FI20215247A1

FI20215247A1 - A method for analysing parameters in a communications network

Info

Publication number: FI20215247A1
Application number: FI20215247A
Authority: FI
Inventors: Tero Isotalo
Original assignee: Elisa Oyj
Priority date: 2021-03-08
Filing date: 2021-03-08
Publication date: 2022-07-15
Also published as: WO2022189699A1; FI129705B

Abstract

A computer implemented method for analysing parameter values of cells of a communications network. The method comprises: obtaining (301) parameter data of a first cell, wherein measured parameter values are stored in data bins; analysing (302) distribution of the measured parameter values to determine if the parameter data is descriptive; and responsive (303) to determining that the parameter data is non-descriptive, modifying the data bins for storing future measured parameter values.

Description

A METHOD FOR ANALYSING PARAMETERS IN A COMMUNICATIONS NETWORK

TECHNICAL FIELD The present disclosure generally relates to analysing parameters in a communications network. The disclosure relates particularly, though not exclusively, to analysing user equipment distances of cells of a communications network.

BACKGROUND This section illustrates useful background information without admission of any technique described herein representative of the state of the art.

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 neighbouring cells.

Detailed knowledge of parameter distribution within a cell coverage area may improve optimization operations of a network. The present invention provides a new approach for analysing parameter information.

SUMMARY S The appended claims define the scope of protection. Any examples and technical 6 descriptions of apparatuses, products and/or methods in the description and/or drawings 2 not covered by the claims are presented not as embodiments of the invention but as © 25 background art or examples useful for understanding the invention.

I a > According to a first example aspect there is provided a computer implemented method for Nn S analysing parameter values of cells of a communications network. The method comprises:

LO N obtaining parameter data of a first cell, wherein measured parameter values are stored in N data bins; analysing distribution of the measured parameter values to determine if the parameter data is descriptive; and responsive to determining that the parameter data is non-descriptive, modifying the data bins for storing future measured parameter values.

In an embodiment, the parameter data is determined non-descriptive if the parameter data comprises a single bin or a group of few bins storing a significant amount of the measured parameter values; and responsively the data bins are modified such that accuracy is increased in values around the values of said bin or said group of few bins.

In an embodiment, the parameter data is determined non-descriptive if a significant amount of the measured parameter values is stored in a bin of largest values or in a group of bins of largest values; and responsively the data bins are modified such that accuracy is increased in larger values.

In an embodiment, the parameter data is determined non-descriptive if a significant amount of the measured parameter values is stored in a bin of smallest values or in a group of bins of smallest values; and responsively the data bins are modified such that accuracy is increased in smaller values. In an embodiment, the parameter data is determined non-descriptive if the parameter data comprises at least one bin storing an insignificant amount of the measured parameter — values; and responsively the data bins are modified such that accuracy is decreased in values around the values of said at least one bin. In an embodiment, the significant amount of the measured parameter values comprises at least 0.5 — 10 %, or 10-20 % or 20-30 % or 30-40 % or 40-50 % of the total count of the measured parameter values. In an embodiment, modifying the data bins comprises changing number of bins and/or changing bin sizes and/or changing bin borders. In an embodiment, modifying the data bins comprises switching between pre-defined bin set configurations. N In an embodiment, measured parameter values stored before previous modification of data N . . . . & 25 bins are ignored in the analysis. o 00 In an embodiment, the parameter data comprises any of: user eguipment distance data; I timing advance, TA, data; signal level data; signal quality data; reference signals received Ao - power, RSRP, data; and reference signal received quality, RSRQ, data.

NN + N In an embodiment, the measured parameter values are stored in 5-50 bins or in 10, 20, 30, N 30 — 40, 50 or 100 bins.

N In an embodiment, the bins are eguidistant or non-eguidistant.

