SyncSense: Towards Cooperative Processing Using Noncoherent Crowdsourced Wireless Networks

Crowdsourced wireless networking is a recognized trend in modern wireless communications. Thanks to their wide distribution, in which one crowdsourced wireless network (CWN) can cover the whole of 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. Nonetheless, one fundamental problem that holds CWNs back from establishing more applications is the synchronization problem. In fact, synchronization is an indispensable requirement in many wireless communication applications. SyncSense's final objective is to realize a synchronized CWN by employing refined dynamic stochastic models and exploiting the advances in machine learning. I will design synchronization algorithms for CWNs, achieving a significant breakthrough in CWNs' supported range of wireless applications. The performance of the proposed synchronization algorithms will be examined on real-world CWNs, enabling accurate analysis of the framework's design parameters for optimal performance. In particular, SyncSense's synchronization framework will be evaluated in the context of three signal processing applications: cooperative decoding, target tracking, and cooperative beamforming.