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. However for the real application, the effect of noise can not be ignored. In order to improve the performance of phase reconstruction with the consideration of noise, a hybrid system for wireless tomography is proposed in this paper. Meanwhile, machine learning is explored here to execute the noise reduction.