SyncSense: Towards cooperative processing using noncoherent crowdsourced networks

Crowdsourced wireless networking (CWN) is a recognized trend in modern wireless communications. Thanks to their wide distribution (one CWN can cover whole Europe), CWNs were able to introduce a set of cutting-edge new applications such as crowdsourced air traffic control (ATC) (e.g., Flightradar24), and large-scale spectrum sensing. These applications have revolutionized their field of research. Nonetheless, one fundamental problem the holds CWN back from establishing more applications is the synchronization problem. In fact, synchronization is an indispensable requirement in many wireless communication applications. SyncSense focuses on realizing synchronized CWN using a combination of refined dynamic stochastic models (DSMs) and deep learning algorithms. Subsequently, the synchronization algorithms introduced, will be evaluated in the context of three signal processing applications: cooperative decoding, target tracking, and cooperative beamforming.

Keywords:crowdsourced wireless networks, synchroization, cooperative processing, localization, deep learning

Disciplines:Wireless communication and positioning systems