WO2013030429A1

WO2013030429A1 - Intelligent capacity management

Info

Publication number: WO2013030429A1
Application number: PCT/FI2011/051103
Authority: WO
Inventors: Rauno Huoviala
Original assignee: Oy Omnitele Ab
Priority date: 2011-09-01
Filing date: 2011-12-13
Publication date: 2013-03-07
Also published as: FI20115857A0

Abstract

The invention discloses a method, a system and computer program product for wireless network capacity management. The method comprises measuring network performance data, measuring reference data from at least one cell using at least two different wireless apparatuses, comparing the measured network performance data to the reference data, obtaining a cell specific performance analysis, applying the performance analysis to a portion of the network and simulating the effect of a capacity configuration upgrade.

Description

INTELLIGENT CAPACITY MANAGEMENT

FIELD OF THE INVENTION

The invention relates to capacity management in a wireless data network.

BACKGROUND OF THE INVENTION

Wireless network capacity management requires careful planning from the network operator. Any upgrade to the existing network or migration to a new wireless carrier technology is usually very expensive. Therefore it is crucial to simulate the effect of the proposed improvement to get the optimal result from the upgrade. The technical effect of the network upgrade should be the most relevant, considering the proposed usage, environmental characteristics and the technological improvement.

The heavily increasing traffic in HSPA (High Speed Packet Access) networks requires network operator to invest significantly in incremental capacity on top of the existing base stations. As more and more capacity solutions are being introduced by the network vendors, the complexity of the capacity planning is increasing. It can be challenging for the network operator to understand the best strategy for capacity dimensioning in this situation. Mobile network radio, traffic, and terminal characteristics vary between different areas, and thus the same capacity configuration is not effective in all locations. Solutions perform in different cell environments in a different manner from the end user QoS perspective (QoS, Quality of Service) , and also the cost effectiveness of the solutions may vary from the capacity investment point of view. By doing more granular capacity planning instead of a homogenous blanket capacity throughout the network, the cost effectiveness of the incremental HSPA capacity can be multiplied .

Mobile Broadband (MBB) traffic in many markets has roughly doubled every year during the recent boom of data modems and smart phones. This requires the operator to invest more and more in the HSPA network to sustain the user experience demanded by the growing customer base. Unlike in the beginning of the HSPA era when the investments were focused on increasing the service coverage with new sites, most 3G network operators also need to start investing in incremental radio access capacity. The HSPA capacity dominantly determines the end user experience of Mobile Broadband.

Network operators have traditionally relied on rather simplified rules in capacity dimensioning. The traditional approaches for capacity expansions are building additional capacity as blanket coverage, i.e. applying same extensions to all sites in the network, or building additional capacity to the most congested cells without analyzing the reason for the congestion or the impact of the applied capacity expansion.

This may lead to homogenous and inefficient capacity dimensioning throughout the network. Since the cell environment (i.e. radio conditions, data traffic, and terminal type distribution) can vary notably between the cells in the network, this leads to an end user QoS that varies greatly in different areas of the network. Further, utilizing the same capacity solutions in different cell environments leads to low cost effectiveness of the capacity implementation .

With increasing variance of the cell environments in the wireless network, it is unclear what the end user QoS is in different cells. Thus, the operator cannot know where to focus the capacity upgrades. With the increasing number of alternative capacity solutions from the network vendor, the network operator does not know what the differences between them are and what the predicted benefit for the end user is. If the operator possessed the information listed above, it could optimize the HSPA capacity configurations (features, baseband, carriers) in the cell level and achieve the best technical effect for the upgrade. At the same time the operator could secure that the network performance achieves the targeted end user MBB QoS in every cell of the network .

SUMMARY

The invention discloses a method, a system and a computer program product for managing network capacity in a wireless data network, wherein the system comprises means for executing the steps of the method .

The system and the apparatus comprise at least one processor and at least one memory including computer program code. The at least one memory and the computer program code are configured, with the at least one processor, to cause these devices to perform according to the method described herein.

The invention discloses a method for managing wireless network capacity. The method comprises steps of measuring network performance and measuring reference data from at least one cell using at least two different wireless apparatuses. Examples of the reference data comprise data throughput with different radio conditions, modulation and coding scheme data or shared control channel usage.

