U-Net Planning Tool

Introduction

Why should simulation be performed in WCDMA radio network planning?

mu at on s



ne o mportant teps or Network Planning

a o

Simulation is oriented to simulate the running situation of networks under the current network configuration so as to facilitate decision-making adjustment. Adopt the planning software to perform simulation based on various types of BTS coverage area, the number of BTSs within the coverage area, and the configuration of each BTS. All these are obtained from traffic coverage analysis.

Atoll 网络规划软件

Introduction to Atoll Software



Be

a

professional

radio

network

design

tool,

supporting

GSM/TDMA, GPRS-EDGE, cdmaOne,W-CDMA/UMTS and CDMA 2000/1x RTT/EVDO. It is specially designed for 3G. 

Realize mobility of planning design, supporting both single system configuration and Enterprise server-based network configuration. The single system configuration does not require connecting external database and users still can share engineering data.



Feature modern software structure as well as open and extendable platform

Simulation step by step -UNet(Atoll)

Are Parameters ready? (site, transmitter, transmitter, cell…)

Coverage by transmitter

Traffic model

Simulation

Coverage prediction

Over

Y

Result OK?

N Parameters modification? (site, transmitter, transmitter, cell…)

Table of Contents



Chapter 1 Importing a Digital Map 



Chapter 2 Data Importing



Chapter 3 Atoll Atoll Propagation Model



Chapter 4 Analog Prediction



Chapter 5 Traffic Model



Chapter 6 Monte carlo Simulation

Composition of a Digital Map

A digital map basically consists of the following three components, stored under three directories respectively. 

 \Heights 

Digital elevation model (DEM): describe basic landforms of this area and directly participate in radio propagation model calculation



 \Clutter 

Digital clutter model (DOM): clutter classification data describes clutter coverage on the ground, such as forest, lake, open area, industrial area, urban area, high-storey building area. It is used during calculating radio propagation path loss.



 \Vector 

Linear vector model (LDM): linear clutter vector data describes plane

Selecting coordinate system

U-Net works with the following two coordinate systems at the same time: 

• Primary coordinate system: It is a coordinate system of

geographical database 

• Display coordinate system:

it is a coordinate system for

display and data-input. All the geographical coordinates are displayed and input according to this system. If the projection coordinate system and the display coordinate

Table of Contents



Chapter 1 Importing a Digital Map 



Chapter 2 Data Importing



Chapter 3 Atoll Atoll Propagation Model



Chapter 4 Analog Prediction



Chapter 5 Traffic Model



Chapter 6 Monte carlo Simulation

Antenna Data and Lobe Pattern



Input antenna type, manufacturer and antenna gain in [General].



Import the corresponding attenuation table at each angle of the antenna in [Horizontal pattern ] and [Vertical pattern].



Input Beamwidth, FMax, FMin or other user-defined parameters in [Other properties ].



Right click ―Antennas －>Properties‖ in the ―Browse -Data‖ window to

open antenna attributes box.

Data Importing



Sites information: refer to BTS equipment type and channel element data Include the following parameters: BTS name, longitude and latitude, height above sea level,



Transmitter TMA, feeder and BTS equipment:



CELL information:

Table of Contents



Chapter 1 Importing a Digital Map 



Chapter 2 Data Importing



Chapter 3 Atoll Atoll Propagation Model



Chapter 4 Analog Prediction



Chapter 5 Traffic Model



Chapter 6 Monte carlo Simulation

Introduction to Propagation Models

Typical models are from repeated CW tests.

Table of Contents



Chapter 1 Importing a Digital Map 



Chapter 2 Data Importing



Chapter 3 Atoll Atoll Propagation Model



Chapter 4 Analog Prediction



Chapter 5 Traffic Model



Chapter 6 Monte carlo Simulation

Coverage Prediction

 A ―Coverage bytransmitter‖ analog prediction is the precondition for simulation.

There are ten analog predictions in all, but only the first three can be performed at the current stage because simulation results are unavailable.

Coverage Prediction

Setting the following parameters: 

Signal level threshold value: defaulted as -110dBm and the maximum value has no upper limit.



All and Best signal level: usually select Best signal level so as to be convenient to observe the coverage of the best cell.



Signal level margin of the best cell: defaulted as 0



Reliability: 50% is usually set.



Carrier wave: it is usually set to ―All carrier waves‖ for coverage area computation.

Coverage Prediction



Drawing a computation area

Select ―Draw‖ from ―Computation zone‖ in the ―Tools‖ menu in the Atoll software. And then draw a polygon with the mouse on the zone to be researched. The computation zone is within the red line. 

Shadowing margins



Compute shadowing margins in each type of landform by inputting the standard variance of each clutter and improving Reliability Level. Reliability level is 50% Calculate or Calculate all by default.

