Patent Description:
Communication networks comprise 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.

Depending on network structure, cell sizes may vary, and coverage area of neighboring cells may overlap. Due to overlapping coverage area, cells may have certain adverse impact on performance of other cells (e.g. interference). Cells should provide sufficient signal level in the coverage area to ensure quality of service. Moreover, some overlap between neighboring cells is necessary to facilitate reliable handovers, but at the same time adverse impact on performance of other cells due too much overlap is not desired. <CIT> discloses measures for cell relations optimization.

In order to perform educated decisions on network management operations, there is a need to analyze how cells impact each other. For example, trace data can be used for this purpose. The challenge is that trace data is expensive or difficult to obtain.

Any devices and/or methods in the description and/or drawings which are not covered by the claims are examples useful for understanding the invention.

According to a first example aspect of the present invention, there is provided a computer implemented method of cell overlap analysis for cells of a communication network. The method comprises.

In an example embodiment, the method further comprises determining a second impact value reflecting impact of the overlap on the second cell as a ratio of the determined intersecting area and the determined coverage area of the second cell.

In an example embodiment, the method further comprises outputting the determined impact value(s).

In an example embodiment, the method further comprises using the determined impact value(s) for determining value for at least one network parameter in the communication network.

In an example embodiment, the method further comprises using the determined impact value(s) for identifying overshooter cells in the communication network.

In an example embodiment, the method further comprises using the determined impact value(s) for analyzing and adjusting antenna tilts in the communication network.

In an example embodiment, the method further comprises using the determined impact value(s) for detecting and/or reducing overlap between cells in the communication network.

In an example embodiment, the method further comprises using the determined impact value(s) for analyzing and adjusting cell neighborhoods in the communication network.

In an example embodiment, the method further comprises using the determined impact value(s) for controlling energy saving procedures in the communication network.

In an example embodiment, predefined percentile of users is taken into account in determination of the cell coverage of a cell. The predefined percentile of users may be different or same for the first cell and the second cell.

In an example embodiment, the user distribution is determined based on timing advance values obtained from the cells of the communication network.

In an example embodiment, the determination of the cell coverage of a cell is further based on cell coordinates, antenna beam width, and antenna bearing of the respective cell. Also antenna patterns may be used.

In an example embodiment, the cell overlap analysis is performed for a plurality of pairs of first and second cells.

In an example embodiment, the method further comprises periodically repeating the cell overlap analysis.

In an example embodiment, the method further comprises omitting user distribution information obtained during periods of time when at least one of the first cell and the second cell is not in use.

In an example embodiment, the method further comprises splitting coverage areas of at least one of the first and second cells into a plurality of sub-areas and performing the cell overlap analysis separately for different sub areas. The method may further comprise taking into account non-uniform user distribution by giving weight to a certain sub-area based on number of users and/or amount of traffic in the respective sub-area.

In an example embodiment, the method further comprises aggregating, for a given first cell, impact values related to multiple second cells to determine total impact on the first cell.

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 of the present invention, there is provided a computer program comprising computer executable program code which when executed by a processor causes an apparatus to perform the method of the first aspect or any related embodiment.

The computer program of the third aspect may be a computer program product stored on a non-transitory memory medium.

Example embodiments of the present invention and its potential advantages are understood by referring to <FIG> of the drawings. In this document, like reference signs denote like parts or steps.

Example embodiments of the invention provide new mechanisms to analyze cell overlap of communication networks. In this way it is possible to obtain information on how cells impact each other, and this information can be used for performing network management operations with the aim to continuously improve operation of the network. The network management operations may relate for example to finding overshooter or interfered cells, adjusting antenna tilts, detecting and/or reducing overlap between cells, adjusting neighbor relations, and identifying cells suited for energy saving operations.