The method comprises further steps of comparing the measured network performance data to the reference data, obtaining a cell specific performance analysis, applying the performance analysis to a portion of the network and simulating the effect of a capacity configuration upgrade. According to the invention normalized measurement data is applied to simulating the effect of the capacity configuration upgrade. Comparing the measured network performance data to the reference data enables one to normalize the network performance data, therefore improving the accuracy of the network measurement data. The effect of the capacity configuration upgrade may be for example improved QoS, better data rate, signal-to- noise ratio, channel quality indicator values or better focus of existing technology. The capacity configuration upgrade may be for example a new cell site or new transmission technology. As another example, the allocation of speech and data in the network or network upgrade may be more optimized. Other examples of capacity configuration upgrades comprise the increased number of radio units, carriers, baseband, HSDPA codes, higher order modulation or spectrum aggregation.

In one exemplary embodiment the method comprises measuring the network performance data being Channel Quality Indicator data from the Operations Support System data of the network operator. In this manner the network performance data is obtained without expensive and tedious local measurements that would require a lot of travelling to different cell sites .

In one exemplary embodiment the method comprises the at least two wireless apparatuses using different wireless transmission methods. Different wireless apparatuses are used to create a profile of single cell characteristics. This profile is used to normalize the network performance data. Examples of different wireless transmission methods are different terminal types such as Cat6, Cat8, Cat14; different multiplexing cases such as Cat6+Cat6, Cat6+Cat8, Cat 8+Cat8; different modulation methods such as QPSK, 16QAM, 64QAM, or any transmission method available in the existing cell or network.

In one exemplary embodiment the method comprises the cell specific performance analysis resulting in a corrected channel quality indicator distribution across the portion of the network. In one exemplary embodiment the method comprises cell specific performance analysis comprising calculating the average wireless apparatus data rate per cell. In one exemplary embodiment the method comprises the portion of the network being the whole network.

In one exemplary embodiment the method comprises prioritizing the order of at least two capacity configuration upgrades in response to simulating the effect of a capacity configuration upgrade .

Another aspect of the invention is a system for managing wireless network capacity, comprising means for measuring network performance data, means for measuring reference data from at least one cell using at least two different wireless apparatuses, means for comparing the measured network performance data to the reference data, means for obtaining a cell specific performance analysis, means for applying the performance analysis to a portion of the network and means for simulating the effect of a capacity configuration upgrade.

In one exemplary embodiment of the invention the system comprises means for measuring the network performance data being Channel Quality Indicator data from the Operations Support System data of the network operator. In one exemplary embodiment of the invention the system comprises the at least two wireless apparatuses comprising different means for wireless transmission. In one exemplary embodiment of the invention the system comprises means for the cell specific performance analysis resulting in a corrected channel quality indicator distribution across the portion of the network. In one exemplary embodiment of the invention the system comprises means for the cell specific performance analysis comprising means for calculating the average wireless apparatus data rate per cell. In one exemplary embodiment of the invention the system comprises the portion of the network being the whole network. In one exemplary embodiment of the invention the system comprises means for prioritizing the order of at least two capacity configuration upgrades in response to means for simulating the effect of a capacity configuration upgrade.

Another aspect of the invention comprises a computer program according to any step of the method, wherein the computer program is a computer program product comprising a computer-readable medium bearing computer program code embodied therein for use with a computer .

The invention provides a detailed analysis of the effect of the new network upgrade, without having to measure each cell of the network. The invention may be implemented without the need for expensive probes to obtain corrected network measurement data, as the invention may utilize the information already available in the network.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a further understanding of the invention and constitute a part of this specification, illustrate embodiments of the invention and together with the description help to explain the principles of the invention. In the drawings:

Fig. 1 is an exemplary chart diagram illustrating user equipment downlink data rates in different cells, Fig. 2 is an exemplary chart diagram illustrating gain variations of different capacity solutions, and

Fig. 3 is an exemplary chart diagram illustrating a performance analysis.

DETAILED DESCRIPTION OF THE INVENTION

Reference will now be made in detail to the embodiments of the present invention, examples of which are illustrated in the accompanying drawings.

Current analyses in live network exercises show that if the HSPA site capacity configurations were optimized according to the cell environments, the MBB capacity investments could be up to five times more cost effective compared to the traditional approaches to capacity dimensioning.

