Traffic & Resource Forecast Principle

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The Principle of Traffic and

Resource Forecast


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Background

Design Concept

Forecast Procedure

Effect Verification

Traffic Forecast Introduction

Resource Forecast Introduction


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Traffic Forecast is to forecast the future traffic of mobile network, and it is the basisof consultingplanning and optimization of mobile network.

Huawei Traffic Forecast tool is based on history traffic trend analysis, and provide

accurate traffic prediction result. It satisfies the requirement of efficient network

operation and maintenance.

Generally, the forecast method is based on history data growth trend to forecast the

traffic in the short / mid term, and it is not applicable to the network with frequent

adjustment such as expansion and evolution.

Application Scenario of Traffic Forecast


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Background

Design Concept

Forecast Procedure

Effect Verification




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1. Pre-process Function

History Traffic Data

Burst Weight Analysis

and Extraction

Trend Weight Analysis

and Extraction

Period Weight Analysis

and Extraction

Random Weight

Analysis and Extraction

Burst Weight Prediction

Trend Weight Prediction

Period Weight Prediction

Random Weight Prediction

X(FutDate) = (1+A)

(TrX(FutDate) +

PerX(FutDate) +RandX(FutDate))

A

TrX(FutDate)

PerX(FutDate)

RandX(FutDate)

Sum all weights

2. Analysis

Function3. Forecast Function

4. Probability Analysis

Function

Traffic forecast Tool is applicable to GSM radio / UMTS radio / Core Network.

History data can be any scale, such as cell level, cluster level and BSC/RNC/GGSN/SGSN level.


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Design Concept Brief Introduction

Algorithm of traffic forecast include four parts:

Module of history data pre-processing

Module of traffic data analysis

Module of traffic data forecast

Module of probability analysis.

Abstract four components in traffic data analysis and forecast:

Burst component and factor

Trending component and slope

Periodic componentRandom component


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History traffic data comes from performance analysis tool, such as OMStar

or PRS. For example,

Data Source

Item UMTS Counter

RRC connections VS.RRC.SuccConnEstab.RNC

CS traffic VS.CSLoad.Erlang.Equiv.RNC

UL R99 PS traffic VS.R99PSLoad.ULThruput.RNC

DL R99 PS traffic VS.R99PSLoad.DLThruput.RNC

HSUPA traffic VS.HSUPAPSLoad.ULThruput.RNC

HSDPA traffic VS.HSDPAPSLoad.DLThruput.RNC

Network registrations VS.RRC.AttConnEstab.Reg

To forecast the traffic of incoming six months to one year, we must input three to

six months history data;

To forecast the traffic of incoming more than one year, we must input six months

to one year data.


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Background

Design Concept

Forecast Procedure

History Data Pre-processing

Traffic Analysis

Traffic ForecastProbability Analysis

Effect Verification




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If the time range of the history data is less than 28days, forecast

wontbe processed.

If the continuous missing data is more than 8 days, forecast wontbe

processed. Inserting data processing. For non-continuous data sequence

(


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Background

Design Concept

Forecast Procedure


Traffic Analysis

Traffic Forecast

Probability Analysis

Effect verification




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Traffic nalysis

Burst component analysis: burst detecting and regular(monthly, weekly) burst extracting

Trend component analysis: provide linear correlation analysis to the traffic data after picking

out burst component, and extract trend component and slope.

Period component analysis: find out the period from the traffic data after picking out the

burst and trending component, then abstract period component.

Random component analysis: random component is the traffic data after picking out burst ,

trending and periodic component.

1. Pre-process Function



and Extraction


and Extraction

Period Weight Analysis

and Extraction

Random Weight Analysis

and Extraction


Trend Weight Prediction

Period Weight Prediction

Random Weight Prediction

X(FutDate) = (1+A)

(TrX(FutDate) +

PerX(FutDate) +

RandX(FutDate))

A

TrX(FutDate)

PerX(FutDate)

RandX(FutDate)

Sum all weights

2. Analysis Function 3. Forecast Function

4. Probability Analysis Function


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Traffic analysis

Burst component analysis

Burst detect: divide whole data into several part, then compare each data to the

average data of the part which it belongs to. If difference is more than a certain

value, this data is burst peak point.

Regular Monthly burst extracting: count the number of certain day with burst in

each month. If the ratio of burst number to total months is more than a certain

percent, store these burst and date.

Regular weekly burst abstracting: count the number of a certain day with burst each

week. If the ratio of burst number to total weeks is more than a certain percent,store these burst and weekday.


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Traffic analysis

Trend component analysis

For the data sequence with burst component picked out, after dividing the

whole sequence to several parts, linear fit at each part, get slope and

trending component ( y = a + b * t).


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Traffic analysis

Period component analysis

Select the possible period

Try some common periodic value (such as 7/15/30 days) as the possible

period, and use F-test algorithm to verify it.

Otherwise, try from 7days to half of the whole data as the period, and get the

value of F-test algorithm for each setting.

Select the period value of max F-test (or next to max) value as the possible

period.

Extract the periodic component

Divide data sequence to several parts by the possible period and get the

periodic component.


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Traffic analysis

Random component analysis

Random Component is the remaining component after picking out burst, trend and

period component in the traffic data.


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Background

Design ConceptForecast Procedure


Traffic Analysis


Effect Verification




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Design Concept

The data of traffic forecasting module comes from the result of traffic analysis, which is burst component,

trending component, period component and random component. We can forecast these four components to

get the whole forecasting result.

