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GSM Query Training

MCI

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Course Structure (2.5 days)

Introduction to the Analysis Manager

Query types overview

Query buildingCommonly-used functions

Analysing information held in different Layer

3 messages

Formatting results

Exporting queries to create report templates

Tips and Tricks for faster and more efficient

query building

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What do we troubleshoot?Event Detection & Drive Test Analysis

Dropped Call

Call SetupFailure

Coverage

Problem

HandoverProblem

Interference

Poor Signal

StrengthPoor Voice

Quality

No serving cell

DominanceMissing neighbor

Cross Feeder

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Query Examples & ExercisesCoverage Holes(bad RxLev coverage-idle, bad server coverage)

Interference Analysis(Cell BCCHs in areas with good coverage but poor quality)

Server Dominance Analysis(Areas where the server is weaker than the strongestneighbour; too many strong signals)

Coverage Island Analysis(Display the distribution of the Timing Advance for quickcoverage island analysis)

File and MS Analysis(Generate statistics for each call in the logfile)

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Query Examples & ExercisesContinued

Dropped Call Statistics & Analysis(Analyse each dropped call to report possible diagnosis)

Merge Handset Data Analysis(Generate statistics at each call setup from both drivetest files)

Call Sequence Messaging

(Analyse the Layer 3 messaging for call setupprocedures)

Create Reports Template(Single & Multiple Files Report Template)

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Before We Begin (page 9)Change theConfigurationCellrefs file set to theCELLREFS_SVS_QUERY.TXTfile

GPS Interpolation set to disabled

Time offsets from GMT should both be set to 0(for swi tchand mobi le)

Binning set to message = 1.

Scanner Load Mode set to Load all scannerdata

Load Speed Default set to Load allGSM Bands Used set to 900, 1800 and 1900.

GPS Transformation set to Default (degrees)

In the Tools > Display Thresholdswindow,ensure the values are set to the defaults.

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GSM Data group in Actix SoftwareStatistics DataInformation about handoverinterval and duration

Dedicated Radio LinkOnce a call has beenestablished, parameters that are associatedwith the cell serving the call are containedhere.

Device InfoInformation about thespecifications of the mobile making the call.

Serving Cell ParametersInformation aboutthe serving cell identity, serving BCCH, andBSIC.

Downlink MeasurementsServing RxLevand RxQual measurements made by themobile, which are also broken out by ARFCN.

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GSM Data group in Actix SoftwareContinueNeighbor Cell InfoBCCH, BSIC, and RxLev foreach neighbor. In addition, all neighbormeasurements are broken out by channel

number.Target Cell InfoInformation about the targetcell for a handoff including BCCH and BSIC.

Event DataCall events triggered by Layer 3messaging or registered by the drive test vendors

equipment. If an event is not present in the tree, itdid not occur in the file.

GPRS MeasurementsMetrics associated withGPRS data calls, including throughput, codingscheme, channel usage, TBF information and

events can be found here.

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GSM Data group in Actix SoftwareContinue

AMR Measurements Call setup and

inband signaling measurements

extracted from AMR-enabled handsetsare contained in this group.

Vendor SpecificMeasurements that

are specific to the particular collection

device used. Specific events registeredby the T+M vendors hardware not

derived from layer 3 messaging by

Analyzer are included here.

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Independent Node Datagroup in Actix Software(Technology & vendorindependent measurements)

Device Info contains settings for the mobile

device on which data is logged.

GPS Data contains mobile longitude, latitude,distance travelled, and speed.

Message InfoThe date and time for the start

of the data stream can be found in this group.

This information is useful when building reporttemplates.

File Info contains label and timestamp

information for the logfile under investigation.

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Independent Node Datagroup in Actix SoftwareContinuedSite Data NodeIf a valid cellref and a logfile are loaded, Actix software

will automatically calculate these measurements take

from both drive test and the cell site information ServingCellDistancedistance, in meters, to the

current serving sector ServingCellLatlatitude of serving cellsite at each

point of the drive route ServingCellLonlongtitude of serving cellsite at

each point of the drive route ServingCellID/SectorIDAlphanumeric identity from

the cellref file of the serving site name and sectorname

NeighborCellDistance/Lat/Long/CellID/SectorIDSame info as above for each neighbor positionalong the drive

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Parameter & Format Groups

Parameters (for frequentuse)page 10 & 11

Useful Format Groups

(for frequent use)

page 46

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How to find/search attributes?

