WO2021260262A1

WO2021260262A1 - Automated prioritization of capacity expansion in communication networks

Info

Publication number: WO2021260262A1
Application number: PCT/FI2021/050447
Authority: WO
Inventors: Teemu PESU
Original assignee: Elisa Oyj
Priority date: 2020-06-23
Filing date: 2021-06-15
Publication date: 2021-12-30
Also published as: FI129885B; FI20205657A1

Abstract

A computer implemented method for prioritizing capacity expansion in a communication network. Performance of a set of cells of the communication network is analyzed (301) by calculating (303) for each cell of the set a weight factor based on a customer impact value of the cell, a target service level and an experienced service level in the cell; and the weight factors are used (304) for providing priority order for capacity expansion of the communication network

Description

AUTOMATED PRIORITIZATION OF CAPACITY EXPANSION IN COMMUNICATION NETWORKS

TECHNICAL FIELD

The present disclosure generally relates to capacity expansions in communication networks. The disclosure relates particularly, though not exclusively, to automated prioritization of capacity expansion.

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 neighboring cells.

In general, there is ongoing need to increase or expand capacity of the networks as number of users and amount of traffic increases in the networks. Each capacity expansion causes additional costs for the operator of the network and therefore there is a need to prioritize capacity expansion so that capacity increases are performed in places where they are most needed.

One possible way to prioritize capacity expansion is to monitor throughput (or data speed) of users during the busiest hour of day and to perform capacity increases in cells where the data speed drops below a predefined threshold, such as 5 Mbit/s, during the busiest hour in the cell. The challenge is that there may be many cells like this. Now a new approach is taken for prioritizing capacity expansion.

SUMMARY

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

According to a first example aspect there is provided a computer implemented method for prioritizing capacity expansion in a communication network. The method comprises analyzing performance of a set of cells of the communication network by calculating for each cell of the set a weight factor based on a customer impact value of the cell, a target service level and an experienced service level in the cell; and using the weight factors for providing priority order for capacity expansion of the communication network.

In some example embodiments, the weight factor is calculated by formula: weight factor = customer impact value ^* target service level / experienced service level.

In some example embodiments, the service level is defined by throughput.

In some example embodiments, the customer impact value of a cell depends on number of users in the cell and/or number of connection establishment requests in the cell.

In some example embodiments, the customer impact value is calculated by formula: customer impact = Max (number of users ion the cell, number of connection establishment requests in the cell / predefined constant).

In some example embodiments, in the predefined constant is 25-100. In certain example embodiment, the predefined constant is 50. In some example embodiments, the method further comprises using the weight factors of the cells for arranging the cells of the set in priority order for capacity expansion.

In some example embodiments, the method further comprises aggregating weight factors on base station sector level to obtain first aggregated weight factors for base station sectors of the communication network; and using the first aggregated weight factors for arranging the base station sectors of the communication network in priority order for capacity expansion.

In some example embodiments, the method further comprises aggregating weight factors on base station site level to obtain second aggregated weight factors for base station sites of the communication network; and using the second aggregated weight factors for arranging base station sites of the communication network in priority order for capacity expansion.

In some example embodiments, the set of cells comprises a subset of all cells of the communication network. Alternatively, the set of cells may comprise all cells of the communication network.

In some example embodiments, the method further comprises receiving performance indicator values from the communication network; and determining the customer impact value and the experienced service level based on the received performance indicator values.

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. 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 the first aspect or any related embodiment.

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 foregoing. The embodiments in the foregoing are used merely to explain selected aspects or steps that may be utilized in different implementations. Some embodiments may be presented only with reference to certain example aspects. It should be appreciated that corresponding embodiments may apply to other example aspects as well.

BRIEF DESCRIPTION OF THE FIGURES

Some example embodiments will be described with reference to the accompanying figures, in which:

Fig. 1 schematically shows an example scenario according to an example embodiment;

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

Fig. 3 shows a flow diagram illustrating example methods according to certain embodiments; and

Figs. 4-5 show graphs illustrating some example cases. DETAILED DESCRIPTION

In the following description, like reference signs denote like elements or steps.

Embodiments of the present disclosure provide such method for prioritizing capacity expansion in communication network that takes into account customer experience instead of simply looking at throughput or some other criterion related to load in a cell.

Fig. 1 schematically 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 operations support system, OSS, 102 configured to manage operations of the communication network 101. Further, the scenario shows an automation system 111. The automation system 111 is configured to implement automated prioritization of capacity expansion in the communication network 101 . The automation system 111 is operable to interact with the OSS 102 for example to receive performance data relating to performance of cells of the communication network 101 from the OSS 102. It is to be noted that in some alternative implementation the performance data may be received through some other system than the OSS 102 and that the data is not necessarily received directly from the OSS 102.

The automation system 111 is configured to implement at least some example embodiments of present disclosure.

In an embodiment of the invention the scenario of Fig. 1 operates as follows: The automation system 111 receives performance data relating to performance of cells of the communication network 101.