In an embodiment, the analysis is performed provided that the parameter data comprises at least a pre-defined number of measured parameter value, wherein the pre-defined number is 1000-200000. 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. 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. According to a fourth example aspect there is provided a computer program product comprising a non-transitory computer readable medium having the computer program of the third example aspect stored thereon. According to a fifth example aspect there is provided an apparatus comprising means for performing the method of any preceding aspect. Any foregoing memory medium may comprise a digital data storage such as a data disc or diskette; optical storage; magnetic storage; holographic storage; opto-magnetic storage; phase-change memory; resistive random-access memory, magnetic random-access memory; solid-electrolyte memory; ferroelectric random-access memory; organic memory; — or polymer memory. The memory medium may be formed into a device without other substantial functions than storing memory or it may be formed as part of a device with other functions, including but not limited to a memory of a computer; a chip set; and a sub assembly of an electronic device. = Different non-binding example aspects and embodiments have been illustrated in the a 25 foregoing. The embodiments in the foregoing are used merely to explain selected aspects 2 or steps that may be utilized in different implementations. Some embodiments may be 2 presented only with reference to certain example aspects. It should be appreciated that = corresponding embodiments may apply to other example aspects as well. a

N BRIEF DESCRIPTION OF THE FIGURES N

LO N 30 Some example embodiments will be described with reference to the accompanying figures,

O N in which: Fig. 1 shows an example scenario according to an embodiment;

Fig. 2 shows a block diagram of an apparatus according to an example embodiment; Figs. 3A-B show flow charts according to example embodiments; and Figs. 4A-B show some example embodiments of selecting descriptive bins.

DETAILED DESCRIPTION In the following description, like reference signs denote like elements or steps. Fig. 1 shows an example scenario according to an embodiment. The scenario shows a communication network 101 comprising a plurality of cells and base station sites and other network devices, and an automated system 111 configured to analyse a distribution of parameter values of a cell of a base station.

In an embodiment of the invention the scenario of Fig. 1 operates as follows: In phase 11, the automated system 111 receives data related to a network. The data may be obtained from the network and/or from a data storage. The received data may comprise any of the following: base station identifier data, cell identifier data, antenna identifier data, antenna configuration data, user equipment distance data, and performance indicators data. In an example embodiment, the user equipment distance data is timing advance (TA) data. Other network and/or user equipment related data may also be comprised.

In phase 12, the automated system 111 uses the received data to analyse a parameter value distribution of a selected cell. In an embodiment, the parameter data is stored as binned data, i.e., the measured parameter values are stored into pre-defined bins. In an embodiment, the automated system 111 analyses the parameter data by investigating how the samples distribute into the bins. Multiple cells may also be analysed.

In phase 13, the automated system 111 may change the bins if the data stored in the bins does not describe parameter values comprehensively. The bins may be changed if a N significant amount of the measured parameter samples is in the bin of smallest values, in N 25 the bin of largest values, or in another single bin. The binned data may also be non- 3 descriptive if a significant part the of measured values are in a few bins of the largest or S smallest values, or in another set of a few bins. The binned data may also be non-descriptive E if it comprises one or more bins comprising insignificant number of measured parameter K samples. A more descriptive measurement data may be obtained if more bins are focused N 30 around values where a significant portion of the measurement values are laid. In an N embodiment, the forthcoming measurement values are stored in the modified bins. - In an example embodiment, the parameter data is used for network analysis and/or network optimization operations provided that the parameter data is analysed to be descriptive.

In an example embodiment, the stored parameter data is determined descriptive if it comprises a representative sample of the parameter values.

In an embodiment, the stored parameter data is determined non-descriptive if comprises an unrepresentative sample or a biased sample of the parameter values.

In an embodiment, the stored parameter data is 5 determined descriptive if its statistical features at least approximately coincide with the statistical features of the parameter values.

In an embodiment, the stored parameter data is determined non-descriptive if its statistical features do not at least approximately correspond to the statistical features of the parameter values.

In an embodiment, the stored parameter data is determined descriptive if it is not non-descriptive.

In an embodiment, the stored parameter data is determined non-descriptive if it is not descriptive.

Fig. 2 shows a block diagram of an apparatus 200 according to an example embodiment.