The factors and network characteristics impacting the Mobile Broadband end user QoS differ greatly from those of the voice QoS. Both share the KPIs (Key Performance Indicator) regarding accessibility and attainability, but while the voice QoS is almost solely determined by these KPIs, the Mobile Broadband user experience is mainly determined by the achieved user data rate and the RTT (Round Trip Time) of the packets. The latter is primarily ruled by the core and switching network, and the first by the radio access network. When it comes to the differences in the end user experience between different cells, the user data rate comprises the most variation. This is due to naturally varying cell environments regarding: radio conditions, i.e. distribution of signal-to-interference plus noise or signal-to-noise ratios that are experienced by the users; traffic levels, i.e. how congested the data capacity is due to simultaneous users, and how much cell resources are being used by other services; or terminal distribution, i.e. how many terminals support the high-end features introduced to the network.

The result of this is a varying end user QoS in the network. The variation in user terminal data rates is illustrated in Figure 1. The x-axis illustrates different individual cells within the network, the example comprising 17 cells. The y-axis illustrates the user equipment downlink data rate in each cell. The cells belong to the same network cluster and have the same baseline HSPA capacity, and still the user equipment data rate can vary from 2500 kbps in cell 1 to 4500 kbps in cell 5; in this example, cells 1 and 5 are neighboring cells.

Analyzing accurately the actual end user MBB QoS on the cell level is a well-known problem. Even if the QoS could be roughly approximated with expensive measurement or probing campaigns, the root cause for poor quality could not be analyzed without knowing the cell environment.

The same capacity solutions are not suitable in all networks. With the current pace of increasing data demand by the end users, simply controlling the data usage will not be a sufficient solution to manage network upgrade needs. The wireless network operator needs to invest in new hardware and software that increases the total capacity provided in the radio access. Possible upgrades to provide more capacity for the network include: additional HSPA cell carriers; higher order modulation (e.g. 64 QAM); aggregation of the cell carriers (i.e. Dual-Cell); or baseband capacity configurations (e.g. Number of HSDPA or EUL users, HS-DSCH codes, Channel Elements) .

The problem of applying these upgrades lies in understanding the benefits or increased gain in different areas of the wireless network. Even if one solution could provide good improvement in some cell, the same solution might be less important in another cell .

A simplified live network example in Figure 2 illustrates the variation of user equipment data rate gains from two different capacity upgrade solutions, 64 QAM and adding a second carrier, which has been analyzed in different cells in the same network. The gains from the solutions vary a lot between the cells. Furthermore, the variation is not coherent between the two solutions. This is because the gain from a second carrier is mostly determined by the cell traffic, whereas the gain from 64 QAM is determined by the radio conditions and terminal distribution in the cell. Understanding these fundamentals that limit the performance of different types of capacity solutions enables one accurately to analyze also the achievable gain to the end user QoS. When this is known, the network operator is able to improve the network performance to meet the set targets in every cell. However, it would still be unclear what the most cost efficient solutions to reach those targets would be.

To meet the end user QoS or budget targets throughout the whole network, the operator needs to be able to prioritize the capacity implementations between all the sites and cells. The prioritization is based on: performance of each site or cell relative to other sites and cells in the network; end user gain available from the implementation in the specific site or cell; cost of each option for implementing the additional capacity to the specific site or cell; or number of subscribers benefitting from the increased capacity. A site is a location comprising one or more apparatuses for operating one or more cells. An example of such an apparatus is a base station or an evolved Node B, eNB . A single site may host several cells of a different diameter or direction. The network operator has a site database at the OSS, Operations Support System, comprising data of location and coverage of each cell and site.

When there are thousands of cells all with dozens of alternative capacity configurations, the task of capacity plan optimization will become a rather complicated task without systematic tools. When analyzing the cost effectiveness of different capacity solutions, the operator must be well aware of the pricing structure of the capacity as well as the limitations that the configurations of different solutions have. Licensing of the HSPA capacity is getting more and more complex as new solutions are brought to the market. The complexity of the HSPA capacity can be illustrated by the following characteristics:

One capacity upgrade may require activation of several different licenses, in addition to possible new HW elements;

The different solutions and licenses need to be differently scaled. Activation may be needed on a cell, site, or cluster level. This may be due to different reasons, e.g. due to the license pricing structure, or to secure the service continuity;

The level of baseband capacity has numerous steps that can be either configured with the existing licenses or by buying new licenses; or

Buying a license may result in vouchers for other licenses or some other discounts, making the cost comparison difficult. Studies have shown that in many cases thorough capacity optimization results in savings in the range of millions of dollars per network, and the MBB end user QoS would still be at the same level or higher as with the conventional capacity upgrade strategy. In some cases the end user QoS can be notably increased without any investments, only by optimizing the current baseband configurations . In one exemplary embodiment the Intelligent Capacity Management prioritization puts all the capacity upgrades in order of cost effect. Further, the Intelligent Capacity Management analysis identifies the cells where new capacity investment is not profitable even if the initial performance was poor. In those locations the root cause for poor performance is not the capacity but rather the radio plan, requiring optimization or new sites.