Certain example embodiments of the invention are based on using user distribution (obtained e.g. from timing advance data and user locations determined based on timing advance data) and network topology data to analyze cell overlap. In some example embodiments, the user distribution may be based on approximate user locations, determined for example based on the timing advance information, which is representative of the signal propagation delay from the user equipment to the base station. Timing advance can be converted into physical distance (e.g. in meters). The timing advance values may be collected as binned histogram, which means that the exact timing advances may not be known, just the number samples falling in certain ranges of values. For example, it could be known that a certain user is between <NUM> and <NUM> meters from the base station. Example embodiments of the invention are applicable also with such limited accuracy data.

Cells may be analyzed in pairs or multiple cells may be taken into account at the same time. The analysis may be limited to certain geographical area or otherwise limited area, but it is possible to analyze a whole network, too.

<FIG> shows an example scenario according to an embodiment. The scenario shows a communication network <NUM> comprising a plurality of cells and base stations and other network devices, and an automation system <NUM> configured to implement (automatic) cell overlap analysis according to example embodiments.

In an embodiment of the invention the scenario of <FIG> operates as follows: In phase <NUM>, the automation system <NUM> obtains data from cells of the network. The data includes e.g. timing advance data that can be used for determining user locations and user distribution in the cells. Also other data may be obtained from the cells or from network design systems. The process may be manually or automatically triggered. The process may be triggered, for example, in response to observing a performance problem or degradation in the network or in a particular area or cell. Additionally or alternatively, the process may be periodically repeated. Data is obtained over a predefined period of time to collect sufficient data for determining cell overlaps. The predefined period of time may be for example minutes, hours, days, weeks, or months and may vary e.g. between <NUM> minutes and <NUM> month. More specific non-limiting examples comprise <NUM> minutes, <NUM> minutes, <NUM> hour, <NUM>-<NUM> hours, <NUM>-<NUM> days, <NUM> week, <NUM> weeks, <NUM> weeks, one month, or some other period of time.

In phase <NUM>, the automation system <NUM> uses the obtained data to analyze cell overlaps in the network. Results of the analysis may be used for determining certain network management operations and for example to determine value for at least one network parameter in the communication network.

In phase <NUM>, any determined network parameter changes or other actions are deployed in the communication network <NUM>.

The process may be repeated for example once a day, every other day, every three days, once a week, every two weeks, once a month, or every two months. By periodically repeating the process, network management operations performed on the basis of the cell overlap analysis adapt to changes in the network load, changes in network usage patterns, and/or changes in the network configuration such as adding new cells or removing or reconfiguring existing cells.

Some of the cells in the network may be temporarily not in use (e.g. powered off) e.g. for energy saving or maintenance purposes. In this case, the neighboring cells typically cover for the cell that is offline (serving users that would be otherwise served by the offline cell). In such cases, cell overlap analysis based on data collected and aggregated over a period including such power offs may be misleading. In an example embodiment, this situation is addressed by adapting the method to only consider the (timing advance) data collected from those time periods where none of the cells whose overlap is being computed is powered off or otherwise offline. The data collected during a time period, when one of the cells is not in use, is omitted.

It is to be noted that although timing advance data is typically collected from a plurality of cells, analysis of cell overlap can be performed individually for individual cell pairs. Alternatively, multiple cell pairs can be analyzed in parallel. Any network management actions resulting from the analyses may be limited so that multiple simultaneous changes are avoided in the same geographical area. In this way it is easier to keep track on changes made in the network and their effects.

<FIG> shows an apparatus <NUM> according to an embodiment. The apparatus <NUM> is for example a general-purpose computer or server 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 diagram illustrating example methods according to certain embodiments. The methods may be implemented in the automation system <NUM> of <FIG> and/or in the apparatus <NUM> of <FIG>. 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 <FIG> 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.

The method of <FIG> provides cell overlap analysis of cells of a communication network for network optimization purposes, and comprises following phases:.

Phase <NUM>: Cell data is obtained. The data may comprise for example dynamic usage data obtained from cells and base stations and network topology data obtained from network design systems. The dynamic usage data may comprise for example timing advance data.

Phase <NUM>: Coverage area of a first cell of the communication network is determined based on user distribution in the first cell.

