Patent Description:
Cellular communications networks are complex systems comprising a plurality of cells serving users of the network. There are various factors that affect operation of individual cells and co-operation between the cells. In order for the communications network to operate as intended and to provide planned quality of service, cells of the communications network need to operate as planned. For example, the cells need to provide sufficient coverage and throughput without interfering with the neighboring cells.

There are various automated measures that monitor operation of the communication networks in order to detect problems as soon as possible so that corrective actions can be taken. Examples are provided in <CIT>, <CIT> and <CIT>.

The challenge is that there are malfunctions that are not detected by current automated monitoring arrangements and therefore there is room for further development of the automated monitoring arrangements.

Now a new approach is taken to analyzing operation of cells of a communications network.

According to a first example aspect there is provided a computer implemented method for analyzing operation of cells of a communications network. The method comprises: obtaining data comprising performance indicators data from a first cell and a group of reference cells for a selected time period; identifying, for the first cell, a first change point of first performance indicators, calculating magnitude of the change of the first performance indicators at the first change point, and defining the calculated magnitude as a first magnitude; identifying, for the group of reference cells, a group of reference change points of the first performance indicators, calculating magnitudes of the changes of the group of reference change points, and defining the calculated magnitudes as a group of reference magnitudes; comparing the first magnitude to the group of reference magnitudes to determine the relevance of the change point of the first cell; and providing output indicating the first change point of the first cell in response to detecting that said change point is determined relevant, or otherwise, providing output indicating that no relevant change point is identified.

In an embodiment, multiple change points of the first performance indicators are identified and analyzed for the first cell.

In an embodiment, the change point is identified using methods comprising at least one of: a binary segmentation algorithm, the Pruned Exact Linear Time, PELT, algorithm, and Z-score based method.

In an embodiment, the value of the first performance indicators degrades at the change point.

In an embodiment, the magnitude of the change at the change point is determined based on an absolute difference or a relative difference of the performance indicators.

In an embodiment, the magnitude of the change at the change point is calculated as a difference of mean values or as a difference of standard deviation values of the performance indicators before and after the change point.

In an embodiment, the relevance of the first change point is determined based on one or more of the following: absolute mean, relative mean, absolute standard deviation, and relative standard deviation of the performance indicator.

In an embodiment, the relevance of the change point is determined based on the magnitude of the first change point being equal to or higher than top values of the group of reference magnitudes, wherein the top values of the group of reference magnitudes comprises a selected percentile of the highest values of the group of reference magnitudes.

In an embodiment, the relevance of the first change point is determined based on clustering the first magnitude and the group of reference magnitudes and classifying outliers compared to the cluster as relevant change points.

In an embodiment, the analysis is performed for multiple different performance indicators.

In an embodiment, the performance indicators data comprises at least one of: throughput, cell availability, handover, reference signal received power, RSRP, reference signal received quality, RSRQ, received signal strength indicator, RSSI, signal to noise ratio, SNR, signal to interference plus noise ratio, SINR, received signal code power, RSCP, and channel quality indicator, CQI.

In an embodiment, the group of reference cells comprises cells which are similar to the first cell.

In an embodiment, the similarity is defined as similarity in one or more of: network density, frequency band, historical performance indicators correlation, historical performance indicator statistics, cell technology, cell size, antenna technology, base station location, and base station surroundings.

In an embodiment, the first cell and the time period are selected based on a user feedback and/or an alarm form a network monitoring system.

In an embodiment, the analysis is repeated for multiple cells of the communications network.

According to a second example aspect of the present invention, there is provided an apparatus comprising a processor and a memory including computer program code; the memory and the computer program code configured to, with the processor, cause the apparatus to perform the method of the first aspect or any related embodiment.

According to a third example aspect there is provided a computer program comprising computer executable program code which when executed by at least one processor causes an apparatus at least to perform the method of the first aspect or any related embodiment.