Intelligent Capacity Management is a thorough but streamlined methodology to optimize the capacity management, being suitable for any HSPA network that can extract basic OSS statistics and site configuration data.

According to an exemplary embodiment the cell specific performance analysis is based on combining the OSS statistics for channel quality and the measured HSPA scheduler dynamics in different radio conditions. The reported Channel Quality Indicator CQI distribution is obtained from the OSS for every cell, and normalized so that any cell specific power attenuation or amplification factors are taken into account, such as cable loss, or mast head amplifications. The HSDPA cell capacity analysis is based on combining of OSS statistics for channel quality and the measured HSPA scheduler dynamics in different radio conditions.

Figure 3 is an exemplary chart illustrating the signal-to-interference plus noise (SINR) distribution and the HSDPA performance. The SINR is the CQI in the current example, but also other known examples of the CQI may be used. The x-axis indicates all cells of the network. Columns 30 indicate the CQI distribution over the cells and the corresponding value is at the left y-axis indicating the percentage value of the CDF, Cumulative Distribution Function. The curves 32, 34, 36, 38 indicate the normalized bearer data rate for each terminal type. According to the present example, curve 32 indicates a Cat64 terminal with a 21.6 Mbps maximum data rate; curve 34 indicates a CatlO terminal with a 14.4 Mbps maximum data rate; curve 36 indicates a Cat8 terminal with a 7.2 Mbps maximum data rate; and curve 38 indicates a Cat6 terminal with a 3.6 Mbps maximum data rate.

According to one embodiment the reported CQI distribution is obtained from the OSS for every cell or for the relevant portion of the network. The OSS information obtained comprises the measured CQI is reported by the user equipment, the corresponding transport block size or received data rate with the CQI. The OSS data results a network performance data that has not been corrected or normalized; for example bit rate as a function of reported CQI .

The OSS data for reported CQI is normalized so that any cell specific power attenuation or amplification factors are taken into account, such as cable loss, mast head amplifications from site data base or the power consumed by R99 from the OSS. R99 refers to the first specification of UMTS, Universal Mobile Telecommunications System. The normalizing means in one embodiment shifting the performance data according to the cell specific attenuation or amplification indicated in dBs, so that the same reference measurement performance curve is applicable in any cell. In one example the performance data or the performance curve obtained from the data, which indicates the bit rate as a function of reported CQI is sumproducted with the reported CQI distribution from the same cell, resulting the average cell throughput of the cell. The sumproduct is a function resulting multiplication with corresponding components in the given arrays, and returns the sum of those products According to one embodiment the OSS provides the used CQI data indicating the actual transport block provided by the HSPA scheduler, which includes the effect of attenuation, amplification, measurement power offset error, or any other form of distortion to the performance data. The normalizing process of the OSS data comprises different steps for the reported CQI data and the used CQI data. The used CQI data is not directly correlated with the measured HSPA scheduler performance curve. In one exemplary embodiment of the reference cell measurement only the reported CQI data is available from the measurement tool. Thus, the used CQI is normalized in accordance to the reported CQI. The reference cell measurement is done in a live network cell or laboratory cell with no other HSPA or R99 traffic. Then the reported and used CQI data is collected from OSS for the reference cell during the reference measurements, and the reported and used CQI value distribution envelope curves are compared against each other. The mean difference between the two CQI distributions can be calculated from the difference of the envelope curves. The difference may be due to error in measurement power offset. It is the common difference between the used and the reported CQI in any cell in the network, excluding the additional errors caused by other attenuation, amplification, or traffic, which are already taken into account in the used CQI OSS data of any cell. The used CQI distribution curve is shifted according to this calculated CQI difference, and by doing that it can be reliably correlated with the measured reference cell performance curve.