Phase <NUM>: Coverage area of a second cell of the communication network is determined based on user distribution in the second cell.

The user distribution is determined based on the obtained cell data and more particularly e.g. based on approximate user locations. For example, timing advance data can be used in phases <NUM> and <NUM> for approximating users' distances from the base station of the cell. The timing advance values may be collected as binned histogram, which means that the exact timing advances may not be known, just the number samples falling in certain ranges of values. For example, it could be known that a certain user is between <NUM> and <NUM> meters from the base station or that there are certain number of users between the <NUM>- and <NUM>-meter distance from the base station. If there is a need to find number of users within some other range than the range defined by the bin edges, the user distribution between the bin edges can be interpolated. For the purpose of extrapolating, it can be for example assumed that the users are uniformly distributed between the bin edges. In addition to user distribution also network topology data such as cell coordinates, antenna bearings, antenna patterns, and antenna beam widths can be used in determination of the cell coverage areas.

Phase <NUM>: An intersecting area is determined as an area where the determined coverage area of the first cell and the determined coverage area of the second cell overlap.

Phase <NUM>: A first impact value is determined. The first impact value reflects impact of the overlap on the first cell and is defined as a ratio of the determined intersecting area and the determined coverage area of the first cell. The first impact value may be alternatively defined as reflecting impact of the second cell on the first cell.

Phase <NUM>: A second impact value is determined. The second impact value reflects impact of the overlap on the second cell and is defined as a ratio of the determined intersecting area and the determined coverage area of the second cell. The second impact value may be alternatively defined as reflecting impact of the first cell on the second cell. It is to be noted that it is not mandatory to determine both the first impact value and the second impact value. Instead, only one of these may suffice.

Phase <NUM>: The determined impact value(s) are output so that they can be used for network management operations in order to optimize network. The impact value(s) may be displayed and/or used for one or more of the following: determining value for at least one network parameter in the communication network, identifying overshooter cells, analyzing and adjusting antenna tilts in the communication network, detecting and/or reducing overlap between cells in the communication network, analyzing and adjusting cell neighborhoods in the communication network, and controlling energy saving procedures in the network. In an example embodiment, it is checked if impact values caused by a certain cell or experienced at a certain cell exceed a threshold value. When it is determined that the threshold is exceeded, network management actions are triggered, while impact values below the threshold may be ignored. The impact value that is considered may be an aggregated impact value taking into account interaction between multiple cells.

Phase <NUM>: The phases <NUM>-<NUM> or some of them are repeated when necessary and/or periodically.

It is to be noted that anywhere in this document, the term cell coverage does not necessarily refer to <NUM>% coverage area of the cell, but instead to a significant or desired or experienced coverage area or area that fulfills predefined criteria. For example, the timing advance data does not convey information of the bearing of the users, so the coverage determined using such distance data is going to have some level of uncertainty. This is, however, a reasonable trade-off considering the feasibility of acquiring and analyzing the data.

In an example embodiment, a predefined percentile of users is taken into account in determination of the cell coverage. For example, it may be considered that it suffices to consider the impact that <NUM>% of the users of certain cell cause or to consider the impact on <NUM>% of the users of the cell. <NUM>% of the users furthest apart from the base station may be ignored. The percentile that is applied may vary depending on the implementation. For example, <NUM>th, <NUM>th, <NUM>th, <NUM>th, <NUM>th, or <NUM>th percentile or some other percentile may be used. The predefined percentile of users may be different or the same for the first cell and the second cell. In an example embodiment, the cell coverage area may be split into sub-areas so that each sub-area covers certain percentile(s) of users.

<FIG> illustrate certain cell overlap examples. <FIG> shows a base station <NUM> of a first cell and coverage area <NUM> of the first cell. Likewise, <FIG> shows a base station <NUM> of a second cell and coverage area <NUM> of the second cell. Coverage areas <NUM> and <NUM> of the first and second cell overlap at intersecting area <NUM>. In this example it is likely that the overlap has relatively small impact on both the first and the second cell because it represents only a small portion of either the first cell coverage area <NUM> or the second cell coverage area <NUM>.