1Pre-process Function



and Extraction


and Extraction

Period Weight Analysisand Extraction

Random Weight

Analysis and

Extraction


Trend WeightPrediction

Period WeightPrediction

Random Weight

Prediction

X(FutDate) = (1+A)

(TrX(FutDate) +

PerX(FutDate) +

RandX(FutDate))

A

TrX(FutDate)

PerX(FutDate)

RandX(FutDat

e)

Sum all weights

2Analysis Function 3Prediction Function

4

Probability Analysis Function


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Traffic Forecast

Trend component forecast

Forecast the future trend slope and trend component by method of periodic

exponential smoothing algorithm

Method of periodic exponential smoothing is a simple method of

gradually decreasing the weight given to an observation as its ageincreases. The method has been used in many applications, such as the

forecasting of demands and prices, and the predication of future target

positions in fire control systems.


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Traffic Prediction

Period Component forecasting

Period component in the future is the average of history period components.

Period component can be adjusted by the user growth factor(can be configured) .

Random component forecasting

Use the history random component as the forecasting one, and revise by the

adjusted factor(can be configured).


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Traffic Prediction

Burst component forecasting

According to burst analysis, predict whether the forecasting date will have

burst.

If a burst is predicted, calculate average value of history burst datasequence which has the same type as forecasting date, burst factor of

forecasting date is this average value multiplied by a logarithm function.

The final forecasting traffic = (1+forecasting burst factor )(forecasting

trending component + forecasting period component +forecasting random

component)


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Background

Design ConceptForecast Procedure


Traffic Analysis


Effect Verification




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Probability nalysis

Purpose: Giving confidence interval of forecasting result under a certain confidence.

Method:

Supposing there is four months history traffic data and the first three months data is

the input data, the last month traffic data will be forecasted with this forecasting

algorithm.

Compare the last month forecasting traffic data to real traffic data, get the relative

difference.

Calculate the confidence interval of forecasting result in a certain confidence.


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Background

Design Concept

Forecast Procedure

Effect Verification (example)




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Data source

there are five months data, and this verification uses the first four monthsdata as input to forecast the last month data(in red), then compare it with real

date(in blue).

CS traffic

The average difference between CS voice forecasting value and real value is 11%.

TATA RNC301 Verification CS traffic


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Average HSUPA user number in busy hourThe difference between forecasting value and real value of average HSUPA user

number in busy hour


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Average HSDPA user number in busy hourThe difference between the forecasting value and real value of averageHSDPA user number in busy hour is 9%


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HSDPA throughput in busy hourThe difference between forecasting value and real value of HSDPA

throughput in busy hour is 7%.


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Verification result: The ratio of the difference below 10% between

forecasting value and real value of traffic is 80%.

TATA RNC301 Verification - Summarization


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Data source: using five moths data(in blue) from India project, to forecast future six

months data(in red) and confidence interval (cyan-blue, purple)with 95% confidence offorecasting result.

CS TrafficCS Traffic is stable

TATA RNC301 Traffic Forecasting


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Average HSUPA user number in busy hourAverage HSUPA user number in busy hour increased rapidly, the usernumber will increase 60% after six months.


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Average HSDPA user number in busy hourAverage HSDPA user number in busy hour increased rapidly, and user numberwill increase 100% after six months.


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HSDPA throughput in busy hour

HSDPA throughput in busy hour increase rapidly, and will increase 45%after six months\


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Verification result:Forecasting results of 4 type of traffic above follow the history rules,and good convincingness has been proved.

TATA RNC301 Verification -Summary


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Principle

Example Result of the Algorithm


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Principle of Resource Forecast

Input

Traffic counters :signaling, CS voice, R99 PS,HSUPA ,HSDPA

Resource counters: ULCE / DLCE / RTWP / TCP /OVSF Code / SPU CPU.

Procedure

Set up a function between resource and traffic:

Resource = f(A1*signaling, A2*CS voice traffic, A3*R99 PS traffic, A4*HSUPA

traffic, A5*HSDPA traffic, B);

Note: coefficient A1,A2,A3,A4,A5 are the weight of the corresponding traffic, B is

constant.

Using regression algorithm to find out the correlation between resource and

history traffic ,and get the weight of A1, A2, A3, A4, A5.

Input the result of traffic forecasting to resource forecasting module above, and

you can forecast future resource consumption.


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Resource forecast Introduction

Principle

ExampleResult of the Algorithm


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ULCE of Resource Forecast

Data source: traffic and data at a UMTS cell from a operator in shanghai.

Y-coordinate---consumed and predicted ULCE at a cell

X-coordinate----number of samples, each sample correspond to a group of traffic value.

Result: red curve in the following picture is resource consumed value of real history data, blue

curve in the following picture is resource predicted value which is calculated from the model of

self-adapting study according to history data.


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Real value

Prediction value

Y-coordinate ---- consumed and predicted DL CE at a cell

X-coordinate ----number of samples

DLCE of Resource Forecast


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Real value

Prediction value

Y-coordinate ---- received total wideband power (unit: dBm)

X-coordinate ---- number of samples

RTWP of Resource Forecast


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X-coordinate ---- transmit carrier power at a cell (unit: mW)

Y-coordinate ---- number of samples

TCP of Resource Forecast

Real value

Prediction value


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SPU Load of Resource Forecast

Y-coordinate ---- average percentage of consumed and predicted SPU board loadX-coordinate ---- the number of samples.

Real value

Prediction value


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Thank you

Documents

Traffic & Resource Forecast Principle