Example embodiments of the invention provide new methods for analyzing operation of cells of a communications network in order to identify anomalously operating cells. Certain example embodiments of the invention are based on identifying change points of performance indicators of a cell. The performance indicators are a time series of performance indicator data points collected at regular time intervals, e.g., hourly or daily samples over a selected time period. The selected time period may be of <NUM>, <NUM>, <NUM> days, or of other duration. A change point represents a point in time which marks a significant change in statistics, e.g., mean or standard deviation of the performance indicators time series. Alternatively, it can be interpreted as a shift in data. In order to determine the relevance of the identified change points, the identified change points may be compared to change points of said performance indicators identified form a group of reference cells. That is, performance of cell that is being analyzed is compared with performance in one or more reference cells. A change point which is determined to be relevant may indicate that the cell is anomalous. As a consequence of detecting an anomaly, further analysis or corrective actions can be applied to the cell.

<FIG> shows an example scenario according to an embodiment. The scenario shows a communications network <NUM> comprising a plurality of cells and base stations and other network devices, and an operations support system (OSS) <NUM> that manages operations of the communications network <NUM>. Further, the scenario shows an automation system <NUM> configured to control the communications network according to example embodiments.

In an embodiment of the invention the scenario of <FIG> operates as follows:.

The process may be manually or automatically triggered. The process may be triggered by user feedback. The process may be periodically repeated. The process may be repeated for example once a day, once a week, every two weeks, or once a month. By periodically repeating the process, effective network monitoring and controlling is achieved and problems, if any, may be timely detected. Additionally or alternatively, the process may be triggered, for example, in response to reported problems in the network. Problems may be reported for example by users through making a service complaint. Still further, the process may be performed in connection with deployment of new cell(s) or base station site, deployment of new physical equipment in the base station site and/or maintenance actions performed in the base station site. In this way any problems with the newly deployed equipment may be detected right away.

<FIG> shows an apparatus <NUM> according to an embodiment. The apparatus <NUM> is for example a general-purpose computer or server or cloud computing environment or some other electronic data processing apparatus. The apparatus <NUM> can be used for implementing embodiments of the invention. That is, with suitable configuration the apparatus <NUM> is suited for operating for example as the automation system <NUM> of foregoing disclosure.

The general structure of the apparatus <NUM> comprises a processor <NUM>, and a memory <NUM> coupled to the processor <NUM>. The apparatus <NUM> further comprises software <NUM> stored in the memory <NUM> and operable to be loaded into and executed in the processor <NUM>. The software <NUM> may comprise one or more software modules and can be in the form of a computer program product. Further, the apparatus <NUM> comprises a communication interface <NUM> coupled to the processor <NUM>.

The processor <NUM> may comprise, e.g., a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a graphics processing unit, or the like. <FIG> shows one processor <NUM>, but the apparatus <NUM> may comprise a plurality of processors.

The memory <NUM> 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 like. The apparatus <NUM> may comprise a plurality of memories.

The communication interface <NUM> may comprise communication modules that implement data transmission to and from the apparatus <NUM>. The communication 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 frequency identification (RF ID), GSM/GPRS, CDMA, WCDMA, LTE (Long Term Evolution) or <NUM> radio module. The wired interface may comprise such as Ethernet or universal serial bus (USB), for example. Further the apparatus <NUM> 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 <NUM>, too.

A skilled person appreciates that in addition to the elements shown in <FIG>, the apparatus <NUM> 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>, but the embodiments of the invention may equally be implemented in a cluster of shown apparatuses.

<FIG> shows a flow chart of a method according to an example embodiment. <FIG> illustrates a computer implemented method for analyzing operation of cells of a communications network comprising various possible process steps including some optional steps while also further steps can be included and/or some of the steps can be performed more than once:.

Also other methods may be used for detecting the change point. In an embodiment, a change point having the highest magnitude of the change is identified as the first change point. In some embodiments, only change points indicating degradation in performance indicators values may be used for analysis.

The magnitude of the change at the change point may be determined based on an absolute difference in values of the performance indicators. Alternatively or additionally, the magnitude can be measured using a relative difference. The magnitude of the change at the change point may be calculated as a difference of mean values of the performance indicators before and after the change point. Alternatively or additionally, a difference in standard deviation values of the performance indicators before and after the change point can be used to calculate the magnitude of the change point.