In an example embodiment, the coverage area of a cell is approximated by a circular sector pointing in the direction of bearing of antenna of the cell. The origin of the sector is at cell's coordinates (latitude, longitude). Sector's width is based on antenna's beam width. The length of the sector is determined by the distance to users. In practice, the users that are furthest away from the base station of the cell determine the length of the sector.

In an example embodiment, the coverage area <NUM> of the first cell is denoted A<NUM> and the coverage area <NUM> of the second cell is denoted A<NUM>. These may be computed by approximating the corresponding circular sectors with polygons. The geographical area of the polygon of the intersecting area <NUM> is denoted I.

In another example embodiment, the area of the circular sector may be approximated by dividing the area of interest into a grid of points (e.g. <NUM> meter spacing and determining which of the grid points are within each area. The area is then approximated to be directly proportional to the number of grid points falling inside it.

Now, a first impact value O<NUM> reflecting impact of the overlap on the first cell is defined as a ratio of the determined intersecting area and the determined coverage area of the first cell. O<NUM> may be defined as <MAT> where the factor <NUM> is for expressing the result as a percentage.

A second impact value <NUM> reflecting impact of the overlap on the second cell is defined as a ratio of the determined intersecting area and the determined coverage area of the second cell. O<NUM> may be de defined as <MAT> where the factor <NUM> is for expressing the result as a percentage.

<FIG> shows a similar example as <FIG> with a base station <NUM> of a first cell, coverage area <NUM> of the first cell, a base station <NUM> of a second cell, and coverage area <NUM> of the second cell. Coverage areas <NUM> and <NUM> of the first and second cell overlap at intersecting area <NUM>. In this example the coverage area <NUM> of the first cell fully overlaps with the coverage area <NUM> of the second cell and thereby the first cell has an impact on the second cell over the whole coverage area <NUM> of the second cell. Coverage area <NUM> of the second cell on the other hand overlaps only part of the coverage area <NUM> of the first cell. It is clear that in this example the second impact value <NUM> is much larger than the first impact value O<NUM>. This example illustrates that one cell may have significant impact on another cell, but the vice versa is not necessarily true. In principle impact of cells on each other could be approximated for example based on antenna angles and distance between the base stations of the cells but in this example case such approximation would not give accurate results as the inter-cell impact is not balanced. Example embodiments of present disclosure, on the other hand, provide accurate results also in this case.

<FIG> shows an example where certain percentile of users is considered for determination of the coverage area of the cells. In the shown example scenario, the cell coverage area is split into sub-areas, each sub-area covering certain percentile of users. <FIG> shows a base station <NUM> of a first cell, a coverage area <NUM> covering <NUM>th percentile of the first cell, a coverage area <NUM> covering <NUM>th-<NUM>th percentile of the first cell, a base station <NUM> of a second cell, and coverage area <NUM> covering <NUM>th percentile of the second cell, and a coverage area <NUM> covering <NUM>th - <NUM>th percentile of the second cell. It can be seen that the coverage areas <NUM> and <NUM> of the first and second cell overlap at intersecting area <NUM>, and the coverage areas <NUM> and <NUM> of the first and second cell overlap at intersecting area <NUM>. The coverage area <NUM> of the first cell does not have any overlap with the second cell.

In an example setup, the following impact values can be obtained in the scenario of <FIG> using the equations discussed in connection with <FIG>:.

User distribution in cells can be non-uniform with respect to time and distance. Also amount of traffic can be non-uniform as certain users generate more traffic than others. <FIG> illustrate examples related to uneven user distribution. <FIG> shows coverage area of a base station <NUM> split into four sub-areas (<NUM>-<NUM>) each having equal number of users. The sub-areas may be associated with for example <NUM>th (sub-area <NUM>), <NUM>th (sub-area <NUM>), <NUM>th (sub-area <NUM>), and <NUM>th (sub-area <NUM>) percentile of users. It can be seen that for example the sub-area <NUM> is smaller than the other sub-areas, whereby user-density in that area is higher than in the other areas. Also amount of traffic in different sub-areas may be non-uniform. For example, area <NUM> may generate significantly more traffic than area <NUM>. Likewise, two equally sized sub-areas may have different traffic density. That is, the amount of traffic generated in one area may be different from the amount of traffic generated in the other area.

