Fig. 3 provides monitoring of performance of a cell of a communication network, and comprises the following phases:
    [0047] [0047] Phase 301: Performance data is obtained. The performance data comprises at least information about performance classes in the cell. More specifically, the performance data may comprise for example samples classified into a plurality of performance classes.
    [0048] [0048] In an embodiment, the performance classes provide classification S related to signal quality or quality of service in the cell. For example, the performance 0 class may depend on a counter related to error correction. In an embodiment, the 7 communication network is 2G network and the performance classes are quality classes z of 2G network technology. Nevertheless, the method is applicable in other network > technologies, too. E [0049] Phase 302: Number of samples in a first performance class is compared N with number of samples in a second performance class. The second performance class N represents better performance than the first performance class.
    [0050] [0050] In an embodiment, the first performance classes that are taken into consideration are one or more performance classes representing worst performance in the cell. In an embodiment, the first performance classes that are taken into consideration are performance classes configured to trigger handover to a neighboring cell. In an embodiment, the first performance classes that are taken into consideration are quality classes 4-7 or quality classes 5-7 of 2G network technology.
    [0051] [0051] In an embodiment, the second performance class represents one step better performance than the first performance class. That is, in an example, where there are performance classes 1-7 (class1 representing the best performance and the class 7 representing the worst performance), the number of samples in performance class 7 is compared to the number of samples in performance class 6; the number of samples in performance class 6 is compared to the number of samples in performance class 5; the number of samples in performance class 5 is compared to the number of samples in performance class 4 etc. Alternatively, the number of samples in performance classes 7, 6 and 5 may be compared to the number of samples in performance class 3 or 4.
    [0052] [0052] Phase 303: Larger or increased number of samples is detected in the first performance class.
    [0053] [0053] Phase 304: Responsive to the detected increased number of samples in phase 303, at least one action is performed. The action may be for example resetting the cell or restarting the cell. Additionally or alternatively, the action may be issuing an alert and/or logging the detected increase.
    [0054] [0054] The method of Fig. 4 provides details of an example implementation of the detection phase 303 of Fig. 3. The method comprises the following phases:
    [0055] [0055] Phase 401: A ratio of the number of samples in the first performance class to the number of samples in the second performance class is determined.
    [0058] [0058] Another technical effect of one or more of the example embodiments disclosed herein is ability to automatically detect and resolve network behavior that causes degraded user experience but does not trigger conventional network alarms.
    [0059] [0059] Yetanother technical effect of one or more of the example embodiments disclosed herein is monitoring method that is easy to implement and that is network supplier independent. Various example implementations are usable for detecting problems in equipment of different network device suppliers.
    [0060] [0060] If desired, the different functions discussed herein may be performed in a different order and/or concurrently with each other. Furthermore, if desired, one or more of the before-described functions may be optional or may be combined.
    [0061] [0061] Although various aspects of the invention are set out in the independent claims, other aspects of the invention comprise other combinations of features from the described embodiments and/or the dependent claims with the features of the independent claims, and not solely the combinations explicitly set out in the claims.
    [0062] [0062] It is also noted herein that while the foregoing describes example embodiments of the invention, these descriptions should not be viewed in a limiting sense. Rather, there are several variations and modifications, which may be made without departing from the scope of the present invention as defined in the appended claims.ONO
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    权利要求:
    Claims (13)
    [1] 1. A computer implemented method of monitoring a cell of a communication network (101), the method comprising receiving (301), from the cell, performance data comprising samples classified into a plurality of performance classes; comparing (302) number of samples in a first performance class with number of samples in a second performance class representing better performance than the first performance class; and detecting (303) increased number of samples in the first performance class compared to the second performance class and responsively performing (304) at least one action.
    [2] 2. The method of claim 1, wherein the at least one action is selected from a group consisting of resetting the cell, restarting the cell, issuing an alert, logging the detected increase.
    [3] 3. The method of any preceding claim, further comprising performing the comparison for selected one or more first performance classes representing worst performance in the cell.
    [4] 4. The method of claim 3, wherein the selected one or more first performance classes are performance classes configured to trigger handover to a neighboring cell.
    [5] N 5. The method of any preceding claim, wherein the communication network is 2G 3 network and the performance classes are quality classes of 2G network technology.
    [6] z 6. The method of claim 5, wherein the selected one or more first performance 3 classes are quality classes 5-7 of 2G network technology.
    [7] LO 2 N 7. The method of any preceding claim, wherein the second performance class N represents one step better performance than the first performance class.
    [8] 8. The method of any preceding claim, wherein detecting increased number of samples in the first performance class requires that ratio of the number of samples in the first performance class to the number of samples in the second performance class exceeds a threshold.
    [9] 9. The method of claim 8, wherein the threshold is 1-2.
    [10] 10. The method of claim 8, wherein the threshold is 1,5.
    [11] 11. An apparatus (20, 111) comprising a processor (21), and a memory (22) including computer program code; the memory and the computer program code configured to, with the processor, cause the apparatus to perform the method of any one of claims 1-10.
    [12] 12. A computer program comprising computer executable program code (23) which when executed by a processor causes an apparatus to perform the method of any one of claims 1-10.
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