The absolute difference of the mean values may be calculated as <MAT> where <MAT> is the mean value of the performance indicators after the change point and <MAT> is the mean value of the performance indicators before the change point.

The absolute difference of the standard deviation values may be calculated as <MAT> where <MAT> is the standard deviation value of the performance indicators after the change point and <MAT> is the standard deviation value of the performance indicators before the change point.

The relative differences of the mean values and the standard deviation values may be calculated as <MAT> and <MAT>.

In an example embodiment, multiple performance indicators may be analyzed for identifying the change points. In an example embodiment, a change point in just one performance indicators may be considered less relevant (and less likely to point to a significant network problem requiring further action) than approximately simultaneously occurring change point in multiple performance indicators of the same cell. In an example embodiment, multiple change points may be identified for the first cell.

<NUM>: Identifying, for the group of reference cells, a group of reference change points of the first performance indicators, calculating magnitudes of the changes of the group of reference change points, and defining the calculated magnitudes as a group of reference magnitudes. In an embodiment, multiple change points may be identified at least for some cells of the group of reference cells. In some embodiments, change points may be identified for some of the cells of the group of reference cells. In some embodiments, the magnitudes of the changes of the group of reference change points are calculated similarly as the magnitude of the first change point of the first cell. In an example embodiment, multiple performance indicators may be analyzed for identifying the change points.

In an example embodiment, the reference change points of the first performance indicators are selected so that if the performance indicators of a cell have multiple change points within the observed time window, only the one with the largest magnitude is selected to the reference set.

<NUM>: Comparing the first magnitude to the group of reference magnitudes to determine the relevance of the change point of the first cell. The relevance of the change point is determined based on at least one of the following: absolute mean, relative mean, absolute standard deviation, and relative standard deviation of the performance indicator. In some example embodiments, the relevance of the change point may also be determined based on other statistical quantities. In an example embodiment, multiple performance indicators are compared simultaneously in order to determine the relevance of the change point. In an example embodiment, the relevance of the first change point is determined based on comparing the magnitude of the first change point to the group of reference magnitudes. The first change point may be determined relevant if its magnitude is higher than any of the values of the group of reference magnitudes. The first change point may be determined relevant if its magnitude is higher than <NUM>% of the values of the group of reference magnitudes. Alternatively the first change point may be determined relevant if its magnitude is higher than <NUM>%, <NUM>%, <NUM>%, or other percentile values of the group of reference magnitudes.

In an example embodiment, the relevance of the change point is determined based on clustering the first magnitude and the group of reference magnitudes and classifying the outliers of the main cluster as relevant change points. The clustering may be performed in a single dimension space comprising one measure of magnitude of the change points: absolute difference in mean, relative difference in mean, absolute difference in standard deviation, relative difference in standard deviation of performance indicators. In an example embodiment, the clustering is performed in a multidimensional space comprising two or more measures of magnitude: absolute mean, relative mean, absolute standard deviation, and relative standard deviation of the performance indicator.

<NUM>: Providing output based on the comparison of the change point magnitudes. The output may indicate the first change point of the first cell in response to detecting that said change point is determined relevant. In response to detecting that the first change point is irrelevant, output indicating that no relevant change points were identified may be provided. In a yet another alternative, the output provides results of the comparison phase and further analysis of the comparison results may be performed elsewhere.

In an example embodiment, multiple change points of the first performance indicators are identified and analyzed for the first cell simultaneously or sequentially. The analysis may be performed for multiple cells of the communications network simultaneously or sequentially. In an example embodiment, the analysis may be performed for a group of cells covering a particular area indicated in the customer feedback, or for example where a software update or a network parameter change has been applied. In another example embodiment, the performance indicators apply and are computed for a group of cells (such as cells served by the same base station). In such a case also the reference set can comprise of similarly grouped cells.