The apparatus 200 comprises a communication interface 210; a processor 220; a user interface 230; and a memory 240. The apparatus 200 can be used for implementing at least some embodiments of the invention.

That is, with suitable configuration the apparatus 200 — is suited for operating for example as the automated system 111. The communication interface 210 comprises in an embodiment a wired and/or wireless communication circuitry, such as Ethernet; Wireless LAN; Bluetooth; GSM; CDMA; WCDMA; LTE; and/or 5G circuitry.

The communication interface can be integrated in the apparatus 200 or provided as a part of an adapter, card or the like, that is attachable to the apparatus 200. The communication interface 210 may support one or more different communication technologies.

The apparatus 200 may also or alternatively comprise more than one of the communication interfaces 210. In this document, a processor may refer to a central processing unit (CPU) a microprocessor; a digital signal processor (DSP); a graphics processing unit; an application — 25 — specific integrated circuit (ASIC); a field programmable gate array; a microcontroller; or a O combination of such elements. g The user interface may comprise a circuitry for receiving input from a user of the apparatus 2 200, e.g., via a keyboard; graphical user interface shown on the display of the apparatus = 200; speech recognition circuitry; or an accessory device; such as a headset; and for N 30 providing output to the user via, e.g., a graphical user interface or a loudspeaker.

S$ © The memory 240 comprises a work memory 242 and a persistent memory 244 configured O to store computer program code 246 and data 248. The memory 240 may comprise any one or more of: a read-only memory (ROM); a programmable read-only memory (PROM); an 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; a solid- state drive (SSD); or the like. The apparatus 200 may comprise a plurality of the memories

240. The memory 240 may be constructed as a part of the apparatus 200 or as an attachment to be inserted into a slot; port; or the like of the apparatus 200 by a user or by another person or by a robot. The memory 240 may serve the sole purpose of storing data, or be constructed as a part of an apparatus 200 serving other purposes, such as processing data. A skilled person appreciates that in addition to the elements shown in Figure 2, the apparatus 200 may comprise other elements, such as microphones; displays; as well as — additional circuitry such as input/output (I/O) circuitry; memory chips; application-specific integrated circuits (ASIC); processing circuitry for specific purposes such as source coding/decoding circuitry; channel coding/decoding circuitry; ciphering/deciphering circuitry; and the like. Additionally, the apparatus 200 may comprise a disposable or rechargeable battery (not shown) for powering the apparatus 200 if external power supply is not available. Figs. 3A-B show flow charts according to example embodiments. Fig. 3A illustrates a computer implemented method for analysing parameter values 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: 301: Obtaining parameter data of a first cell, wherein measured parameter values are stored in data bins. In an embodiment, the parameter data is stored as binned data to save storage space. In an embodiment, the parameter data is stored as binned data to obtain a statistical distribution of the parameter values. - 25 In an embodiment, the measured parameter values are stored in 5-50 bins. In an O embodiment, the measured parameter values are stored in 10, 20, 30, 40, 50 or 100 bins. se In an embodiment, the bins are eguidistant. In an embodiment, the bins are non-eguidistant. 0 In an example embodiment, the parameter data comprises user eguipment distance data. 7 In an example embodiment, the user equipment distances are measured using timing & 30 advance (TA) method. Other methods for obtaining approximate or accurate user i eguipment distances may also be used. In an example embodiment, the parameter data O comprises signal level or signal guality data, for example, reference signals received power O (RSRP) data or reference signal received quality (RSRQ) data. 302: Analysing distribution of the measured parameter values to determine if the parameter data is descriptive. A distribution calculated from parameter data stored in unsuitable bins may not describe a distribution of measured parameter values correctly.

In an embodiment, the analysis is performed once a week, every two weeks, or once a month or with other suitable intervals. In an embodiment, the analysis is performed first time 7-14 days after a previous bin modification and then daily.

In an embodiment, the analysis is performed provided that the parameter data comprises at least a pre-defined number of measured parameter values. In an example embodiment the pre-defined number is 1000-200000. The pre-defined number may depend on analysis interval. In an example embodiment, the pre-defined number is on average 1000-2000 measured samples per day during the analysis interval.