In an example embodiment, non-uniform user distribution and/or amount of traffic can be taken into account by giving weights to the different sub-areas of cells. The sub-areas can be assigned a weight based on number of users in respective sub-area. This is beneficial if certain cell is divided into equally sized sub-areas. Likewise, the amount of traffic in sub-areas (either equally or unequally sized) of a cell can be used to determine and assign weight for the respective sub-area. In this way sub-areas with more users or more traffic cause higher impact values. The weight can be assigned based on one of the following: number of samples at a given distance, area of timing advance zones of a cell, signal attenuation at different distances, number of samples at given distance collected from two or more cells that impact each other.

<FIG> illustrates time varying user distribution. <FIG> shows number of users at different distances in one cell at three different moments of time <NUM>, <NUM> and <NUM>. Distance from the base station of the cell increases from left to right. Vertical line <NUM> indicates a static reference distance from the base station. Vertical lines <NUM>, <NUM> and <NUM> show approximate distance from the base station for <NUM>th percentile of users. It can be seen that at the moment of time <NUM>, the distance of the <NUM>th percentile is less than the reference distance, at the moment of time <NUM>, the distance of the <NUM>th percentile is about the same as the reference distance, and at the moment of time <NUM>, the distance of the <NUM>th percentile is more than the reference distance.

Time variance can be taken into account by collecting information about user distribution over a time period to obtain average user distribution. The information can be collected for example for one week, two weeks, a month or for some other period of time. In this way individual divergent usage patterns and/or sudden short-term changes do not affect the analysis. It is to be noted that in view of implementation of various embodiments, it is possible to analyze data from a shorter or longer period of time. At minimum information at one moment of time is enough for the analysis.

<FIG> illustrate examples related to multiple overlapping cells.

In an example embodiment, impact values related to multiple second cells are aggregated for a given first cell to determine total impact on the first cell. The impact of individual second cells or sub-areas of one or more second cells can be determined using the equations discussed in connection with <FIG>. The impact values thus obtained may then be aggregated (e.g. by adding up the individual impact values) to obtain total impact value for the first cell.

<FIG> shows an example scenario with three partially overlapping cells. The shown example scenario comprises a base station <NUM> of a cell A and coverage area <NUM> of the cell A, a base station <NUM> of a cell B and coverage area <NUM> of the cell B, and a base station <NUM> of a cell C and coverage area <NUM> of the cell C.

In view of the cell A, the cells B and C are second cells possibly causing impact on the cell A. There is overlap with the cell C at intersecting area <NUM> and overlap with the cell B at intersecting area <NUM>. At the intersecting area <NUM> both the cell C and the cell B have an impact on the cell A. An aggregated impact value reflecting total impact on the cell A may be calculated by aggregating the impact values caused by the overlap with the cell C and overlap with the cell B.

In view of the cell B, the cells A and C are second cells possibly causing impact on the cell B. There is overlap with the cell C at intersecting area <NUM> and overlap with the cell A at intersecting area <NUM>. At the intersecting area <NUM> both the cell C and the cell A have an impact on the cell B. An aggregated impact value reflecting total impact on the cell B may be calculated by aggregating the impact values caused by the overlap with the cell C and overlap with the cell A.

In view of the cell C, the cells B and A are second cells possibly causing impact on the cell C. There is overlap with the cell A at intersecting area <NUM> and overlap with the second cell at intersecting area <NUM>. The intersecting areas <NUM> and <NUM> overlap at the intersecting area <NUM>, where both the cell A and the cell B have an impact on the cell C. An aggregated impact value reflecting total impact on the cell C may be calculated by aggregating the impact values caused by the overlap with the cell A and overlap with the cell B.