Table of Contents



Chapter 1 Importing a Digital Map 



Chapter 2 Data Importing



Chapter 3 Atoll Atoll Propagation Model



Chapter 4 Analog Prediction



Chapter 5 Traffic Model



Chapter 6 Monte carlo Simulation

Traffic Modeling 

Traffic data involved in traffic modeling includes service type, terminals, mobility type, user profile, environment and traffic map.

Creating a Traffic Map



Based on Environments (raster): refer to the raster map based on traffic model



Based on User profiles (vector): refer to the vector map based on user profile



Based on Transmitters and Services (throughput): refer to throughput map based on sector and service type

Table of Contents



Chapter 1 Importing a Digital Map 



Chapter 2 Data Importing



Chapter 3 Atoll Atoll Propagation Model



Chapter 4 Analog Prediction



Chapter 5 Traffic Model



Chapter 6 Monte carlo Simulation

Monte Carlo Simulation

The process of Monte-Carlo simulation is as follows: 

Perform Monte-Carlo simulation based on traffic map. Atoll randomly distribute user location and user profile on the traffic map based on the number of users and density.



Perform uplink/downlink power simulation based on results from step 1.

Static Simulation

1.

Generate a certain quantity of network instantaneous state—―Snapshot‖ Here, some some terminals are distributed based based on a certain rule (such as random even distribution) at each ―Snapshot‖. 2.

Acquire connection capability between terminals and networks by incremental operation. Here, it is required to consider the possibility of multiple connection failure (uplink/downlink traffic channel maximum transmit power, unavailable channels, low Ec/Io and uplink/downlink interference).

3.

Measure and analyze results of multiple ―Snapshots‖ to have a overall understanding of network performance. Monte Carlo simulation is one type of static simulation.

Monte Carlo Simulation



Coverage Probability

The following takes coverage probability for an example to further understand how Monte Carlo simulation is performed.

100%

20%

60%

100%

0%

75%

60%

40%

Simulation Report

Analysis Report on Simulation Results

Statistics 

In the Request is total users accessed into the network, uplink/downlink total volume required by the network, and details classification of each type of service.



In the Result is refused users and relevant causes, users successfully accessed, actual volume of the network, and details classification of each type of service.

Sites Include BTS rated maximum channel elements, FCH and SCH channel elements actually used for uplinks and downlinks, channel elements of uplink/downlink overhead channels for soft handoff, speech/data volume of

Analysis Report on Simulation Results

The following initial conditions must be satisfied:  Setting global parameters of the transmitter  Setting original parameters of this simulation Setting parameters related to landform, such as the orthogonal factor and standard variance of each

Propagation Model Tuning

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Propagation Model Tuning 

Propagation Model Tuning Flow CW Data

Perform Appropriate Filtering

YES

NO

Is Filtering Necessary

Change Model Parameter Document Change

SPM CELIBRATION

Analysis Results

NO

Error Satisfactorily Low? YES

SPM Model

Propagation Model Tuning





Establishing a model 

Establish a standard macrocell model to be tuned.



Select the effective antenna height.



Select a calculation method of diffraction loss.

Importing data 

Import CW test data file into the project.

Propagation Model Tuning 

Map correction 

GPS locating in CW test usually adopts WGS84 and UTM projection. However, digital maps in China do not use such projections and reference plane. Correct digital maps if CW test data does not correspond to them.



Correction method:  Correct four parameters on rectangular coordinates in a

digital map to realize the optimal match with the test data.

Propagation Model Tuning



Setting Filtering 

Distance filtering:  Filter the data of which r is less than 150m or r is greater

than 3000m. 

Signal strength filtering:  Filter the data of which Signal is greater than -40dBm or

Signal is less than -121dB. 

Clutter filtering

Propagation Model Tuning



Parameter tuning 

L=K1 + K2log(d) + K3log(Heff) + K4 + K5log(d)

Diffraction

log(HTxeff) + K6(HRxeff)

+ Kclutterf(clutter)

Tune such parameters as log(d), log(Heff), Diff, log(d)log(Heff), Hmeff and Klutter to finally tune SPM propagation model.

Propagation Model Tuning

Propagation Model Tuning ERROR (measurement – (measurement  – prediction)

Regression line calculated values for the variable

Propagation Model Tuning

Propagation Model Tuning 

Correction of propagation model parameters in a city

Parameter

Reference value

K

K1

23.2

K2

44.90

K3

5.83

K4

0.5

K5

-6.55

K6

0

Propagation Model Tuning 

Analysis of correction results 

Analyze correctness of the acquired model after correction.



Evaluate the correctness of the model with Std Dev, which refer to the binding degree of the acquired model and actual test environment.



Make Std Dev less than 8 as much as possible in actual model tuning, which indicates that the tuned model and actual test environment are well bound.

T  h   a n k   y  o  u