Phase Reconstruction Using Machine Learning For Wireless Tomography
This is the following paper in a series on a new initiative of wireless tomography. The goal is to combine two areas: wireless communication and radio tomography. This paper primarily focuses on phase reconstruction using machine learning for wireless tomography. When only communication components instead of sophisticated equipment are exploited to perform wireless tomography, phase information of the received field is hard to obtain. Thus self-coherent tomography is proposed, which has two main steps. First, phase reconstruction is achieved using the received amplitude only data. Second, the standard radio tomographic imaging algorithms are used for data analysis.