cellular_network_map
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cellular_network_map [2015/07/20 01:15] – [Prerequisites] samer | cellular_network_map [2015/07/20 16:10] – [6- Raw Data] samer | ||
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Real deployment data for cellular networks can be very convenient when elaborating simulation scenarios in scientific papers. In the following, we present an automation tool that enables to extract the geographical data of the base stations for the mobile operators in France. | Real deployment data for cellular networks can be very convenient when elaborating simulation scenarios in scientific papers. In the following, we present an automation tool that enables to extract the geographical data of the base stations for the mobile operators in France. | ||
- | ===== Prerequisites ===== | + | ===== -- Prerequisites ===== |
You need a working python distribution with the following packages: | You need a working python distribution with the following packages: | ||
* [[https:// | * [[https:// | ||
Line 8: | Line 8: | ||
You also need to have a valid subscription on the [[http:// | You also need to have a valid subscription on the [[http:// | ||
- | ===== Output at a Glance | + | ===== -- Method of Operation |
- | 1- Go to http://www.antennesmobiles.fr/, select the antenna types, the city and copy the antenna ids (at the bottom of the page) in a text file (see for example orange-3G-rennes.csv) | + | 1. Download or fork my repository from [[https://github.com/ |
- | 2- Go to http://www.cartoradio.fr/ | + | 2. Launch the crawler script from your command line. The argument of the script corresponds to the postal code as in: |
- | 3- Open python | + | |
- | 4- Launch the python | + | You should obtain a text file named '' |
+ | |||
+ | 66007; 2G 3G 4G; BOUYGUES TELECOM; 1995-09-01; 2014-09-26; 156 R LÉON MAURICE NORDMANN; 75013; PARIS-13E--ARRONDISSEMENT; | ||
+ | 351124; 2G 3G 4G; BOUYGUES TELECOM; 2004-08-20; 2014-12-05; 22/24 R DU FAUBOURG ST JACQUES; 75014; PARIS-14E--ARRONDISSEMENT; | ||
+ | |||
+ | The only missing information is the geographic position of the antennas. For this, you need the information from [[http:// | ||
+ | |||
+ | 3. Go to [[http:// | ||
+ | |||
+ | 4. Launch the coordinate generation | ||
+ | |||
+ | python antenna-coordinate-generation.py 75014 orange 4G | ||
+ | |||
+ | * The first argument is the postal code. | ||
+ | * The second argument is the operator name and must be chosen from: orange - free - bouygues - sfr | ||
+ | * The last argument is the network type: 2G - 3G - 4G | ||
+ | |||
+ | 5. Watch back and enjoy! | ||
+ | |||
+ | ===== -- Output at a Glance ===== | ||
+ | The output of the automation tool consists of three files: | ||
+ | * A command file '' | ||
+ | * A figure plot of the antenna positions '' | ||
+ | * A figure plot of the Voronoi diagram (the set of points in the geographic area closer to each antenna) '' | ||
+ | |||
+ | [{{ : | ||
+ | |||
+ | [{{ : | ||
+ | ===== -- Scientific Work ===== | ||
+ | |||
+ | We used the antenna positions generated by the automation tool in our work entitled '' | ||
+ | |||
+ | [{{ : | ||
+ | ===== -- Eye Candies ===== | ||
+ | The automation tool can be used in different scenarios. For instance, in order to illustrate the densification of the network deployment, a comparative study can be performed on the 2G, 3G, and 4G networks. The case of the city of Rennes is presented hereafter. | ||
+ | [{{ : | ||
+ | [{{ : | ||
+ | [{{ : | ||
+ | |||
+ | ===== -- Raw Data ===== | ||
+ | {{: | ||
+ | {{: |
cellular_network_map.txt · Last modified: 2015/07/20 17:33 by samer