ToolFind attributes

orPress Ctrl-Shift-F

GSM_Um_Msg_Type

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Queries

Queries are simple or complex expressions

used to extractmeaningful performance

data, based onuser-defined thresholdsor

the value of other expressions.

The result of the query helps you to highlight

possible radio problems, generate KPIstatisticsor investigate problems.

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Analysis Manager

Central point for managing

queries

Allows new query to bewritten, edited can deleted

Allows existing queries to be

imported (retrieved) and

exported (saved)

Tool Analysis Manager, or

press Ctrl-A to go to Analysis Manager

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6 types of Queries

FilterBinned (Time-Series )

Crosstab (Multi-Dimensional Statistics )

Histogram

Statistic

Event (Event-Triggered Window Statistics )

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FilterA filter analysis tests data on a single

criterionand displays the data only if the

criterion is met

Example 1:

To quickly identify areas with bad RxLev

coverage(idle and dedicated mode)

ServRxLevEither < -95 dBmExample 2:

Interference:

ServRxLevSub > -90 AND ServRxQualSub > 3

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Filter Wizard

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Making use of HELPto obtain more

useful information

Example:

Help Index state function, overview(Here you find all explanation and examples

with logfiles + cellrefs regarding array indexer)

You can also find the state() function

explanation in page 41

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Turn on a Filter

Right Click on StreamName

Click on Filter

Select the Filter you wish to turn on

Turn off a FilterRight Click on StreamName

Click on Filter

Select the filter marked with you

wish to turn off

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Delete a Filter

Press Ctrl-Ato go toAnalysis Manager

Click on Existing Analysestab

Scroll down to Filterfolder

Select the filter you want todelete

Click on Deletebutton todelete the filter

Click on OK button toconfirm and exit

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Binned Query

The Binned Query allows you to

define a new parameter based onexisting parameters, using

functions and inequalities.

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Expression Builder

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Format Group

Format groups control howinformation is displayed to the

user in Analyzer.

To find out which format to use,

when to use, make use of the

HELP Index format groups,

queries

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useful information

Example:

Help Index building expressions(This explains how you build and edit an

expression using the Expression builder)

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useful information

Example:

Help Index array indexer queries(Here you find all explanation and examples

with logfiles + cellrefs regarding array indexer)

Binned Queries, creating

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Histogram

Histogram query processes data for a

single dimension into a bar chart, which is

good for producing a high-level view ofthe data. This data is available for any

time-series data displayed in a workbook.

Example:

Application Measurement (Throughput)RxLevSub

Call Setup Time Analysis (to look at the Statistics)

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Statistic

Statistic query allows you to generate

data based on the statistics available for

a single dimension. It is useful forgenerating a high-level view for system

metrics purposes.

These return the Mean, Mode, Median,

Maximum, Minimum, Count, StandardDeviation and Varianceof the

parameter or expression used.

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Crosstab Query

(Multidimensional Statistical Queries)

provides high-level overviews, typically of the

state of the network.

processes the sequential message data and

extract and calculate summary information

(called statistics), which is broken down by

one or more dimensions(such as region andserving cell).

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Crosstab Query (continued)Dimension

An attribute that is used to define how the data isgrouped in a crosstab query.

StatisticThe results that you would like to include foreach dimension, where the parameter youchoose will be displayed as the columns in theStatistic Explorer(i.e.: mean ServRxLevSub, Count/Total # of dropped calls)

FilterThe filter button on the Statistics Explorer may beused to quickly filter query results in the StatisticsExplorer and in any other data view

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Crosstab Query (continued)

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Crosstab Query Wizard

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useful information

Example:

Help SEARCH building expressions(This explains how you build and edit an

expression using the Expression builder)

Help SEARCH Event Query Functions

Demo

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REMEMBER!!!

