abstract josé a. j. figueira

7/30/2019 Abstract Jos A. J. Figueira

1/6

1

AbstractBasic Idea: 3D Wireless Networks Planning Tool & Simulation Tool

Thesis Title: Planning & Simulation Tool for LTE-A with Femtocells considering aMulti-Agent System

Student: Jos Albuquerque Jardim Figueira

The abstract is organized by a short abstract and some sections for easier reading and interpretation of the

concepts described below.

Short Abstract

The 4G Planning Tool is a simulation tool for wireless communications, for 4G wireless

communication. This planning tool was developed considering multi-agent system and Google Maps.

This tool can be seen as a mobile traffic simulator (MTS). This one permits to analyze the capacity of

base stations in cellular networks for a proper capacity and coverage planning, using the Google Maps.

The MTS was developed using JavaScript programming language. It used a multi-agent model

considering actual data, and allows to simulate real scenarios. Results from simulations were obtained

and analyzed, allowing to check if there is necessary to upgrade the cellular network.

I. Introduction

Several new services and the improved Quality of Service (QoS) of mobile telecommunications

systems results on the increasing volume of data transmission. In addition to this change, there is more

variation in population distribution due to the greater ease of mobility and the existence, increasingly,

social events, bringing in new implications, including the characteristics and topology of cellular

networks.

With this constant change becomes necessary to plan existing and new networks of base stations (BS)

to cover and provide useful services to the people and events in a defined geographic space and reduced

time. To this end, the development of a system for analyzing the ability of BSs for cellular mobile

communications based on maps is critical.

In most cases, the use of maps is solved importing small and restricted geographic maps by

simulation tools.

The construction of the basic tool for the simulation of social systems with more realistic data,

updated and geographically scalable makes the analysis more competitive.

This abstract describes the simulation tool on section II. The section III specifies the agent model.

Results of a case study are present in section IV. Finally the main conclusions and future work are present

in section V.


2/6

2

II. Simulation Tool

The mobile traffic simulator (MTS) is characterized by implementing an easy and intuitive interface,

in order to facilitate its use by users by providing them simulation variable options and presenting

graphics data of the evolution over time. This reflects the options taken by the user before the start of the

simulation.

In order to make simulation as accurate as possible and realistic, the MTS implements the calculation

and representation of the coverage of BSs according to

the calculation of losses in the free space in conjunction

with the calculation of the recursive major obstacle

method.

In order to improve the outcome of planning, i.e.,

creating a more efficient network in order to meet the

needs required, we make simulations. The results of

these simulations, if the requirements are not met, thetopology of the network can be changed to reach an

optimal network, such as model optimization tool

presented in Figure 1.

The tool is built entirely in JavaScript programming language based on object oriented programming

over HTML and CSS. The basis API used is Google Maps JavaScript API V3 [1], with several

instruments, which were used as follows: Map Types, Controls, Overlays, Events, Base, Services

(Directions API, Elevation API, Geocoding API), Geometry Library, Places Library and Drawing

Library.

Apart from this enormous API were used other API's for the auxiliary clustering [2], for example,

representing various markers in one, and thereby enhancing visualization of the same on the map. We

have implemented the features to show the circuit to be traveled at each instant by the agents.

Along with the previous API, it was used auxiliary tools like Google Maps API Styled Map Wizard

[3] which is based on Section Map Types, and helps in building new types of maps interactively and

quickly. The type of map style created is called "Blue Roads", as shown in Figure 2.

There is consider in each parish population data from Census 2011 study [4] held in Portugal,bringing reality to simulation. To use the census data and represent them correctly it is necessary to

represent the geographical boundaries of the parish geographies considered in coordinates (latitude and

longitude). These spatial data were downloaded from the Website Administrative Areas [5], in kmz

format.

In order to clarify the operation process of the tool, this is divided into three main blocks. By simply

inputs made by the user, occurs the beginning of planning. This section shows user controls before

starting the simulation. In all menus available are present sliders [6] limited by minimum, maximum and

jump. These inputs include simulation and event times, BS parameters, coverage resolution and map

events as BS and event positions.

MTS Optimization Tool

Cellular Planning

Tool

Multi-Agent

Simulation

+ OutputsInputs

Fig. 1. Optimization model for cellular

planning tool using multi-agent simulation


3/6

3

Fig. 2. Graphical representation by Google Maps of the simulation agents processing and BS

coverage levels

The next block of the MTS corresponds to the processing of the simulation and the carrying out of

decisions of agents, taking into account the characteristics that differentiate them.

