  We have recently proposed a scheme to use the channel equalization blocks of
telecommunication systems to sense changes in an environment. We call this
communication-sensing, CommSense for short. After some initial positive results
we tried to use our global system for mobile communication (GSM) based
CommSense system for a through-the-wall sensing application. As the system was
inherently highly under-determined we used statistical machine learning
techniques to help us sense environmental changes in the behind-the-wall
experiments. We observed that with limited amount of data per GSM frame of 577
{\mu}s a person can be detected across a wall to an accuracy of 77.458% and a
person carrying a weapon can be detected to an accuracy of 95.208%. We present
details of the experiments and the encouraging results that we have obtained in
this article.