Always view and display the result

of your query after building a few

dimensions and statistics

DO NOT WAIT UNTIL YOU HAVE

CREATED THE WHOLE

CROSSTAB QUERY!!! It will bevery difficult to back track!

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When Crosstab and event queries are processed, Analyzer evaluates

each message in turn in the following order:

Filter

If the message does not pass the filter, evaluation stops and Analyzer

immediately moves on to the next message.

Dimensions

Each dimension is evaluated in turn in the order in which they appear in the

Analysis Manager dialog box. When the evaluation of one dimension

fails, the evaluation stops and Analyzer moves on to the next message

(example of Attach failure report).

Statistics

The statistics are evaluated for every row and are also evaluated in the

order in which they appear in the Analysis Manager dialog box. For each

statistic, the filter is evaluated first and then the statistic expression.

Use the order of evaluation

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The following diagram shows the

order of evaluation for a crosstab

query that has:

- two dimensions

- two statistics

- nofilters

Sometimes speed up a query by

reordering the dimensions.

Order of evaluation

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Often dimension expressions are too general to

be of use for screening out messages

(for example Cell ID or IMSI are set in most of the messages).If your query is only concerned with one or two

message types:

By making a filter that tests for them, you will

immediately eliminate all of the other message

typesThis can hugely reduce the number of

evaluations because no further evaluations will be

performed on the eliminated messages

Use Filtersto Eliminate Messagesthat are Not Relevant

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That's where placing a filter at

the top level (on the query itself)

comes in:

Note that in an event query, the

combination of trigger

expression and window act as a

filter.

Order of evaluation

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Event QueryAlso known aswindow queries or triggeredqueriesis a specialtype of crosstab querythatare used to create a list of individual occurrences of

a problem so that users can drill downinto thedetails of what was going at that time.

Generally you use an event query to create a listoffailureor warning events, such as dropped calls,handover failures, or throughput measurementsthat are less than a given threshold. You specifythe problem you want to list asthe trigger(sometimes called the triggering event). It can beany attribute or expression, although typically it willbe an event attribute.

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Standard Drive Test AnalysisWhen you create the event query, you specify the triggering

eventand whether you want to investigate a period(called the

window) before, after, or both before and afterthe triggering

event. Typically you would then define statistics to be calculated

on the messages that appear in that window.

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Event Query Wizard

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Discriminated Event QueryAn event query for which a discriminatorhas been

specified.

The discriminator is an attribute(i.e. Call IDor Session

ID) that is used to identify messages that belong to a

particular call or session

Running the query at load time triggers the tracking and

indexing of the messages for each separate call or

session, so that they can subsequently be loaded into

Actix Software as a separate stream for detailed analysisDiscriminated event queries are usually used with

protocol link data, in which the messages from multiple

calls and sessions are multiplexed and interleaved, or

with Repository Manager.

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Discriminated Event Query(Continued)

Note that adding a discriminator will

make the query run more slowlyandso discriminators should not be used

when not really necessary.

For example, users should not

normally create discriminated queriesfor use with drive test data.

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useful information

Example:

Help SEARCH Event Query

Functions Demo

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Statistical Aggregation Methods

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Expression and Function groups

Help Index filtering,

expressions

(Here you can find all the available

functions to be used in building

your query)

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state() Function

Returns the value of an attribute at the current

message position or, if that has not been set, the

previous valid value.

The state function is useful when working with

attributes that are not set in every message

such as those that record an instantaneousmeasurement of signal strength.

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Tips for query

optimisation

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Should use the statefunction on the dimensionsratherthan the statistics because you are more likely to createdimensions using values (such as the serving cell) that areset only when the value changes and partly because usingthe statefunction in statistics can sometimes distort theresults.

The main places where it is valid to use the statefunction ina statistic is when retrieving, for example, the last value, ofan attribute in an event query or when retrieving the value ofa threshold for use in a comparison operation.

When to use state() function?