From inputs and the values obtained in the processing the results obtained by MTS, outputs, are

presented in the form of graphs and tables. Over graphical representations users can analyses the progress

of simulation.

o InputsThe simulator has a setup menu of simulation time, a menu for size and geographically a temporal

event and setup menu of the parameters of the BSs.

o ProcessingWhen the simulation starts, the simulator iterates through each element of the list of agents scrambled

previously [7] and ask each agent what the next action (geographical shift and communications). This

process is performed repeatedly until the limit of iterations defined by the user.

o OutputsThe coverage of the BS is computed and represented on the map, as can be shown in Figure 2. The

BS has three different levels of coverage (low, medium and high) and different geometries, depending on

the received power at each point, which is calculated as a function of distance and obstructions due to

terrain elevation.

During the simulation are presented three current capacity barometers level (Figure 3) in each one of

three shows coverage levels, and also the evolution through simulation.

Fig. 3. Barometers of users charge for each level of BS coverage.


4/6

4

III. Agent Model

Agents are distributed and moves through blue roads (fig. 2), and the initial position of each is

calculated by Agent Distribution Algorithm (ADA). This algorithm creates a non-uniform distribution

becoming more realistic.

Agents will be distributed as they are along roads, where is that the population resides, and where

agents will have the standard position. With this new map we can predict in advance the actual

distribution of the population.

The agent model has three main blocks: inputs, processing and outputs.

o InputsThe simulator has a setup menu for the distribution of agents, resulting in the density of agents.

o ProcessingADA algorithm was established based on data of the streets to obtain agents distribution closer to

reality, as compared to the uniform spatial distribution. The position of the agents is calculated from a

grid of points and via Google Directions service. This algorithm allows agents to be added on the roads,

excluding the "green zones" (areas where there are no roads), approximating the distribution of the agents

of the actual population.

The distance, from the grid point to the point nearest road as calculated by the Google Directions

service, limits the addition of an agent (represented by a

marker) on the map. This distance must be less than a

threshold, which is defined by the distance between the grid

points. If the calculated distance is less than the defined

limit, then an agent is created with the default position as the

geographic point calculated by the Google service above.

This limitation on distance prevents placing markers around

"green areas", when there are no streets.

The agents speed of travel differs from the type of

transport they are using, where the two possible are by foot

or car, considering the speeds of 5 and 50 km/h, respectively.

The car's speed is limited by the maximum allowed by the

Portugal driving code in rural roads.

o OutputsAgents have the ability to move through the streets of the map with different routes and speeds, so

not everyone is moving, or have the same number of traveled miles. Thus, it was obtained several graphs

illustrating the percentage and number of agents that are in motion and, by other hand the traveled

distance of each one.

Fig. 4People in the event


5/6


6/6

6

The planning can be obtained anywhere in the world by Google Maps using local social statistics.

This tool allows manipulating the different variables of simulation.

The distribution of agents is efficient and very close to real. The presence of an event inside the

coverage area of one BS can saturate the traffic and dont satisfy all requests for long periods of time, due

to the concentration of users.

Some subjects of this work were accepted for presentation in RWW 2013 conference [8]. Others

parts was submitted to PAEWN 2012 [9].

Some additional work is being developed for optimize the tool characteristics.

As a future work it will be interesting and helpful do some work to solve some problems about cars

traffic. By other hand, the Specific Absorption Radiation can be simulated by each user in function of his

route. The population behavior can be used to model the agent behavior to obtain a more realistic

simulation.

References

[1]Google Maps JavaScript API V3 (Online)

https://developers.google.com/maps/documentation/javascript/reference, accessed 15-06-2012.

[2]markerclustererplus, google-maps-utility-library-v3(Online) http://google-maps-utility-library-

v3.googlecode.com/svn/trunk/markerclustererplus/, accessed 15-06-2012.

[3]Google Maps API Styled Map Wizard (Online) http://gmaps-samples-

v3.googlecode.com/svn/trunk/styledmaps/wizard/index.html, accessed 15-06-2012.

[4]Provisional Result of the 2011 Census (Online)

http://censos.ine.pt/xportal/xmain?xpid=CENSOS&xpgid=censos2011_apresentacao, accessed

15-06-2012.

[5]Global Administrative Areas, (Portugal, Google Earth .kmz) (Online)

http://www.gadm.org/country, accessed 15-06-2012.

[6] dhtmlxSlider: Neat and Simple JavaScript Slider Control (Online)

http://dhtmlx.com/docs/products/dhtmlxSlider/, accessed 15-06-2012.

[7]Array Shuffle - JavaScript Function (Online) http://dzone.com/snippets/array-shuffle-javascript,

accessed 15-06-2012.

[8] J. A. Figueira, P. Sebastio, F. Cercas and N. David, Simulator for Capacity Analysis of Base

Stations for Mobile Networks using Google Maps in Radio and Wireless Week, Austin, TX,

USA, Jan. 2013.

[9] J. Albuquerque Figueira, Pedro Sebastio, Francisco Cercas, Nuno David, 4G Planning Tool

considering Multi-Agent System using Google Maps in 8th IEEE International Workshop on the

Performance Analysis and Enhancement of Wireless Networks (PAEWN), Barcelona, Spain,March, 2013.

abstract josé a. j. figueira

Documents