<FIG> show an example according to an embodiment. <FIG> show obtained performance indicators data of a first performance indicator according to step <NUM> for two different cells 400A and 400B. The cell analysis in both cases 400A-B is initiated by a user complaint that generated ticket <NUM> in the system. Due to the received user ticket <NUM>, the time period for analysis, or an analysis window, is selected to mainly cover time before the user ticket <NUM> as shown in <FIG>. Then, the change point <NUM> is identified and the magnitude of the change is calculated according to step <NUM> for both cells 400A and 400B. In this example, cells 400A and 400B are similar according to a specified criterion of similarity so that the same group of reference cells can be used for the analysis. The change points of the group of reference cells are then identified according to step <NUM>. The relevance of the change points <NUM> of cells 400A and 400B is determined at step <NUM> using a clustering method as shown in <FIG>. The dots in <FIG> indicate the magnitude of the change points of the first performance indicators identified for cells 400A and 400B and for the group of reference cells. In the shown example two-dimensional clustering analysis is performed using change in absolute mean and relative mean values. In some other examples, the clustering may be performed in four dimensions including also change in absolute and relative standard deviation values. Curve <NUM> in <FIG> indicates an approximate clustering border. In <FIG>, data points towards the top-right corner form the curve <NUM> are considered normal, while rest of the data points may be considered anomalous (they are more separated from other data points, whereas the points within the cluster of normal points are less separated from one another). Thus, the magnitude of the change point <NUM> of cell 400A indicates that the change point <NUM> of cell 400A is not relevant or such that it does not indicate a problem in the cell. However, the magnitude of change point <NUM> of cell 400B indicates that change point <NUM> of cell 400B is relevant and shows a deterioration of performance indicator value likely caused by a problem in the cell or the network. Consequently, operation of cell 400B is classified as anomalous and further investigations, maintenance, or corrective actions may be initiated for cell 400B. Since operation of cell 400A is classified normal, no further actions are presently required for cell 400A.

In the context of this method, clustering has the role of differentiating between the datapoints showcasing measures of magnitudes of the change points that are alike or similar in values, forming the main cluster and denoting the normal behavior of the cell population and the outliers. The outliers are the datapoints that are falling outside the main clusters and likely to indicate anomalies in the respective cells' operations. There are various clustering algorithms that could be used, the one used in the example of <FIG> is Isolation Forest. Other alternatives comprise for example PCA (principal component analysis) based methods and (H)DBSCAN (hierarchical density-based spatial clustering of applications with noise).

The embodiments of the present invention provide automated methods for analyzing operation of cells of a communications network. An advantage provided by the embodiments is that the efficiency of network problem resolution may be improved. This is due to narrowing down the list of identified anomalous cells shown to a level that is manageable to the end user, i.e., only cells with relevant change points in their performance indicators are indicated or highlighted to the administration personnel of the network. Thus, maintenance actions may be initiated faster for the most significant issues. Further, automatic maintenance actions may be enabled as certainty of identifying true and significant problems increases. A further advantage is that automatic processing enables continuous network-wide cell monitoring through monitoring performance indicators data. Yet another advantage is that a proactive approach for finding and resolving network faults may be provided, instead of waiting for user complaints to react to.

Various embodiments have been presented. It should be appreciated that in this document, words comprise, include and contain are each used as open-ended expressions with no intended exclusivity.

Claim 1:
A computer implemented method for analyzing operation of cells of a communications network, the method comprising:
obtaining data (<NUM>) comprising performance indicators data from a first cell and a group of reference cells for a selected time period;
identifying, for the first cell, (<NUM>) a first change point of first performance indicators, calculating magnitude of the change of the first performance indicators at the first change point, and defining the calculated magnitude as a first magnitude;
identifying, for the group of reference cells, (<NUM>) a group of reference change points of the first performance indicators, calculating magnitudes of the changes of the group of reference change points, and defining the calculated magnitudes as a group of reference magnitudes;
comparing (<NUM>) the first magnitude to the group of reference magnitudes to determine the relevance of the change point of the first cell; and
providing output (<NUM>) indicating the first change point of the first cell in response to detecting that said change point is determined relevant, or otherwise, providing output indicating that no relevant change points are identified.