Installing the WARPnet Framework

Installing the software

1. First ensure that you are running ​Python 2.6 or higher. If not, install the latest 2.6 version of Python. We have not tested the server with Python 3+ and since the syntax has changed for several important commands it will probably not work yet.
2. Download and install the ​Twisted networking library from source.
3. Download and install ​Zope.interface from source. The Twisted library depends on this.
4. WARPnet uses PCAP to communicate with the WARP board directly. To allow the Python-based server/client to access PCAP, the Pcapy module needs to be installed. As WARPnet works with ​Pcapy version 10.5 only, download this to your computer.
5. Before installing Pcapy, it needs to be patched for some WARPnet specific functionality. Download pcapy_warpnet_patch.diff​ from the repository into the Pcapy folder and run the following.

    patch < pcapy_warpnet_patch.diff
    python setup.py install
   

6. Download and install ​Impacket from source. This package helps in analyzing raw Ethernet packets. Again, to install run the following from the Impacket folder.

    python setup.py install
   

Installing the Framework

The WARPnet Framework is a standalone module that works just like any Python module. However, for now it is not installed into site-packages but added to the Python path on a per shell basis. The current stable release is version 2.2

1. Download the WARPnet Framework folder and store it anywhere on your computer. Note the absolute path to it as it will be important later.
2. Download warpnet_client_experiment_structs.py​ and TxPower_vs_PER.py​. These are an example definitions file and example script that work with OFDM Reference Design v15.

Running the server

Running the server is explained in more detail here?.

Setting up a shell for the Client

1. Navigate to the warpnet_framework folder.
2. Add the warpnet_framework location to the local environment variable PYTHONPATH. For example in bash:

    export PYTHONPATH='~/warpnet_framework'
   

3. Now, browse to your TxPower_vs_PER.py. To run your experiment type:

    python TxPower_vs_PER.py