Computer Vision Based Pre-processing for Channel Sensing in Non-stationary Environment

With the evolution of wireless networks, new techniques including massive multiple-input multiple-output (MIMO) and millimeter wave are adopted to satisfy the demands for diversified services. However, it has been verified by field tests that the traditional wide sense stationary assumption for wireless channel does not hold anymore. As a result, traditional channel state information (CSI) acquisition methods, especially the statistical CSI acquisition, cannot be applied straightforwardly in such a circumstance. In this paper, we propose a pre-processing method for channel sensing in the non-stationary environment. Specifically, the data sampled from channel training is treated as a channel image, where the statistical channel state is represented by gray-scale. Then the computer vision technique, specifically, the edge detection method, is used on the channel image to detect the homogeneous sub-regions. Within each sub-region, the channel is statistically stationary, and then the CSI can be obtained by existing methods. It is verified by simulation results that, the proposed method can help to improve the CSI acquisition accuracy in the non-stationary environment.