<FIG> shows an example scenario with three partially overlapping cells. In this example scenario, cell coverage area of at least some cells is further split into sub-areas representing predefined percentiles of users. The shown example scenario comprises a base station <NUM> of a cell A, a base station <NUM> of a cell B, and a base station <NUM> of a cell C. Coverage area of the cell A is split into two sub-areas <NUM> and <NUM>. The sub-area <NUM> may represent for example <NUM>th percentile of users and the sub-area <NUM> may represent for example <NUM>th - <NUM>th percentile of users. Coverage area <NUM> of the cell B may represent for example <NUM>th percentile of users. Coverage area of the cell C is split into two sub-areas <NUM> and <NUM>. The sub-area <NUM> may represent for example <NUM>th percentile of users and the sub-area <NUM> may represent for example <NUM>th - <NUM>th percentile of users. It is to be noted that percentiles defined herein area given simply as an example and different selection of percentiles of users can be used. For example, same percentiles may be used in all cells to provide comparable results, but also other choices are possible.

In view of the cell A, the cells B and C are second cells possibly causing impact on the cell A. The coverage area <NUM> of the cell B overlaps and impacts the sub-area <NUM> of the cell A but not the sub-area <NUM> of the cell A. The sub-areas <NUM> and <NUM> of the cell C partially overlap and thereby impact both the sub-areas <NUM> and <NUM> of the cell A.

In view of the cell B, the cells A and C are second cells possibly causing impact on the cell A. The sub-area <NUM> of the cell A fully overlaps and thus impact the coverage area <NUM> of the cell B. The sub-area <NUM> of the cell C partially overlaps and thereby impacts the coverage area <NUM> of the cell B. The sub-area <NUM> of the cell C and the sub-area <NUM> of the cell B do not have overlapping area with the cell B.

In view of the cell C, the cells B and A are second cells possibly causing impact on the cell A. The coverage area <NUM> of the cell B partially overlaps and impacts the sub-area <NUM> of the cell C but not the sub-area <NUM> of the cell C. The sub-areas <NUM> and <NUM> of the cell A partially overlap and thereby impact both the sub-areas <NUM> and <NUM> of the cell C.

Without in any way limiting the scope, interpretation, or application of the claims appearing below, a technical effect of one or more of the example embodiments disclosed herein is ability to dynamically analyze cell overlaps in an efficient and adaptive manner. The results thus obtained may be used for network management operations and consequently network performance may be improved.

Another technical effect of one or more of the example embodiments disclosed herein is ability to analyze cell overlaps based on data that is easily available without more complicated data such as trace data. Thereby the solution is easy to implement and reliable to follow.

Another technical effect of one or more of the example embodiments disclosed herein is improved analysis as the results of the analysis are based on actual cell size and actual user distribution instead of planned cell sizes. In this way actually experienced cell overlap can be determined. As the cell overlaps are analyzed based on actual experienced service areas and actual user distributions of each cell, there is no need to rely for example on potential of the mobile devices to be served by different cells or on signal levels from neighboring cells.

Yet another technical effect of one or more of the example embodiments disclosed herein is that the analysis is suitable for heterogenous network deployments with varying cell sizes.

Claim 1:
A computer implemented method of cell overlap analysis for cells of a communication network (<NUM>) for the purpose of controlling the communication network, the method comprising
determining (<NUM>, <NUM>) coverage area of a first and a second cell of the communication network;
determining (<NUM>) intersecting area as an area where the determined coverage area of the first cell and the determined coverage area of the second cell overlap; and
determining (<NUM>) a first impact value reflecting impact of the overlap on the first cell as a ratio of the determined intersecting area and the determined coverage area of the first cell;
characterized in that the determination of the coverage area of a cell is based on user distribution in the respective cell and wherein the user distribution of a cell is based on approximate user locations in the respective cell.