The HSPA scheduler dynamics is measured in a reference cell in the operator network. There may be more than one reference cell. The measurement is done with wireless apparatuses comprising different radio interface characteristics. Such wireless apparatuses may be implemented to a single terminal or to separate terminals. Examples of radio interface characteristics comprise all common terminal type measures such as Cat6, Cat8, Catl4 or common multiplexing cases such as Cat6+Cat6, Cat6+Cat8, Cat8+Cat8 or any other radio transmission technology relevant to obtaining the measurement data. Different categories, CatN, refer to different types of user equipments, for example in terms of the maximum data rate or modulation technique. Examples of measured attributes comprise transport block size, code and modulation usage and SCCH usage, in different channel conditions.

Knowing the scheduler behavior, also performance curves for any code limited case can be calculated from the measurements, for example how the Catl4 performance dynamics works if the codes are limited to 12 codes in the cell due to dynamic code allocation. The performance may not be linear in relation to the codes, because the power remains original. The tool according to the invention may comprise more than 300 different performance dynamic curves for different cases.

Examples of different types of cell specific traffic information obtained from the OSS comprise: R99 voice erlangs and R99 DCH erlangs result in CEs for R99; HS A-DCH erlangs result in the number of HS users; HS users per TTI (Transmission Time Interval) result in HSPA traffic load (value of 0-1); from the R99 traffic analysis, the HSPA resources limited by R99 are known. From the HSPA traffic load, the average user data rate per cell = (Cell capacity) /( 1+loading) can be calculated.

The load factor and the user data rate are calculated per cell if additional carriers are added; traffic distributed between the cell carriers = (Cell capacity) /( 1+loading/number of carriers). The cell capacity in case of multiple carriers can be calculated by dividing the one carrier case R99 power into the multiple carriers and dividing the R99 code usage into the multiple carriers.

Shifting the CQI distribution in dBs according to the average interference attenuation that is achieved from adding additional carriers (estimated from the OSS by before-after analysis from already implemented second/third carrier cases) results in the amount by which, in average, the CQI distribution shifts due to the added carrier.

Running the cell performance analysis again with upgraded HSDPA power and code limit and CQI distribution, the shares of time when different terminal combinations are served by the scheduler can be calculated from the HSPA traffic load (together with terminal distribution) . For example: 13% Cat6 alone, 2% Cat6+Cat6, 35% Cat8, alone 12% Cat8+Cat8 etc. From this information, the user data rate per terminal category can be calculated.

According to one embodiment of the invention, which may be partially used in combination with any of the previous embodiments, the first step is to analyze the cell throughput or capacity to each terminal type by combining the CQI statistics and the CQI vs. bitrate measurement. The cell throughput equals the user equipment throughput if the user equipment is the only one attached to the cell, not sharing capacity with other HSDPA terminals. This is measured and calculated also to situations with multiple user terminals, resulting to different performance curves (for example curves 32-38, as in Figure 3), when code multiplexing is in use sharing the code and the power to several user equipment in the same TTI. The multiplexed throughput is better throughput than merely combined throughput. This step includes also the normalization, correcting the CQI values in a cell level. Performance curves or the performance data are calculated to all conceivable situations with limited codes by measuring every code-limited situation or calculating the effect of the limitation to the power modulation coding dynamics. As a result the cell throughput to each cell or combination thereof is known .

The terminal category distribution may be calculated by several methods. In one embodiment the reference data measurement process indicates the characteristics of the scheduler providing different amount of codes 1-15 to each terminal category or the combinations of categories for different CQIs. The information obtained from the OSS provides the actual usage of channelization codes by the HSDPA scheduler in total, for example a percentage of each code 1-15 used. As the cell CQI distribution is known, the terminal distribution is calculated to match the real radio transmission environment data. In the situation where the actual terminal category distribution cannot be directly obtained from the OSS, there can be only be one terminal category distribution that results in the known actual channelization code usage with the known CQI distribution. This distribution can be calculated by knowing the other variables mentioned.

The loading and terminal combinations are calculated to achieve real user data rate. For example to the situation of one user equipment the user data rate equals to dividing by (1+loading) . The loading is obtained from the OSS, from the number of users per TTI. For example 40% 0, 38% 1, 17% 2, 5% 3; resulting to 60% of TTI being distributed to one or more terminals. As the terminal distribution is known within the cell, the actual user data rate experienced by each terminal can be calculated. For example loading value of 0,6 and the terminal distribution of 50% cat6 and 50% of cat8 results to any moment of transmission cat6 is 40% of the time alone in the cell transmitting; 30% of the time with another cat6 terminal and 30% with cat8 terminal. Any combination of other terminal codes may be calculated by the same method. In case of added HSPA carriers, the effect on user bit rate can be calculated with the same formula and by decreasing the TTI loading according to the selected traffic balancing between the carriers, e.g. 50%-50% load balancing between two carriers will result in loading of 60%/2=30% loading.