The performance data is automatically analysed in the automation system 111 to arrange network locations (e.g. cells, base station sites or sectors) of the communication network 101 in priority order for the purpose of capacity expansion. The results of the analysis may be provided for further automated processes running in the automation system 111 or shown on a display or otherwise output to users such as network operator personnel. The network operator personnel may then implement capacity expansion in the provided priority order. Capacity expansion may involve for example adding new cells, new frequencies and/or new network equipment and/or to updating or replacing cells, frequencies and/or network equipment.

The analysis may be automatically or manually triggered. The analysis may be periodically repeated for example once a week, every two weeks, once a month or even once a day.

Fig. 2 shows a block diagram of 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 at least some embodiments of the invention. That is, with suitable configuration the apparatus 20 is suited for operating for example as the automation system 111 or the expert profile module 112 of foregoing disclosure.

The apparatus 20 comprises a communication interface 25; a processor 21 ; a user interface 24; and a memory 22. 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.

The processor 21 may comprise 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.

The user interface 24 is configured for providing interaction with a user of the apparatus. Additionally or alternatively, the user interaction may be implemented through the communication interface 25. The user interface 24 may comprise a circuitry for receiving input from a user of the apparatus 20, e.g., via a keyboard, graphical user interface shown on the display of the apparatus 20, speech recognition circuitry, or an accessory device, such as a headset, and for providing output to the user via, e.g., a graphical user interface or a loudspeaker.

The memory 22 may comprise 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 20 may comprise a plurality of memories. The memory 22 may serve the sole purpose of storing data, or be constructed as a part of an apparatus 20 serving other purposes, such as processing data.

The communication interface 25 may comprise communication modules that implement data transmission to and from the apparatus 20. The communication modules may comprise a wireless or a wired interface module(s) or both. 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 4G) or NR (New Radio or 5G) radio module. The wired interface may comprise such as Ethernet or universal serial bus (USB), for example. The communication interface 25 may support one or more different communication technologies. The apparatus 20 may additionally or alternatively comprise more than one of the communication interfaces 25.

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.

Fig. 3 shows a flow diagram 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 the flow diagrams 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 diagrams is not mandatory.

The method of Fig. 3 provides prioritizing capacity expansion in a communication network. The method of Fig. 3 comprises the following phases:

301 : Analysis of performance of a set of cells is started. This may be based on performance indicator values received from the communication network. The performance indicator values may comprise for example one or more of the following non-exclusive list: throughput, data speed, MCS (modulation and coding scheme), information about MIMO (multiple input, multiple output) usage, signal level, signal quality, number of users, number of connection establishment requests in the cells of the set.

The set of cells may comprise all cells of the communication network or a subset of all cells of the communication network. That is, is not necessary to analyze the whole network at a time, but clearly that is possible.

302: Customer impact value is determined for the cells of the set.

In an embodiment, the customer impact value of a cell depends on number of users in the cell. Additionally or alternatively, the customer impact value of a cell may depend on number of connection establishment requests in the cell.

In certain example implementation, the customer impact value is calculated by formula: customer impact = Max (number of users in the cell, number of connection establishment requests in the cell / predefined constant).

That is, the number of users in the cell and the number of connection establishment requests in the cell are considered and the larger one is chosen.

The predefined constant is selected so that it will make the number of users and number of connection establishment requests comparable. In certain examples it may be considered that 50 connection establishment requests corresponds to one user based on that it may be considered that one users creates 50 connection establishment requests in an hour. In such case the constant may be selected to be 50. In general, the predefined constant may be for example between 25-100. The suitable value for the predefined constant may vary depending on usage profile of the network.

By taking into account the number of connection establishment requests, the method is able to take into account traffic generated by users that briefly visit the cell in question, but do not necessarily stay in the cell for a longer period of time. In this way, cells that may have short period of usage, but nevertheless have significant customer impact due to the amount of connection establishment requests, can be properly treated in prioritization.

By taking into account the combined effect of number of users and number of connection establishment requests, the customer impact value reflects actual customer impact that the service level in the cell may have.

303: A weight factor is calculated for each cell of the set of cells. The weight factor is calculated based on the customer impact value of the cell, a target service level and an experienced service level in the cell.

In certain example implementation, the weight factor is calculated by formula: weight factor = customer impact value ^* target service level / experienced service level.

The service level may be given in terms of throughput, but some other definition of service level may be used, too. The experienced service level may be average or median service level. The average or median may be calculated over different users in the cell and/or over a period of time (e.g. over a plurality of days). Additionally or alternatively, the experienced service level may be experienced service level during a busy hour (the busiest hour of the day). In an example, the target service level is 5 Mbit/s throughput, but clearly other target values can be applied, too. E.g. 10 Mbit/s throughput could be set as the target service level. Still further, the target service level and the experienced service level may be defined in downlink direction or in uplink direction or in both directions.