Using the statefunction is slowerthan not using

it, so do not use it when it's not necessary.

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The first thing to understand is what is an evaluation:

each sub-expression within a complex expression is a separate evaluation

so simplifying a complex expression will reduce the number of evaluations.

For example, consider this complex expression for determining the serving cell:

default(state(ServingSectorID),Sector with SC + default(Uu_ActiveSet_SC[0],Undefined))

This can be broken down into the following sub-expressions:

Obviously the overall complex expression will take longer to evaluate than any

one of these sub-expression on its own.

Impact of the Number of Evaluations

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That is what happens when you run a crosstab query:

the tool passes each message to the query

the message then passes through the query until it's either excludedor the evaluation of all of the statistics is complete

Therefore, the fewer the evaluations, the faster the query

If you want your query to run fast, you should try to find the simplest

solutionthat is, the one that requires the fewest evaluations.

Reducing the Number of Evaluations

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For expressions of comparable complexity, each evaluation takes about

the same length of time.

Often you will find that there is more than one way of arriving at a result.

You can therefore speed up queries by reducing the number of evaluations

that take place or reducing the number of sub-expressions in a complex

expression

One of the main ways of doing this is to make the order of evaluation work

for you (see next point).

Reduce the number of evaluations

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Each message is matched against each unique value in each

dimension, so the more unique values there are in a dimension, the

slower the query will be.

An example of reducing the number of unique values in a dimension is

for example using a function like mround:

substitute (ServRxLevFull) with mround(ServRxLevFull, 5)

to round the values to the nearest multiple of five.

Reduce the number of rows

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Instead of creating a list of calls and iterating across them looking for the

interesting ones, it's generally better to create event queries based on the

events you are interested in

Example of failures during attach: use the failure events as trigger points

If necessary, write an event diagram to detect the event to be used as the

trigger in the event query. Event diagrams (especially simple ones) are

generally much faster than queries.

Consider event detection

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For example, rather than create a dimension or statistic with a complicated

expression that tracks the gap between GPRS packets:

If((Time prev_time_where(IsValid(Task_Packets_Cum_IncRetr_DL))) > 1000,

(Timeprev_time_where(IsValid(Task_Packets_Cum_IncRetr_DL))), Null)

With this filter: IsValid(Task_Packets_Cum_IncRetr_DL)

it would usually be preferable to create an event diagram that tracks the gap

between packets and sets an event if the gap is greater than a specified

threshold.

Then you could create an event query using that event as the trigger.

Consider event detection

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The Count Distinctand Modeaggregation methods require a count to be

maintained of every value that is found in the data

That can have a serious negative impact on performance when used inqueries that are run against large volumes of data, particularly when the

range of potential values being evaluated is large and can grow indefinitely

(for example, when the values are IMSIs)

When necessary, consider whether you can get the equivalent information in

a different wayfor example, by using a separate histogram query rather

than a crosstab query

If this is not possible, because, for example, you need to break down the

results by a dimension (such as the cell), make sure that you minimize the

number of dimensions (see active subscribers

Beware the Count Distinct and Mode

aggregation methods

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strings are much slower to evaluate than numerical values

avoid basing dimensions on string values, because the lookup for strings

is slow. If you need to dimension on a string value, see if there is a numeric

value that you could use instead.

The query will be faster if you dimension on a numeric value and make

the string value a statistic (you can use format groups) to render numeric

data as strings in the output.

For example, you can use the Service_Protocol_Type format group to

render progressive numbers into protocol names (see Subscribers per

Application). This will make the query faster than using the function, which

actually converts the time to a string.

Sometimes it is impossible to avoid string, though (see APN usage).

Strings are slow

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When you create queries it's easy to think of the table you

want in Excel and then create dimensions for the rows and

statistics for the columns.

However, this m ight not always be the mos t eff ic ient wayto c reate the query

Sometimes it might be better to construct it in a different way.

For example, suppose you want to create a table that has a

column for every message type and rows that show the count.

Rather than creating a statistic for each message type, itwould be more efficient to simply use the message type as the

dimension.

Think laterally

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Questions?

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