Additionally the cell loading may be used to calculate how different sectors of a single site limit each other' s code resources when the baseband distributed the limited code pool dynamically to all cells. For example the common code pool comprising 15 codes, where all three cells have loading value of 0,6, one cell may use all 15 codes for only 52% of the time; as calculated by:

0,4*0,/l (no users in other sectors) +0,4*0,6*2/2 (user in one other sector, codes shared round robin with that user) +0,6*0,6/3 (users in both other sectors, codes shared round robin between all users) = 52%

As the OSS information provides code usage distribution within different cells (for example 92% of TTIs scheduled with less than 15 codes, 80% less than 14 codes etc.), also the probability of other free code amounts may be calculated. Using the same calculation method can be calculated the number of additional codes available in a cell for a user terminal, when the code pool is increased in baseband (for example from 15 to 30 codes shared)

The user data rate is calculated to different terminals. According to previous example cat6 receives 40% of time the cell capacity assigned to cat6 alone (for example 2Mbps) ; 30% of the time cat6+cat6 multiplexed throughput (for example 1.2 MBps) ; and 30% of the time cat6+cat8 multiplexed throughput to cat6 (for example 1,1 Mbps - less than with another cat6 since HSDPA scheduler often prioritizes higher categories) . As a result the average cat6 throughput is 1,49Mbps. Similar value may be calculated to all categories with the cell-specific all user' s average user data rate.

According to one embodiment the effect of different capacity configuration upgrades to the user data rate are calculated. For example the effect of 64QAM upgrade improving only catl4 terminals is calculated by using the catlO performance data with 15 codes 16QAM that was obtained from previous steps is now used by replaced by the catlO data with the catl4 data. For the effect of additional carrier the traffic is divided by carriers, basically dividing the loading value by the number of carriers and repeating all calculations and taking into account the less R99 traffic using resources. The effect of baseband upgrade is basically additional codes if all are not already used. Then the code limiting code pool size may be increased for example from 15 coeds to 30 codes; calculating probabilities to different code limits and the resulting user data rate. The effect of a dual-cell situation may be calculated by doubling the cat 24 terminal user data rate taking into account the limitation to other user terminal's resources. As a result the baseline cell capacity and the user data rate is known in every cell to all terminals with the information of improved cell user data rate with different upgrades.

The prioritization of different capacity configuration upgrades is based on maximized cost effectiveness to the whole customer base. An example of the prioritization can be obtained by calculating (User data gain / customer) * (Amount of customers in cell) / (Cost of the upgraded configuration) . For every site or cell, different upgrades may be arranged in a prioritized order of for example the most cost effective upgrade compared to the baseline configuration; or the most cost effective upgrade compared to the earlier upgrade.

The best configuration upgrades relevant to each cell or site are compared to every other site or cell. All upgrades throughout the network are put in a prioritized order of for example the most cost effective upgrade compared to the whole customer base; or the next cost effective upgrade compared to the whole customer base. Also other prioritizing orders are possible.

Examples of the results obtained with the invention comprise highlighting of areas where capacity extension is not feasible to reach the target QoS, indicating the radio optimization or a new site needed. Other examples comprise detailed capacity configuration change recommendations to meet the targets; cluster, site, and cell level configuration changes, BoQ for the upgraded configurations, expected MBB QoS after configuration changes; and network level, cluster level, site/cell level observations.

Embodiments of the present invention may be implemented in software, hardware, application logic or a combination of software, hardware and application logic. In an example embodiment, the application logic, software or instruction set is maintained on any one of various conventional computer-readable media. In the context of this document, a "computer- readable medium" may be any media or means that can contain, store, communicate, propagate or transport the instructions for use by or in connection with an instruction execution system, apparatus, or device, such as a computer. A computer-readable medium may comprise a computer-readable storage medium that may be any media or means that can contain or store the instructions for use by or in connection with an instruction execution system, apparatus, or device, such as a computer. The exemplary embodiments can store information relating to various processes described herein. This information can be stored in one or more memories, such as a hard disk, optical disk, magneto-optical disk, RAM, and the like. One or more databases can store the information used to implement the exemplary embodiments of the present inventions. The databases can be organized using data structures (e.g., records, tables, arrays, fields, graphs, trees, lists, and the like) included in one or more memories or storage devices listed herein. The processes described with respect to the exemplary embodiments can include appropriate data structures for storing data collected and/or generated by the processes of the devices and subsystems of the exemplary embodiments in one or more databases.