In an example embodiment, measured parameter values stored before previous modification of data bins are ignored in the analysis.

303: Responsive to determining that the parameter data is non-descriptive, modifying the data bins for storing future measured parameter values.

In an embodiment, modifying the data bins comprises changing bin sizes or bin widths. In an embodiment, modifying the data bins comprises changing bin borders. In an embodiment, modifying the data bins comprises changing number of bins. In an embodiment, only some of the bins are modified. In an example embodiment, modifying the data bins comprises switching between pre-defined bin set configurations.

304: Optionally, the parameter data is determined non-descriptive if the parameter data comprises a single bin or a group of few bins storing a significant amount of the measured parameter values. The parameter data may be determined non-descriptive if a significant amount of the measured parameter values is stored in a single bin. The parameter data may also be determined non-descriptive if a significant amount of the measured parameter — values is stored in a group of few bins. The parameter data may also be determined non- N descriptive if the parameter data comprises multiple bins each storing a significant amount N of the measured parameter values. As a conseguence, the data bins are modified such that S accuracy is increased in values around the values of said bin or said bins.

3 The number of bins in the group of few bins may depend on the total number of bins in the E 30 parameter data. The group of few bins may comprise adjacent bins. The group of few bins KN may comprise nearby bins. The group of few bins may comprise apart bins.

N In an embodiment, the significant amount of the measured parameter values comprises at N least 0.5— 10 % of the total count of the measured parameter values. In some embodiments, N the significant amount of the measured parameter values comprises at least 10-50 % of the total count of the measured parameter values, or 10-20 %, or 20-30 %, or 30-40, or

40-50 %. The percentage for the significant amount may depend on the total number of bins. For example, for binned data having 10 bins the percentage for the significant amount may be higher than for binned data having 50 bins.

305: Optionally, the parameter data is determined non-descriptive if a significant amount of the measured parameter values is stored in a bin of largest values. The parameter data may also be determined non-descriptive if a significant amount of the measured parameter values is stored in a group of bins of largest values or in a few bins of largest values. As a consequence, data bins are modified such that accuracy is increased in larger values.

306: Optionally, the parameter data is determined non-descriptive if a significant amount of the measured parameter values is stored in a bin of smallest values. The parameter data may also be determined non-descriptive if a significant amount of the measured parameter values is stored in a group of bins of smallest values or in a few bins of smallest values. As a conseguence, the data bins are modified such that accuracy is increased in smaller values.

307: Optionally, the parameter data is determined non-descriptive if the parameter data comprises at least one bin storing an insignificant amount of the measured parameter values. As a conseguence, the data bins are modified such that accuracy is decreased in values around the values of said at least one bin. The percentage defining the insignificant amount may depend on total number of bins. In an example embodiment, the percentage is 0-5 % or 0-10%.

Fig. 3B illustrates the method of Fig. 3A applied to analysing user equipment distances of cells of a communications network comprising steps: 321: Obtaining user equipment distance data of a first cell, wherein measured user equipment distances are stored in data bins. N 25 In an embodiment, the user eguipment distance data is stored as binned data to obtain a N statistical distribution of the user eguipment distances. 2 In an example embodiment, the user eguipment distances are measured using timing © advance (TA) method. Other methods for obtaining approximate or accurate user = eguipment distances may also be used. i 30 322: Analysing distribution of the measured user eguipment distances to determine if the 5 user equipment distance data is descriptive. N 323: Responsive to determining that the user equipment distance data is non-descriptive, modifying the data bins for storing future measured user equipment distances.

324: Optionally, the user equipment distance data is determined non-descriptive if the user equipment distance data comprises a single bin or a group of few bins storing a significant amount of the measured user equipment distances.

The user equipment distance data may be determined non-descriptive if a significant amount of the measured user equipment distances is stored in a single bin.

The user equipment distance data may also be determined non-descriptive if a significant amount of the measured user equipment distances is stored in a group of few bins.

