Facilitating the coexistence of radar systems with communication systems has been a major area of research in radar engineering. The current work presents a new way to sense the environment using the channel equalization block of existing communication systems. We have named this system CommSense.In the current paper we demonstrate the feasibility of the system using Global System for Mobile Communications (GSM) signals. The implementation has been done using open-source Software Defined Radio (SDR) environment. In the preliminary results obtained in our work we show that it is possible to distinguish environmental changes using the proposed system. The major advantage of the system is that it is inexpensive as channel estimation is an inherent block in any communication system and hence the added cost to make it work as an environment sensor is minimal. The major challenge, on which we are continuing our work, is how to characterize the features in the environmental changes. This is an acute challenge given the fact that the bandwidth available is narrow and the system is inherently a forward looking radar. However the initial results, as shown in this paper, are encouraging and we intend to use an application specific instrumentation (ASIN) scheme to distinguish the environmental changes.

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Channel estimation and equalization is a major area of research and development in communications engineering.However in the open literature there is no elaborate report on how to implement it on real signals from base-stations. In this paper we give the detailed steps to implement channel estimation block on real GSM signals using open-source software defined radio (SDR) environment. This work is expected to help all the researchers trying to implement and test channel equalization algorithms on real signal.

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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 µs 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.