In a further embodiment, the calculation of the weight factor takes into account duration of experienced poor service level. This may be implemented for example by taking into account time period during which the experienced service level is below the target service level may. That is, for example time period during which average or median throughput is below a target throughput is taken into account.

304: The weight factors of the cells are used for providing priority order for capacity expansion of the communication network. The weight factors may be output or provided for further processing.

In a first alternative, the cells are arranged in priority order based on the weight factors. In a second alternative, the weight factors of the cells are aggregated in predefined batches and the prioritization is performed based on the aggregated weight factors. The weight factors may be aggregated for example on base station sector level or on base station site level. It may be considered that weight factors of cells that provide at least partially overlapping service area are aggregated to evaluate performance and capacity expansion need in that area. In this way a single poorly behaving cell does not necessarily trigger the need for capacity expansion if the other cells serving the same area may compensate the overall customer experience. Additionally or alternatively, it may be considered that weight factors of cells that use at least partially the same network equipment or originate from the same base station site are aggregated to evaluate performance in the area served by certain base station site. In this way, capacity expansion in certain base station site is not necessarily triggered by single poorly behaving cell. Capacity expansion usually requires that maintenance personnel physically visits the base station site and therefore it is beneficial to perform capacity expansion for more than one co located cell at a time, if possible.

It is to be noted that even though multiple cells are analysed and arranged in priority order, the weight factor of each cell can be determined individually without needing to have data from other cells.

Figs. 4-5 show graphs illustrating some example cases.

Fig. 4 shows how average throughput per user 401 varies during a day. Line 405 shows a target throughput threshold. The aim is to maintain the throughput above the threshold 405. It can be seen that the throughput briefly drops below the threshold 405 around 21 :00.

Fig. 5 shows how average throughput per user of three different cells within one sector 501-503 vary during a day. Also in this example, line 405 shows a target throughput threshold. The aim is to maintain the throughput above the threshold 405. It can be seen that the throughput of all three cells is below the threshold most of the day.

In a conventional method, where solely throughput, average throughput, cell resource utilization degree or the like is used for prioritizing or deciding on capacity expansion, examples of Figs. 4 and 5 would both evaluate equally important for capacity expansion. Whereas, with the methods of present disclosure, the example of Fig. 5 would be ranked higher in priority order than the example of Fig. 4. That is, the example where customer impact is more significant (more users are affected), is given higher priority. In this way, customer experience can be taken into account without additional manual evaluation of cases that result in requiring capacity expansion.

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 improved prioritization of capacity expansion in a communication network. In particular, the example embodiments take into account user experience so that capacity expansion can be directed to locations where the capacity expansion is most effective in increasing customer experience and ensuring customer satisfaction hence making operator investments more effective. In certain practical experiments, it has been seen that customer experience can improve as much as 50% with given budget by using methods of present disclosure.

Another technical effect is that, the network operating personnel may obtain a priority order that can be directly used for performing capacity expansion without requiring additional work to evaluate which locations would be the most beneficial for capacity expansion.

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.

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.

Claims

1. A computer implemented method for prioritizing capacity expansion in a communication network (101 ), the method comprising analyzing (301 ) performance of a set of cells of the communication network by calculating (303) for each cell of the set a weight factor based on a customer impact value of the cell, a target service level and an experienced service level in the cell; and using (304) the weight factors for providing priority order for capacity expansion of the communication network.

2. The method of claim 1 , wherein the weight factor is calculated by formula: weight factor = customer impact value ^* target service level / experienced service level.

3. The method of any preceding claim, wherein the service level is defined by throughput.

4. The method of any preceding claim, wherein the customer impact value of a cell depends on number of users in the cell and/or number of connection establishment requests in the cell.

5. The method of any preceding claim, wherein the customer impact value is calculated (302) by formula: customer impact = Max (number of users in the cell, number of connection establishment requests in the cell / predefined constant).

6. The method of claim 5, wherein the predefined constant is 25-100.

7. The method of claim 5 or 6, wherein the predefined constant is 50.

8. The method of any preceding claim, further comprising using the weight factors of the cells for arranging the cells of the set in priority order for capacity expansion.

9. The method of any one of claims 1-7, further comprising aggregating weight factors on base station sector level to obtain first aggregated weight factors for base station sectors of the communication network; and using the first aggregated weight factors for arranging the base station sectors of the communication network in priority order for capacity expansion.

10. The method of any one of claims 1-7, further comprising aggregating weight factors on base station site level to obtain second aggregated weight factors for base station sites of the communication network; and using the second aggregated weight factors for arranging base station sites of the communication network in priority order for capacity expansion.

11. The method of any preceding claim, wherein the set of cells comprises a subset of all cells of the communication network.

12. The method of any preceding claim, wherein the set of cells comprises all cells of the communication network.

13. The method of any preceding claim, further comprising receiving performance indicator values from the communication network; and determining the customer impact value and the experienced service level based on the received performance indicator values.

14. An apparatus (20, 111, 112) 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 -13.

15. 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 -13.