The user equipment distance data may also be determined non-descriptive if the user equipment distance data comprises multiple bins each storing a significant amount of the measured user equipment distances.

As a consequence, the data bins are modified such that accuracy is increased in distances around the distances of said bin or said bins. 325: Optionally, the user equipment distance data is determined non-descriptive if a significant amount of the measured user equipment distances is stored in a bin of largest distances.

The user equipment distance data may also be determined non-descriptive if a — significant amount of the measured user equipment distances is stored in a group of bins of largest distances or in a few bins of largest distances.

As a consequence, data bins are modified such that accuracy is increased in larger distances. 326: Optionally, the user equipment distance data is determined non-descriptive if a significant amount of the measured user equipment distances is stored in a bin of shortest distances.

The user equipment distance data may also be determined non-descriptive if a significant amount of the measured user equipment distances is stored in a group of bins of shortest distances or in a few bins of shortest distances.

As a consequence, the data bins are modified such that accuracy is increased in shorter distances. 327: Optionally, the user equipment distance data is determined non-descriptive if the user - 25 equipment distance data comprises at least one bin storing an insignificant amount of the O measured user eguipment distances.

As a conseguence, the data bins are modified such se that accuracy is decreased in distances around the distances of said at least one bin. 2 Figs. 4A-B show some example embodiments of selecting descriptive bins.

In the example = embodiments of Figs. 4A-B, user equipment distance data is analysed according to the N 30 method of Fig. 3B.

Fig. 4A shows an example with three pre-defined bin set configurations N A, B, and C.

In this example, each bin set configuration has 10 bins.

The upper bin border N is shown in the corresponding column in kilometres (km), except for the bin with highest N values, i.e., the tenth bin.

In the last bin, all values exceeding the upper bin border of the ninth bin are stored.

Bin set A has eguidistant bins, while bin sets B and C have non-

equidistant bins.

If bin set A is used and a significant amount of measured user equipment distances are stored in bin 10 or bins 9-10, it may be concluded that user equipment distance data is non- descriptive, and bin set A may be switched to bin set B.

Switching from bin set A to bin set Bincreases accuracy at longer distances.

If bin set B is used and a significant amount of measured user equipment distances are stored in bin 1 and in bin 10, it may be beneficial to switch to bin set C to increase accuracy at small distances and also at long distances.

If bin set C is used and a significant amount of measured user equipment distances are stored in bin 7, i.e., in a single bin storing distance between 1-5 km, it may be beneficial to switch to bin set B to increase accuracy between 1-5 km.

Fig. 4B shows an example of bin sets D and E which may be non-pre-defined bin set configurations.

At the beginning bin set D with 6 bins may be used.

If a significant amount of the measured user equipment distances is between 1-2 km, i.e., stored in bin 3 of bin set D, it may be determined that more dense binning is required at said distance interval to obtain more descriptive user equipment distance data.

Thus, new bins with upper borders at1.2, 1.4, 1.6, and 1.8 km may be added, as shown in bin set E.

In another example, while using bin set E, it may be determined that insignificant amount of user equipment distances is stored in bins 3-6. Consequently, said bins may be removed — so that bins shown in bin set D are used.

Alternatively, only some of bins 3-6 may be removed.

Without in any way limiting the scope, interpretation, or application of the appended claims, a technical effect of one or more of the example embodiments disclosed herein is that more = accurate distribution of the parameter values may be obtained.

An advantage is also that < 25 data bins for storing parameter values may be customised for each cell.

Consequently, a se further advantage is that network analysis, optimization, maintenance and/or repair © operations may be enhanced due to improved accuracy of stored parameter values, and/or I especially due to improved user location knowledge or improved user ranging information. a KN Any of the afore described methods, method steps, or combinations thereof, may be N 30 controlled or performed using hardware; software; firmware; or any combination thereof.

N The software and/or hardware may be local; distributed; centralised; virtualised; or any N combination thereof.