All or a portion of the exemplary embodiments can be conveniently implemented using one or more general purpose processors, microprocessors, digital signal processors, micro-controllers, and the like, programmed according to the teachings of the exemplary embodiments of the present inventions, as will be appreciated by those skilled in the computer and/or software art(s) . Appropriate software can be readily prepared by programmers of ordinary skill based on the teachings of the exemplary embodiments, as will be appreciated by those skilled in the software art. In addition, the exemplary embodiments can be implemented by the preparation of application-specific integrated circuits or by interconnecting an appropriate network of conventional component circuits, as will be appreciated by those skilled in the electrical art(s) . Thus, the exemplary embodiments are not limited to any specific combination of hardware and/or software. 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 above-described functions may be optional or may be combined. Although various aspects of the invention are set out in the independent claims, other aspects of the invention comprise other combinations of features from the described embodiments and/or the dependent claims with the features of the independent claims, and not solely the combinations explicitly set out in the claims.

It is obvious to a person skilled in the art that with the advancement of technology, the basic idea of the invention may be implemented in various ways. The invention and its embodiments are thus not limited to the examples described above; instead they may vary within the scope of the claims.

Claims

1. A method for managing wireless network capacity, c h a r a c t e r i z e d by:

measuring network performance data;

measuring reference data from at least one cell using at least two different wireless apparatuses;

comparing the measured network performance data to the reference data;

obtaining a cell specific performance analysis; applying the performance analysis to a portion of the network; and

simulating the effect of a capacity configuration upgrade .

2. The method according to claim 1, c h a r a c t e r i z e d by measuring the network performance data being Channel Quality Indicator data from the Operations Support System data of the network operator .

3. The method according to claim 1, c h a r a c t e r i z e d by the at least two wireless apparatuses using different wireless transmission methods .

4. The method according to claim 1, c h a r a c t e r i z e d by the cell specific performance analysis resulting in a corrected channel quality indicator distribution across the portion of the network.

5. The method according to claim 1, c h a r a c t e r i z e d by the cell specific performance analysis comprising calculating the average wireless apparatus data rate per cell.

6. The method according to claim 1, c h a r a c t e r i z e d by the portion of the network being the whole network.

7. The method according to claim 1, c h a r a c t e r i z e d by prioritizing the order of at least two capacity configuration upgrades in response to simulating the effect of a capacity configuration upgrade .

8. A system for managing wireless network capacity, c h a r a c t e r i z e d in that the system comprises :

means for measuring network performance data;

means for measuring reference data from at least one cell using at least two different wireless

apparatuses ;

means for comparing the measured network

performance data to the reference data;

means for obtaining a cell specific performance analysis ;

means for applying the performance analysis to a portion of the network; and

means for simulating the effect of a capacity configuration upgrade.

9. The system according to claim 8, c h a r a c t e r i z e d in that the system comprises means for measuring the network performance data being Channel Quality Indicator data from the Operations Support System data of the network operator.

10. The system according to claim 8, c h a r a c t e r i z e d by the at least two wireless apparatuses comprising different means for wireless transmission .

11. The system according to claim 8, c h a r a c t e r i z e d in that the system comprises means for the cell specific performance analysis resulting in a corrected channel quality indicator distribution across the portion of the network.

12. The system according to claim 8, c h a r a c t e r i z e d by the cell specific performance analysis comprising means for calculating the average wireless apparatus data rate per cell.

13. The system according to claim 8, c h a r a c t e r i z e d by the portion of the network being the whole network.

14. The system according to claim 8, c h a r a c t e r i z e d in that the system comprises means for prioritizing the order of at least two capacity configuration upgrades in response to means for simulating the effect of a capacity configuration upgrade .

15. A computer program according to any of the method claims 1 to 7, wherein the computer program is a computer program product comprising a computer- readable medium bearing computer program code embodied therein for use with a computer.