Moreover, any form of computing, including computational intelligence, may be used for controlling or performing any of the afore described methods,

method steps, or combinations thereof. Computational intelligence may refer to, for example, any of artificial intelligence; neural networks; fuzzy logics; machine learning; genetic algorithms; evolutionary computation; or any combination thereof. 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. The foregoing description has provided by way of non-limiting examples of particular implementations and embodiments a full and informative description of the best mode presently contemplated by the inventors for carrying out the invention. It is however clear to a person skilled in the art that the invention is not restricted to details of the embodiments presented in the foregoing, but that it can be implemented in other embodiments using equivalent means or in different combinations of embodiments without deviating from the characteristics of the invention. Furthermore, some of the features of the afore-disclosed example embodiments may be used to advantage without the corresponding use of other features. As such, the foregoing description shall be considered as merely illustrative of the principles of the present invention, and not in limitation thereof. Hence, the scope of the invention is only restricted by the appended patent claims.

N O N

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Claims

1. A computer implemented method for analysing parameter values of cells of a communications network, the method comprising: obtaining (301) parameter data of a first cell, wherein measured parameter values are stored in data bins; analysing (302) distribution of the measured parameter values to determine if the parameter data is descriptive; and responsive (303) to determining that the parameter data is non-descriptive, modifying — the data bins for storing future measured parameter values.

2. The method of claim 1, wherein: the parameter data is determined (304) non-descriptive if the parameter data comprises a single bin or a group of few bins storing a significant amount of the measured parameter values; and responsively the data bins are modified such that accuracy is increased in values around the values of said bin or said group of few bins.

3. The method of claim 1 or 2, wherein: the parameter data is determined (305) non-descriptive if a significant amount of the measured parameter values is stored in a bin of largest values or in a group of bins of largest — values; and responsively the data bins are modified such that accuracy is increased in larger values.

4. The method of claim 1 or 2 or 3, wherein: the parameter data is determined (306) non-descriptive if a significant amount of the measured parameter values is stored in a bin of smallest values or in a group of bins of N 25 smallest values; and responsively N the data bins are modified such that accuracy is increased in smaller values.

O 2

5. The method of any of the preceding claims, wherein: © the parameter data is determined (307) non-descriptive if the parameter data

I a. comprises at least one bin storing an insignificant amount of the measured parameter i 30 values; and responsively O the data bins are modified such that accuracy is decreased in values around the O values of said at least one bin.

6. The method of any of any of claims 2-4, the significant amount of the measured parameter values comprises at least 0.5 — 10 %, or 10-20 % or 20-30 % or 30-40 % or 40-50 % of the total count of the measured parameter values.

7. The method of any of the preceding claims, wherein modifying the data bins comprises changing number of bins and/or changing bin sizes and/or changing bin borders.

8. The method of any of the preceding claims, wherein modifying the data bins comprises switching between pre-defined bin set configurations.

9. The method of any of the preceding claims, wherein measured parameter values stored before previous modification of data bins are ignored in the analysis.

10. The method of any of the preceding claims, wherein the parameter data comprises any of: user equipment distance data; timing advance, TA, data; signal level data; signal guality data; reference signals received power, RSRP, data; and reference signal received guality, RSRO, data.

11. Themethodofany of the preceding claims, wherein the measured parameter values are stored in 5-50 bins or in 10, 20, 30, 40, 50 or 100 bins.

12. The method of any of the preceding claims, wherein the bins are equidistant or non- equidistant.

13. The method of any of the preceding claims, wherein the analysis is performed provided that the parameter data comprises at least a pre-defined number of measured parameter value, wherein the pre-defined number is 1000-200000.

14. An apparatus (200, 111) comprising: a processor (220); and a memory (240) including computer program code (246); the memory (240) and the — computer program code (246) configured to, with the processor (220), cause the apparatus N - S (200, 111) to perform the method of any of the claims 1-13.

O O 25

15. A computer program comprising computer executable program code (246) which S when executed by a processor (220) causes an apparatus (200, 111) to perform the method E of any of the claims 1-13.

NN +

N

LO

N

O

N